Datablist is a <a href="/csv-editor">CSV editor</a> with a built-in character count feature. Many character counters count the characters across all your CSV content. Datablist Character Counter lets you count the character on a specific column. Making it more suitable to real-life use cases.
<h2 id="user-content-how-many-characters-are-in-my-csv-file"><a href="#how-many-characters-are-in-my-csv-file" aria-hidden="true" tabindex="-1"></a>How many characters are in my CSV file?</h2>
First, load your CSV file into Datablist. The Character Count feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load <a href="/how-to/edit-big-csv-files-online">CSV files with up to 1.5 million rows</a>.
Click the "+" button to create a new collection and then click "Import CSV" to load your file.
<img src="/learn_images/csv/characters-count-column/import-csv.png#2155x1326" alt="Import CSV file">
Once loaded, you will see your CSV columns and your CSV content directly in Datablist. Click on the CSV column on which you want to count characters.
<img src="/learn_images/csv/characters-count-column/start-calculations.png#2152x910" alt="Open Calculations tool">
Select "Characters count" in the calculation options. The character count is displayed directly in the drawer.
<img src="/learn_images/csv/characters-count-column/character-count-results.png#2155x1321" alt="Character Count Result">
<h2 id="user-content-how-character-count-is-calculated"><a href="#how-character-count-is-calculated" aria-hidden="true" tabindex="-1"></a>How character count is calculated?</h2>
Every character counts. Spaces, punctuations, digits, smileys, etc. count.
Please note that some smileys are coded on 2 characters. For example, the smiley "🍆" counts as 2 characters.
Line breaks don't count as characters. In the text:
<pre><code>My


Word
</code></pre>
The character count is 6.
<h2 id="user-content-when-to-use-character-count"><a href="#when-to-use-character-count" aria-hidden="true" tabindex="-1"></a>When to use character count?</h2>
Here are some situations where character count is important:
<ul>
<li>SEO Optimization - In the digital age, character count plays a crucial role in SEO. Meta descriptions, titles, and URLs with optimal character lengths are more likely to be indexed effectively by search engines, improving your online visibility.</li>
<li>Social Media - Platforms like "X" (Twitter) have character limits, making it essential to keep tweets within a specific length to convey your message effectively.</li>
<li>Cost Analysis - When using third-party tools, for example, <a href="/how-to/translate-csv-files-online">translation services</a>, where the cost is charged based on word or character count.</li>
</ul>

Datablist is a [CSV editor](/csv-editor) with a built-in character count feature. Many character counters count the characters across all your CSV content. Datablist Character Counter lets you count the character on a specific column. Making it more suitable to real-life use cases.

## How many characters are in my CSV file?

First, load your CSV file into Datablist. The Character Count feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load [CSV files with up to 1.5 million rows](/how-to/edit-big-csv-files-online).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

Click the "+" button to create a new collection and then click "Import CSV" to load your file.

![Import CSV file](/learn_images/csv/characters-count-column/import-csv.png#2155x1326)

Once loaded, you will see your CSV columns and your CSV content directly in Datablist. Click on the CSV column on which you want to count characters.

![Open Calculations tool](/learn_images/csv/characters-count-column/start-calculations.png#2152x910)

Select "Characters count" in the calculation options. The character count is displayed directly in the drawer.

![Character Count Result](/learn_images/csv/characters-count-column/character-count-results.png#2155x1321)

## How character count is calculated?

Every character counts. Spaces, punctuations, digits, smileys, etc. count.

Please note that some smileys are coded on 2 characters. For example, the smiley "🍆" counts as 2 characters.

Line breaks don't count as characters. In the text:

```
My


Word
```

The character count is 6.


## When to use character count?

Here are some situations where character count is important:

- **SEO Optimization** - In the digital age, character count plays a crucial role in SEO. Meta descriptions, titles, and URLs with optimal character lengths are more likely to be indexed effectively by search engines, improving your online visibility.
- **Social Media** - Platforms like "X" (Twitter) have character limits, making it essential to keep tweets within a specific length to convey your message effectively.
- **Cost Analysis** - When using third-party tools, for example, [translation services](/how-to/translate-csv-files-online), where the cost is charged based on word or character count.

How to count characters in a CSV column

Datablist is a CSV editor with a built-in character count feature. Many character counters count the characters across all your CSV content. Datablist Character Counter lets you count the character on a specific column. Making it more suitable to real-life use cases.
How many characters are in my CSV file?
First, load your CSV file into Datablist. The Character Count feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load CSV files with up to 1.5 million rows.
Click the &ampquot;+&ampquot; button to create a new collection and then click &ampquot;Import CSV&ampquot; to load your file.
Import CSV file
Once loaded, you will see your CSV columns and your CSV content directly in Datablist. Click on the CSV column on which you want to count characters.
Open Calculations tool
Select &ampquot;Characters count&ampquot; in the calculation options. The character count is displayed directly in the drawer.
Character Count Result
How character count is calculated?
Every character counts. Spaces, punctuations, digits, smileys, etc. count.
Please note that some smileys are coded on 2 characters. For example, the smiley &ampquot;🍆&ampquot; counts as 2 characters.
Line breaks don&amp#39;t count as characters. In the text:
My


Word
The character count is 6.
When to use character count?
Here are some situations where character count is important:
SEO Optimization - In the digital age, character count plays a crucial role in SEO. Meta descriptions, titles, and URLs with optimal character lengths are more likely to be indexed effectively by search engines, improving your online visibility.
Social Media - Platforms like &ampquot;X&ampquot; (Twitter) have character limits, making it essential to keep tweets within a specific length to convey your message effectively.
Cost Analysis - When using third-party tools, for example, translation services, where the cost is charged based on word or character count.



CSV files, or comma-separated values, are a common way to store and share tabular data. They're often used to store time-related information, which can be represented using different formats. However, to effectively analyze and manipulate this data, converting timestamp columns into DateTime format is essential.
DateTime is a data type that represents both date and time information in a unified manner. It enables efficient manipulation and analysis of time-series data, facilitating tasks such as filtering, aggregation, and comparison.
The word "Timestamp" usually refers to the Epoch Unix Timestamp. Which is the number of seconds between the Unix Epoch (00:00:00 UTC on 1 January 1970) and a particular date.
In this guide, you'll learn how to parse and convert a CSV column with timestamp values into DateTime format. The guide focuses on the "Unix Timestamp" format, but the tool works with many other DateTime string representations.
<h2 id="user-content-step-1-load-your-csv-file"><a href="#step-1-load-your-csv-file" aria-hidden="true" tabindex="-1"></a>Step 1: Load your CSV file</h2>
The first step in converting a CSV timestamp column into DateTime format is to import the CSV file into Datablist. Datablist is a powerful <a href="/csv-editor">CSV editor</a> with <a href="/how-to/data-cleaning">data-cleaning</a> features. Perfect for this task!
Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.
<img src="/learn_images/csv/convert-timestamp-datetime/import-csv.png#2155x1326" alt="Import CSV file">
Once the CSV file has been imported into Datablist, you can proceed to the next step: converting the timestamp column into DateTime format.
<h2 id="user-content-step-2-use-the-text--datetime-converter"><a href="#step-2-use-the-text--datetime-converter" aria-hidden="true" tabindex="-1"></a>Step 2: Use the "Text &#x3C;=> DateTime" converter</h2>
The next step is to convert the timestamp column into DateTime format. This can be done using the "Text &#x3C;=> DateTime" converter.
Click on "Clean -> Text &#x3C;=> Date, Number, etc."
It opens a data converter tool. The converter allows conversion between various types such as Checkbox, Number, DateTime and Text.
<img src="/learn_images/csv/convert-timestamp-datetime/convert-tool.png#2133x1112" alt="Open Convert Tool">
Select the column with the timestamp values in "Convert data from". Then, select the "Convert to DateTime" in the "Convert To" setting.
<img src="/learn_images/csv/convert-timestamp-datetime/convert-to-datetime.png#2102x1207" alt="Convert to DateTime setting">
The last configuration step is to select the format in which your DateTime values are defined. Here, you have to select "Timestamp/Unix time" if your timestamps are Unix Timestamps. Check the different available formats otherwise. If none of the preset formats match your data, you can write one or more custom DateTime patterns.
<blockquote>
Note
Datablist will automatically detect if the timestamp values refer to seconds or milliseconds.
</blockquote>
<img src="/learn_images/csv/convert-timestamp-datetime/timestamp-format.png#2098x1196" alt="Select date format">
<h2 id="user-content-step-3-preview-and-conversion"><a href="#step-3-preview-and-conversion" aria-hidden="true" tabindex="-1"></a>Step 3: Preview and conversion</h2>
Then you'll be presented with a preview of the converted data. This allows you to check that the conversion has been performed correctly and that the timestamps are now in the correct format.
<img src="/learn_images/csv/convert-timestamp-datetime/conversion-preview.png#2101x1213" alt="Conversion Preview">
Once you've checked the preview and confirmed that the conversion has been successful, you can run the conversion. Datablist will create a new column to store the results. This will allow you to leverage the DateTime format to analyze and manipulate your data with more precision.
<img src="/learn_images/csv/convert-timestamp-datetime/conversion-results.png#2099x1202" alt="Conversion Results">
<blockquote>
Note
If you need to convert back DateTime values to text in another format. Use the same converter tool with "DateTime => Text" settings.
</blockquote>

CSV files, or comma-separated values, are a common way to store and share tabular data. They're often used to store time-related information, which can be represented using different formats. However, to effectively analyze and manipulate this data, converting timestamp columns into DateTime format is essential.

DateTime is a data type that represents both date and time information in a unified manner. It enables efficient manipulation and analysis of time-series data, facilitating tasks such as filtering, aggregation, and comparison.

The word "Timestamp" usually refers to the Epoch Unix Timestamp. Which is the number of seconds between the Unix Epoch (00:00:00 UTC on 1 January 1970) and a particular date.

**In this guide, you'll learn how to parse and convert a CSV column with timestamp values into DateTime format.** The guide focuses on the "Unix Timestamp" format, but the tool works with many other DateTime string representations.

## Step 1: Load your CSV file

The first step in converting a CSV timestamp column into DateTime format is to import the CSV file into Datablist. Datablist is a powerful [CSV editor](/csv-editor) with [data-cleaning](/how-to/data-cleaning) features. Perfect for this task!

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>


Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.

![Import CSV file](/learn_images/csv/convert-timestamp-datetime/import-csv.png#2155x1326)

Once the CSV file has been imported into Datablist, you can proceed to the next step: converting the timestamp column into DateTime format.

## Step 2: Use the "Text <=> DateTime" converter

The next step is to convert the timestamp column into DateTime format. This can be done using the "Text <=> DateTime" converter.

Click on "Clean -> Text <=> Date, Number, etc."

It opens a data converter tool. The converter allows conversion between various types such as Checkbox, Number, DateTime and Text.



![Open Convert Tool](/learn_images/csv/convert-timestamp-datetime/convert-tool.png#2133x1112)

Select the column with the timestamp values in "Convert data from". Then, select the "Convert to DateTime" in the "Convert To" setting.

![Convert to DateTime setting](/learn_images/csv/convert-timestamp-datetime/convert-to-datetime.png#2102x1207)

The last configuration step is to select the format in which your DateTime values are defined. Here, you have to select "Timestamp/Unix time" if your timestamps are Unix Timestamps. Check the different available formats otherwise. If none of the preset formats match your data, you can write one or more custom DateTime patterns.

> **Note** 
> Datablist will automatically detect if the timestamp values refer to seconds or milliseconds.

![Select date format](/learn_images/csv/convert-timestamp-datetime/timestamp-format.png#2098x1196)

## Step 3: Preview and conversion

Then you'll be presented with a preview of the converted data. This allows you to check that the conversion has been performed correctly and that the timestamps are now in the correct format.

![Conversion Preview](/learn_images/csv/convert-timestamp-datetime/conversion-preview.png#2101x1213)

Once you've checked the preview and confirmed that the conversion has been successful, you can run the conversion. Datablist will create a new column to store the results. This will allow you to leverage the DateTime format to analyze and manipulate your data with more precision.

![Conversion Results](/learn_images/csv/convert-timestamp-datetime/conversion-results.png#2099x1202)

> **Note** 
> If you need to convert back DateTime values to text in another format. Use the same converter tool with "DateTime => Text" settings.

How to convert a CSV timestamp column to DateTime format?

CSV files, or comma-separated values, are a common way to store and share tabular data. They&amp#39;re often used to store time-related information, which can be represented using different formats. However, to effectively analyze and manipulate this data, converting timestamp columns into DateTime format is essential.
DateTime is a data type that represents both date and time information in a unified manner. It enables efficient manipulation and analysis of time-series data, facilitating tasks such as filtering, aggregation, and comparison.
The word &ampquot;Timestamp&ampquot; usually refers to the Epoch Unix Timestamp. Which is the number of seconds between the Unix Epoch (00:00:00 UTC on 1 January 1970) and a particular date.
In this guide, you&amp#39;ll learn how to parse and convert a CSV column with timestamp values into DateTime format. The guide focuses on the &ampquot;Unix Timestamp&ampquot; format, but the tool works with many other DateTime string representations.
Step 1: Load your CSV file
The first step in converting a CSV timestamp column into DateTime format is to import the CSV file into Datablist. Datablist is a powerful CSV editor with data-cleaning features. Perfect for this task!
Create a new collection (&ampquot;+&ampquot; button in the left sidebar) and then click &ampquot;Import CSV&ampquot; to load your file.
Import CSV file
Once the CSV file has been imported into Datablist, you can proceed to the next step: converting the timestamp column into DateTime format.
Step 2: Use the &ampquot;Text &amplt;=&ampgt; DateTime&ampquot; converter
The next step is to convert the timestamp column into DateTime format. This can be done using the &ampquot;Text &amplt;=&ampgt; DateTime&ampquot; converter.
Click on &ampquot;Clean -&ampgt; Text &amplt;=&ampgt; Date, Number, etc.&ampquot;
It opens a data converter tool. The converter allows conversion between various types such as Checkbox, Number, DateTime and Text.
Open Convert Tool
Select the column with the timestamp values in &ampquot;Convert data from&ampquot;. Then, select the &ampquot;Convert to DateTime&ampquot; in the &ampquot;Convert To&ampquot; setting.
Convert to DateTime setting
The last configuration step is to select the format in which your DateTime values are defined. Here, you have to select &ampquot;Timestamp/Unix time&ampquot; if your timestamps are Unix Timestamps. Check the different available formats otherwise. If none of the preset formats match your data, you can write one or more custom DateTime patterns.
Note&ltbr/&gt
Datablist will automatically detect if the timestamp values refer to seconds or milliseconds.

Select date format
Step 3: Preview and conversion
Then you&amp#39;ll be presented with a preview of the converted data. This allows you to check that the conversion has been performed correctly and that the timestamps are now in the correct format.
Conversion Preview
Once you&amp#39;ve checked the preview and confirmed that the conversion has been successful, you can run the conversion. Datablist will create a new column to store the results. This will allow you to leverage the DateTime format to analyze and manipulate your data with more precision.
Conversion Results
Note&ltbr /&gt
If you need to convert back DateTime values to text in another format. Use the same converter tool with &ampquot;DateTime =&ampgt; Text&ampquot; settings.



Managing contacts across devices can be tricky, especially when different platforms prefer different file formats. While CSV files are great for organizing contact data in spreadsheets, most modern devices and apps use VCF (vCard) files, which allow richer contact information like photos, addresses, and notes.
Datablist is an online tool to <a href="/csv-editor">manipulate CSV files</a>. Datablist can be used to export a CSV file with contacts to a VCF file using the vCard data format.
Datablist support multiple contacts conversion. All your contacts are stored in a single VCF file (vCard) file.
<h2 id="user-content-step-1-load-your-csv-file"><a href="#step-1-load-your-csv-file" aria-hidden="true" tabindex="-1"></a>Step 1: Load your CSV file</h2>
First, upload your CSV file to Datablist. The CSV to vCard feature is accessible without needing to register. As an anonymous user, you can import CSV files with up to 10,000 rows. For larger files, create a free account. Registered users can import <a href="/how-to/edit-big-csv-files-online">CSV files with up to 1.5 million rows</a>.
To get started, create a new collection by clicking the "+" button in the left sidebar, then select "Import CSV" to upload your file.
<img src="/learn_images/csv/convert-csv-vcard-vcf/import-csv.png#2155x1326" alt="Import CSV file">
In just a few seconds, your CSV columns and data will appear in Datablist, ready for you to work with.
<h2 id="user-content-step-2-map-your-columns-to-vcard-fields"><a href="#step-2-map-your-columns-to-vcard-fields" aria-hidden="true" tabindex="-1"></a>Step 2: Map your columns to vCard fields</h2>
Now your CSV is loaded, converting the data to vCard is simple and can be done in just a few steps.
Click on the Export button available in the application header.
<img src="/learn_images/csv/convert-csv-vcard-vcf/csv-export-action.png#1574x757" alt="Export CSV to vCard">
In the export options, choose the vCard format. This ensures your contacts are saved in a format compatible with most modern devices and apps.
<img src="/learn_images/csv/convert-csv-vcard-vcf/select-vcard.png#1458x920" alt="Select vCard (VCF) data format">
The next step is to match your CSV columns (like Name, Email, and Phone) to the corresponding vCard fields. This ensures all your data is accurately transferred.
The vCard format is a standard for electronic business cards. The format includes a list of standard properties to store the name, the profile picture, email addresses, etc.
<blockquote>
Visit the <a href="https://en.wikipedia.org/wiki/VCard" target="_blank" rel="nofollow">vCard Wikipedia page</a> to see all available vCard properties.
</blockquote>
The format also allows custom properties to store any data other than the standard ones.
<blockquote>
Advice: To improve the compatibility of your exported VCF file (vCard) with other tools, be sure to map your CSV column with a standard property when possible. Use custom property only when no matching property exists.
</blockquote>
Datablist lists all your CSV columns.
For each of them:
<ul>
<li>Select a standard vCard property</li>
<li>Or define a custom property</li>
</ul>
<img src="/learn_images/csv/convert-csv-vcard-vcf/map-vcard-fields.png#1876x1230" alt="Map your CSV columns with vCard fields">
When selecting "Map with custom vCard property", an input appears. Write the name of the property to be added in the exported vCard file (.vcf).
<img src="/learn_images/csv/convert-csv-vcard-vcf/custom-property.png#1616x982" alt="Custom vCard property">
Once you've mapped the fields, you’re ready to export and use your contacts in the vCard format!
Just click on the "Export" button at the bottom of the export modal.
A file is generated with the <code>.vcf</code> file extension. All your CSV rows are converted to the vCard format.
Conclusion: vCard is a good text format for storing and exchanging contacts between tools. With its powerful features to view and edit CSV, Datablist is the perfect tool for converting your contacts from a CSV file to a VCF file.
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-what-is-the-version-of-vcard-generated"><a href="#what-is-the-version-of-vcard-generated" aria-hidden="true" tabindex="-1"></a>What is the version of vCard generated?</h3>
Datablist sets the vCard version to the last available to date: VERSION:4.0.
It also adds the <code>KIND</code> property to <code>individual</code> to define the type of entity to contacts.
<h3 id="user-content-how-many-contacts-be-converted-at-once"><a href="#how-many-contacts-be-converted-at-once" aria-hidden="true" tabindex="-1"></a>How many contacts be converted at once?</h3>
The limit depends on the CSV import limits. As anonymous, you can load and convert CSV files up to 10,000 rows.
While registered users can load files up to 1,000,000 rows.
<h3 id="user-content-is-it-free-to-convert-a-csv-file-to-vcard-vcf"><a href="#is-it-free-to-convert-a-csv-file-to-vcard-vcf" aria-hidden="true" tabindex="-1"></a>Is it free to convert a CSV file to vCard (VCF)?</h3>
Yes, it is. Data conversion is available in the Datablist free plan.
<h2 id="user-content-when-is-csv-to-vcf-vcard-conversion-useful"><a href="#when-is-csv-to-vcf-vcard-conversion-useful" aria-hidden="true" tabindex="-1"></a>When is CSV to VCF (vCard) conversion useful</h2>
<ul>
<li>
Transferring Contacts Between Devices - Easily move your contacts from a spreadsheet to your phone, email client, or contact management app.
</li>
<li>
Backup Contacts - Convert and store contacts as a VCF file for secure and standardized backup that can be imported anywhere.
</li>
<li>
Sync Contacts Across Platforms - Ensure contacts are compatible across different systems, like transferring from Outlook to Google Contacts.
</li>
<li>
Sharing Contact Lists - Share your contact list with colleagues, clients, or friends in a universal format that’s easily importable.
</li>
<li>
Import Contacts to CRM - Upload contacts from a CSV file to CRM platforms that accept vCard format for streamlined data integration.
</li>
<li>
Organizing Large Contact Databases - Organize and migrate large contact lists in a structured vCard format, which supports additional data fields like addresses and notes.
</li>
<li>
Personalizing Contact Information - Add richer details such as photos, social media profiles, and custom fields when converting to the vCard format.
</li>
</ul>



Managing contacts across devices can be tricky, especially when different platforms prefer different file formats. While CSV files are great for organizing contact data in spreadsheets, most modern devices and apps use VCF (vCard) files, which allow richer contact information like photos, addresses, and notes.

Datablist is an online tool to [manipulate CSV files](/csv-editor). Datablist can be used to export a CSV file with contacts to a VCF file using the vCard data format.

Datablist support multiple contacts conversion. All your contacts are stored in a single VCF file (vCard) file.

## Step 1: Load your CSV file

First, upload your CSV file to Datablist. The CSV to vCard feature is accessible without needing to register. As an anonymous user, you can import CSV files with up to 10,000 rows. For larger files, create a free account. Registered users can import [CSV files with up to 1.5 million rows](/how-to/edit-big-csv-files-online).


To get started, create a new collection by clicking the "+" button in the left sidebar, then select "Import CSV" to upload your file.

![Import CSV file](/learn_images/csv/convert-csv-vcard-vcf/import-csv.png#2155x1326)

In just a few seconds, your CSV columns and data will appear in Datablist, ready for you to work with.

## Step 2: Map your columns to vCard fields

Now your CSV is loaded, converting the data to vCard is simple and can be done in just a few steps.

Click on the Export button available in the application header.

![Export CSV to vCard](/learn_images/csv/convert-csv-vcard-vcf/csv-export-action.png#1574x757)

In the export options, choose the vCard format. This ensures your contacts are saved in a format compatible with most modern devices and apps.

![Select vCard (VCF) data format](/learn_images/csv/convert-csv-vcard-vcf/select-vcard.png#1458x920)

The next step is to match your CSV columns (like Name, Email, and Phone) to the corresponding vCard fields. This ensures all your data is accurately transferred.

The vCard format is a standard for electronic business cards. The format includes a list of standard properties to store the name, the profile picture, email addresses, etc.

> Visit the [vCard Wikipedia page](https://en.wikipedia.org/wiki/VCard) to see all available vCard properties.

The format also allows custom properties to store any data other than the standard ones.

> **Advice**: To improve the compatibility of your exported VCF file (vCard) with other tools, be sure to map your CSV column with a standard property when possible. Use custom property only when no matching property exists.

Datablist lists all your CSV columns.

For each of them:

- Select a standard vCard property
- Or define a custom property

![Map your CSV columns with vCard fields](/learn_images/csv/convert-csv-vcard-vcf/map-vcard-fields.png#1876x1230)

When selecting "Map with custom vCard property", an input appears. Write the name of the property to be added in the exported vCard file (.vcf).

![Custom vCard property](/learn_images/csv/convert-csv-vcard-vcf/custom-property.png#1616x982)

Once you've mapped the fields, you’re ready to export and use your contacts in the vCard format!

Just click on the "Export" button at the bottom of the export modal.

A file is generated with the `.vcf` file extension. All your CSV rows are converted to the vCard format.


**Conclusion**: vCard is a good text format for storing and exchanging contacts between tools. With its powerful features to view and edit CSV, Datablist is the perfect tool for converting your contacts from a CSV file to a VCF file.


## FAQ

### What is the version of vCard generated?

Datablist sets the vCard version to the last available to date: VERSION:4.0.
It also adds the `KIND` property to `individual` to define the type of entity to contacts.

### How many contacts be converted at once?

The limit depends on the CSV import limits. As anonymous, you can load and convert CSV files up to 10,000 rows.
While registered users can load files up to 1,000,000 rows.


### Is it free to convert a CSV file to vCard (VCF)?

Yes, it is. Data conversion is available in the Datablist free plan.


## When is CSV to VCF (vCard) conversion useful

- **Transferring Contacts Between Devices** -  Easily move your contacts from a spreadsheet to your phone, email client, or contact management app.

- **Backup Contacts** - Convert and store contacts as a VCF file for secure and standardized backup that can be imported anywhere.

- **Sync Contacts Across Platforms** - Ensure contacts are compatible across different systems, like transferring from Outlook to Google Contacts.

- **Sharing Contact Lists** - Share your contact list with colleagues, clients, or friends in a universal format that’s easily importable.

- **Import Contacts to CRM** - Upload contacts from a CSV file to CRM platforms that accept vCard format for streamlined data integration.

- **Organizing Large Contact Databases** - Organize and migrate large contact lists in a structured vCard format, which supports additional data fields like addresses and notes.

- **Personalizing Contact Information** -  Add richer details such as photos, social media profiles, and custom fields when converting to the vCard format.









How to convert a CSV file to vCard (vcf) online?

Managing contacts across devices can be tricky, especially when different platforms prefer different file formats. While CSV files are great for organizing contact data in spreadsheets, most modern devices and apps use VCF (vCard) files, which allow richer contact information like photos, addresses, and notes.
Datablist is an online tool to manipulate CSV files. Datablist can be used to export a CSV file with contacts to a VCF file using the vCard data format.
Datablist support multiple contacts conversion. All your contacts are stored in a single VCF file (vCard) file.
Step 1: Load your CSV file
First, upload your CSV file to Datablist. The CSV to vCard feature is accessible without needing to register. As an anonymous user, you can import CSV files with up to 10,000 rows. For larger files, create a free account. Registered users can import CSV files with up to 1.5 million rows.
To get started, create a new collection by clicking the &ampquot;+&ampquot; button in the left sidebar, then select &ampquot;Import CSV&ampquot; to upload your file.
Import CSV file
In just a few seconds, your CSV columns and data will appear in Datablist, ready for you to work with.
Step 2: Map your columns to vCard fields
Now your CSV is loaded, converting the data to vCard is simple and can be done in just a few steps.
Click on the Export button available in the application header.
Export CSV to vCard
In the export options, choose the vCard format. This ensures your contacts are saved in a format compatible with most modern devices and apps.
Select vCard (VCF) data format
The next step is to match your CSV columns (like Name, Email, and Phone) to the corresponding vCard fields. This ensures all your data is accurately transferred.
The vCard format is a standard for electronic business cards. The format includes a list of standard properties to store the name, the profile picture, email addresses, etc.
Visit the vCard Wikipedia page to see all available vCard properties.

The format also allows custom properties to store any data other than the standard ones.
Advice: To improve the compatibility of your exported VCF file (vCard) with other tools, be sure to map your CSV column with a standard property when possible. Use custom property only when no matching property exists.

Datablist lists all your CSV columns.
For each of them:
Select a standard vCard property
Or define a custom property

Map your CSV columns with vCard fields
When selecting &ampquot;Map with custom vCard property&ampquot;, an input appears. Write the name of the property to be added in the exported vCard file (.vcf).
Custom vCard property
Once you&amp#39;ve mapped the fields, you’re ready to export and use your contacts in the vCard format!
Just click on the &ampquot;Export&ampquot; button at the bottom of the export modal.
A file is generated with the .vcf file extension. All your CSV rows are converted to the vCard format.
Conclusion: vCard is a good text format for storing and exchanging contacts between tools. With its powerful features to view and edit CSV, Datablist is the perfect tool for converting your contacts from a CSV file to a VCF file.
FAQ
What is the version of vCard generated?
Datablist sets the vCard version to the last available to date: VERSION:4.0.
It also adds the KIND property to individual to define the type of entity to contacts.
How many contacts be converted at once?
The limit depends on the CSV import limits. As anonymous, you can load and convert CSV files up to 10,000 rows.
While registered users can load files up to 1,000,000 rows.
Is it free to convert a CSV file to vCard (VCF)?
Yes, it is. Data conversion is available in the Datablist free plan.
When is CSV to VCF (vCard) conversion useful
Transferring Contacts Between Devices -  Easily move your contacts from a spreadsheet to your phone, email client, or contact management app.

Backup Contacts - Convert and store contacts as a VCF file for secure and standardized backup that can be imported anywhere.

Sync Contacts Across Platforms - Ensure contacts are compatible across different systems, like transferring from Outlook to Google Contacts.

Sharing Contact Lists - Share your contact list with colleagues, clients, or friends in a universal format that’s easily importable.

Import Contacts to CRM - Upload contacts from a CSV file to CRM platforms that accept vCard format for streamlined data integration.

Organizing Large Contact Databases - Organize and migrate large contact lists in a structured vCard format, which supports additional data fields like addresses and notes.

Personalizing Contact Information -  Add richer details such as photos, social media profiles, and custom fields when converting to the vCard format.




Datablist is a <a href="/csv-editor">CSV editor</a> with a built-in tool to get distinct values from a CSV column. And you get the sum of occurrences for each value.
<img src="/learn_images/csv/distinct-values-column/distinct-values-header.png#1862x1009" alt="Example Distinct Values">
This tutorial shows you the step-by-step process to count and aggregate distinct values with a free online tool. No development or technical skills are required.
<h2 id="user-content-get-distinct-values-in-a-csv-file"><a href="#get-distinct-values-in-a-csv-file" aria-hidden="true" tabindex="-1"></a>Get distinct values in a CSV file</h2>
First, load your CSV file into Datablist. The Distinct Values feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load <a href="/how-to/edit-big-csv-files-online">CSV files with up to 1.5 million rows</a>.
Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.
<img src="/learn_images/csv/distinct-values-column/import-csv.png#2155x1326" alt="Import CSV file">
CSV columns and your CSV content is visible directly in Datablist in a few seconds. Click on the CSV column you want to analyze.
<img src="/learn_images/csv/distinct-values-column/start-calculations.png#2314x1164" alt="Open Calculations tool">
Select "Count distinct values" in the calculation options. Other options include <a href="/learn/csv/word-count-csv-file">Word Count</a>, and <a href="/learn/csv/characters-count-csv-file">Character Count</a>.
<img src="/learn_images/csv/distinct-values-column/select-calculation-distinct.png#1792x1030" alt="Calculation Selection">
Then run the process. Datablist analyses your data in real-time without sending the data to servers. You get results in a few seconds.
<img src="/learn_images/csv/distinct-values-column/distinct-values-results.png#2320x1202" alt="Distinct Value Results">
<h2 id="user-content-distinct-value-algorithm-explained"><a href="#distinct-value-algorithm-explained" aria-hidden="true" tabindex="-1"></a>Distinct value algorithm explained</h2>
Datablist converts all values to lowercase before doing the aggregation. The algorithm is thus "case insensitive".
Leading and trailing spaces are also removed.
The terms:
<pre><code>PARIS
</code></pre>
and
<pre><code> paRis
</code></pre>
Are similar and will be aggregated.
<h2 id="user-content-when-to-use-the-distinct-values-feature"><a href="#when-to-use-the-distinct-values-feature" aria-hidden="true" tabindex="-1"></a>When to use the distinct values feature?</h2>
This is great for aggregation of limited choice values (countries, status, etc.). For example if your CSV c
<ul>
<li>Leads Analysis - Get insights on your <a href="/use-cases/lead-list-building">prospects list</a> by running the distinct values counter on your prospect outreach status.</li>
<li>Geographical Analytics - Countries, Cities, and other location information can be aggregated to have a better understanding of your dataset.</li>
<li>Data Cleaning - Find outliers and irrelevant data by looking at the values with few occurrences. <a href="/how-to/data-cleaning">Learn more about Data Cleaning</a>.</li>
</ul>

Datablist is a [CSV editor](/csv-editor) with a built-in tool to get distinct values from a CSV column. And you get the sum of occurrences for each value.

![Example Distinct Values](/learn_images/csv/distinct-values-column/distinct-values-header.png#1862x1009)

This tutorial shows you the step-by-step process to count and aggregate distinct values with a free online tool. No development or technical skills are required.

## Get distinct values in a CSV file

First, load your CSV file into Datablist. The Distinct Values feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load [CSV files with up to 1.5 million rows](/how-to/edit-big-csv-files-online).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.

![Import CSV file](/learn_images/csv/distinct-values-column/import-csv.png#2155x1326)

CSV columns and your CSV content is visible directly in Datablist in a few seconds. Click on the CSV column you want to analyze.

![Open Calculations tool](/learn_images/csv/distinct-values-column/start-calculations.png#2314x1164)

Select "Count distinct values" in the calculation options. Other options include [Word Count](/learn/csv/word-count-csv-file), and [Character Count](/learn/csv/characters-count-csv-file).

![Calculation Selection](/learn_images/csv/distinct-values-column/select-calculation-distinct.png#1792x1030)

Then run the process. Datablist analyses your data in real-time without sending the data to servers. You get results in a few seconds.

![Distinct Value Results](/learn_images/csv/distinct-values-column/distinct-values-results.png#2320x1202)

## Distinct value algorithm explained

Datablist converts all values to lowercase before doing the aggregation. The algorithm is thus "case insensitive".
Leading and trailing spaces are also removed.

The terms:

```
PARIS
```

and

```
 paRis
```

Are similar and will be aggregated.


## When to use the distinct values feature?

This is great for aggregation of limited choice values (countries, status, etc.). For example if your CSV c

- **Leads Analysis** - Get insights on your [prospects list](/use-cases/lead-list-building) by running the distinct values counter on your prospect outreach status.
- **Geographical Analytics** - Countries, Cities, and other location information can be aggregated to have a better understanding of your dataset.
- **Data Cleaning** - Find outliers and irrelevant data by looking at the values with few occurrences. [Learn more about Data Cleaning](/how-to/data-cleaning).

How to count distinct values in a CSV column?

Datablist is a CSV editor with a built-in tool to get distinct values from a CSV column. And you get the sum of occurrences for each value.
Example Distinct Values
This tutorial shows you the step-by-step process to count and aggregate distinct values with a free online tool. No development or technical skills are required.
Get distinct values in a CSV file
First, load your CSV file into Datablist. The Distinct Values feature is available without registration. You can import a CSV file with a maximum of 10000 rows as an anonymous. For larger CSV files, just create an account for free. Registered users can load CSV files with up to 1.5 million rows.
Create a new collection (&ampquot;+&ampquot; button in the left sidebar) and then click &ampquot;Import CSV&ampquot; to load your file.
Import CSV file
CSV columns and your CSV content is visible directly in Datablist in a few seconds. Click on the CSV column you want to analyze.
Open Calculations tool
Select &ampquot;Count distinct values&ampquot; in the calculation options. Other options include Word Count, and Character Count.
Calculation Selection
Then run the process. Datablist analyses your data in real-time without sending the data to servers. You get results in a few seconds.
Distinct Value Results
Distinct value algorithm explained
Datablist converts all values to lowercase before doing the aggregation. The algorithm is thus &ampquot;case insensitive&ampquot;.
Leading and trailing spaces are also removed.
The terms:
PARIS
and
      paRis
Are similar and will be aggregated.
When to use the distinct values feature?
This is great for aggregation of limited choice values (countries, status, etc.). For example if your CSV c
Leads Analysis - Get insights on your prospects list by running the distinct values counter on your prospect outreach status.
Geographical Analytics - Countries, Cities, and other location information can be aggregated to have a better understanding of your dataset.
Data Cleaning - Find outliers and irrelevant data by looking at the values with few occurrences. Learn more about Data Cleaning.



Have you ever faced the challenge of counting distinct words in a CSV or Excel file? Whether you're working with survey responses, customer feedback, or any text-heavy data, identifying unique words can offer invaluable insights.
With spreadsheet tools, you can count the number of distinct values. But when each cell contains several words, Excel formulas becomes too complicated.
In this article, we will guide you through counting distinct words (and splitting cells by separator) in a column using Datablist, a <a href="/csv-editor">robust CSV viewer and editor</a> with advanced <a href="/how-to/data-cleaning">data-cleaning features</a>.
<img src="/learn_images/csv/distinct-word-count/count-distinct-words-results.png#2070x1313" alt="Count Distinct words with separator">
<h2 id="user-content-how-many-distinct-words-are-in-my-csv-file"><a href="#how-many-distinct-words-are-in-my-csv-file" aria-hidden="true" tabindex="-1"></a>How many distinct words are in my CSV file?</h2>
<h3 id="user-content-step-1-import-your-csv-or-excel-file"><a href="#step-1-import-your-csv-or-excel-file" aria-hidden="true" tabindex="-1"></a>Step 1: Import Your CSV or Excel File</h3>
The first step to get the distinct word count is to load your CSV file. Datablist is a powerful <a href="/csv-editor">CSV viewer</a> that loads <a href="/how-to/edit-big-csv-files-online">CSV files with up to 1.5 million rows</a>.
In the <a href="https://app.datablist.com/" target="_blank" rel="nofollow">Datablist Application</a>, create a new collection (using the "+" in the sidebar) to load your CSV file.
<img src="/learn_images/csv/distinct-word-count/import-csv.png#2155x1326" alt="Import CSV file">
Follow the import wizard to get your CSV data in Datablist.
<h3 id="user-content-step-2-open-the-calculation-tool"><a href="#step-2-open-the-calculation-tool" aria-hidden="true" tabindex="-1"></a>Step 2: Open the Calculation tool</h3>
Once the import is done, click on the column you want to count words in. Select "Perform calculation".
<img src="/learn_images/csv/distinct-word-count/open-calculations.png#2455x1183" alt="Open Calculations tool">
The "Calculation" tool opens. Datablist provides several calculation algorithms.
For text columns:
<ul>
<li>Distinct values counter (What we use in this guide! 💪)</li>
<li><a href="/learn/csv/word-count-csv-file">Total Words Count</a> - Sum of all words</li>
<li><a href="/learn/csv/characters-count-csv-file">Characters Count</a> - Sum of all characters</li>
</ul>
For numeric columns:
<ul>
<li><a href="/learn/csv/sum-min-max-average-csv-column">Min, Max, Average for numeric columns</a></li>
</ul>
<h3 id="user-content-step-3-count-distinct-words-with-or-without-a-separator"><a href="#step-3-count-distinct-words-with-or-without-a-separator" aria-hidden="true" tabindex="-1"></a>Step 3: Count Distinct Words with or without a separator</h3>
Select Count distinct values. An extra option appears to define a "Splitting Rule". The splitting rule defines if a cell contains one or several words.
Possible separators: Comma, Semicolon, Dot, Space, or Custom. When you select custom, another option will pop up to write your custom separator. You can write any string.
<img src="/learn_images/csv/distinct-word-count/count-distinct-words-settings.png#2391x1377" alt="Configure separator">
Then click on the "Run calculation" to start the process. A list of results appears in the drawer.
<img src="/learn_images/csv/distinct-word-count/count-distinct-words-results.png#2070x1313" alt="Count Distinct words with separator">
<h4 id="user-content-step-4-review"><a href="#step-4-review" aria-hidden="true" tabindex="-1"></a>Step 4: Review</h4>
Review the analysis results directly within Datablist. For each term, a shortcut button appears on mouse hover to create a filter.
Conclusion: Counting distinct words using a separator is a breeze with Datablist. Datablist opens CSV and Excel files alike. Its intuitive interface and powerful features make it an essential tool for data professionals.
<h2 id="user-content-when-is-counting-distinct-words-useful"><a href="#when-is-counting-distinct-words-useful" aria-hidden="true" tabindex="-1"></a>When is Counting Distinct Words useful</h2>
Counting distinct words is useful for several tasks:
<ul>
<li>Analysis Tags in a product catalog - Example: A Tags column in a product catalog might contain values like "electronics, gadgets, accessories". Using the "Distinct words" feature gives you a summary of occurence for each tag across the products.</li>
<li>Text Analysis - Helps in text mining, sentiment analysis, and understanding frequency patterns. Example: Consider a survey asking for user opinions. Counting distinct words in the feedback column helps identify common sentiments and themes.</li>
<li>Data Normalization - Identifies and reduces redundancy in datasets.</li>
<li>Content Creation - Assists in keyword research and content optimization.</li>
</ul>

Have you ever faced the challenge of counting distinct words in a CSV or Excel file? Whether you're working with survey responses, customer feedback, or any text-heavy data, identifying unique words can offer invaluable insights.

With spreadsheet tools, you can count the number of distinct values. But when each cell contains several words, Excel formulas becomes too complicated. 

In this article, we will guide you through counting distinct words (and splitting cells by separator) in a column using Datablist, a [robust CSV viewer and editor](/csv-editor) with advanced [data-cleaning features](/how-to/data-cleaning).

![Count Distinct words with separator](/learn_images/csv/distinct-word-count/count-distinct-words-results.png#2070x1313)

## How many distinct words are in my CSV file?

### Step 1: Import Your CSV or Excel File

The first step to get the distinct word count is to load your CSV file. Datablist is a powerful [CSV viewer](/csv-editor) that loads [CSV files with up to 1.5 million rows](/how-to/edit-big-csv-files-online).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

In the [Datablist Application](https://app.datablist.com/), create a new collection (using the "+" in the sidebar) to load your CSV file.

![Import CSV file](/learn_images/csv/distinct-word-count/import-csv.png#2155x1326)

Follow the import wizard to get your CSV data in Datablist. 

### Step 2: Open the Calculation tool

Once the import is done, click on the column you want to count words in. Select "Perform calculation".

![Open Calculations tool](/learn_images/csv/distinct-word-count/open-calculations.png#2455x1183)

The "Calculation" tool opens. Datablist provides several calculation algorithms.

For text columns:

- **Distinct values counter** (What we use in this guide! 💪)
- [Total Words Count](/learn/csv/word-count-csv-file) - Sum of all words
- [Characters Count](/learn/csv/characters-count-csv-file) - Sum of all characters

For numeric columns:

- [Min, Max, Average for numeric columns](/learn/csv/sum-min-max-average-csv-column)

### Step 3: Count Distinct Words with or without a separator

Select **Count distinct values**. An extra option appears to define a "Splitting Rule". The splitting rule defines if a cell contains one or several words.

Possible separators: **Comma**, **Semicolon**, **Dot**, **Space**, or **Custom**. When you select custom, another option will pop up to write your custom separator. You can write any string.

![Configure separator](/learn_images/csv/distinct-word-count/count-distinct-words-settings.png#2391x1377)

Then click on the "Run calculation" to start the process. A list of results appears in the drawer.

![Count Distinct words with separator](/learn_images/csv/distinct-word-count/count-distinct-words-results.png#2070x1313)

#### Step 4: Review

Review the analysis results directly within Datablist. For each term, a shortcut button appears on mouse hover to create a filter.

**Conclusion**: Counting distinct words using a separator is a breeze with Datablist. Datablist opens CSV and Excel files alike. Its intuitive interface and powerful features make it an essential tool for data professionals.


## When is Counting Distinct Words useful

Counting distinct words is useful for several tasks:

- **Analysis Tags in a product catalog** - Example: A Tags column in a product catalog might contain values like "electronics, gadgets, accessories". Using the "Distinct words" feature gives you a summary of occurence for each tag across the products.
- **Text Analysis** - Helps in text mining, sentiment analysis, and understanding frequency patterns. Example: Consider a survey asking for user opinions. Counting distinct words in the feedback column helps identify common sentiments and themes.
- **Data Normalization** - Identifies and reduces redundancy in datasets.
- **Content Creation** - Assists in keyword research and content optimization.

How to count distinct words/tags in a CSV Column?

Have you ever faced the challenge of counting distinct words in a CSV or Excel file? Whether you&amp#39;re working with survey responses, customer feedback, or any text-heavy data, identifying unique words can offer invaluable insights.
With spreadsheet tools, you can count the number of distinct values. But when each cell contains several words, Excel formulas becomes too complicated. 
In this article, we will guide you through counting distinct words (and splitting cells by separator) in a column using Datablist, a robust CSV viewer and editor with advanced data-cleaning features.
Count Distinct words with separator
How many distinct words are in my CSV file?
Step 1: Import Your CSV or Excel File
The first step to get the distinct word count is to load your CSV file. Datablist is a powerful CSV viewer that loads CSV files with up to 1.5 million rows.
In the Datablist Application, create a new collection (using the &ampquot;+&ampquot; in the sidebar) to load your CSV file.
Import CSV file
Follow the import wizard to get your CSV data in Datablist. 
Step 2: Open the Calculation tool
Once the import is done, click on the column you want to count words in. Select &ampquot;Perform calculation&ampquot;.
Open Calculations tool
The &ampquot;Calculation&ampquot; tool opens. Datablist provides several calculation algorithms.
For text columns:
Distinct values counter (What we use in this guide! 💪)
Total Words Count - Sum of all words
Characters Count - Sum of all characters

For numeric columns:
Min, Max, Average for numeric columns

Step 3: Count Distinct Words with or without a separator
Select Count distinct values. An extra option appears to define a &ampquot;Splitting Rule&ampquot;. The splitting rule defines if a cell contains one or several words.
Possible separators: Comma, Semicolon, Dot, Space, or Custom. When you select custom, another option will pop up to write your custom separator. You can write any string.
Configure separator
Then click on the &ampquot;Run calculation&ampquot; to start the process. A list of results appears in the drawer.
Count Distinct words with separator
Step 4: Review
Review the analysis results directly within Datablist. For each term, a shortcut button appears on mouse hover to create a filter.
Conclusion: Counting distinct words using a separator is a breeze with Datablist. Datablist opens CSV and Excel files alike. Its intuitive interface and powerful features make it an essential tool for data professionals.
When is Counting Distinct Words useful
Counting distinct words is useful for several tasks:
Analysis Tags in a product catalog - Example: A Tags column in a product catalog might contain values like &ampquot;electronics, gadgets, accessories&ampquot;. Using the &ampquot;Distinct words&ampquot; feature gives you a summary of occurence for each tag across the products.
Text Analysis - Helps in text mining, sentiment analysis, and understanding frequency patterns. Example: Consider a survey asking for user opinions. Counting distinct words in the feedback column helps identify common sentiments and themes.
Data Normalization - Identifies and reduces redundancy in datasets.
Content Creation - Assists in keyword research and content optimization.



Comma Separated Value(s), also known as CSV, is a format to store structured data using text files.
The CSV format is not standardized, so various implementations exist.
In 2005, <a href="https://www.internetsociety.org/" target="_blank" rel="nofollow">the Internet Society</a> published <a href="https://www.ietf.org/rfc/rfc4180.txt" target="_blank" rel="nofollow">guidelines for creating CSV files</a>. They wrote down best practices to structure and process CSV data.
From those guidelines and giving the lack of standardization, the header line is optional in a CSV file.
When present, the header line must be the first line in the file and must contain the same number of fields as the records. Header and records lines must use the same field delimiters.
Example:
<pre><code>column_1, column2, column3
aaa, bbb, ccc
111, 222, 333
</code></pre>


Comma Separated Value(s), also known as CSV, is a format to store structured data using text files.
**The CSV format is not standardized, so various implementations exist.**

In 2005, [the Internet Society](https://www.internetsociety.org/) published [guidelines for creating CSV files](https://www.ietf.org/rfc/rfc4180.txt). They wrote down best practices to structure and process CSV data.

From those guidelines and giving the lack of standardization, the header line is **optional** in a CSV file.

When present, the header line must be the first line in the file and must contain the same number of fields as the records. Header and records lines must use the same field delimiters.

Example:

```
column_1, column2, column3
aaa, bbb, ccc
111, 222, 333
```


Do I need to have a header line in my Comma Separated Value file?

Comma Separated Value(s), also known as CSV, is a format to store structured data using text files.
The CSV format is not standardized, so various implementations exist.
In 2005, the Internet Society published guidelines for creating CSV files. They wrote down best practices to structure and process CSV data.
From those guidelines and giving the lack of standardization, the header line is optional in a CSV file.
When present, the header line must be the first line in the file and must contain the same number of fields as the records. Header and records lines must use the same field delimiters.
Example:
column_1, column2, column3
aaa, bbb, ccc
111, 222, 333


CSV stands for "Comma Separated Value(s)" and is a format to store structured data using text files. In a CSV file, each line is a data record. And each record is made of fields separated by commas (or sometimes by semicolons ";" or tabular keys).
CSV files are a common way to transfer data between applications. Because it relies on text files, the CSV format is a simple way to export database or spreadsheet data.
Unfortunately, the CSV file format is not standardized. Encoding format, delimiters, or escaping rules might differ between implementations. As a result, most applications accepting CSV files as data sources have different options to read the CSV data.
To view the content of an CSV file, you can uses an online <a href="/csv-editor">CSV editor</a> that is compatible with CSV exported by most applications.


CSV stands for "Comma Separated Value(s)" and is a format to store structured data using text files. In a CSV file, each line is a data record. And each record is made of fields separated by commas (or sometimes by semicolons ";" or tabular keys).

CSV files are a common way to transfer data between applications. Because it relies on text files, the CSV format is a simple way to export database or spreadsheet data.

Unfortunately, the CSV file format is not standardized. Encoding format, delimiters, or escaping rules might differ between implementations. As a result, most applications accepting CSV files as data sources have different options to read the CSV data.

To view the content of an CSV file, you can uses an online [CSV editor](/csv-editor) that is compatible with CSV exported by most applications.


What is a CSV file?

CSV stands for &ampquot;Comma Separated Value(s)&ampquot; and is a format to store structured data using text files. In a CSV file, each line is a data record. And each record is made of fields separated by commas (or sometimes by semicolons &ampquot;;&ampquot; or tabular keys).
CSV files are a common way to transfer data between applications. Because it relies on text files, the CSV format is a simple way to export database or spreadsheet data.
Unfortunately, the CSV file format is not standardized. Encoding format, delimiters, or escaping rules might differ between implementations. As a result, most applications accepting CSV files as data sources have different options to read the CSV data.
To view the content of an CSV file, you can uses an online CSV editor that is compatible with CSV exported by most applications.


A CSV file is a text file used to store structured data. The CSV format is used to transfer structured data between applications or to export data in order to perform data analytics.
The first use case is data transfer. Applications such as e-commerce software, mailing list providers, or CRM deal can import and export data in the CSV format. In e-commerce software, it is common to import a CSV containing the product catalog. When managing a mailing list, importing a CSV with all the email addresses is faster than adding each contact one by one.
The second use case is data analytics. The CSV format is flat and is perfectly compatible with spreadsheet or data analytics tools. As a result, it is a good source to analyze and visualize data. A CSV file has no size limit and can contain millions or more of records. It is a good file format to use in Big Data.


A CSV file is a text file used to store structured data. The CSV format is used to transfer structured data between applications or to export data in order to perform data analytics.

The first use case is data transfer. Applications such as e-commerce software, mailing list providers, or CRM deal can import and export data in the CSV format. In e-commerce software, it is common to import a CSV containing the product catalog. When managing a mailing list, importing a CSV with all the email addresses is faster than adding each contact one by one.

The second use case is data analytics. The CSV format is flat and is perfectly compatible with spreadsheet or data analytics tools. As a result, it is a good source to analyze and visualize data. A CSV file has no size limit and can contain millions or more of records. It is a good file format to use in Big Data.


When to use a CSV file?

A CSV file is a text file used to store structured data. The CSV format is used to transfer structured data between applications or to export data in order to perform data analytics.
The first use case is data transfer. Applications such as e-commerce software, mailing list providers, or CRM deal can import and export data in the CSV format. In e-commerce software, it is common to import a CSV containing the product catalog. When managing a mailing list, importing a CSV with all the email addresses is faster than adding each contact one by one.
The second use case is data analytics. The CSV format is flat and is perfectly compatible with spreadsheet or data analytics tools. As a result, it is a good source to analyze and visualize data. A CSV file has no size limit and can contain millions or more of records. It is a good file format to use in Big Data.


Dedupe is a synonym for data deduplication or de-duplicate. Dedupe is the process of eliminating duplicate entries from a data set.
The term is used both in computing and for non-technical people.
In computing, deduping is performed in software development to remove duplicates in a list. Still with computing, data deduplication is used with files too. In a system that store files, deduplication is a technique to list all similar files and their reference in documents, emails, etc., and to remove all redundant files to keep only one version and have all references point to those single files.
Dedupe is also used for non-technical people to define the action of finding and merging redundant duplicates. A good example is CRM deduplication. CRM applications store contact information for leads, customers, etc. Several CRM records can represent the same person or business and hold different and complementary data. In this example, <a href="/features/duplicates-remover">merging duplicates</a> in the CRM is important to build a single view for every user. It also lower software cost as it reduces the number of items to store in a database.
<img src="/learn_images/csv/merge-duplicate-rows-modal.png#1732x1060" alt="Merging tool to dedupe contacts">
<a href="/how-to/remove-csv-duplicates">👉 Read our guide to dedupe CSV files</a>


Dedupe is a synonym for **data deduplication** or **de-duplicate**. Dedupe is the process of eliminating duplicate entries from a data set.

The term is used both in **computing** and for **non-technical people**.

**In computing**, deduping is performed in software development to remove duplicates in a list. Still with computing, data deduplication is used with files too. In a system that store files, deduplication is a technique to list all similar files and their reference in documents, emails, etc., and to remove all redundant files to keep only one version and have all references point to those single files.

**Dedupe is also used for non-technical people to define the action of finding and merging redundant duplicates.** A good example is CRM deduplication. CRM applications store contact information for leads, customers, etc. Several CRM records can represent the same person or business and hold different and complementary data. In this example, [merging duplicates](/features/duplicates-remover) in the CRM is important to build a single view for every user. It also lower software cost as it reduces the number of items to store in a database.

![Merging tool to dedupe contacts](/learn_images/csv/merge-duplicate-rows-modal.png#1732x1060)

[👉 Read our guide to dedupe CSV files](/how-to/remove-csv-duplicates)


What tools to dedupe a CSV file?

Dedupe is a synonym for data deduplication or de-duplicate. Dedupe is the process of eliminating duplicate entries from a data set.
The term is used both in computing and for non-technical people.
In computing, deduping is performed in software development to remove duplicates in a list. Still with computing, data deduplication is used with files too. In a system that store files, deduplication is a technique to list all similar files and their reference in documents, emails, etc., and to remove all redundant files to keep only one version and have all references point to those single files.
Dedupe is also used for non-technical people to define the action of finding and merging redundant duplicates. A good example is CRM deduplication. CRM applications store contact information for leads, customers, etc. Several CRM records can represent the same person or business and hold different and complementary data. In this example, merging duplicates in the CRM is important to build a single view for every user. It also lower software cost as it reduces the number of items to store in a database.
Merging tool to dedupe contacts
👉 Read our guide to dedupe CSV files


Join and Merge are two operations to combine data from several files. When merging, you are combining several files with the same structure into a single listing.
When joining, you are combining several files with different data structure but with at least one common field. This common field will be used as a key to combine the data and generate a single listing combining the fields from all the files.
<h2 id="user-content-merge-and-join-examples"><a href="#merge-and-join-examples" aria-hidden="true" tabindex="-1"></a>Merge and Join examples</h2>
<h3 id="user-content-merge-example"><a href="#merge-example" aria-hidden="true" tabindex="-1"></a>Merge example</h3>
Let's say you have a File A:
<pre><code> firstName | lastName | jobTitle
 Jean | Doe | CEO
 Morgan | Stanlet | Marketing Officer
</code></pre>
And a File B:
<pre><code> firstName | lastName | jobTitle
 Joe | Kanigan | Finance
 Marie | Filman | Dev
</code></pre>
Then, the resulting of a merge operation would be:
<pre><code> firstName | lastName | jobTitle
 Jean | Doe | CEO
 Morgan | Stanlet | Marketing Officer
 Joe | Kanigan | Finance
 Marie | Filman | Dev
</code></pre>
<h3 id="user-content-join-example"><a href="#join-example" aria-hidden="true" tabindex="-1"></a>Join example</h3>
Now, let's look at the join operation. When performing a join, your files can have different structures but must have a common identifier that will be used to combine the data together.
We have a File C:
<pre><code> email | jobTitle
 jean.doe@gmail.com | CEO
 morgan@gmail.com | Marketing Officer
</code></pre>
Than we want to join with a File D:
<pre><code> email | City
 jean.doe@gmail.com | Paris
 morgan@gmail.com | New York
</code></pre>
The resulting join operation using the <code>email</code> identifier is
<pre><code> email | jobTitle | City
 jean.doe@gmail.com | CEO | Paris
 morgan@gmail.com | Marketing Officer | New York
</code></pre>
A very common data manipulation task is to bring two or more sets of data together based on a common key.


Join and Merge are two operations to combine data from several files. When merging, you are combining several files **with the same structure** into a single listing.

When joining, you are combining several files **with different data structure** but with at least one common field. This common field will be used as a key to combine the data and generate a single listing combining the fields from all the files.


## Merge and Join examples

### Merge example

Let's say you have a *File A*:

```
  firstName |    lastName    |  jobTitle
    Jean    |       Doe      |  CEO
   Morgan   |     Stanlet    |  Marketing Officer
```


And a *File B*:

```
  firstName |    lastName    |  jobTitle
    Joe     |     Kanigan    |  Finance
   Marie    |     Filman     |  Dev
```

Then, the resulting of a merge operation would be:

```
  firstName |    lastName    |  jobTitle
    Jean    |       Doe      |  CEO
   Morgan   |     Stanlet    |  Marketing Officer
    Joe     |     Kanigan    |  Finance
   Marie    |     Filman     |  Dev
```


### Join example

Now, let's look at the join operation. When performing a join, your files can have different structures but must have a common identifier that will be used to combine the data together.

We have a *File C*:

```
          email           |      jobTitle
    jean.doe@gmail.com    |        CEO
     morgan@gmail.com     |    Marketing Officer
```

Than we want to join with a *File D*:

```
          email           |         City
    jean.doe@gmail.com    |        Paris
     morgan@gmail.com     |       New York
```

The resulting join operation using the `email` identifier is

```
          email           |      jobTitle         |       City
    jean.doe@gmail.com    |        CEO            |       Paris
     morgan@gmail.com     |    Marketing Officer  |      New York
```

A very common data manipulation task is to bring two or more sets of data together based on a common key.


Join and Merge are two operations to combine data from several files. When merging, you are combining several files with the same structure into a single listing.
When joining, you are combining several files with different data structure but with at least one common field. This common field will be used as a key to combine the data and generate a single listing combining the fields from all the files.
Merge and Join examples
Merge example
Let&amp#39;s say you have a File A:
  firstName |    lastName    |  jobTitle
    Jean    |       Doe      |  CEO
   Morgan   |     Stanlet    |  Marketing Officer
And a File B:
  firstName |    lastName    |  jobTitle
    Joe     |     Kanigan    |  Finance
   Marie    |     Filman     |  Dev
Then, the resulting of a merge operation would be:
  firstName |    lastName    |  jobTitle
    Jean    |       Doe      |  CEO
   Morgan   |     Stanlet    |  Marketing Officer
    Joe     |     Kanigan    |  Finance
   Marie    |     Filman     |  Dev
Join example
Now, let&amp#39;s look at the join operation. When performing a join, your files can have different structures but must have a common identifier that will be used to combine the data together.
We have a File C:
          email           |      jobTitle
    jean.doe@gmail.com    |        CEO
     morgan@gmail.com     |    Marketing Officer
Than we want to join with a File D:
          email           |         City
    jean.doe@gmail.com    |        Paris
     morgan@gmail.com     |       New York
The resulting join operation using the email identifier is
          email           |      jobTitle         |       City
    jean.doe@gmail.com    |        CEO            |       Paris
     morgan@gmail.com     |    Marketing Officer  |      New York
A very common data manipulation task is to bring two or more sets of data together based on a common key.


You can download sample CSV files here for testing purposes. The datasets can be used in any software application compatible with CSV files. An easy tool to edit CSV files online is our <a href="/csv-editor">CSV Editor</a>.
Five datasets are available:
<ul>
<li><a href="#customers-dataset">Customers</a> - <a href="#download-customers-sample-csv-files">Download</a></li>
<li><a href="#people-dataset">People</a> - <a href="#download-people-sample-csv-files">Download</a></li>
<li><a href="#organizations-dataset">Organizations</a> - <a href="#download-organizations-sample-csv-files">Download</a></li>
<li><a href="#leads-dataset">Leads</a> - <a href="#download-leads-sample-csv-files">Download</a></li>
<li><a href="#products-dataset">Products</a> - <a href="#download-products-sample-csv-files">Download</a></li>
</ul>
For each dataset, several CSV sizes are available, from 100 to 2 million records. The first line contains the CSV headers. An index column is set on each file. Rows have an index value which is incremental and starts at 1 for the first data row.
All datasets are free to download and play with. All the data is random and those files must only be used for testing.
The script used to generate all those CSV files is open source and <a href="https://github.com/datablist/sample-csv-files" target="_blank" rel="nofollow">available on Github</a>.
<h2 id="user-content-customers-dataset"><a href="#customers-dataset" aria-hidden="true" tabindex="-1"></a>Customers Dataset</h2>
<h3 id="user-content-customers-dataset-fields"><a href="#customers-dataset-fields" aria-hidden="true" tabindex="-1"></a>Customers Dataset Fields</h3>
<ul>
<li>Index</li>
<li>Customer Id</li>
<li>First Name</li>
<li>Last Name</li>
<li>Company</li>
<li>City</li>
<li>Country</li>
<li>Phone 1</li>
<li>Phone 2</li>
<li>Email</li>
<li>Subscription Date</li>
<li>Website</li>
</ul>
<h3 id="user-content-download-customers-sample-csv-files"><a href="#download-customers-sample-csv-files" aria-hidden="true" tabindex="-1"></a>Download Customers Sample CSV files</h3>
<ul>
<li><a href="https://drive.google.com/uc?id=1zO8ekHWx9U7mrbx_0Hoxxu6od7uxJqWw&#x26;export=download" target="_blank" rel="nofollow">customers-100.csv</a> - <a href="https://drive.google.com/uc?id=1yyL20BNKv3PxJRJVjJ_2Q-HidvIUis45&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Customers CSV with 100 records</li>
<li><a href="https://drive.google.com/uc?id=1OT84-j5J5z2tHoUvikJtoJFInWmlyYzY&#x26;export=download" target="_blank" rel="nofollow">customers-1000.csv</a> - <a href="https://drive.google.com/uc?id=17CIha7N1jOJWFd3G-CdlA5JplCnGj67x&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Customers CSV with 1000 records</li>
<li><a href="https://drive.google.com/uc?id=1x2IdSNcHGLmot9i1h90gwMJr5lULC2QV&#x26;export=download" target="_blank" rel="nofollow">customers-10000.csv</a> - <a href="https://drive.google.com/uc?id=1peI5sBNUVN_Q7qhgZwwCEejpMR1NjOwd&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Customers CSV with 10000 records</li>
<li><a href="https://drive.google.com/uc?id=1N1xoxgcw2K3d-49tlchXAWw4wuxLj7EV&#x26;export=download" target="_blank" rel="nofollow">customers-100000.csv</a> - <a href="https://drive.google.com/uc?id=1ZCVi_08A8W0f6q-2sVHDPyGzC0S7o4r4&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Customers CSV with 100000 records</li>
<li><a href="https://drive.google.com/uc?id=1f_lRSEobcCqoigHnc9mzli8fbK18loQm&#x26;export=download" target="_blank" rel="nofollow">customers-500000.csv</a> - Customers CSV with 500000 records</li>
<li><a href="https://drive.google.com/uc?id=16WH96smhIT0KK0ZVJRpjymLa_XDhKOoD&#x26;export=download" target="_blank" rel="nofollow">customers-1000000.csv</a> - Customers CSV with 1000000 records</li>
<li><a href="https://drive.google.com/uc?id=1IXQDp8Um3d-o7ysZLxkDyuvFj9gtlxqz&#x26;export=download" target="_blank" rel="nofollow">customers-2000000.csv</a> - Customers CSV with 2000000 records</li>
</ul>
<h3 id="user-content-customers-sample-csv-preview"><a href="#customers-sample-csv-preview" aria-hidden="true" tabindex="-1"></a>Customers Sample CSV Preview</h3>
Here are the 10 first lines for the Customers CSV sample:
<h2 id="user-content-people-dataset"><a href="#people-dataset" aria-hidden="true" tabindex="-1"></a>People Dataset</h2>
<h3 id="user-content-people-dataset-fields"><a href="#people-dataset-fields" aria-hidden="true" tabindex="-1"></a>People Dataset Fields</h3>
<ul>
<li>Index</li>
<li>User Id</li>
<li>First Name</li>
<li>Last Name</li>
<li>Sex</li>
<li>Email</li>
<li>Phone</li>
<li>Date of birth</li>
<li>Job Title</li>
</ul>
<h3 id="user-content-download-people-sample-csv-files"><a href="#download-people-sample-csv-files" aria-hidden="true" tabindex="-1"></a>Download People Sample CSV files</h3>
<ul>
<li><a href="https://drive.google.com/uc?id=1phaHg9objxK2MwaZmSUZAKQ8kVqlgng4&#x26;export=download" target="_blank" rel="nofollow">people-100.csv</a> - <a href="https://drive.google.com/uc?id=1BVcEHEiXiXCcfG7CIbvX3EotJhDknSw-&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - People CSV with 100 records</li>
<li><a href="https://drive.google.com/uc?id=1AWPf-pJodJKeHsARQK_RHiNsE8fjPCVK&#x26;export=download" target="_blank" rel="nofollow">people-1000.csv</a> - <a href="https://drive.google.com/uc?id=1s3761PwSCu8JzTRUctOy8CtncfXry2V8&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - People CSV with 1000 records</li>
<li><a href="https://drive.google.com/uc?id=1VEi-dnEh4RbBKa97fyl_Eenkvu2NC6ki&#x26;export=download" target="_blank" rel="nofollow">people-10000.csv</a> - <a href="https://drive.google.com/uc?id=1lJD9_S2XwIVLWyNSDqcIpT8Bqt7408p-&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - People CSV with 10000 records</li>
<li><a href="https://drive.google.com/uc?id=1NW7EnwxuY6RpMIxOazRVibOYrZfMjsb2&#x26;export=download" target="_blank" rel="nofollow">people-100000.csv</a> - <a href="https://drive.google.com/uc?id=1yDP1I1cbiY8LrZdamdfwwVlM7di4SEE_&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - People CSV with 100000 records</li>
<li><a href="https://drive.google.com/uc?id=1gYcKeeF2KIx3jHsn-Egc_zjv-VaI4LFw&#x26;export=download" target="_blank" rel="nofollow">people-500000.csv</a> - People CSV with 500000 records</li>
<li><a href="https://drive.google.com/uc?id=1CUl7o2GAsNuMas2pyjqmz5CavGV08Mje&#x26;export=download" target="_blank" rel="nofollow">people-1000000.csv</a> - People CSV with 1000000 records</li>
<li><a href="https://drive.google.com/uc?id=1fveqbEJIr4o4oMqswF03NA2Qrk1zF7v4&#x26;export=download" target="_blank" rel="nofollow">people-2000000.csv</a> - People CSV with 2000000 records</li>
</ul>
<h3 id="user-content-people-sample-csv-preview"><a href="#people-sample-csv-preview" aria-hidden="true" tabindex="-1"></a>People Sample CSV Preview</h3>
Here are the 10 first lines for the People CSV sample:
<h2 id="user-content-organizations-dataset"><a href="#organizations-dataset" aria-hidden="true" tabindex="-1"></a>Organizations Dataset</h2>
<ul>
<li>Index</li>
<li>Organization Id</li>
<li>Name</li>
<li>Website</li>
<li>Country</li>
<li>Description</li>
<li>Founded</li>
<li>Industry</li>
<li>Number of employees</li>
</ul>
<h3 id="user-content-download-organizations-sample-csv-files"><a href="#download-organizations-sample-csv-files" aria-hidden="true" tabindex="-1"></a>Download Organizations Sample CSV files</h3>
<ul>
<li><a href="https://drive.google.com/uc?id=13a2WyLoGxQKXbN_AIjrOogIlQKNe9uPm&#x26;export=download" target="_blank" rel="nofollow">organizations-100.csv</a> - <a href="https://drive.google.com/uc?id=1FE22Q358zt0DPgJu-Bflo2FCXD1X_pxl&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Organizations CSV with 100 records</li>
<li><a href="https://drive.google.com/uc?id=1AjP7Vy0apmPaBkMBzbtv_XiDATuoBJyg&#x26;export=download" target="_blank" rel="nofollow">organizations-1000.csv</a> - <a href="https://drive.google.com/uc?id=1nClHbAB2od_E4-Wx7V00ENLrNqsm_SDU&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Organizations CSV with 1000 records</li>
<li><a href="https://drive.google.com/uc?id=13p-box0F9kou4wE9AyeBNKMSfE767xT-&#x26;export=download" target="_blank" rel="nofollow">organizations-10000.csv</a> - <a href="https://drive.google.com/uc?id=1Xi1_OLMLS4ehBG4CGeyMt4kSyxrcsW7p&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Organizations CSV with 10000 records</li>
<li><a href="https://drive.google.com/uc?id=1g4wqEIsKyiBWeCAwd0wEkiC4Psc4zwFu&#x26;export=download" target="_blank" rel="nofollow">organizations-100000.csv</a> - <a href="https://drive.google.com/uc?id=1wtYMAcAHHwdgoSQoe6BJfTHDnkXrJt2d&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Organizations CSV with 100000 records</li>
<li><a href="https://drive.google.com/uc?id=1w50f3dqKgzSqdeBhB8tXQHCmahq_a6cL&#x26;export=download" target="_blank" rel="nofollow">organizations-500000.csv</a> - Organizations CSV with 500000 records</li>
<li><a href="https://drive.google.com/uc?id=1uaUCN5vAMVz73RgfJykJzzlIq2yQTlYB&#x26;export=download" target="_blank" rel="nofollow">organizations-1000000.csv</a> - Organizations CSV with 1000000 records</li>
<li><a href="https://drive.google.com/uc?id=18vlOi20KcMR328ewc2NBsoBNPrV3vL9Q&#x26;export=download" target="_blank" rel="nofollow">organizations-2000000.csv</a> - Organizations CSV with 2000000 records</li>
</ul>
<h3 id="user-content-organizations-sample-csv-preview"><a href="#organizations-sample-csv-preview" aria-hidden="true" tabindex="-1"></a>Organizations Sample CSV Preview</h3>
Here are the 10 first lines for the Organizations CSV sample:
<h2 id="user-content-leads-dataset"><a href="#leads-dataset" aria-hidden="true" tabindex="-1"></a>Leads Dataset</h2>
<h3 id="user-content-leads-sample-fields"><a href="#leads-sample-fields" aria-hidden="true" tabindex="-1"></a>Leads Sample Fields</h3>
<ul>
<li>Index</li>
<li>Account Id</li>
<li>Lead Owner</li>
<li>First Name</li>
<li>Last Name</li>
<li>Company</li>
<li>Phone 1</li>
<li>Phone 2</li>
<li>Email 1</li>
<li>Email 2</li>
<li>Website</li>
<li>Source</li>
<li>Deal Stage</li>
<li>Notes</li>
</ul>
<h3 id="user-content-download-leads-sample-csv-files"><a href="#download-leads-sample-csv-files" aria-hidden="true" tabindex="-1"></a>Download Leads Sample CSV files</h3>
<ul>
<li><a href="https://drive.google.com/uc?id=1lGNKLeKjS7n03JrEgtiGsw6hUMbdnOO5&#x26;export=download" target="_blank" rel="nofollow">leads-100.csv</a> - <a href="https://drive.google.com/uc?id=1v4cIPFyS38utm2LG3wr1Lj7czfgK0x7i&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Leads CSV with 100 records</li>
<li><a href="https://drive.google.com/uc?id=1oRXTuvdsZ0HKcpVzV7I5I9-BYKheR1RM&#x26;export=download" target="_blank" rel="nofollow">leads-1000.csv</a> - <a href="https://drive.google.com/uc?id=1EXby0tTtKWN_V75z7ezVuVHnPr99_oIV&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Leads CSV with 1000 records</li>
<li><a href="https://drive.google.com/uc?id=1IHpMYaUbCwOjzVANMm818FlFG0u3-9iC&#x26;export=download" target="_blank" rel="nofollow">leads-10000.csv</a> - <a href="https://drive.google.com/uc?id=1BKpNWE1NcKTl7rN-52o83nZbTX3sqX9E&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Leads CSV with 10000 records</li>
<li><a href="https://drive.google.com/uc?id=1mCFMwc_Y0nU8G99-AUznqqBvh6t2Kg8B&#x26;export=download" target="_blank" rel="nofollow">leads-100000.csv</a> - <a href="https://drive.google.com/uc?id=1qW9jbDnlFzxVr8QbYipEohvvSAj6yXSi&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Leads CSV with 100000 records</li>
</ul>
<h3 id="user-content-leads-sample-csv-preview"><a href="#leads-sample-csv-preview" aria-hidden="true" tabindex="-1"></a>Leads Sample CSV Preview</h3>
Here are the 10 first lines for the Leads CSV sample:
<h2 id="user-content-products-dataset"><a href="#products-dataset" aria-hidden="true" tabindex="-1"></a>Products Dataset</h2>
<h3 id="user-content-products-sample-fields"><a href="#products-sample-fields" aria-hidden="true" tabindex="-1"></a>Products Sample Fields</h3>
<ul>
<li>Index</li>
<li>Name</li>
<li>Description</li>
<li>Brand</li>
<li>Category</li>
<li>Price</li>
<li>Currency</li>
<li>Stock</li>
<li>EAN</li>
<li>Color</li>
<li>Size</li>
<li>Availability</li>
<li>Internal ID</li>
</ul>
<h3 id="user-content-download-products-sample-csv-files"><a href="#download-products-sample-csv-files" aria-hidden="true" tabindex="-1"></a>Download Products Sample CSV files</h3>
<ul>
<li><a href="https://drive.google.com/uc?id=107Sqmt1sk6oGcKL_TwNVqz5wMJBavsoq&#x26;export=download" target="_blank" rel="nofollow">products-100.csv</a> - <a href="https://drive.google.com/uc?id=1QelGM4sjuCbLQ8eJBkHuagsDyQZm5YKC&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 100 records</li>
<li><a href="https://drive.google.com/uc?id=1aZNLCVO0VoHmvPNvSC2teeFmZfiViE6u&#x26;export=download" target="_blank" rel="nofollow">products-1000.csv</a> - <a href="https://drive.google.com/uc?id=1uyd43hna_IVm6ZitUBLty5-GVUSGfMVK&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 1000 records</li>
<li><a href="https://drive.google.com/uc?id=1BE-dfkrb6oyLKDuqXAq2fDYMkDz2f9hM&#x26;export=download" target="_blank" rel="nofollow">products-10000.csv</a> - <a href="https://drive.google.com/uc?id=1MOB6cYB3GtzU88lf4XKqNEJMl2HRYrZd&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 10000 records</li>
<li><a href="https://drive.google.com/uc?id=1xkWZDg0qDEsDBDeSg7VSn5YcCLxQTmin&#x26;export=download" target="_blank" rel="nofollow">products-100000.csv</a> - <a href="https://drive.google.com/uc?id=103kKeLAlkdgn1oysubJPU7vnE0x0x58a&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 100000 records</li>
<li><a href="https://drive.google.com/uc?id=1u5t7wfkWDW4W2TYJDuh1m0pa-7E0_Sr3&#x26;export=download" target="_blank" rel="nofollow">products-1000000.csv</a> - <a href="https://drive.google.com/uc?id=1kAyOE06v069e-i9XA45Y2kHJpNAMqZiJ&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 1000000 records</li>
<li><a href="https://drive.google.com/uc?id=18BLAZDeH74Ll3b4GsMNY3s-YVnNmWblC&#x26;export=download" target="_blank" rel="nofollow">products-2000000.csv</a> - <a href="https://drive.google.com/uc?id=1LU_DT7j5YjTMnNHeI0VSTWMsHveSRpoz&#x26;export=download" target="_blank" rel="nofollow">Zip version</a> - Products CSV with 2000000 records</li>
</ul>
<h3 id="user-content-products-sample-csv-preview"><a href="#products-sample-csv-preview" aria-hidden="true" tabindex="-1"></a>Products Sample CSV Preview</h3>
Here are the 10 first lines for the Products CSV sample:

You can download sample CSV files here for testing purposes. The datasets can be used in any software application compatible with CSV files. An easy tool to edit CSV files online is our CSV Editor.
Five datasets are available:
Customers - Download
People - Download
Organizations - Download
Leads - Download
Products - Download

For each dataset, several CSV sizes are available, from 100 to 2 million records. The first line contains the CSV headers. An index column is set on each file. Rows have an index value which is incremental and starts at 1 for the first data row.
All datasets are free to download and play with. All the data is random and those files must only be used for testing.
The script used to generate all those CSV files is open source and available on Github.
Customers Dataset
Customers Dataset Fields
Index
Customer Id
First Name
Last Name
Company
City
Country
Phone 1
Phone 2
Email
Subscription Date
Website

Download Customers Sample CSV files
customers-100.csv - Zip version - Customers CSV with 100 records
customers-1000.csv - Zip version - Customers CSV with 1000 records
customers-10000.csv - Zip version - Customers CSV with 10000 records
customers-100000.csv - Zip version - Customers CSV with 100000 records
customers-500000.csv - Customers CSV with 500000 records
customers-1000000.csv - Customers CSV with 1000000 records
customers-2000000.csv - Customers CSV with 2000000 records

Customers Sample CSV Preview
Here are the 10 first lines for the Customers CSV sample:
People Dataset
People Dataset Fields
Index
User Id
First Name
Last Name
Sex
Email
Phone
Date of birth
Job Title

Download People Sample CSV files
people-100.csv - Zip version - People CSV with 100 records
people-1000.csv - Zip version - People CSV with 1000 records
people-10000.csv - Zip version - People CSV with 10000 records
people-100000.csv - Zip version - People CSV with 100000 records
people-500000.csv - People CSV with 500000 records
people-1000000.csv - People CSV with 1000000 records
people-2000000.csv - People CSV with 2000000 records

People Sample CSV Preview
Here are the 10 first lines for the People CSV sample:
Organizations Dataset
Index
Organization Id
Name
Website
Country
Description
Founded
Industry
Number of employees

Download Organizations Sample CSV files
organizations-100.csv - Zip version - Organizations CSV with 100 records
organizations-1000.csv - Zip version - Organizations CSV with 1000 records
organizations-10000.csv - Zip version - Organizations CSV with 10000 records
organizations-100000.csv - Zip version - Organizations CSV with 100000 records
organizations-500000.csv - Organizations CSV with 500000 records
organizations-1000000.csv - Organizations CSV with 1000000 records
organizations-2000000.csv - Organizations CSV with 2000000 records

Organizations Sample CSV Preview
Here are the 10 first lines for the Organizations CSV sample:
Leads Dataset
Leads Sample Fields
Index
Account Id
Lead Owner
First Name
Last Name
Company
Phone 1
Phone 2
Email 1
Email 2
Website
Source
Deal Stage
Notes

Download Leads Sample CSV files
leads-100.csv - Zip version - Leads CSV with 100 records
leads-1000.csv - Zip version - Leads CSV with 1000 records
leads-10000.csv - Zip version - Leads CSV with 10000 records
leads-100000.csv - Zip version - Leads CSV with 100000 records

Leads Sample CSV Preview
Here are the 10 first lines for the Leads CSV sample:
Products Dataset
Products Sample Fields
Index
Name
Description
Brand
Category
Price
Currency
Stock
EAN
Color
Size
Availability
Internal ID

Download Products Sample CSV files
products-100.csv - Zip version - Products CSV with 100 records
products-1000.csv - Zip version - Products CSV with 1000 records
products-10000.csv - Zip version - Products CSV with 10000 records
products-100000.csv - Zip version - Products CSV with 100000 records
products-1000000.csv - Zip version - Products CSV with 1000000 records
products-2000000.csv - Zip version - Products CSV with 2000000 records

Products Sample CSV Preview
Here are the 10 first lines for the Products CSV sample:


Filtering CSV files is an essential skill for data analysts, marketers, and anyone working with CSV datasets.
Whether you're cleaning web-scraped data or organizing customer information, filtering CSV files with a NoCode tool will save you time and improve your productivity.
<img src="/learn_images/csv/filter-csv/filter-csv.png#2878x1385" alt="Filter CSV File">
<h2 id="user-content-step-by-step-guide-to-filter-csv-files"><a href="#step-by-step-guide-to-filter-csv-files" aria-hidden="true" tabindex="-1"></a>Step-by-Step Guide to Filter CSV Files</h2>
<h3 id="user-content-step-1-load-your-csv-file"><a href="#step-1-load-your-csv-file" aria-hidden="true" tabindex="-1"></a>Step 1: Load Your CSV File</h3>
The first step is to load your CSV file into Datablist. Datablist is a powerful free NoCode tool that revolutionizes the way users <a href="/csv-editor">handle CSV data</a> filtering and manipulation.
Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.
<img src="/learn_images/csv/filter-csv/import-csv.png#2155x1326" alt="Filter CSV File">
<h3 id="user-content-step-2-apply-filters"><a href="#step-2-apply-filters" aria-hidden="true" tabindex="-1"></a>Step 2: Apply filters</h3>
Datablist offers two ways to filter your data:
<ul>
<li>Property filtering - Define filter criteria on one or several properties</li>
<li>Full-text search - Will match any rows in your CSV that contain the keyword</li>
</ul>
<h3 id="user-content-property-filtering"><a href="#property-filtering" aria-hidden="true" tabindex="-1"></a>Property Filtering</h3>
Once the CSV file is loaded, add filters by clicking on a property header.
<img src="/learn_images/csv/filter-csv/filter-property.png#2008x1076" alt="Add Filter">
Or just open the "Filtering" tool.
<img src="/learn_images/csv/filter-csv/filters-modal.png#1596x822" alt="Filters Modal">
This opens the "Filtering" modal.
Define your filter operators to select or exclude specific data.
<img src="/learn_images/csv/filter-csv/datablist-filters-condition-modal.png#1840x772" alt="Filters modal">
List of filter operators for Text:
<ul>
<li>is - Insensitive equal comparison. Leading and trailing spaces are not removed.
<ul>
<li><code>HeLLo</code> and <code>hello</code> match (case insensitive)</li>
<li>"   john  " (notice the spaces) and <code>john</code> don't match</li>
</ul>
</li>
<li>is not - Opposite of "is".</li>
<li>contains" - Insensitive text contains.
<ul>
<li>The value <code>john@GMAIL.com</code> with the "contains" filter <code>gmail</code> matches</li>
</ul>
</li>
<li>does not contain - Opposite of "contains".</li>
<li>startswith - Insensitive text starts with
<ul>
<li>The value <code>+3302934092309</code> with the "startswith" filter <code>+33</code> matches</li>
<li>The value <code>JOHN doe</code> with the "startswith" filter <code>john</code> matches</li>
</ul>
</li>
<li>endswith - Insentivice text ends with
<ul>
<li>The value <code>john@GMAIL.com</code> with the "endswith" filter <code>gmail.com</code> matches</li>
</ul>
</li>
<li>in - Return items that match at least one of the comma-separated values. The comparison is case-insensitive.
<ul>
<li>If the "in" filter is "France, Italy, Germany, USA". An item with the value "italy" matches.</li>
</ul>
</li>
<li>not in - Return items with values that match none of the comma-separated values. The comparison is case-insensitive.
<ul>
<li>If the "not in** filter is "France, Italy, Germany, USA". An the item with the value "belgium" matches.</li>
</ul>
</li>
<li>is empty - Match on empty or only spaces text values
<ul>
<li>"    " (spaces) match</li>
<li>"" match</li>
</ul>
</li>
<li>is not empty - Opposite of "is empty".</li>
<li>regexp - Insensitive Regex matching. See below.</li>
</ul>
List of filter operators for DateTime:
<ul>
<li>is before - Compare the DateTime value with an absolute date and time.</li>
<li>is before - relative - Check <a href="/docs/search-filter-items#relative-datetime-filtering">Relative datetime filtering</a></li>
<li>is after - Compare the DateTime value with an absolute date and time.</li>
<li>is after - relative - Check <a href="/docs/search-filter-items#relative-datetime-filtering">Relative datetime filtering</a></li>
<li>is empty - Empty DateTime value</li>
<li>is not empty - Opposite of <code>is empty</code></li>
</ul>
List of filter operators for Numbers:
<ul>
<li>= - Equal to</li>
<li>≠ - Not Equal to</li>
<li>&#x3C; - Less than (strict) - Equal numbers don't match</li>
<li>> - Greater than (strict) - Equal numbers don't match</li>
<li>≤ - Less than or equal to</li>
<li>≥ - Greater than or equal to</li>
<li>is empty - Empty cell. 0 doesn't match.</li>
<li>is not empty - Opposite of <code>is empty</code></li>
</ul>
You can combine multiple filter criterias using "AND" and "OR" operators. <a href="/docs/search-filter-items#combining-several-filter-conditions">Read more about combining multiple filter criterias.</a>
<h3 id="user-content-full-text-search"><a href="#full-text-search" aria-hidden="true" tabindex="-1"></a>Full-text search</h3>
Filtering your items using full-text search is simple. Datablist performs a full-text search on all of your item property values in seconds.
The search input is located in the <a href="/docs/introduction#collection-header">collection header</a>. A fast way to start a search is using the keyboard shortcut Ctrl + f.
<img src="/learn_images/csv/filter-csv/datablist-full-search.png#1956x65" alt="Filter CSV File">
<h3 id="user-content-step-4-save--export-filtered-csv-data"><a href="#step-4-save--export-filtered-csv-data" aria-hidden="true" tabindex="-1"></a>Step 4: Save &#x26; export filtered CSV data</h3>
Once your CSV file is filtered, click the "Export" button to generate a new CSV file with your cleaned and filtered data.
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-what-is-a-csv-file"><a href="#what-is-a-csv-file" aria-hidden="true" tabindex="-1"></a>What is a CSV file?</h3>
CSV (Comma Separated Value) files store structured data in text files, with each line representing a data record and fields separated by commas, semicolons, or tabs. They are widely used for transferring data between applications due to their simplicity.
However, because the CSV format isn't standardized, encoding, delimiters, and escaping rules can vary. Most applications offer different options for reading CSV files. <a href="/learn/csv/csv-meaning">More about CSV files.</a>
<h3 id="user-content-how-much-does-it-cost-to-filter-a-csv-file"><a href="#how-much-does-it-cost-to-filter-a-csv-file" aria-hidden="true" tabindex="-1"></a>How much does it cost to filter a CSV file?</h3>
Datablist provides filtering features for free. Some advanced filtering operators such as RegEx, In, Not-In filterings, require a paid plan. <a href="/pricing">See pricing.</a>
<h3 id="user-content-why-filter-csv-files"><a href="#why-filter-csv-files" aria-hidden="true" tabindex="-1"></a>Why Filter CSV Files?</h3>
CSV (Comma-Separated Values) files are widely used for storing and transferring data. However, raw CSV files often contain irrelevant or messy data. Filtering allows you to:
<ul>
<li>Remove duplicate entries</li>
<li>Extract specific information</li>
<li>Clean and normalize data</li>
<li>Prepare datasets for analysis</li>
</ul>

Filtering CSV files is an essential skill for data analysts, marketers, and anyone working with CSV datasets. 

Whether you're cleaning web-scraped data or organizing customer information, filtering CSV files with a NoCode tool will save you time and improve your productivity.

![Filter CSV File](/learn_images/csv/filter-csv/filter-csv.png#2878x1385)

## Step-by-Step Guide to Filter CSV Files

### Step 1: Load Your CSV File

The first step is to load your CSV file into Datablist. Datablist is a powerful **free** NoCode tool that revolutionizes the way users [handle CSV data](/csv-editor) filtering and manipulation.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

Create a new collection ("+" button in the left sidebar) and then click "Import CSV" to load your file.

![Filter CSV File](/learn_images/csv/filter-csv/import-csv.png#2155x1326)

### Step 2: Apply filters

Datablist offers two ways to filter your data:

- **Property filtering** - Define filter criteria on one or several properties
- **Full-text search** - Will match any rows in your CSV that contain the keyword

### Property Filtering

Once the CSV file is loaded, add filters by clicking on a property header.

![Add Filter](/learn_images/csv/filter-csv/filter-property.png#2008x1076)

Or just open the "Filtering" tool.

![Filters Modal](/learn_images/csv/filter-csv/filters-modal.png#1596x822)

This opens the "Filtering" modal.
Define your filter operators to select or exclude specific data. 

![Filters modal](/learn_images/csv/filter-csv/datablist-filters-condition-modal.png#1840x772)

List of filter operators for Text:

- **is** - Insensitive equal comparison. Leading and trailing spaces are not removed.
 - `HeLLo` and `hello` match (case insensitive)
 - "&nbsp;&nbsp;&nbsp;john&nbsp;&nbsp;" (notice the spaces) and `john` **don't** match
- **is not** - Opposite of "is".
- **contains"** - Insensitive text contains. 
 - The value `john@GMAIL.com` with the "contains" filter `gmail` matches
- **does not contain** - Opposite of "contains".
- **startswith** - Insensitive text starts with
 - The value `+3302934092309` with the "startswith" filter `+33` matches
 - The value `JOHN doe` with the "startswith" filter `john` matches
- **endswith** - Insentivice text ends with
 - The value `john@GMAIL.com` with the "endswith" filter `gmail.com` matches
- **in** - Return items that match at least one of the comma-separated values. The comparison is case-insensitive.
 - If the "in" filter is "France, Italy, Germany, USA". An item with the value "italy" matches.
- **not in** - Return items with values that match none of the comma-separated values. The comparison is case-insensitive.
 - If the "not in** filter is "France, Italy, Germany, USA". An the item with the value "belgium" matches.
- **is empty** - Match on empty or only spaces text values
 - "&nbsp;&nbsp;&nbsp;&nbsp;" (spaces) match
 - "" match
- **is not empty** - Opposite of "is empty".
- **regexp** - Insensitive Regex matching. See below.


List of filter operators for DateTime:

- **is before** - Compare the DateTime value with an absolute date and time.
- **is before - relative** - Check [Relative datetime filtering](/docs/search-filter-items#relative-datetime-filtering)
- **is after** - Compare the DateTime value with an absolute date and time.
- **is after - relative** - Check [Relative datetime filtering](/docs/search-filter-items#relative-datetime-filtering)
- **is empty** - Empty DateTime value
- **is not empty** - Opposite of `is empty`

List of filter operators for Numbers:

- **=** - Equal to
- **≠** - Not Equal to
- **<** - Less than (strict) - Equal numbers don't match
- **>** - Greater than (strict) - Equal numbers don't match
- **≤** - Less than or equal to
- **≥** - Greater than or equal to
- **is empty** - Empty cell. 0 doesn't match.
- **is not empty** - Opposite of `is empty`

You can combine multiple filter criterias using "AND" and "OR" operators. [Read more about combining multiple filter criterias.](/docs/search-filter-items#combining-several-filter-conditions)

### Full-text search

Filtering your items using full-text search is simple. Datablist performs a full-text search on all of your item property values in seconds.

The search input is located in the [collection header](/docs/introduction#collection-header). A fast way to start a search is using the keyboard shortcut **Ctrl + f**.

![Filter CSV File](/learn_images/csv/filter-csv/datablist-full-search.png#1956x65)


### Step 4: Save & export filtered CSV data

Once your CSV file is filtered, click the "Export" button to generate a new CSV file with your cleaned and filtered data.

## FAQ

### What is a CSV file?

CSV (Comma Separated Value) files store structured data in text files, with each line representing a data record and fields separated by commas, semicolons, or tabs. They are widely used for transferring data between applications due to their simplicity.
However, because the CSV format isn't standardized, encoding, delimiters, and escaping rules can vary. Most applications offer different options for reading CSV files. [More about CSV files.](/learn/csv/csv-meaning)

### How much does it cost to filter a CSV file?

Datablist provides filtering features for free. Some advanced filtering operators such as RegEx, In, Not-In filterings, require a paid plan. [See pricing.](/pricing)

### Why Filter CSV Files?

CSV (Comma-Separated Values) files are widely used for storing and transferring data. However, raw CSV files often contain irrelevant or messy data. Filtering allows you to:

- Remove duplicate entries
- Extract specific information
- Clean and normalize data
- Prepare datasets for analysis

How to filter a CSV file?

Filtering CSV files is an essential skill for data analysts, marketers, and anyone working with CSV datasets. 
Whether you&amp#39;re cleaning web-scraped data or organizing customer information, filtering CSV files with a NoCode tool will save you time and improve your productivity.
Filter CSV File
Step-by-Step Guide to Filter CSV Files
Step 1: Load Your CSV File
The first step is to load your CSV file into Datablist. Datablist is a powerful free NoCode tool that revolutionizes the way users handle CSV data filtering and manipulation.
Create a new collection (&ampquot;+&ampquot; button in the left sidebar) and then click &ampquot;Import CSV&ampquot; to load your file.
Filter CSV File
Step 2: Apply filters
Datablist offers two ways to filter your data:
Property filtering - Define filter criteria on one or several properties
Full-text search - Will match any rows in your CSV that contain the keyword

Property Filtering
Once the CSV file is loaded, add filters by clicking on a property header.
Add Filter
Or just open the &ampquot;Filtering&ampquot; tool.
Filters Modal
This opens the &ampquot;Filtering&ampquot; modal.
Define your filter operators to select or exclude specific data. 
Filters modal
List of filter operators for Text:
is - Insensitive equal comparison. Leading and trailing spaces are not removed.HeLLo and hello match (case insensitive)
&ampquot;&ampnbsp;&ampnbsp;&ampnbsp;john&ampnbsp;&ampnbsp;&ampquot; (notice the spaces) and john don&amp#39;t match


is not - Opposite of &ampquot;is&ampquot;.
contains&ampquot; - Insensitive text contains. The value john@GMAIL.com with the &ampquot;contains&ampquot; filter gmail matches


does not contain - Opposite of &ampquot;contains&ampquot;.
startswith - Insensitive text starts withThe value +3302934092309 with the &ampquot;startswith&ampquot; filter +33 matches
The value JOHN doe with the &ampquot;startswith&ampquot; filter john matches


endswith - Insentivice text ends withThe value john@GMAIL.com with the &ampquot;endswith&ampquot; filter gmail.com matches


in - Return items that match at least one of the comma-separated values. The comparison is case-insensitive.If the &ampquot;in&ampquot; filter is &ampquot;France, Italy, Germany, USA&ampquot;. An item with the value &ampquot;italy&ampquot; matches.


not in - Return items with values that match none of the comma-separated values. The comparison is case-insensitive.If the &ampquot;not in** filter is &ampquot;France, Italy, Germany, USA&ampquot;. An the item with the value &ampquot;belgium&ampquot; matches.


is empty - Match on empty or only spaces text values&ampquot;&ampnbsp;&ampnbsp;&ampnbsp;&ampnbsp;&ampquot; (spaces) match
&ampquot;&ampquot; match


is not empty - Opposite of &ampquot;is empty&ampquot;.
regexp - Insensitive Regex matching. See below.

List of filter operators for DateTime:
is before - Compare the DateTime value with an absolute date and time.
is before - relative - Check Relative datetime filtering
is after - Compare the DateTime value with an absolute date and time.
is after - relative -  Check Relative datetime filtering
is empty - Empty DateTime value
is not empty - Opposite of is empty

List of filter operators for Numbers:
= - Equal to
≠ - Not Equal to
&amplt; - Less than (strict) - Equal numbers don&amp#39;t match
&ampgt; - Greater than (strict) - Equal numbers don&amp#39;t match
≤ - Less than or equal to
≥ - Greater than or equal to
is empty - Empty cell. 0 doesn&amp#39;t match.
is not empty - Opposite of is empty

You can combine multiple filter criterias using &ampquot;AND&ampquot; and &ampquot;OR&ampquot; operators. Read more about combining multiple filter criterias.
Full-text search
Filtering your items using full-text search is simple. Datablist performs a full-text search on all of your item property values in seconds.
The search input is located in the collection header. A fast way to start a search is using the keyboard shortcut Ctrl + f.
Filter CSV File
Step 4: Save &ampamp; export filtered CSV data
Once your CSV file is filtered, click the &ampquot;Export&ampquot; button to generate a new CSV file with your cleaned and filtered data.
FAQ
What is a CSV file?
CSV (Comma Separated Value) files store structured data in text files, with each line representing a data record and fields separated by commas, semicolons, or tabs. They are widely used for transferring data between applications due to their simplicity.
However, because the CSV format isn&amp#39;t standardized, encoding, delimiters, and escaping rules can vary. Most applications offer different options for reading CSV files. More about CSV files.
How much does it cost to filter a CSV file?
Datablist provides filtering features for free. Some advanced filtering operators such as RegEx, In, Not-In filterings, require a paid plan. See pricing.
Why Filter CSV Files?
CSV (Comma-Separated Values) files are widely used for storing and transferring data. However, raw CSV files often contain irrelevant or messy data. Filtering allows you to:
Remove duplicate entries
Extract specific information
Clean and normalize data
Prepare datasets for analysis



<ol>
<li>Open the Terminal. (on macOs, you can use Spotlight with keyboard shortcut ⌘ + Space)
<img src="/learn_images/csv/words-count/open-terminal.png#1675x607" alt="Open Terminal using Mac Spotlight"></li>
<li>Move terminal working directory to the folder containing your CSV file. Use ls to show current directory files and folders and cd to move inside a folder.</li>
<li>Enter the following command. Replace filepath with your CSV file path:</li>
</ol>
<pre><code>wc -l filepath.csv
</code></pre>
<ol start="4">
<li>The line count will be printed before the file name
<img src="/learn_images/csv/words-count/word-count.png#1448x518" alt="Word Count Command"></li>
</ol>
The wc command is used to count the number of words or lines (with the -l option) in a file. In a CSV file, each line is a data record. Counting the number of rows is an easy way to have the number of records in your CSV file.
<h2 id="user-content-get-the-number-of-rows-containing-a-keyword-in-a-csv-file"><a href="#get-the-number-of-rows-containing-a-keyword-in-a-csv-file" aria-hidden="true" tabindex="-1"></a>Get the number of rows containing a keyword in a CSV file</h2>
If you want to filter rows in your CSV file and count the matching lines, combine wc with the command grep.
<pre><code>grep "keyword" filepath.csv | wc -l
</code></pre>
<img src="/learn_images/csv/words-count/matching-rows-count.png#1336x518" alt="Filter and Count Lines">
You can also search for multiple keywords with grep using a regular expression (the -E option is for --extended-regexp). Any line with at least one of the keywords will be counted.
<pre><code>grep -E "keyword1|keyword2" filepath.csv | wc -l
</code></pre>
<img src="/learn_images/csv/words-count/sum-rows-multiple-patterns.png#1336x462" alt="Filter and Count Lines with multiple keywords">
By default, grep is case sensitive (uppercase and lowercase characters are distinct). To ignore case in the search, add the -i (for --ignore-case) in your command:
<pre><code>grep -i "keyword" filepath.csv | wc -l
</code></pre>
or
<pre><code>grep -i -E "keyword1|keyword2" filepath.csv | wc -l
</code></pre>
<h2 id="user-content-get-the-number-of-rows-of-multiple-csv-files"><a href="#get-the-number-of-rows-of-multiple-csv-files" aria-hidden="true" tabindex="-1"></a>Get the number of rows of multiple CSV files</h2>
To have the sum of the rows of all the CSV files in the current folder, use the following command:
<pre><code>cat *.csv | wc -l
</code></pre>
<img src="/learn_images/csv/words-count/sum-rows-csv-files.png#1336x518" alt="Get the sum of all csv files">
To view the content of your CSV file, you can uses an online <a href="/csv-editor">CSV editor</a>.



 1. Open the Terminal. (on macOs, you can use Spotlight with keyboard shortcut **⌘** + **Space**)
    ![Open Terminal using Mac Spotlight](/learn_images/csv/words-count/open-terminal.png#1675x607)
 2. Move terminal working directory to the folder containing your CSV file. Use **ls** to show current directory files and folders and **cd** to move inside a folder.
 3. Enter the following command. Replace *filepath* with your CSV file path:
 ```
 wc -l filepath.csv
 ```
 4. The line count will be printed before the file name
 ![Word Count Command](/learn_images/csv/words-count/word-count.png#1448x518)


 The **wc** command is used to count the number of words or lines (with the **-l** option) in a file. In a CSV file, each line is a data record. Counting the number of rows is an easy way to have the number of records in your CSV file.


## Get the number of rows containing a keyword in a CSV file

If you want to filter rows in your CSV file and count the matching lines, combine **wc** with the command **grep**.

```
grep "keyword" filepath.csv | wc -l
```

![Filter and Count Lines](/learn_images/csv/words-count/matching-rows-count.png#1336x518)

You can also search for multiple keywords with **grep** using a regular expression (the **-E** option is for **--extended-regexp**). Any line with at least one of the keywords will be counted.

```
grep -E "keyword1|keyword2" filepath.csv | wc -l
```

![Filter and Count Lines with multiple keywords](/learn_images/csv/words-count/sum-rows-multiple-patterns.png#1336x462)

By default, **grep** is case sensitive (uppercase and lowercase characters are distinct). To ignore case in the search, add the **-i** (for --ignore-case) in your command:

```
grep -i "keyword" filepath.csv | wc -l
```
or
```
grep -i -E "keyword1|keyword2" filepath.csv | wc -l
```


## Get the number of rows of multiple CSV files

To have the sum of the rows of all the CSV files in the current folder, use the following command:

```
cat *.csv | wc -l
```

![Get the sum of all csv files](/learn_images/csv/words-count/sum-rows-csv-files.png#1336x518)



To view the content of your CSV file, you can uses an online [CSV editor](/csv-editor).


Count the number of rows of a CSV file on Linux or macOS Terminal

Open the Terminal. (on macOs, you can use Spotlight with keyboard shortcut ⌘ + Space)
Open Terminal using Mac Spotlight
Move terminal working directory to the folder containing your CSV file. Use ls to show current directory files and folders and cd to move inside a folder.
Enter the following command. Replace filepath with your CSV file path:wc -l filepath.csv

The line count will be printed before the file name
Word Count Command

 The wc command is used to count the number of words or lines (with the -l option) in a file. In a CSV file, each line is a data record. Counting the number of rows is an easy way to have the number of records in your CSV file.
Get the number of rows containing a keyword in a CSV file
If you want to filter rows in your CSV file and count the matching lines, combine wc with the command grep.
grep &quotkeyword&quot filepath.csv | wc -l
Filter and Count Lines
You can also search for multiple keywords with grep using a regular expression (the -E option is for --extended-regexp). Any line with at least one of the keywords will be counted.
grep -E &quotkeyword1|keyword2&quot filepath.csv | wc -l
Filter and Count Lines with multiple keywords
By default, grep is case sensitive (uppercase and lowercase characters are distinct). To ignore case in the search, add the -i (for --ignore-case) in your command:
grep -i &quotkeyword&quot filepath.csv | wc -l
or
grep -i -E &quotkeyword1|keyword2&quot filepath.csv | wc -l
Get the number of rows of multiple CSV files
To have the sum of the rows of all the CSV files in the current folder, use the following command:
cat *.csv | wc -l
Get the sum of all csv files
To view the content of your CSV file, you can uses an online CSV editor.


CSV files are simple text files. A way to look at it is to use a text editor. But CSV files contain structured data and can be hard to navigate with a text editor.
The easier way to open a CSV file is using a spreadsheet application.
Microsoft Excel, Apple Numbers, or Google Sheets can open <code>.csv</code> files. Once open, you will be able to view and edit your data directly in the spreadsheet application.
<img src="/learn_images/csv/google_sheet_csv.png#1616x886" alt="CSV file in Google Sheets">
However, spreadsheet apps are designed for accounting and lack common features when it comes to structured data management. In <a href="https://www.datablist.com/" target="_blank" rel="nofollow">Datablist</a>, CSV files are first-class citizens! Open, define data types, and manage your data directly in our <a href="https://www.datablist.com/csv-editor" target="_blank" rel="nofollow">CSV Editor</a>.
Larger files (more than 1 million rows) require specific tools. For those files, use data analytics software, big data tools, or custom development to process your files.


CSV files are simple text files. A way to look at it is to use a text editor. But CSV files contain structured data and can be hard to navigate with a text editor.

The easier way to open a CSV file is using a spreadsheet application.
Microsoft Excel, Apple Numbers, or Google Sheets can open `.csv` files. Once open, you will be able to view and edit your data directly in the spreadsheet application.

![CSV file in Google Sheets](/learn_images/csv/google_sheet_csv.png#1616x886)

However, spreadsheet apps are designed for accounting and lack common features when it comes to structured data management. In [Datablist](https://www.datablist.com/), CSV files are first-class citizens! Open, define data types, and manage your data directly in our [CSV Editor](https://www.datablist.com/csv-editor).

**Larger files (more than 1 million rows) require specific tools.** For those files, use data analytics software, big data tools, or custom development to process your files.


What tools to open a CSV file?

CSV files are simple text files. A way to look at it is to use a text editor. But CSV files contain structured data and can be hard to navigate with a text editor.
The easier way to open a CSV file is using a spreadsheet application.
Microsoft Excel, Apple Numbers, or Google Sheets can open .csv files. Once open, you will be able to view and edit your data directly in the spreadsheet application.
CSV file in Google Sheets
However, spreadsheet apps are designed for accounting and lack common features when it comes to structured data management. In Datablist, CSV files are first-class citizens! Open, define data types, and manage your data directly in our CSV Editor.
Larger files (more than 1 million rows) require specific tools. For those files, use data analytics software, big data tools, or custom development to process your files.


Swapping and/or removing CSV columns is common in data manipulation.
<a href="/csv-editor">Datablist CSV Editor</a> is the easiest way to reorder and/or remove columns in a CSV file online. For free.
<h2 id="user-content-step-1-open-your-csv-online"><a href="#step-1-open-your-csv-online" aria-hidden="true" tabindex="-1"></a>Step 1: Open your CSV online</h2>
First, open your CSV file online with Datablist <a href="/csv-editor">CSV Editor</a>.
Then, click on the "Start with a CSV/Excel file" button.
<img src="/learn_images/csv/reorder-csv-columns/open-csv-file.png#2145x1320" alt="Open CSV file">
Drag and drop your CSV file, or click on the grey zone to browse your files.
<img src="/learn_images/csv/reorder-csv-columns/load-csv-file.png#2147x1319" alt="Load CSV file">
Follow the instructions. Check your data in the preview step. If you see incorrect characters, try changing the encoding options in the first step.
If the preview data is good, click "Import" to import your CSV data.
<img src="/learn_images/csv/reorder-csv-columns/preview-csv-file.png#2436x1399" alt="CSV Preview">
<h2 id="user-content-step-2-swap-andor-remove-csv-columns"><a href="#step-2-swap-andor-remove-csv-columns" aria-hidden="true" tabindex="-1"></a>Step 2: Swap and/or remove CSV columns</h2>
Datablist is a <a href="/csv-editor">CSV Editor</a>. Once your CSV is imported, you can see your CSV data in the data table. Edit your data: cell by cell or with bulk edit; search items; or perform <a href="/how-to/data-cleaning">data cleaning manipulations</a>.
CSV columns are called properties within Datablist. Click on the "Manage Properties" button to reorder and/or hide CSV columns.
<img src="/learn_images/csv/reorder-csv-columns/manage-properties.png#2433x1392" alt="Properties Configuration">
<ul>
<li>Drag and drop the properties by clicking on the left icon to rearrange them.</li>
<li>Toggle the visibility button on the right to hide a property.</li>
</ul>
<img src="/learn_images/csv/reorder-csv-columns/reorder-hide-csv-columns.png#2437x1411" alt="ReOrder-Remove CSV columns">
<img src="/learn_images/csv/reorder-csv-columns/reordering-csv-columns.png#1869x1107" alt="ReOrdering CSV column">
<h2 id="user-content-step-3-export-the-cleaned-csv-data"><a href="#step-3-export-the-cleaned-csv-data" aria-hidden="true" tabindex="-1"></a>Step 3: Export the cleaned CSV data</h2>
When your manipulations are finished. Click the "Export" button and select "Export only visible properties".
A CSV file will be generated with columns in the same order as displayed.
<img src="/learn_images/csv/reorder-csv-columns/export-cleaned-csv.png#2164x1164" alt="Export CSV">
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-do-i-need-to-be-a-developer-to-reorder-andor-remove-csv-columns"><a href="#do-i-need-to-be-a-developer-to-reorder-andor-remove-csv-columns" aria-hidden="true" tabindex="-1"></a>Do I need to be a developer to reorder and/or remove CSV columns?</h3>
No. Datablist is a no-code online tool for everyone!
<h3 id="user-content-how-to-export-in-a-microsoft-excel-file"><a href="#how-to-export-in-a-microsoft-excel-file" aria-hidden="true" tabindex="-1"></a>How to export in a Microsoft Excel file?</h3>
Datablist is compatible with Microsoft Excel format. Select "Microsoft Excel" in the export modal to generate an Excel file from your CSV data.

Swapping and/or removing CSV columns is common in data manipulation.

**[Datablist CSV Editor](/csv-editor) is the easiest way to reorder and/or remove columns in a CSV file online. For free.**

## Step 1: Open your CSV online

First, open your CSV file online with Datablist [CSV Editor](/csv-editor).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

Then, click on the "Start with a CSV/Excel file" button.

![Open CSV file](/learn_images/csv/reorder-csv-columns/open-csv-file.png#2145x1320)

Drag and drop your CSV file, or click on the grey zone to browse your files.

![Load CSV file](/learn_images/csv/reorder-csv-columns/load-csv-file.png#2147x1319)

Follow the instructions. Check your data in the preview step. If you see incorrect characters, try changing the encoding options in the first step.

**If the preview data is good, click "Import" to import your CSV data.**

![CSV Preview](/learn_images/csv/reorder-csv-columns/preview-csv-file.png#2436x1399)


## Step 2: Swap and/or remove CSV columns

Datablist is a [CSV Editor](/csv-editor). Once your CSV is imported, you can see your CSV data in the data table. Edit your data: cell by cell or with bulk edit; search items; or perform [data cleaning manipulations](/how-to/data-cleaning).

CSV columns are called properties within Datablist. **Click on the "Manage Properties" button to reorder and/or hide CSV columns.**

![Properties Configuration](/learn_images/csv/reorder-csv-columns/manage-properties.png#2433x1392)

- Drag and drop the properties by clicking on the left icon to rearrange them.
- Toggle the visibility button on the right to hide a property.

![ReOrder-Remove CSV columns](/learn_images/csv/reorder-csv-columns/reorder-hide-csv-columns.png#2437x1411)

![ReOrdering CSV column](/learn_images/csv/reorder-csv-columns/reordering-csv-columns.png#1869x1107)



## Step 3: Export the cleaned CSV data

When your manipulations are finished. Click the "Export" button and select "Export only visible properties".

A CSV file will be generated with columns in the same order as displayed.

![Export CSV](/learn_images/csv/reorder-csv-columns/export-cleaned-csv.png#2164x1164)


## FAQ

### Do I need to be a developer to reorder and/or remove CSV columns?

No. Datablist is a no-code online tool for everyone!

### How to export in a Microsoft Excel file?

Datablist is compatible with Microsoft Excel format. Select "Microsoft Excel" in the export modal to generate an Excel file from your CSV data.

How to reorder and/or remove CSV columns online?

Swapping and/or removing CSV columns is common in data manipulation.
Datablist CSV Editor is the easiest way to reorder and/or remove columns in a CSV file online. For free.
Step 1: Open your CSV online
First, open your CSV file online with Datablist CSV Editor.
Then, click on the &ampquot;Start with a CSV/Excel file&ampquot; button.
Open CSV file
Drag and drop your CSV file, or click on the grey zone to browse your files.
Load CSV file
Follow the instructions. Check your data in the preview step. If you see incorrect characters, try changing the encoding options in the first step.
If the preview data is good, click &ampquot;Import&ampquot; to import your CSV data.
CSV Preview
Step 2: Swap and/or remove CSV columns
Datablist is a CSV Editor. Once your CSV is imported, you can see your CSV data in the data table. Edit your data: cell by cell or with bulk edit; search items; or perform data cleaning manipulations.
CSV columns are called properties within Datablist. Click on the &ampquot;Manage Properties&ampquot; button to reorder and/or hide CSV columns.
Properties Configuration
Drag and drop the properties by clicking on the left icon to rearrange them.
Toggle the visibility button on the right to hide a property.

ReOrder-Remove CSV columns
ReOrdering CSV column
Step 3: Export the cleaned CSV data
When your manipulations are finished. Click the &ampquot;Export&ampquot; button and select &ampquot;Export only visible properties&ampquot;.
A CSV file will be generated with columns in the same order as displayed.
Export CSV
FAQ
Do I need to be a developer to reorder and/or remove CSV columns?
No. Datablist is a no-code online tool for everyone!
How to export in a Microsoft Excel file?
Datablist is compatible with Microsoft Excel format. Select &ampquot;Microsoft Excel&ampquot; in the export modal to generate an Excel file from your CSV data.


Few <a href="/csv-editor">CSV editors</a> can manage large CSV files. Spreadsheet tools like Excel and Google Sheet break before reaching the million of lines. And dedicated tools target data scientists so they are expensive and complex to use.
A free alternative is to use your terminal and manage your big CSV file using the command line.
A CSV file is a text file, with each line being a data record. And each record is made of fields separated by commas (or sometimes by semicolons ";" or tabular keys). Because a CSV file is a text file and each line is a record, you can use Linux/Unix commands for manipulating texts.
The command line might seem scary but they are also very powerful. I'm listed here the command I use to search in big CSV files.
<blockquote>
Note: All the commands listed bellow are just reading your files. They don't overwrite or delete your CSV files.
</blockquote>
A summary of the commands listed on this page:
<ul>
<li><a href="#searching-in-a-big-csv-file-for-a">Searching in a CSV file for a single keyword</a></li>
<li><a href="#searching-in-a-big-csv-file-for-multiple-keywords">Searching in a CSV file for multiple keywords</a></li>
<li><a href="#how-to-count-the-number-of-matching-rows">How to count the number of matching rows in a big CSV file</a></li>
</ul>
Before moving to the commands, open your Terminal. On macOs, you can use Spotlight with keyboard shortcut ⌘ + Space and type Terminal. Then hit "Enter"/"Return" to open the Terminal.
<img src="/learn_images/csv/words-count/open-terminal.png#1675x607" alt="Open Terminal using Mac Spotlight">
<h2 id="user-content-searching-in-a-big-csv-file-for-a-single-keyword"><a href="#searching-in-a-big-csv-file-for-a-single-keyword" aria-hidden="true" tabindex="-1"></a>Searching in a big CSV file for a single keyword</h2>
To perform a simple search and list all rows that match a specific keyword, use <a href="#grep">grep</a>.
<pre><code>grep "keyword" filepath.csv
</code></pre>
To be case insensitive, add the -i option.
<pre><code>grep -i "keyword" filepath.csv
</code></pre>
<img src="/learn_images/csv/search-big-csv-file-terminal/simple-search-case-insensitive.png#916x406=.with-shadow.rounded-3.mb-4" alt="Search CSV for a single keyword">
In my example, I search in a companies dataset the lines matching the keyword airtable.com. I get the two matching rows. One for the company with the website airtable.com (the good one), and another for chinaairtable.com (unexpected).
<h4 id="user-content-search-for-whole-keyword"><a href="#search-for-whole-keyword" aria-hidden="true" tabindex="-1"></a>Search for whole keyword</h4>
To avoid unexpected results like that, you can switch to Regular Expression (RegEx) and search for the whole keyword.
By adding the -E option to grep, I tell grep to interpret the pattern as a regular expression. And I add \b before and after my keyword. \b is a RegEx operator that means word boundary.
<pre><code>grep -E -i "\bkeyword\b" filepath.csv
</code></pre>
<img src="/learn_images/csv/search-big-csv-file-terminal/simple-search-whole-word.png#986x322=.with-shadow.rounded-3.mb-4" alt="Search CSV for a single keyword with word boundary">
<h4 id="user-content-search-terms-starting-with-a-keyword"><a href="#search-terms-starting-with-a-keyword" aria-hidden="true" tabindex="-1"></a>Search terms starting with a keyword</h4>
By using the same RegEx operator \b at the beginning of your pattern, you are searching all the lines with a word starting with your keyword.
<pre><code>grep -E -i "\bkeyword" filepath.csv
</code></pre>
<h4 id="user-content-search-terms-ending-with-a-keyword"><a href="#search-terms-ending-with-a-keyword" aria-hidden="true" tabindex="-1"></a>Search terms ending with a keyword</h4>
And by using it at the end of your pattern, you are searching the lines with a word ending with your keyword.
<pre><code>grep -E -i "keyword\b" filepath.csv
</code></pre>
<img src="/learn_images/csv/search-big-csv-file-terminal/simple-search-ending-width.png#1476x1106=.with-shadow.rounded-3.mb-4" alt="Search CSV for words ending with a keyword">
<h2 id="user-content-searching-in-a-big-csv-file-for-multiple-keywords"><a href="#searching-in-a-big-csv-file-for-multiple-keywords" aria-hidden="true" tabindex="-1"></a>Searching in a big CSV file for multiple keywords</h2>
When searching for multiple keywords in a CSV file, you can search for one of the keywords or for all the keywords.
<h3 id="user-content-searching-for-one-of-several-keywords-in-a-big-csv-file"><a href="#searching-for-one-of-several-keywords-in-a-big-csv-file" aria-hidden="true" tabindex="-1"></a>Searching for one of several keywords in a big CSV file</h3>
Grep with a RegEx is once again the perfect choice. In a RegEx, the symbol | means OR.
<pre><code>grep -i -E "keyword1|keyword2" filepath.csv
</code></pre>
And to search for whole keywords, we can add \b for each keyword.
<pre><code>grep -i -E "\bkeyword1\b|\bkeyword2\b" filepath.csv
</code></pre>
<img src="/learn_images/csv/search-big-csv-file-terminal/multiple-keywords-or.png#1252x434=.with-shadow.rounded-3.mb-4" alt="Search CSV for multiple keywords">
<h3 id="user-content-searching-for-all-the-keywords-in-a-big-csv-file"><a href="#searching-for-all-the-keywords-in-a-big-csv-file" aria-hidden="true" tabindex="-1"></a>Searching for all the keywords in a big CSV file</h3>
To search for lines in your CSV file that have all the requested keywords, you can use another command: awk. Awk is another command tool to deal with text files. It has more advanced features.
Awk also works with RegEx and interprets text enclosed in slashes (/) as regular expressions.
<pre><code>awk '/keyword1/ &#x26;&#x26; /keyword2/' filepath.csv
</code></pre>
Awk processes the file line by line. To be case insensitive, you can convert the line to lower text before searching your keywords:
<pre><code>awk '{lc=tolower($0)}; (lc ~ /keyword1/) &#x26;&#x26; (lc ~ /keyword2/)' filepath.csv
</code></pre>
Here, we take the current line $0 and create a variable lc containing the current line in lower case. Then we check for keywords in it.
In the screenshot below, I search in my CSV for a line with the keywords france AND google.
<img src="/learn_images/csv/search-big-csv-file-terminal/exact-multiple-keywords.png#1378x938=.with-shadow.rounded-3.mb-4" alt="Search CSV for all keywords">
<h2 id="user-content-how-to-count-the-number-of-matching-rows"><a href="#how-to-count-the-number-of-matching-rows" aria-hidden="true" tabindex="-1"></a>How to count the number of matching rows?</h2>
Several commands can be combined. An important command is wc for word count. It does a great job at counting words or lines in a text file.
Use the -l option to count lines in a CSV file or after another command.
For example, to count how many lines match a keyword, combine it with the <a href="#searching-in-a-big-csv-file-for-a-single-keyword">single keyword search</a>.
<pre><code>grep -i "keyword" filepath.csv | wc -l
</code></pre>
<img src="/learn_images/csv/search-big-csv-file-terminal/search-and-count.png#1028x350=.with-shadow.rounded-3.mb-4" alt="Count how many lines match a keyword">


Few [CSV editors](/csv-editor) can manage large CSV files. Spreadsheet tools like Excel and Google Sheet break before reaching the million of lines. And dedicated tools target data scientists so they are expensive and complex to use.

**A free alternative** is to use your terminal and manage your big CSV file using the command line.

A CSV file is a text file, with each line being a data record. And each record is made of fields separated by commas (or sometimes by semicolons ";" or tabular keys). Because a CSV file is a text file and each line is a record, you can use Linux/Unix commands for manipulating texts.

The command line might seem scary but they are also very powerful. I'm listed here the command I use to search in big CSV files.

> **Note**: All the commands listed bellow are just **reading** your files. They don't overwrite or delete your CSV files.

A summary of the commands listed on this page:

- [Searching in a CSV file for a single keyword](#searching-in-a-big-csv-file-for-a)
- [Searching in a CSV file for multiple keywords](#searching-in-a-big-csv-file-for-multiple-keywords)
- [How to count the number of matching rows in a big CSV file](#how-to-count-the-number-of-matching-rows)


Before moving to the commands, open your Terminal. On macOs, you can use Spotlight with keyboard shortcut **⌘** + **Space** and type **Terminal**. Then hit "Enter"/"Return" to open the Terminal.

![Open Terminal using Mac Spotlight](/learn_images/csv/words-count/open-terminal.png#1675x607)


## Searching in a big CSV file for a single keyword


To perform a simple search and list all rows that match a specific keyword, use [grep](#grep).

```
grep "keyword" filepath.csv
```

To be case insensitive, add the **-i** option.

```
grep -i "keyword" filepath.csv
```

![Search CSV for a single keyword](/learn_images/csv/search-big-csv-file-terminal/simple-search-case-insensitive.png#916x406=.with-shadow.rounded-3.mb-4)


In my example, I search in a companies dataset the lines matching the keyword **airtable.com**. I get the two matching rows. One for the company with the website **airtable.com** (the good one), and another for **chinaairtable.com** (unexpected).

#### Search for whole keyword

To avoid unexpected results like that, you can switch to Regular Expression (RegEx) and search for the whole keyword.

By adding the **-E** option to **grep**, I tell grep to interpret the pattern as a regular expression. And I add **\b** before and after my keyword. **\b** is a RegEx operator that means word boundary.

```
grep -E -i "\bkeyword\b" filepath.csv
```
![Search CSV for a single keyword with word boundary](/learn_images/csv/search-big-csv-file-terminal/simple-search-whole-word.png#986x322=.with-shadow.rounded-3.mb-4)


#### Search terms starting with a keyword

By using the same RegEx operator **\b** at the beginning of your pattern, you are searching all the lines with a word starting with your keyword.

```
grep -E -i "\bkeyword" filepath.csv
```

#### Search terms ending with a keyword

And by using it at the end of your pattern, you are searching the lines with a word ending with your keyword.

```
grep -E -i "keyword\b" filepath.csv
```

![Search CSV for words ending with a keyword](/learn_images/csv/search-big-csv-file-terminal/simple-search-ending-width.png#1476x1106=.with-shadow.rounded-3.mb-4)



## Searching in a big CSV file for multiple keywords

When searching for multiple keywords in a CSV file, you can search for **one of** the keywords or for **all** the keywords.

### Searching for one of several keywords in a big CSV file

Grep with a RegEx is once again the perfect choice. In a RegEx, the symbol **|** means OR.

```
grep -i -E "keyword1|keyword2" filepath.csv
```

And to search for whole keywords, we can add **\b** for each keyword.

```
grep -i -E "\bkeyword1\b|\bkeyword2\b" filepath.csv
```

![Search CSV for multiple keywords](/learn_images/csv/search-big-csv-file-terminal/multiple-keywords-or.png#1252x434=.with-shadow.rounded-3.mb-4)


### Searching for all the keywords in a big CSV file

To search for lines in your CSV file that have all the requested keywords, you can use another command: **awk**. Awk is another command tool to deal with text files. It has more advanced features.

Awk also works with RegEx and interprets text enclosed in slashes (**/**) as regular expressions.

```
awk '/keyword1/ && /keyword2/' filepath.csv
```

Awk processes the file line by line. To be case insensitive, you can convert the line to lower text before searching your keywords:

```
awk '{lc=tolower($0)}; (lc ~ /keyword1/) && (lc ~ /keyword2/)' filepath.csv
```

Here, we take the current line **$0** and create a variable **lc** containing the current line in lower case. Then we check for keywords in it.

In the screenshot below, I search in my CSV for a line with the keywords **france** AND **google**.

![Search CSV for all keywords](/learn_images/csv/search-big-csv-file-terminal/exact-multiple-keywords.png#1378x938=.with-shadow.rounded-3.mb-4)


## How to count the number of matching rows?

Several commands can be combined. An important command is **wc** for **word count**. It does a great job at counting words or lines in a text file.

Use the **-l** option to count lines in a CSV file or after another command.

For example, to count how many lines match a keyword, combine it with the [single keyword search](#searching-in-a-big-csv-file-for-a-single-keyword).


```
grep -i "keyword" filepath.csv | wc -l
```

![Count how many lines match a keyword](/learn_images/csv/search-big-csv-file-terminal/search-and-count.png#1028x350=.with-shadow.rounded-3.mb-4)


Search big CSV file using Linux or macOS Terminal

Few CSV editors can manage large CSV files. Spreadsheet tools like Excel and Google Sheet break before reaching the million of lines. And dedicated tools target data scientists so they are expensive and complex to use.
A free alternative is to use your terminal and manage your big CSV file using the command line.
A CSV file is a text file, with each line being a data record. And each record is made of fields separated by commas (or sometimes by semicolons &ampquot;;&ampquot; or tabular keys). Because a CSV file is a text file and each line is a record, you can use Linux/Unix commands for manipulating texts.
The command line might seem scary but they are also very powerful. I&amp#39;m listed here the command I use to search in big CSV files.
Note: All the commands listed bellow are just reading your files. They don&amp#39;t overwrite or delete your CSV files.

A summary of the commands listed on this page:
Searching in a CSV file for a single keyword
Searching in a CSV file for multiple keywords
How to count the number of matching rows in a big CSV file

Before moving to the commands, open your Terminal. On macOs, you can use Spotlight with keyboard shortcut ⌘ + Space and type Terminal. Then hit &ampquot;Enter&ampquot;/&ampquot;Return&ampquot; to open the Terminal.
Open Terminal using Mac Spotlight
Searching in a big CSV file for a single keyword
To perform a simple search and list all rows that match a specific keyword, use grep.
grep &quotkeyword&quot filepath.csv
To be case insensitive, add the -i option.
grep -i &quotkeyword&quot filepath.csv
Search CSV for a single keyword
In my example, I search in a companies dataset the lines matching the keyword airtable.com. I get the two matching rows. One for the company with the website airtable.com (the good one), and another for chinaairtable.com (unexpected).
Search for whole keyword
To avoid unexpected results like that, you can switch to Regular Expression (RegEx) and search for the whole keyword.
By adding the -E option to grep, I tell grep to interpret the pattern as a regular expression. And I add \b before and after my keyword. \b is a RegEx operator that means word boundary.
grep -E -i &quot\bkeyword\b&quot filepath.csv
Search CSV for a single keyword with word boundary
Search terms starting with a keyword
By using the same RegEx operator \b at the beginning of your pattern, you are searching all the lines with a word starting with your keyword.
grep -E -i &quot\bkeyword&quot filepath.csv
Search terms ending with a keyword
And by using it at the end of your pattern, you are searching the lines with a word ending with your keyword.
grep -E -i &quotkeyword\b&quot filepath.csv
Search CSV for words ending with a keyword
Searching in a big CSV file for multiple keywords
When searching for multiple keywords in a CSV file, you can search for one of the keywords or for all the keywords.
Searching for one of several keywords in a big CSV file
Grep with a RegEx is once again the perfect choice. In a RegEx, the symbol | means OR.
grep -i -E &quotkeyword1|keyword2&quot filepath.csv
And to search for whole keywords, we can add \b for each keyword.
grep -i -E &quot\bkeyword1\b|\bkeyword2\b&quot filepath.csv
Search CSV for multiple keywords
Searching for all the keywords in a big CSV file
To search for lines in your CSV file that have all the requested keywords, you can use another command: awk. Awk is another command tool to deal with text files. It has more advanced features.
Awk also works with RegEx and interprets text enclosed in slashes (/) as regular expressions.
awk &#39/keyword1/ &amp&amp /keyword2/&#39 filepath.csv
Awk processes the file line by line. To be case insensitive, you can convert the line to lower text before searching your keywords:
awk &#39{lc=tolower($0)}; (lc ~ /keyword1/) &amp&amp (lc ~ /keyword2/)&#39 filepath.csv
Here, we take the current line $0 and create a variable lc containing the current line in lower case. Then we check for keywords in it.
In the screenshot below, I search in my CSV for a line with the keywords france AND google.
Search CSV for all keywords
How to count the number of matching rows?
Several commands can be combined. An important command is wc for word count. It does a great job at counting words or lines in a text file.
Use the -l option to count lines in a CSV file or after another command.
For example, to count how many lines match a keyword, combine it with the single keyword search.
grep -i &quotkeyword&quot filepath.csv | wc -l
Count how many lines match a keyword


You have a very big CSV file, and you want to split it into several files of n lines. Smaller files let you open them with <a href="/csv-editor">CSV editors</a> without reaching their limits.
If you are not afraid of using the terminal, macOS and Linux come with a simple utility to split files into parts: <a href="https://man7.org/linux/man-pages/man1/split.1.html" target="_blank" rel="nofollow">split</a>. <a href="https://man7.org/linux/man-pages/man1/split.1.html" target="_blank" rel="nofollow">See full documentation on split man page.</a>
<pre><code>split [-l line_count] [-b byte_count] [-n chunk_count] [file [prefix]]
</code></pre>
The command <code>split</code> breaks up files using:
<ul>
<li>lines - useful for text-based files</li>
<li>bytes - for binary files</li>
<li>number - if you want to split a file into a specific number of parts.</li>
</ul>
<a href="/learn/csv/csv-meaning">CSV is a format to store structured data using text files.</a> In a CSV file, each line is a data record. To split a CSV file, we'll use <code>split</code> with the <code>lines</code> parameter.
<pre><code>split -l 100000 filename.csv
</code></pre>
<img src="/learn_images/csv/split-csv-file/basic-split-file.png#1392x350=.with-shadow.rounded-3.mb-4" alt="Basic split CSV file">
<code>Split</code> generates files with basic names beginning with x: <code>xaa</code>, <code>xab</code>, <code>xac</code>, etc. See the next section to split your CSV files and rename the generated files.
<h3 id="user-content-using-split-and-renaming-generated-files"><a href="#using-split-and-renaming-generated-files" aria-hidden="true" tabindex="-1"></a>Using split and renaming generated files</h3>
<code>Split</code> has two interesting parameters. The first is -d to use numeric suffixes (1, 2, 3, etc.) instead of letters. The second is the prefix parameter for the generated files. To define the prefix, just add a string after your CSV filename.
<pre><code>split -l 100000 -d filename.csv file_
</code></pre>
<img src="/learn_images/csv/split-csv-file/split-numeric-prefix.png#1182x350=.with-shadow.rounded-3.mb-4" alt="split CSV file with prefix and numeric suffixes">
Then, loop on all files starting with <code>file_</code> and rename them to add <code>.csv</code>.
<pre><code>for i in $(find file_*); do mv $i "$i.csv"; done
</code></pre>
<img src="/learn_images/csv/split-csv-file/split-prefix-rename.png#944x518=.with-shadow.rounded-3.mb-4" alt="Renaming generated files with .csv">
<h3 id="user-content-adding-csv-header-to-generated-files"><a href="#adding-csv-header-to-generated-files" aria-hidden="true" tabindex="-1"></a>Adding CSV header to generated files</h3>
You have several generated files like: <code>file_00.csv</code>, <code>file_01.csv</code>, etc.
But the <a href="/learn/csv/csv-headers">CSV header line</a> is on the first file (<code>file_00.csv</code>) and all other files don't have a CSV header line.
To copy the file_00.csv first line on all other files, run the following command:
<pre><code>for i in $(find . -type f -name "file_*.csv" -not -name "file_00.csv");
 do echo -e "$(head -1 file_00.csv)\n$(cat $i)" > $i;
done
</code></pre>
What it does is simple:
Step1
Loop on all file that begin with file_ but are not file_00.csv:
<code>find . -type f -name "file_*.csv" -not -name "file_00.csv"</code>
Step2
For each file in the loop, copy the first line of <code>file_00.csv</code> to it. There is no easy way to prepend a line to a file using the command line. We just use <code>echo</code> with the first line from file_00.csv and the file content from the current file <code>cat $i</code>.
<h3 id="user-content-to-sum-up-the-whole-process"><a href="#to-sum-up-the-whole-process" aria-hidden="true" tabindex="-1"></a>To Sum up the whole process</h3>
Step 1: Split the CSV file into parts of 100000 lines and prefix the generated files with "file_".
<pre><code>split -l 100000 -d filename.csv file_
</code></pre>
Step 2: Add <code>.csv</code> to all generated files.
<pre><code>for i in $(find file_*); do mv $i "$i.csv"; done
</code></pre>
Step 3: Copy header from first generated file at the beginning of the other files.
<pre><code>for i in $(find . -type f -name "file_*.csv" -not -name "file_00.csv");
 do echo -e "$(head -1 file_00.csv)\n$(cat $i)" > $i;
done
</code></pre>


You have a very big CSV file, and you want to split it into several files of n lines. Smaller files let you open them with [CSV editors](/csv-editor) without reaching their limits.

If you are not afraid of using the terminal, macOS and Linux come with a simple utility to split files into parts: [**split**](https://man7.org/linux/man-pages/man1/split.1.html). [See full documentation on split man page.](https://man7.org/linux/man-pages/man1/split.1.html)

```
split [-l line_count] [-b byte_count] [-n chunk_count] [file [prefix]]
```

The command `split` breaks up files using:
- **lines** - useful for text-based files
- **bytes** - for binary files
- **number** - if you want to split a file into a specific number of parts.

[CSV is a format to store structured data using text files.](/learn/csv/csv-meaning) In a CSV file, each line is a data record. **To split a CSV file, we'll use `split` with the `lines` parameter.**

```
split -l 100000 filename.csv
```

![Basic split CSV file](/learn_images/csv/split-csv-file/basic-split-file.png#1392x350=.with-shadow.rounded-3.mb-4)

`Split` generates files with basic names beginning with **x**: `xaa`, `xab`, `xac`, etc. See the next section to split your CSV files and rename the generated files.

### Using split and renaming generated files

`Split` has two interesting parameters. The first is **-d** to use numeric suffixes (1, 2, 3, etc.) instead of letters. The second is the **prefix** parameter for the generated files. To define the **prefix**, just add a string after your CSV filename.

```
split -l 100000 -d filename.csv file_
```

![split CSV file with prefix and numeric suffixes](/learn_images/csv/split-csv-file/split-numeric-prefix.png#1182x350=.with-shadow.rounded-3.mb-4)

Then, loop on all files starting with `file_` and rename them to add `.csv`.

```
for i in $(find file_*); do mv $i "$i.csv"; done
```

![Renaming generated files with .csv](/learn_images/csv/split-csv-file/split-prefix-rename.png#944x518=.with-shadow.rounded-3.mb-4)


### Adding CSV header to generated files

You have several generated files like: `file_00.csv`, `file_01.csv`, etc.

But the [CSV header line](/learn/csv/csv-headers) is on the first file (`file_00.csv`) and all other files don't have a CSV header line.

To copy the file_00.csv first line on all other files, run the following command:

```
for i in $(find . -type f -name "file_*.csv" -not -name "file_00.csv");
    do echo -e "$(head -1 file_00.csv)\n$(cat $i)" > $i;
done
```

What it does is simple:

**Step1**

Loop on all file that begin with **file_** but are not **file_00.csv**:

`find . -type f -name "file_*.csv" -not -name "file_00.csv"`


**Step2**

For each file in the loop, copy the first line of `file_00.csv` to it. There is no easy way to prepend a line to a file using the command line. We just use `echo` with the first line from file_00.csv and the file content from the current file `cat $i`.


### To Sum up the whole process

Step 1: Split the CSV file into parts of 100000 lines and prefix the generated files with "file_".

```
split -l 100000 -d filename.csv file_
```

Step 2: Add `.csv` to all generated files.

```
for i in $(find file_*); do mv $i "$i.csv"; done
```

Step 3: Copy header from first generated file at the beginning of the other files.
```
for i in $(find . -type f -name "file_*.csv" -not -name "file_00.csv");
    do echo -e "$(head -1 file_00.csv)\n$(cat $i)" > $i;
done
```


Split big CSV file using Linux or Mac OS Terminal

You have a very big CSV file, and you want to split it into several files of n lines. Smaller files let you open them with CSV editors without reaching their limits.
If you are not afraid of using the terminal, macOS and Linux come with a simple utility to split files into parts: split. See full documentation on split man page.
split [-l line_count] [-b byte_count] [-n chunk_count] [file [prefix]]
The command split breaks up files using:
lines - useful for text-based files
bytes - for binary files
number - if you want to split a file into a specific number of parts.

CSV is a format to store structured data using text files. In a CSV file, each line is a data record. To split a CSV file, we&amp#39;ll use split with the lines parameter.
split -l 100000 filename.csv
Basic split CSV file
Split generates files with basic names beginning with x: xaa, xab, xac, etc. See the next section to split your CSV files and rename the generated files.
Using split and renaming generated files
Split has two interesting parameters. The first is -d to use numeric suffixes (1, 2, 3, etc.) instead of letters. The second is the prefix parameter for the generated files. To define the prefix, just add a string after your CSV filename.
split -l 100000 -d filename.csv file_
split CSV file with prefix and numeric suffixes
Then, loop on all files starting with file_ and rename them to add .csv.
for i in $(find file_*); do mv $i &quot$i.csv&quot; done
Renaming generated files with .csv
Adding CSV header to generated files
You have several generated files like: file_00.csv, file_01.csv, etc.
But the CSV header line is on the first file (file_00.csv) and all other files don&amp#39;t have a CSV header line.
To copy the file_00.csv first line on all other files, run the following command:
for i in $(find . -type f -name &quotfile_*.csv&quot -not -name &quotfile_00.csv&quot);
    do echo -e &quot$(head -1 file_00.csv)\n$(cat $i)&quot &gt $i;
done
What it does is simple:
Step1
Loop on all file that begin with file_ but are not file_00.csv:
find . -type f -name &ampquot;file_*.csv&ampquot; -not -name &ampquot;file_00.csv&ampquot;
Step2
For each file in the loop, copy the first line of file_00.csv to it. There is no easy way to prepend a line to a file using the command line. We just use echo with the first line from file_00.csv and the file content from the current file cat $i.
To Sum up the whole process
Step 1: Split the CSV file into parts of 100000 lines and prefix the generated files with &ampquot;file_&ampquot;.
split -l 100000 -d filename.csv file_
Step 2: Add .csv to all generated files.
for i in $(find file_*); do mv $i &quot$i.csv&quot; done
Step 3: Copy header from first generated file at the beginning of the other files.
for i in $(find . -type f -name &quotfile_*.csv&quot -not -name &quotfile_00.csv&quot);
    do echo -e &quot$(head -1 file_00.csv)\n$(cat $i)&quot &gt $i;
done


Datablist is a CSV editor and provides calculation features. Calculations include <a href="/learn/csv/word-count-csv-file">word count</a>, <a href="/learn/csv/characters-count-csv-file">character count</a>, empty-filled counts, etc.
For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.
<h2 id="user-content-how-to-get-the-sum-min-max-and-average-values-of-a-csv-column"><a href="#how-to-get-the-sum-min-max-and-average-values-of-a-csv-column" aria-hidden="true" tabindex="-1"></a>How to get the Sum, Min, Max, and Average values of a CSV column?</h2>
Begin by loading your CSV file in Datablist. Anonymous users (without signup) can load files with up to 10,000 rows. For larger files, just <a href="https://app.datablist.com/auth/signup" target="_blank" rel="nofollow">sign up and create a free account</a>.
<img src="/learn_images/csv/sum-min-max-average-csv-column/import-csv.png#2155x1326" alt="Import CSV file">
Choose the specific column for which you want to perform numeric calculations. Then click on "Perform calculation".
<img src="/learn_images/csv/sum-min-max-average-csv-column/start-calculations.png#2152x910" alt="Open Calculation tool">
Select the desired calculation operation – whether it's sum, minimum, maximum, or average.
<img src="/learn_images/csv/sum-min-max-average-csv-column/min-max-average-calculation.png#2156x1322" alt="Select numeric calculation">
Watch as our feature swiftly processes your data and presents you with the calculated result. No more waiting around or navigating through complex formulas.
<img src="/learn_images/csv/sum-min-max-average-csv-column/min-max-average-calculation-results.png#2156x1322" alt="Get the Sum, Min, Max, and Average value">
<h2 id="user-content-when-to-use-numeric-calculations-on-a-csv-file"><a href="#when-to-use-numeric-calculations-on-a-csv-file" aria-hidden="true" tabindex="-1"></a>When to use Numeric Calculations on a CSV file?</h2>
<ul>
<li>Data Analysts - For fast analytical processes without the need to use a complex data analytic tool.</li>
<li>Traffic Analysis - Get export from the Google Search Console and perform calculations to get aggregated results.</li>
<li>E-commerce Product Catalog analysis - Easily play with numeric values such as prices, order counts, etc. in your e-commerce</li>
</ul>

Datablist is a CSV editor and provides calculation features. Calculations include [word count](/learn/csv/word-count-csv-file), [character count](/learn/csv/characters-count-csv-file), empty-filled counts, etc.

For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.

## How to get the Sum, Min, Max, and Average values of a CSV column?

Begin by loading your CSV file in Datablist. Anonymous users (without signup) can load files with up to 10,000 rows. For larger files, just [sign up and create a free account](https://app.datablist.com/auth/signup).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

![Import CSV file](/learn_images/csv/sum-min-max-average-csv-column/import-csv.png#2155x1326)

Choose the specific column for which you want to perform numeric calculations. Then click on "Perform calculation".

![Open Calculation tool](/learn_images/csv/sum-min-max-average-csv-column/start-calculations.png#2152x910)

Select the desired calculation operation – whether it's sum, minimum, maximum, or average.

![Select numeric calculation](/learn_images/csv/sum-min-max-average-csv-column/min-max-average-calculation.png#2156x1322)

Watch as our feature swiftly processes your data and presents you with the calculated result. No more waiting around or navigating through complex formulas.

![Get the Sum, Min, Max, and Average value](/learn_images/csv/sum-min-max-average-csv-column/min-max-average-calculation-results.png#2156x1322)

## When to use Numeric Calculations on a CSV file?

- **Data Analysts** - For fast analytical processes without the need to use a complex data analytic tool.
- **Traffic Analysis** - Get export from the Google Search Console and perform calculations to get aggregated results.
- **E-commerce Product Catalog analysis** - Easily play with numeric values such as prices, order counts, etc. in your e-commerce

Get the Sum, Min, Max, and Average values of a CSV column

Datablist is a CSV editor and provides calculation features. Calculations include word count, character count, empty-filled counts, etc.
For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.
How to get the Sum, Min, Max, and Average values of a CSV column?
Begin by loading your CSV file in Datablist. Anonymous users (without signup) can load files with up to 10,000 rows. For larger files, just sign up and create a free account.
Import CSV file
Choose the specific column for which you want to perform numeric calculations. Then click on &ampquot;Perform calculation&ampquot;.
Open Calculation tool
Select the desired calculation operation – whether it&amp#39;s sum, minimum, maximum, or average.
Select numeric calculation
Watch as our feature swiftly processes your data and presents you with the calculated result. No more waiting around or navigating through complex formulas.
Get the Sum, Min, Max, and Average value
When to use Numeric Calculations on a CSV file?
Data Analysts - For fast analytical processes without the need to use a complex data analytic tool.
Traffic Analysis - Get export from the Google Search Console and perform calculations to get aggregated results.
E-commerce Product Catalog analysis - Easily play with numeric values such as prices, order counts, etc. in your e-commerce 



Datablist is a <a href="/csv-editor">CSV editor</a> and provides calculation features. Calculations provide word counts, character counts, empty-filled counts, etc.
For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.
<h2 id="user-content-how-many-words-are-in-my-csv-file"><a href="#how-many-words-are-in-my-csv-file" aria-hidden="true" tabindex="-1"></a>How many words are in my CSV file?</h2>
The first step to get the word count is to load your CSV file. Datablist is a powerful <a href="/csv-editor">CSV viewer</a> that loads <a href="/how-to/edit-big-csv-files-online">CSV files with up to 1.5 million rows</a>.
In the <a href="https://app.datablist.com/" target="_blank" rel="nofollow">Datablist Application</a>, create a new collection (using the "+" in the sidebar) to load your CSV file.
<img src="/learn_images/csv/words-count-column/import-csv.png#2155x1326" alt="Import CSV file">
Follow the import wizard to get your CSV data in Datablist. Once the import is done, click on the column you want to count words in. Select "Perform calculation".
<img src="/learn_images/csv/words-count-column/start-calculations.png#2152x910" alt="Open Calculations tool">
The "Calculation" tool opens on the right with the column name pre-filled. Datablist provides several calculations:
<ul>
<li>Words Count</li>
<li><a href="/learn/csv/count-distinct-words-csv">Distinct Words Count</a></li>
<li><a href="/learn/csv/characters-count-csv-file">Characters Count</a></li>
<li><a href="/learn/csv/sum-min-max-average-csv-column">Min, Max, Average for numeric columns</a></li>
</ul>
<img src="/learn_images/csv/words-count-column/words-count-column.png#2156x1322" alt="Word Count result">
<h2 id="user-content-how-word-count-is-calculated"><a href="#how-word-count-is-calculated" aria-hidden="true" tabindex="-1"></a>How word count is calculated?</h2>
Datablist Word Count analyzes the text of each column's cell. For each, it splits the text on any whitespace character. The word count is the sum of the split parts.
For compound words, such as "rule-breaking", the word count is 1.
Punctuation characters are excluded from the word count. In the French sentence (In French, we have a space before punctuation letters) "Quel est ton nom ?", the word count is 4 ("quel", "est", "ton", "nom").
Numbers are counted as words. In the text "My phone number is 04 38 58 19 38.", the word count is 9.
Spaces before or after a text are ignored, and multi-space characters are ignored. In the text " Datablist is great ", the word count is 3 ("datablist", "is", "great").
<h2 id="user-content-when-to-use-word-count"><a href="#when-to-use-word-count" aria-hidden="true" tabindex="-1"></a>When to use word count?</h2>
Here are some situations where word count is important:
<ul>
<li>Blogging and Online Content - Online content including blog posts and articles, may benefit from maintaining an optimal word count for search engine optimization (SEO). Search engines often favor longer, comprehensive content.</li>
<li>Academic Writing and Professional Writing. - Many academic assignments and professional documents come with specific word count requirements.</li>
<li>Social Media - Platforms like "X" (Twitter) have character limits, making it essential to keep tweets within a specific word count to convey your message effectively.</li>
<li>Cost Analysis - When using third-party tools, for example, <a href="/how-to/translate-csv-files-online">translation services</a>, where the cost is charged based on word or character count.</li>
</ul>

Datablist is a [CSV editor](/csv-editor) and provides calculation features. Calculations provide word counts, character counts, empty-filled counts, etc.

For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.

## How many words are in my CSV file?

The first step to get the word count is to load your CSV file. Datablist is a powerful [CSV viewer](/csv-editor) that loads [CSV files with up to 1.5 million rows](/how-to/edit-big-csv-files-online).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

In the [Datablist Application](https://app.datablist.com/), create a new collection (using the "+" in the sidebar) to load your CSV file.

![Import CSV file](/learn_images/csv/words-count-column/import-csv.png#2155x1326)

Follow the import wizard to get your CSV data in Datablist. Once the import is done, click on the column you want to count words in. Select "Perform calculation".

![Open Calculations tool](/learn_images/csv/words-count-column/start-calculations.png#2152x910)

The "Calculation" tool opens on the right with the column name pre-filled. Datablist provides several calculations:

- Words Count
- [Distinct Words Count](/learn/csv/count-distinct-words-csv)
- [Characters Count](/learn/csv/characters-count-csv-file)
- [Min, Max, Average for numeric columns](/learn/csv/sum-min-max-average-csv-column)

![Word Count result](/learn_images/csv/words-count-column/words-count-column.png#2156x1322)

## How word count is calculated?

Datablist Word Count analyzes the text of each column's cell. For each, it splits the text on any whitespace character. The word count is the sum of the split parts.

For compound words, such as "rule-breaking", the word count is 1.

Punctuation characters are excluded from the word count. In the French sentence (In French, we have a space before punctuation letters) "Quel est ton nom ?", the word count is 4 ("quel", "est", "ton", "nom").

Numbers are counted as words. In the text "My phone number is 04 38 58 19 38.", the word count is 9.

Spaces before or after a text are ignored, and multi-space characters are ignored. In the text " Datablist is great ", the word count is 3 ("datablist", "is", "great").

## When to use word count?

Here are some situations where word count is important:

- **Blogging and Online Content** - Online content including blog posts and articles, may benefit from maintaining an optimal word count for search engine optimization (SEO). Search engines often favor longer, comprehensive content.
- **Academic Writing and Professional Writing.** - Many academic assignments and professional documents come with specific word count requirements.
- **Social Media** - Platforms like "X" (Twitter) have character limits, making it essential to keep tweets within a specific word count to convey your message effectively.
- **Cost Analysis** - When using third-party tools, for example, [translation services](/how-to/translate-csv-files-online), where the cost is charged based on word or character count.

How to count words in a CSV Column?

Datablist is a CSV editor and provides calculation features. Calculations provide word counts, character counts, empty-filled counts, etc.
For CSV columns with numeric values, calculations return Sum, Average, Min, and Max.
How many words are in my CSV file?
The first step to get the word count is to load your CSV file. Datablist is a powerful CSV viewer that loads CSV files with up to 1.5 million rows.
In the Datablist Application, create a new collection (using the &ampquot;+&ampquot; in the sidebar) to load your CSV file.
Import CSV file
Follow the import wizard to get your CSV data in Datablist. Once the import is done, click on the column you want to count words in. Select &ampquot;Perform calculation&ampquot;.
Open Calculations tool
The &ampquot;Calculation&ampquot; tool opens on the right with the column name pre-filled. Datablist provides several calculations:
Words Count
Distinct Words Count
Characters Count
Min, Max, Average for numeric columns

Word Count result
How word count is calculated?
Datablist Word Count analyzes the text of each column&amp#39;s cell. For each, it splits the text on any whitespace character. The word count is the sum of the split parts.
For compound words, such as &ampquot;rule-breaking&ampquot;, the word count is 1.
Punctuation characters are excluded from the word count. In the French sentence (In French, we have a space before punctuation letters) &ampquot;Quel est ton nom ?&ampquot;, the word count is 4 (&ampquot;quel&ampquot;, &ampquot;est&ampquot;, &ampquot;ton&ampquot;, &ampquot;nom&ampquot;).
Numbers are counted as words. In the text &ampquot;My phone number is 04 38 58 19 38.&ampquot;, the word count is 9.
Spaces before or after a text are ignored, and multi-space characters are ignored. In the text &ampquot;     Datablist        is great    &ampquot;, the word count is 3 (&ampquot;datablist&ampquot;, &ampquot;is&ampquot;, &ampquot;great&ampquot;).
When to use word count?
Here are some situations where word count is important:
Blogging and Online Content - Online content including blog posts and articles, may benefit from maintaining an optimal word count for search engine optimization (SEO). Search engines often favor longer, comprehensive content.
Academic Writing and Professional Writing. - Many academic assignments and professional documents come with specific word count requirements.
Social Media - Platforms like &ampquot;X&ampquot; (Twitter) have character limits, making it essential to keep tweets within a specific word count to convey your message effectively.
Cost Analysis - When using third-party tools, for example, translation services, where the cost is charged based on word or character count.



Apple Numbers has a 1,000,000 rows limit.
When importing a big CSV file, only the first million rows are imported. Extra rows are skipped.
<img src="/learn_images/csv/apple-numbers-rows-limit/numbers-import-rows-limits.png#1232x536" alt="Importing a CSV file with more than 1m rows">
This is a hard limit defined in the software. The operational limit is way lower. When editing a 1,000,000 rows spreadsheet with a Macbook Pro M1 16Go, the application freezes for several seconds every time I want to edit a cell.
<img src="/learn_images/csv/apple-numbers-rows-limit/editing-big-csv-numbers.png#2504x1588" alt="Editing big spreadsheet Apple Numbers">
<h2 id="user-content-how-to-get-around-apple-numberss-max-row-limit"><a href="#how-to-get-around-apple-numberss-max-row-limit" aria-hidden="true" tabindex="-1"></a>How to get around Apple Numbers's max row limit?</h2>
Spreadsheet tools are not suited for big spreadsheets. They must keep all the data in memory so users can link any cell in their formulas.
Apple Numbers is not a database and have poor performance on big listing. See our guide on how to <a href="/how-to/edit-big-csv-files-online">edit big CSV files online</a>.


**Apple Numbers has a 1,000,000 rows limit.**

When importing a big CSV file, only the first million rows are imported. Extra rows are skipped.

![Importing a CSV file with more than 1m rows](/learn_images/csv/apple-numbers-rows-limit/numbers-import-rows-limits.png#1232x536)

This is a hard limit defined in the software. The operational limit is way lower. When editing a 1,000,000 rows spreadsheet with a Macbook Pro M1 16Go, the application freezes for several seconds every time I want to edit a cell.

![Editing big spreadsheet Apple Numbers](/learn_images/csv/apple-numbers-rows-limit/editing-big-csv-numbers.png#2504x1588)

## How to get around Apple Numbers's max row limit?

**Spreadsheet tools are not suited for big spreadsheets.** They must keep all the data in memory so users can link any cell in their formulas.
Apple Numbers is not a database and have poor performance on big listing. See our guide on how to [edit big CSV files online](/how-to/edit-big-csv-files-online).


Apple Numbers maximum rows per spreadsheet

Apple Numbers has a 1,000,000 rows limit.
When importing a big CSV file, only the first million rows are imported. Extra rows are skipped.
Importing a CSV file with more than 1m rows
This is a hard limit defined in the software. The operational limit is way lower. When editing a 1,000,000 rows spreadsheet with a Macbook Pro M1 16Go, the application freezes for several seconds every time I want to edit a cell.
Editing big spreadsheet Apple Numbers
How to get around Apple Numbers&amp#39;s max row limit?
Spreadsheet tools are not suited for big spreadsheets. They must keep all the data in memory so users can link any cell in their formulas.
Apple Numbers is not a database and have poor performance on big listing. See our guide on how to edit big CSV files online.


To convert a Numbers (<code>.numbers</code>) file into a <code>.csv</code> file, you can directly use the Apple Numbers app.
First, load your Numbers file file with the Apple Numbers app. Then, use the File -> Export To -> CSV menu action.
<img src="/learn_images/csv/export_apple_numbers_to_csv/apple_numbers_to_csv.png#1488x966" alt="File -> Export To -> CSV">
If needed, change the text encoding format in the "Advanced Options".
<blockquote>
Do not check the "Include table names" option! It adds a first row with the name of the table in your CSV file, which will be considered as a <a href="/learn/csv/csv-headers">CSV header</a> with only one column by most applications.
</blockquote>
<img src="/learn_images/csv/export_apple_numbers_to_csv/apple_numbers_csv_options.png#1410x826" alt="CSV Exporting Options">
Then, click the Next button to select a save location. Finally, click the Export button.


To convert a Numbers (`.numbers`) file into a `.csv` file, you can directly use the Apple Numbers app.

First, load your Numbers file file with the Apple Numbers app. Then, use the **File -> Export To -> CSV** menu action.

![File -> Export To -> CSV](/learn_images/csv/export_apple_numbers_to_csv/apple_numbers_to_csv.png#1488x966)

If needed, change the text encoding format in the "Advanced Options".

> Do not check the "Include table names" option! It adds a first row with the name of the table in your CSV file, which will be considered as a [CSV header](/learn/csv/csv-headers) with only one column by most applications.

![CSV Exporting Options](/learn_images/csv/export_apple_numbers_to_csv/apple_numbers_csv_options.png#1410x826)

Then, click the **Next** button to select a save location. Finally, click the **Export** button.


Export Numbers spreadsheet into a CSV file

To convert a Numbers (.numbers) file into a .csv file, you can directly use the Apple Numbers app.
First, load your Numbers file file with the Apple Numbers app. Then, use the File -&ampgt; Export To -&ampgt; CSV menu action.
File -&ampgt; Export To -&ampgt; CSV
If needed, change the text encoding format in the &ampquot;Advanced Options&ampquot;.
Do not check the &ampquot;Include table names&ampquot; option! It adds a first row with the name of the table in your CSV file, which will be considered as a CSV header with only one column by most applications.

CSV Exporting Options
Then, click the Next button to select a save location. Finally, click the Export button.


Spreadsheet and database both store sets of data. However, each one has advantages and disadvantages and is suited to specific use cases.
A spreadsheet represents a digital worksheet and was invented for accounting tasks. In a spreadsheet, data is stored in a flat structure consisting of rows and columns, also known as tabular format. Spreadsheets are simple to use and require no technical knowledge. Because a spreadsheet is flexible and doesn't have a data schema, any value can be entered in any cell. The drawback of this flexibility is the lack of data schema. Spreadsheets cannot be used safely as a source of data for other applications as they cannot provide a strict structure.
Finally, spreadsheets are designed to handle a limited volume of data.
In databases, data is stored in tables, which have a defined structure. This data structure describes for each table, the attributes and data types expected. For example, attributes can be text, date, text, or relationship to another table. This data structure adds constraints and triggers errors directly when inserting a new record in the database. Thanks to this strict structure, other applications can rely on the data provided by a database.
Unlike spreadsheets, databases are better for storing large amounts of data and when several users or applications access the data simultaneously.


Spreadsheet and database both store sets of data. However, each one has advantages and disadvantages and is suited to specific use cases.

A spreadsheet represents a digital worksheet and was invented for accounting tasks. In a spreadsheet, data is stored in a flat structure consisting of rows and columns, also known as tabular format. Spreadsheets are simple to use and require no technical knowledge. Because a spreadsheet is flexible and doesn't have a data schema, any value can be entered in any cell. The drawback of this flexibility is the lack of data schema. **Spreadsheets cannot be used safely as a source of data for other applications as they cannot provide a strict structure**.
Finally, spreadsheets are designed to handle a limited volume of data.

In databases, data is stored in tables, which have a defined structure. This data structure describes for each table, the attributes and data types expected. For example, attributes can be text, date, text, or relationship to another table. This data structure adds constraints and triggers errors directly when inserting a new record in the database. **Thanks to this strict structure, other applications can rely on the data provided by a database.**
Unlike spreadsheets, databases are better for storing large amounts of data and when several users or applications access the data simultaneously.


How spreadsheets and databases differ technically?

Spreadsheet and database both store sets of data. However, each one has advantages and disadvantages and is suited to specific use cases.
A spreadsheet represents a digital worksheet and was invented for accounting tasks. In a spreadsheet, data is stored in a flat structure consisting of rows and columns, also known as tabular format. Spreadsheets are simple to use and require no technical knowledge. Because a spreadsheet is flexible and doesn&amp#39;t have a data schema, any value can be entered in any cell. The drawback of this flexibility is the lack of data schema. Spreadsheets cannot be used safely as a source of data for other applications as they cannot provide a strict structure.
Finally, spreadsheets are designed to handle a limited volume of data.
In databases, data is stored in tables, which have a defined structure. This data structure describes for each table, the attributes and data types expected. For example, attributes can be text, date, text, or relationship to another table. This data structure adds constraints and triggers errors directly when inserting a new record in the database. Thanks to this strict structure, other applications can rely on the data provided by a database.
Unlike spreadsheets, databases are better for storing large amounts of data and when several users or applications access the data simultaneously.


Microsoft Excel comes with a <a href="https://support.microsoft.com/en-us/office/split-text-into-different-columns-with-the-convert-text-to-columns-wizard-30b14928-5550-41f5-97ca-7a3e9c363ed7" target="_blank" rel="nofollow">Columns Wizard</a> to split terms from a column into multiple columns using one or more patterns.
This is enough for simple use cases. But Excel will fall short with more advanced text-splitting needs.
For example, if you need to split text and have the same terms to be moved to the same columns.
<img src="/learn_images/excel/split-distinct/start-example.png#1243x391" alt="From single column">
We want to split the text so each column gets a distinct term:
<img src="/learn_images/excel/split-distinct/result-example.png#1231x423" alt="Specific column for each term">
In this use case, splitting the column with Microsft Excel Columns Wizard would generate this result:
<img src="/learn_images/excel/split-distinct/not-what-we-want.png#1375x481" alt="Microsoft Excel Text to Column Wizard">
<h2 id="user-content-how-to-split-text-into-columnscategories-based-on-the-values"><a href="#how-to-split-text-into-columnscategories-based-on-the-values" aria-hidden="true" tabindex="-1"></a>How to split text into columns/categories based on the values?</h2>
We want the result values into be inserted to corresponding columns.
To perform this splitting operation, you have to use <a href="/">Datablist</a>. Datablist is the perfect tool to perform this <a href="/how-to/data-cleaning">data-cleaning</a> task.
Datablist is an online tool. Just <a href="https://app.datablist.com/" target="_blank" rel="nofollow">click here</a> to open Datablist.
Import your Excel or <a href="/csv-editor">CSV file</a> directly from the "getting started" page. Or create a new collection and follow the import wizard.
<img src="/learn_images/excel/split-distinct/import-excel.png#2440x1002" alt="Import Excel or CSV">
Once your data is imported into Datablist. Open the "Split Property" tool from the "Edit" menu.
<img src="/learn_images/excel/split-distinct/split-property-menu.png#1974x922" alt="Open Split Property tool">
The tool lets you define the property with the text to be split. You can define one or more delimiters.
Delimiters can be one of the built-in patterns (comma, semi-colon, tab, etc.) or a custom pattern.
For this use case, where we want the text to be split into specific columns, open the "More settings" panel and enable the "One term per property" setting.
Then, click on "Analyse data".
<img src="/learn_images/excel/split-distinct/split-property-settings.png#1964x1125" alt="Check the &#x22;one term per property&#x22; option">
Datablist will scan your data to extract the distinct terms. The process is case-insensitive, and extra leading and trailing spaces around terms will be removed.
If the analysis results are consistent with the expected columns, click on "Preview Split".
<img src="/learn_images/excel/split-distinct/analysis-result.png#2366x1131" alt="Analysis Result">
A preview split shows the expected result after the text-splitting process. Click "Split property" to split the data.
<img src="/learn_images/excel/split-distinct/preview-split.png#2368x1143" alt="Preview Split">
And the data is split.
<img src="/learn_images/excel/split-distinct/split-result.png#1751x586" alt="Text Split Results">
Click "Export" to export the results into a CSV or Excel file.
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-can-i-split-texts-into-columns-with-a-custom-pattern"><a href="#can-i-split-texts-into-columns-with-a-custom-pattern" aria-hidden="true" tabindex="-1"></a>Can I split texts into columns with a custom pattern?</h3>
Yes. Datablist handles custom patterns. A custom pattern is matched as a whole. You can match several custom patterns using the multiple delimiters feature.
<h3 id="user-content-what-happens-to-leading-or-trailing-spaces-when-splitting-text-into-columns"><a href="#what-happens-to-leading-or-trailing-spaces-when-splitting-text-into-columns" aria-hidden="true" tabindex="-1"></a>What happens to leading or trailing spaces when splitting text into columns?</h3>
Datablist removes all leading and trailing spaces during the process. It also compares the terms without the case. The final case for each term is the one from the first match.
<h3 id="user-content-how-many-items-are-analyzed-to-generate-the-list-of-distinct-terms"><a href="#how-many-items-are-analyzed-to-generate-the-list-of-distinct-terms" aria-hidden="true" tabindex="-1"></a>How many items are analyzed to generate the list of distinct terms?</h3>
For performance reasons, Datablist analyzes the first 2000 items for your collection. The list of distinct terms must be present in those 2000 items. If other terms appear after the 2000 items, they will be ignored during the split process.
<h3 id="user-content-are-there-any-limitations-on-the-number-of-columns-created"><a href="#are-there-any-limitations-on-the-number-of-columns-created" aria-hidden="true" tabindex="-1"></a>Are there any limitations on the number of columns created?</h3>
The text-to-column operation is performed in your browser. There is currently no limitation on the number of extracted terms. Please note that if you run this cleaning operation on incoherent data, you can end up with thousands of distinct terms. Resulting in performance issues.
<h3 id="user-content-how-do-i-split-text-into-multiple-columns-with-a-csv-file"><a href="#how-do-i-split-text-into-multiple-columns-with-a-csv-file" aria-hidden="true" tabindex="-1"></a>How do I split text into multiple columns with a CSV file?</h3>
Datablist opens Excel and CSV files. And to be honest, Datablist is the <a href="/csv-editor">best online CSV editor</a> on the market. The text-to-columns tool is also available with CSV files.

Microsoft Excel comes with a [Columns Wizard](https://support.microsoft.com/en-us/office/split-text-into-different-columns-with-the-convert-text-to-columns-wizard-30b14928-5550-41f5-97ca-7a3e9c363ed7) to split terms from a column into multiple columns using one or more patterns.

This is enough for simple use cases. But Excel will fall short with more advanced text-splitting needs.

For example, if you need to **split text and have the same terms to be moved to the same columns.**

![From single column](/learn_images/excel/split-distinct/start-example.png#1243x391)

We want to split the text so each column gets a distinct term:

![Specific column for each term](/learn_images/excel/split-distinct/result-example.png#1231x423)

In this use case, splitting the column with Microsft Excel Columns Wizard would generate this result:

![Microsoft Excel Text to Column Wizard](/learn_images/excel/split-distinct/not-what-we-want.png#1375x481)


## How to split text into columns/categories based on the values?

We want the result values into be inserted to corresponding columns.

To perform this splitting operation, you have to use [Datablist](/). Datablist is the perfect tool to perform this [data-cleaning](/how-to/data-cleaning) task.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

Datablist is an online tool. Just [click here](https://app.datablist.com/) to open Datablist.

Import your Excel or [CSV file](/csv-editor) directly from the "getting started" page. Or create a new collection and follow the import wizard.

![Import Excel or CSV](/learn_images/excel/split-distinct/import-excel.png#2440x1002)

Once your data is imported into Datablist. Open the "Split Property" tool from the "Edit" menu.

![Open Split Property tool](/learn_images/excel/split-distinct/split-property-menu.png#1974x922)

The tool lets you define the property with the text to be split. You can define one or more delimiters.

Delimiters can be one of the built-in patterns (comma, semi-colon, tab, etc.) or a custom pattern.

For this use case, where we want the text to be split into specific columns, open the "More settings" panel and enable the "One term per property" setting.

Then, click on "Analyse data".

![Check the "one term per property" option](/learn_images/excel/split-distinct/split-property-settings.png#1964x1125)

Datablist will scan your data to extract the distinct terms. The process is case-insensitive, and extra leading and trailing spaces around terms will be removed.

If the analysis results are consistent with the expected columns, click on "Preview Split".

![Analysis Result](/learn_images/excel/split-distinct/analysis-result.png#2366x1131)

A preview split shows the expected result after the text-splitting process. Click "Split property" to split the data.

![Preview Split](/learn_images/excel/split-distinct/preview-split.png#2368x1143)

And the data is split.

![Text Split Results](/learn_images/excel/split-distinct/split-result.png#1751x586)

Click "Export" to export the results into a CSV or Excel file.


## FAQ

### Can I split texts into columns with a custom pattern?

Yes. Datablist handles custom patterns. A custom pattern is matched as a whole. You can match several custom patterns using the multiple delimiters feature.

### What happens to leading or trailing spaces when splitting text into columns?

Datablist removes all leading and trailing spaces during the process. It also compares the terms without the case. The final case for each term is the one from the first match.

### How many items are analyzed to generate the list of distinct terms?

For performance reasons, Datablist analyzes the first 2000 items for your collection. The list of distinct terms must be present in those 2000 items. If other terms appear after the 2000 items, they will be ignored during the split process.

### Are there any limitations on the number of columns created?

The text-to-column operation is performed in your browser. There is currently no limitation on the number of extracted terms. Please note that if you run this cleaning operation on incoherent data, you can end up with thousands of distinct terms. Resulting in performance issues.

### How do I split text into multiple columns with a CSV file?

Datablist opens Excel and CSV files. And to be honest, Datablist is the [best online CSV editor](/csv-editor) on the market. The text-to-columns tool is also available with CSV files.

Split text in a Microsoft Excel file into specific columns

Microsoft Excel comes with a Columns Wizard to split terms from a column into multiple columns using one or more patterns.
This is enough for simple use cases. But Excel will fall short with more advanced text-splitting needs.
For example, if you need to split text and have the same terms to be moved to the same columns.
From single column
We want to split the text so each column gets a distinct term:
Specific column for each term
In this use case, splitting the column with Microsft Excel Columns Wizard would generate this result:
Microsoft Excel Text to Column Wizard
How to split text into columns/categories based on the values?
We want the result values into be inserted to corresponding columns.
To perform this splitting operation, you have to use Datablist. Datablist is the perfect tool to perform this data-cleaning task.
Datablist is an online tool. Just click here to open Datablist.
Import your Excel or CSV file directly from the &ampquot;getting started&ampquot; page. Or create a new collection and follow the import wizard.
Import Excel or CSV
Once your data is imported into Datablist. Open the &ampquot;Split Property&ampquot; tool from the &ampquot;Edit&ampquot; menu.
Open Split Property tool
The tool lets you define the property with the text to be split. You can define one or more delimiters.
Delimiters can be one of the built-in patterns (comma, semi-colon, tab, etc.) or a custom pattern.
For this use case, where we want the text to be split into specific columns, open the &ampquot;More settings&ampquot; panel and enable the &ampquot;One term per property&ampquot; setting.
Then, click on &ampquot;Analyse data&ampquot;.
Check the &ampquot;one term per property&ampquot; option
Datablist will scan your data to extract the distinct terms. The process is case-insensitive, and extra leading and trailing spaces around terms will be removed.
If the analysis results are consistent with the expected columns, click on &ampquot;Preview Split&ampquot;.
Analysis Result
A preview split shows the expected result after the text-splitting process. Click &ampquot;Split property&ampquot; to split the data.
Preview Split
And the data is split.
Text Split Results
Click &ampquot;Export&ampquot; to export the results into a CSV or Excel file.
FAQ
Can I split texts into columns with a custom pattern?
Yes. Datablist handles custom patterns. A custom pattern is matched as a whole. You can match several custom patterns using the multiple delimiters feature.
What happens to leading or trailing spaces when splitting text into columns?
Datablist removes all leading and trailing spaces during the process. It also compares the terms without the case. The final case for each term is the one from the first match.
How many items are analyzed to generate the list of distinct terms?
For performance reasons, Datablist analyzes the first 2000 items for your collection. The list of distinct terms must be present in those 2000 items. If other terms appear after the 2000 items, they will be ignored during the split process.
Are there any limitations on the number of columns created?
The text-to-column operation is performed in your browser. There is currently no limitation on the number of extracted terms. Please note that if you run this cleaning operation on incoherent data, you can end up with thousands of distinct terms. Resulting in performance issues.
How do I split text into multiple columns with a CSV file?
Datablist opens Excel and CSV files. And to be honest, Datablist is the best online CSV editor on the market. The text-to-columns tool is also available with CSV files.


Exporting a sheet from Microsoft Excel to a CSV file is straightforward. But how to export selected cells to a CSV file?
The short answer is you can't export selected cells in CSV quickly. Exporting a selection to a CSV file or another Excel file is not directly possible. Here is an alternative solution: copy the data to another sheet and export the sheet to a CSV file.
<h2 id="user-content-step-1-copy-paste-selected-cells"><a href="#step-1-copy-paste-selected-cells" aria-hidden="true" tabindex="-1"></a>Step 1: Copy-Paste selected cells</h2>
Select the cells you want to export. And copy the data to your clipboard (<code>ctrl+c</code> or <code>Menu Edit => Copy</code>)
<img src="/learn_images/excel/export-selection-csv/copy-selection.png#1934x1550" alt="File -> Export To -> CSV">
<h3 id="user-content-export-only-visible-cells-without-hidden-columns"><a href="#export-only-visible-cells-without-hidden-columns" aria-hidden="true" tabindex="-1"></a>Export only visible cells (without hidden columns)</h3>
If you have hidden columns, Excel will add their content to your clipboard. This is not what we want. We want to export only our selected and visible cells.
To prevent hidden columns to be copied, open the <code>Go to Special</code> menu in <code>Find &#x26; Select</code>.
<img src="/learn_images/excel/export-selection-csv/go-to-special.png#2676x1552" alt="Find &#x26; Select -> Go to Special">
And select <code>Visible cells only</code>. It will tell Microsoft Excel we only want to copy the visible cells.
<img src="/learn_images/excel/export-selection-csv/visible-cells-only.png#1630x992" alt="Go to special -> Visible cells only">
<h2 id="user-content-step-2-create-a-new-sheet-and-paste-your-data"><a href="#step-2-create-a-new-sheet-and-paste-your-data" aria-hidden="true" tabindex="-1"></a>Step 2: Create a new sheet and paste your data</h2>
Then, create a new sheet and <code>paste</code> your data (<code>ctrl + v</code> or <code>Menu Edit => Paste</code>)
<img src="/learn_images/excel/export-selection-csv/paste-cells.png#2190x1292" alt="Paste data in a new sheet">
<h3 id="user-content-add-columns-to-have-csv-headers"><a href="#add-columns-to-have-csv-headers" aria-hidden="true" tabindex="-1"></a>Add columns to have CSV headers</h3>
<a href="/learn/csv/csv-uses">CSV files are used to store structured data</a>, and CSV headers add context to know what your CSV contains.
<a href="/learn/csv/csv-headers">CSV headers are optional</a>. But if you need to import your CSV into another software or <a href="/csv-editor">clean it with a CSV editor</a>, you better have them.
Just <code>copy - paste</code> the columns from the first sheet or write them directly as the first line in the new sheet.
<img src="/learn_images/excel/export-selection-csv/paste-headers.png#1732x1294" alt="Paste columns in the new sheet">
<h2 id="user-content-step-3-export-sheet-to-csv"><a href="#step-3-export-sheet-to-csv" aria-hidden="true" tabindex="-1"></a>Step 3: Export sheet to CSV</h2>
The last step is to export the new Excel sheet to a CSV file. Once you are on the new sheet, select <code>Save As</code> in the <code>File</code> menu.
<blockquote>
Important: Set the active sheet to the sheet to export!
</blockquote>
<img src="/learn_images/excel/export-selection-csv/save-as-to-export-csv.png#1374x1076" alt="File -> Save As">
Then change the <code>File format</code> to <code>CSV UTF-8 (Comma-delimited) (.csv)</code>. And click <code>Save</code>.
<img src="/learn_images/excel/export-selection-csv/select-csv-as-export-format.png#1780x1294" alt="Save As with CSV format">
A warning message will pop up with the message "This workbook cannot be saved in the selected file format because it contains multiple sheets.".
Bypass the warning message by clicking on <code>OK</code>. Microsoft Excel tells you that it can't export all sheets to a single CSV. Only your active sheet will be exported to a CSV file. This is what we want.
<img src="/learn_images/excel/export-selection-csv/confirm-to-export-active-sheet.png#1512x1076" alt="Confirm with OK">
<h2 id="user-content-edit-it-with-a-csv-editor"><a href="#edit-it-with-a-csv-editor" aria-hidden="true" tabindex="-1"></a>Edit it with a CSV Editor</h2>
If you need to further clean your CSV data, use a <a href="/csv-editor">CSV Editor</a> such as <a href="/csv-editor">Datablist</a>.
Datablist provides advanced tools to <a href="/features/duplicates-remover">deduplicate data</a>, <a href="/features/combine-csv-files">consolidate several CSV files</a>, or <a href="/how-to/clean-web-scraping-data-online">clean your data</a>.


Exporting a sheet from Microsoft Excel to a CSV file is straightforward. But how to export selected cells to a CSV file?

**The short answer is you can't export selected cells in CSV quickly. Exporting a selection to a CSV file or another Excel file is not directly possible. Here is an alternative solution: copy the data to another sheet and export the sheet to a CSV file.** 


## Step 1: Copy-Paste selected cells

Select the cells you want to export. And copy the data to your clipboard (`ctrl+c` or `Menu Edit => Copy`)

![File -> Export To -> CSV](/learn_images/excel/export-selection-csv/copy-selection.png#1934x1550)

### Export only visible cells (without hidden columns)

If you have hidden columns, Excel will add their content to your clipboard. This is not what we want. We want to export only our selected and visible cells.

To prevent hidden columns to be copied, open the `Go to Special` menu in `Find & Select`.


![Find & Select -> Go to Special](/learn_images/excel/export-selection-csv/go-to-special.png#2676x1552)

And select `Visible cells only`. It will tell Microsoft Excel we only want to copy the visible cells.

![Go to special -> Visible cells only](/learn_images/excel/export-selection-csv/visible-cells-only.png#1630x992)


## Step 2: Create a new sheet and paste your data

Then, create a new sheet and `paste` your data (`ctrl + v` or `Menu Edit => Paste`)


![Paste data in a new sheet](/learn_images/excel/export-selection-csv/paste-cells.png#2190x1292)


### Add columns to have CSV headers

[CSV files are used to store structured data](/learn/csv/csv-uses), and CSV headers add context to know what your CSV contains.

[CSV headers are optional](/learn/csv/csv-headers). But if you need to import your CSV into another software or [clean it with a CSV editor](/csv-editor), you better have them.

Just `copy - paste` the columns from the first sheet or write them directly as the first line in the new sheet.

![Paste columns in the new sheet](/learn_images/excel/export-selection-csv/paste-headers.png#1732x1294)

## Step 3: Export sheet to CSV

The last step is to export the new Excel sheet to a CSV file. Once you are on the new sheet, select `Save As` in the `File` menu.

> **Important**: Set the active sheet to the sheet to export!

![File -> Save As](/learn_images/excel/export-selection-csv/save-as-to-export-csv.png#1374x1076)

Then change the `File format` to `CSV UTF-8 (Comma-delimited) (.csv)`. And click `Save`.

![Save As with CSV format](/learn_images/excel/export-selection-csv/select-csv-as-export-format.png#1780x1294)

A warning message will pop up with the message "This workbook cannot be saved in the selected file format because it contains multiple sheets.".

Bypass the warning message by clicking on `OK`. Microsoft Excel tells you that it can't export all sheets to a single CSV. Only your active sheet will be exported to a CSV file. This is what we want.

![Confirm with OK](/learn_images/excel/export-selection-csv/confirm-to-export-active-sheet.png#1512x1076)


## Edit it with a CSV Editor

If you need to further clean your CSV data, use a [CSV Editor](/csv-editor) such as [Datablist](/csv-editor).

Datablist provides advanced tools to [deduplicate data](/features/duplicates-remover), [consolidate several CSV files](/features/combine-csv-files), or [clean your data](/how-to/clean-web-scraping-data-online).







Export Microsoft Excel selection into a CSV file

Exporting a sheet from Microsoft Excel to a CSV file is straightforward. But how to export selected cells to a CSV file?
The short answer is you can&amp#39;t export selected cells in CSV quickly. Exporting a selection to a CSV file or another Excel file is not directly possible. Here is an alternative solution: copy the data to another sheet and export the sheet to a CSV file. 
Step 1: Copy-Paste selected cells
Select the cells you want to export. And copy the data to your clipboard (ctrl+c or Menu Edit =&ampgt; Copy)
File -&ampgt; Export To -&ampgt; CSV
Export only visible cells (without hidden columns)
If you have hidden columns, Excel will add their content to your clipboard. This is not what we want. We want to export only our selected and visible cells.
To prevent hidden columns to be copied, open the Go to Special menu in Find &ampamp; Select.
Find &ampamp; Select -&ampgt; Go to Special
And select Visible cells only. It will tell Microsoft Excel we only want to copy the visible cells.
Go to special -&ampgt; Visible cells only
Step 2: Create a new sheet and paste your data
Then, create a new sheet and paste your data (ctrl + v or Menu Edit =&ampgt; Paste)
Paste data in a new sheet
Add columns to have CSV headers
CSV files are used to store structured data, and CSV headers add context to know what your CSV contains.
CSV headers are optional. But if you need to import your CSV into another software or clean it with a CSV editor, you better have them.
Just copy - paste the columns from the first sheet or write them directly as the first line in the new sheet.
Paste columns in the new sheet
Step 3: Export sheet to CSV
The last step is to export the new Excel sheet to a CSV file. Once you are on the new sheet, select Save As in the File menu.
Important: Set the active sheet to the sheet to export!

File -&ampgt; Save As
Then change the File format to CSV UTF-8 (Comma-delimited) (.csv). And click Save.
Save As with CSV format
A warning message will pop up with the message &ampquot;This workbook cannot be saved in the selected file format because it contains multiple sheets.&ampquot;.
Bypass the warning message by clicking on OK. Microsoft Excel tells you that it can&amp#39;t export all sheets to a single CSV. Only your active sheet will be exported to a CSV file. This is what we want.
Confirm with OK
Edit it with a CSV Editor
If you need to further clean your CSV data, use a CSV Editor such as Datablist.
Datablist provides advanced tools to deduplicate data, consolidate several CSV files, or clean your data.


Manually finding and replacing values in an Excel file is a pain. Excel's built-in "Find and Replace" tool works for simple cases, but what if you need to replace dozens or hundreds of terms? Doing it one by one is slow and frustrating.
With Datablist's AI Editing feature, you can automate bulk find and replace with a simple prompt. No formulas, no coding—just write what you need, and let AI do the work.
<img src="/learn_images/excel/multiple-find-replace/hero_article.png#880x436" alt="Multiple Find&#x26;Replace in a Excel File">
<h2 id="user-content-how-to-find-and-replace-multiple-values-in-an-excel-file"><a href="#how-to-find-and-replace-multiple-values-in-an-excel-file" aria-hidden="true" tabindex="-1"></a>How to Find and Replace Multiple Values in an Excel file</h2>
Follow these steps to transform your data effortlessly.
<h3 id="user-content-step-1-upload-your-excel-file"><a href="#step-1-upload-your-excel-file" aria-hidden="true" tabindex="-1"></a>Step 1: Upload Your Excel File</h3>
Datablist is a free online tool to open and edit CSV and Excel files. It comes with data cleaning and data enrichment tools.
Create a new collection and Upload your Excel file (supports .xlsx and .csv).
<img src="/learn_images/excel/multiple-find-replace/create-collection-cleaned.png#2598x1177" alt="Create a new collection">
Your data will appear in a clean, editable table.
<h3 id="user-content-step-2-open-ai-editing"><a href="#step-2-open-ai-editing" aria-hidden="true" tabindex="-1"></a>Step 2: Open AI Editing</h3>
AI Editing in Datablist is a powerful feature that generates scripts based on your instructions. Instead of manually creating complex Excel formulas or VBA macros, you describe what you need in a prompt, and Datablist does the rest.
For find and replace, you simply provide a list of terms to match and their replacements. AI Editing generates the script and runs it on your Excel file instantly. This feature also works with terms inside multi-value cells. It doesn’t have to match the full cell value, making it perfect for cleaning up complex data structures.
<img src="/learn_images/excel/multiple-find-replace/select-ai-editing.png#1261x720" alt="Select AI Editing">
<h3 id="user-content-step-3-write-your-find-and-replace-instructions"><a href="#step-3-write-your-find-and-replace-instructions" aria-hidden="true" tabindex="-1"></a>Step 3: Write Your Find and Replace Instructions</h3>
In the AI Editing prompt, write the find and replace instruction with the list of terms to replace.
Use {{XX}} to specify which column you want to edit.
An example of prompt is:
<pre><code>Replace matching values in the Countries column.
The text in the Countries column can contain multiple values.

KR => South Korea 
SG => Singapore 
AE => United Arab Emirates 
CA => Canada 
CN => China 
ZA => South Africa 
SE => Sweden 
US => United States 
FR => France 
ES => Spain 
NL => Netherlands 
BR => Brazil 
MX => Mexico
</code></pre>
<img src="/learn_images/excel/multiple-find-replace/write_prompt.png#2435x1259" alt="Select AI Editing">
The AI generates a script based on your instructions.
Click "Generate" to have a preview of the results.
<img src="/learn_images/excel/multiple-find-replace/preview.png#2284x1406" alt="Preview changes">
Click Run on items, and Datablist will run the script on all your items.
<h3 id="user-content-step-4-download-your-updated-file"><a href="#step-4-download-your-updated-file" aria-hidden="true" tabindex="-1"></a>Step 4: Download Your Updated File</h3>
Once the replacements are made, export your file back to Excel.
Click on the "Export" button in the header.
<img src="/learn_images/excel/multiple-find-replace/export-file.png#1558x1116" alt="Export results">
And select the "Excel" export format.
<img src="/learn_images/excel/multiple-find-replace/excel-format.png#1304x736" alt="Export in Excel">
Done! No manual search and replace needed.
<h2 id="user-content-why-use-datablist-for-bulk-find-and-replace"><a href="#why-use-datablist-for-bulk-find-and-replace" aria-hidden="true" tabindex="-1"></a>Why Use Datablist for Bulk Find and Replace?</h2>
<ul>
<li>Faster than Excel – No need to run find-and-replace one by one.</li>
<li>No formulas or macros – Just describe what you need.</li>
<li>Handles large datasets – Perfect for cleaning and standardizing big files.</li>
<li>Works inside multi-value cells – No need to match the full cell value.</li>
<li>Accurate and repeatable – AI follows your instructions precisely.</li>
</ul>
<h2 id="user-content-conclusion"><a href="#conclusion" aria-hidden="true" tabindex="-1"></a>Conclusion</h2>
If you're spending hours manually updating spreadsheets, stop. With AI Editing in Datablist, bulk find and replace is as easy as writing a to-do list. Try it today and make your Excel workflow effortless!

Manually finding and replacing values in an Excel file is a pain. Excel's built-in "Find and Replace" tool works for simple cases, but what if you need to replace dozens or hundreds of terms? Doing it one by one is slow and frustrating.

**With Datablist's AI Editing feature, you can automate bulk find and replace with a simple prompt. No formulas, no coding—just write what you need, and let AI do the work.**


![Multiple Find&Replace in a Excel File](/learn_images/excel/multiple-find-replace/hero_article.png#880x436)

## How to Find and Replace Multiple Values in an Excel file

Follow these steps to transform your data effortlessly.

### Step 1: Upload Your Excel File

Datablist is a free online tool to open and edit CSV and Excel files. It comes with data cleaning and data enrichment tools.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/auth/signup" className="btn btn-primary btn-large" target="_blank">
 Sign up on Datablist
 </a>
 </div>
 It's Free!
</aside>

Create a new collection and Upload your Excel file (supports .xlsx and .csv).

![Create a new collection](/learn_images/excel/multiple-find-replace/create-collection-cleaned.png#2598x1177)

Your data will appear in a clean, editable table.


### Step 2: Open AI Editing

AI Editing in Datablist is a powerful feature that generates scripts based on your instructions. Instead of manually creating complex Excel formulas or VBA macros, you describe what you need in a prompt, and Datablist does the rest.

**For find and replace, you simply provide a list of terms to match and their replacements. AI Editing generates the script and runs it on your Excel file instantly.** This feature also works with terms inside multi-value cells. It doesn’t have to match the full cell value, making it perfect for cleaning up complex data structures.

![Select AI Editing](/learn_images/excel/multiple-find-replace/select-ai-editing.png#1261x720)

### Step 3: Write Your Find and Replace Instructions

In the AI Editing prompt, write the find and replace instruction with the list of terms to replace.

Use {{XX}} to specify which column you want to edit.

An example of prompt is:

```
Replace matching values in the Countries column.
The text in the Countries column can contain multiple values.

KR => South Korea 
SG => Singapore 
AE => United Arab Emirates 
CA => Canada 
CN => China 
ZA => South Africa 
SE => Sweden 
US => United States 
FR => France 
ES => Spain 
NL => Netherlands 
BR => Brazil 
MX => Mexico
```


![Select AI Editing](/learn_images/excel/multiple-find-replace/write_prompt.png#2435x1259)

The AI generates a script based on your instructions.

Click "Generate" to have a preview of the results.

![Preview changes](/learn_images/excel/multiple-find-replace/preview.png#2284x1406)

Click **Run on items**, and Datablist will run the script on all your items.


### Step 4: Download Your Updated File

Once the replacements are made, export your file back to Excel.

Click on the "Export" button in the header.

![Export results](/learn_images/excel/multiple-find-replace/export-file.png#1558x1116)

And select the "Excel" export format.

![Export in Excel](/learn_images/excel/multiple-find-replace/excel-format.png#1304x736)

Done! No manual search and replace needed.


## Why Use Datablist for Bulk Find and Replace?

- Faster than Excel – No need to run find-and-replace one by one.
- No formulas or macros – Just describe what you need.
- Handles large datasets – Perfect for cleaning and standardizing big files.
- Works inside multi-value cells – No need to match the full cell value.
- Accurate and repeatable – AI follows your instructions precisely.

## Conclusion

If you're spending hours manually updating spreadsheets, stop. With AI Editing in Datablist, bulk find and replace is as easy as writing a to-do list. Try it today and make your Excel workflow effortless!

Find and replace multiple values in a Excel file

Manually finding and replacing values in an Excel file is a pain. Excel&amp#39;s built-in &ampquot;Find and Replace&ampquot; tool works for simple cases, but what if you need to replace dozens or hundreds of terms? Doing it one by one is slow and frustrating.
With Datablist&amp#39;s AI Editing feature, you can automate bulk find and replace with a simple prompt. No formulas, no coding—just write what you need, and let AI do the work.
Multiple Find&ampamp;Replace in a Excel File
How to Find and Replace Multiple Values in an Excel file
Follow these steps to transform your data effortlessly.
Step 1: Upload Your Excel File
Datablist is a free online tool to open and edit CSV and Excel files. It comes with data cleaning and data enrichment tools.
Create a new collection and Upload your Excel file (supports .xlsx and .csv).
Create a new collection
Your data will appear in a clean, editable table.
Step 2: Open AI Editing
AI Editing in Datablist is a powerful feature that generates scripts based on your instructions. Instead of manually creating complex Excel formulas or VBA macros, you describe what you need in a prompt, and Datablist does the rest.
For find and replace, you simply provide a list of terms to match and their replacements. AI Editing generates the script and runs it on your Excel file instantly. This feature also works with terms inside multi-value cells. It doesn’t have to match the full cell value, making it perfect for cleaning up complex data structures.
Select AI Editing
Step 3: Write Your Find and Replace Instructions
In the AI Editing prompt, write the find and replace instruction with the list of terms to replace.
Use {{XX}} to specify which column you want to edit.
An example of prompt is:
Replace matching values in the Countries column.
The text in the Countries column can contain multiple values.

KR =&gt South Korea 
SG =&gt Singapore 
AE =&gt United Arab Emirates 
CA =&gt Canada 
CN =&gt China 
ZA =&gt South Africa 
SE =&gt Sweden 
US =&gt United States 
FR =&gt France 
ES =&gt Spain 
NL =&gt Netherlands 
BR =&gt Brazil 
MX =&gt Mexico
Select AI Editing
The AI generates a script based on your instructions.
Click &ampquot;Generate&ampquot; to have a preview of the results.
Preview changes
Click Run on items, and Datablist will run the script on all your items.
Step 4: Download Your Updated File
Once the replacements are made, export your file back to Excel.
Click on the &ampquot;Export&ampquot; button in the header.
Export results
And select the &ampquot;Excel&ampquot; export format.
Export in Excel
Done! No manual search and replace needed.
Why Use Datablist for Bulk Find and Replace?
Faster than Excel – No need to run find-and-replace one by one.
No formulas or macros – Just describe what you need.
Handles large datasets – Perfect for cleaning and standardizing big files.
Works inside multi-value cells – No need to match the full cell value.
Accurate and repeatable – AI follows your instructions precisely.

Conclusion
If you&amp#39;re spending hours manually updating spreadsheets, stop. With AI Editing in Datablist, bulk find and replace is as easy as writing a to-do list. Try it today and make your Excel workflow effortless!


To convert a <code>.xlsx</code> or <code>.xls</code> file into a <code>.csv</code> file, Microsoft Excel is the perfect tool.
First, load your <code>.xlsx</code> or <code>.xls</code> file with Excel. Then, use the File -> Save As menu action.
<img src="/learn_images/csv/export_excel_to_csv/microsoft_excel_convert_to_csv_save_as.png#1488x966" alt="File -> Save As">
Choose where you want to save your CSV file and select CSV UTF-8 (Comma delimited) (.csv) in the File format dropdown.
<img src="/learn_images/csv/export_excel_to_csv/microsoft_excel_convert_to_csv_select_format.png#1378x892" alt="Select CSV in file formats">
Finally, click the Save button.


To convert a `.xlsx` or `.xls` file into a `.csv` file, Microsoft Excel is the perfect tool.

First, load your `.xlsx` or `.xls` file with Excel. Then, use the **File -> Save As** menu action.

![File -> Save As](/learn_images/csv/export_excel_to_csv/microsoft_excel_convert_to_csv_save_as.png#1488x966)

Choose where you want to save your CSV file and select **CSV UTF-8 (Comma delimited) (.csv)** in the File format dropdown.

![Select CSV in file formats](/learn_images/csv/export_excel_to_csv/microsoft_excel_convert_to_csv_select_format.png#1378x892)


Finally, click the **Save** button.


Export Microsoft Excel spreadsheet into a CSV file

To convert a .xlsx or .xls file into a .csv file, Microsoft Excel is the perfect tool.
First, load your .xlsx or .xls file with Excel. Then, use the File -&ampgt; Save As menu action.
File -&ampgt; Save As
Choose where you want to save your CSV file and select CSV UTF-8 (Comma delimited) (.csv) in the File format dropdown.
Select CSV in file formats
Finally, click the Save button.


When it comes to storing data online, spreadsheets and databases are two possible options. Understanding the differences between them is crucial to find the best tool for your task.
Spreadsheets (Excel and Google Sheets) are easy to use and inexpensive. They are the go-to solution when it comes to simple analytics and data manipulation. For example, spreadsheet tools offer analytics features to display charts or create pivot tables.
Databases are designed to handle larger datasets and structured data. They offer built-in data constraints to force field format or to avoid duplicate information. Databases have powerful query languages to search and filter their data. These features are powerful but add complexity compared to spreadsheets. As a result, databases have a steep learning curve.
If you need to connect to your data programmatically, databases are the go-to solution. Thanks to its data structure, a database ensure the data is valid before saving it.
If you need to store less than 1 million records and don't need to connect your data to an application, spreadsheets do the job.


When it comes to storing data online, spreadsheets and databases are two possible options. Understanding the differences between them is crucial to find the best tool for your task.

Spreadsheets (Excel and Google Sheets) are easy to use and inexpensive. They are the go-to solution when it comes to simple analytics and data manipulation. For example, spreadsheet tools offer analytics features to display charts or create pivot tables.

Databases are designed to handle larger datasets and structured data. They offer built-in data constraints to force field format or to avoid duplicate information. Databases have powerful query languages to search and filter their data. These features are powerful but add complexity compared to spreadsheets. As a result, databases have a steep learning curve.

If you need to connect to your data programmatically, databases are the go-to solution. Thanks to its data structure, a database ensure the data is valid before saving it.

If you need to store less than 1 million records and don't need to connect your data to an application, spreadsheets do the job.


When to use a spreadsheet over a database?

When it comes to storing data online, spreadsheets and databases are two possible options. Understanding the differences between them is crucial to find the best tool for your task.
Spreadsheets (Excel and Google Sheets) are easy to use and inexpensive. They are the go-to solution when it comes to simple analytics and data manipulation. For example, spreadsheet tools offer analytics features to display charts or create pivot tables.
Databases are designed to handle larger datasets and structured data. They offer built-in data constraints to force field format or to avoid duplicate information. Databases have powerful query languages to search and filter their data. These features are powerful but add complexity compared to spreadsheets. As a result, databases have a steep learning curve.
If you need to connect to your data programmatically, databases are the go-to solution. Thanks to its data structure, a database ensure the data is valid before saving it.
If you need to store less than 1 million records and don&amp#39;t need to connect your data to an application, spreadsheets do the job.


If you’ve got a long list of email addresses, manually sorting out business from personal emails can feel like finding a needle in a haystack.
But here’s the good news: Datablist’s <a href="/enrichments/free-email-validator-bulk">Free Email Validator</a> can do this for you in bulk, with just a few clicks.
It’s a simple, fast way to identify whether an email is linked to a business domain or a personal email provider. For free!
We’ve all seen personal email addresses like <code>john.doe@gmail.com</code> or <code>sarah@yahoo.com</code>. These are free, generic email providers, and people use them for everyday communication. Business emails, on the other hand, usually have a custom domain like <code>info@companyname.com</code> or <code>jane@startup.com</code>.
If you’re sending marketing campaigns or segmenting contacts, this distinction can help you tailor your approach. For example, business emails may require a more formal message than personal ones.
<h2 id="user-content-how-to-identify-business-email-addresses-from-personal-ones-from-a-csv-file"><a href="#how-to-identify-business-email-addresses-from-personal-ones-from-a-csv-file" aria-hidden="true" tabindex="-1"></a>How to identify business email addresses from personal ones from a CSV file</h2>
<h3 id="user-content-step-1-import-your-email-list"><a href="#step-1-import-your-email-list" aria-hidden="true" tabindex="-1"></a>Step 1: Import Your Email List</h3>
First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.
<blockquote>
Note: The "Free Email Validator" enrichment is free and unlimited. You just need to create a free account.
</blockquote>
Create a new collection in Datablist, click on the "+" button, and select "Import CSV/Excel". <a href="/csv-editor">Datablist opens CSV files</a> and Excel files.
<img src="/learn_images/marketing/business-or-personal/create-collection-cleaned.png#2598x1177" alt="Import Email List">
<h3 id="user-content-step-2-run-the-free-email-validator-enrichment"><a href="#step-2-run-the-free-email-validator-enrichment" aria-hidden="true" tabindex="-1"></a>Step 2: Run the Free Email Validator Enrichment</h3>
Once your email list is uploaded, use the "Free Email Validator"(/enrichments/free-email-validator-bulk) to know if each email address belongs to a personal email provider or a business domain.
Click on the "Enrich" button in the header.
<img src="/learn_images/marketing/business-or-personal/enrich-free-email-validation.png#2627x1236" alt="Click Enrich">
Then select "<a href="/enrichments/free-email-validator-bulk">Free Email Validator</a>".
<img src="/learn_images/marketing/business-or-personal/enrichment-free-email-validation.png#2235x1304" alt="Select Free Email Validator">
Choose the property containing the email addresses from your collection.
<img src="/learn_images/marketing/business-or-personal/map-properties.png#2185x1339" alt="Map your email property">
Configure the output options to store the results. This will include whether the email is valid and the "Business Email" boolean information.
<img src="/learn_images/marketing/business-or-personal/business-boolean-outputs.png#2877x1326" alt="Configure outputs">
<h3 id="user-content-step-3-review-the-results"><a href="#step-3-review-the-results" aria-hidden="true" tabindex="-1"></a>Step 3: Review the Results</h3>
Datablist processes the email list and returns the following:
<ul>
<li>Valid Email - Confirms whether the email address is valid based on its syntax and MX records.</li>
<li>Error Status - The reason why the email address is invalid. Empty if valid.</li>
<li>Business Email - Return True if the domain is not from a generic email provider (gmail, yahoo, etc.)</li>
</ul>
<h2 id="user-content-examples-of-business-email-analysis"><a href="#examples-of-business-email-analysis" aria-hidden="true" tabindex="-1"></a>Examples of Business Email analysis</h2>
Here are some examples:
<ul>
<li>john.doe@gmail.com will be tagged as a personal email</li>
<li>info@companyname.com will be marked as a business email</li>
<li>random@disposable.com will be marked as invalid with an error message in the Error Status. The <code>Business Email</code> property will be marked as <code>False</code>.</li>
</ul>
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-why-should-you-care-about-checking-if-an-email-belongs-to-a-company"><a href="#why-should-you-care-about-checking-if-an-email-belongs-to-a-company" aria-hidden="true" tabindex="-1"></a>Why should you care about checking if an email belongs to a company?</h3>
Understanding whether an email is personal or business can help you target your messages more effectively. For example, a personalized approach might be better for personal email users, while business addresses may require a more formal tone.
<h3 id="user-content-is-datablists-free-email-validator-really-free"><a href="#is-datablists-free-email-validator-really-free" aria-hidden="true" tabindex="-1"></a>Is Datablist’s Free Email Validator really free?</h3>
Yes! The Free Email Validator is available to all Datablist users without any cost. You can upload as many email addresses as you need and run the validator as often as necessary.
<h3 id="user-content-how-does-datablist-determine-if-an-email-is-personal-or-business"><a href="#how-does-datablist-determine-if-an-email-is-personal-or-business" aria-hidden="true" tabindex="-1"></a>How does Datablist determine if an email is personal or business?</h3>
Datablist uses MX records to check the email’s provider. MX records reveal the mail servers associated with the email domain, which allows Datablist to identify whether the email is handled by a personal provider (like Gmail or Yahoo) or a custom domain typically used for business purposes.
<h3 id="user-content-can-i-use-datablist-for-large-email-lists"><a href="#can-i-use-datablist-for-large-email-lists" aria-hidden="true" tabindex="-1"></a>Can I use Datablist for large email lists?</h3>
Absolutely! Datablist is designed for bulk processing. Whether you have a few dozen or thousands of email addresses in a <a href="/csv-editor">CSV file</a>, Datablist can handle the list efficiently, providing results quickly.
<h3 id="user-content-what-other-information-does-the-free-email-validator-provide"><a href="#what-other-information-does-the-free-email-validator-provide" aria-hidden="true" tabindex="-1"></a>What other information does the Free Email Validator provide?</h3>
In addition to identifying whether an email is personal or business, the validator checks for the email’s validity. This includes flagging invalid addresses and identifying disposable or temporary email services.
<h3 id="user-content-what-file-formats-can-i-upload-for-validation"><a href="#what-file-formats-can-i-upload-for-validation" aria-hidden="true" tabindex="-1"></a>What file formats can I upload for validation?</h3>
You can upload email lists in CSV or Excel format. Just drag and drop your file into Datablist, and the platform will automatically process the data.
<h3 id="user-content-can-datablist-help-me-remove-fake-or-temporary-emails"><a href="#can-datablist-help-me-remove-fake-or-temporary-emails" aria-hidden="true" tabindex="-1"></a>Can Datablist help me remove fake or temporary emails?</h3>
Yes! Datablist’s <a href="/enrichments/free-email-validator-bulk">Free Email Validator</a> flags emails from disposable services like @mailinator.com or @10minutemail.com. This helps you clean up your list and avoid contacting temporary or fake addresses.
<h3 id="user-content-how-long-does-it-take-to-validate-a-large-list-of-emails"><a href="#how-long-does-it-take-to-validate-a-large-list-of-emails" aria-hidden="true" tabindex="-1"></a>How long does it take to validate a large list of emails?</h3>
Processing time depends on the size of your list, but Datablist is optimized for fast, bulk validation. For most lists, you’ll have results within minutes.

If you’ve got a long list of email addresses, manually sorting out business from personal emails can feel like finding a needle in a haystack. 

But here’s the good news: Datablist’s [Free Email Validator](/enrichments/free-email-validator-bulk) can do this for you in bulk, with just a few clicks.

**It’s a simple, fast way to identify whether an email is linked to a business domain or a personal email provider. For free!**

We’ve all seen personal email addresses like `john.doe@gmail.com` or `sarah@yahoo.com`. These are free, generic email providers, and people use them for everyday communication. Business emails, on the other hand, usually have a custom domain like `info@companyname.com` or `jane@startup.com`.

If you’re sending marketing campaigns or segmenting contacts, this distinction can help you tailor your approach. For example, business emails may require a more formal message than personal ones.

## How to identify business email addresses from personal ones from a CSV file

### Step 1: Import Your Email List

First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.

> **Note:** The "Free Email Validator" enrichment is **free** and unlimited. You just need to create a free account.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 You need a free Datablist account
</aside>

Create a new collection in Datablist, click on the "+" button, and select "Import CSV/Excel". [Datablist opens CSV files](/csv-editor) and Excel files.

![Import Email List](/learn_images/marketing/business-or-personal/create-collection-cleaned.png#2598x1177)


### Step 2: Run the Free Email Validator Enrichment

Once your email list is uploaded, use the "Free Email Validator"(/enrichments/free-email-validator-bulk) to know if each email address belongs to a personal email provider or a business domain.

Click on the "Enrich" button in the header.

![Click Enrich](/learn_images/marketing/business-or-personal/enrich-free-email-validation.png#2627x1236)

Then select "[Free Email Validator](/enrichments/free-email-validator-bulk)".

![Select Free Email Validator](/learn_images/marketing/business-or-personal/enrichment-free-email-validation.png#2235x1304)

Choose the property containing the email addresses from your collection.

![Map your email property](/learn_images/marketing/business-or-personal/map-properties.png#2185x1339)


Configure the output options to store the results. This will include whether the email is valid and the "Business Email" boolean information.

![Configure outputs](/learn_images/marketing/business-or-personal/business-boolean-outputs.png#2877x1326)


### Step 3: Review the Results

Datablist processes the email list and returns the following:

- **Valid Email** - Confirms whether the email address is valid based on its syntax and MX records.
- **Error Status** - The reason why the email address is invalid. Empty if valid.
- **Business Email** - Return True if the domain is not from a generic email provider (gmail, yahoo, etc.)


## Examples of Business Email analysis

Here are some examples:

- john.doe@gmail.com will be tagged as a personal email
- info@companyname.com will be marked as a business email
- random@disposable.com will be marked as invalid with an error message in the Error Status. The `Business Email` property will be marked as `False`.



## FAQ


### Why should you care about checking if an email belongs to a company?
Understanding whether an email is personal or business can help you target your messages more effectively. For example, a personalized approach might be better for personal email users, while business addresses may require a more formal tone.


### Is Datablist’s Free Email Validator really free?
Yes! The Free Email Validator is available to all Datablist users without any cost. You can upload as many email addresses as you need and run the validator as often as necessary.

### How does Datablist determine if an email is personal or business?
Datablist uses MX records to check the email’s provider. MX records reveal the mail servers associated with the email domain, which allows Datablist to identify whether the email is handled by a personal provider (like Gmail or Yahoo) or a custom domain typically used for business purposes.

### Can I use Datablist for large email lists?
Absolutely! Datablist is designed for bulk processing. Whether you have a few dozen or thousands of email addresses in a [CSV file](/csv-editor), Datablist can handle the list efficiently, providing results quickly.

### What other information does the Free Email Validator provide?
In addition to identifying whether an email is personal or business, the validator checks for the email’s validity. This includes flagging invalid addresses and identifying disposable or temporary email services.

### What file formats can I upload for validation?
You can upload email lists in CSV or Excel format. Just drag and drop your file into Datablist, and the platform will automatically process the data.


### Can Datablist help me remove fake or temporary emails?
Yes! Datablist’s [Free Email Validator](/enrichments/free-email-validator-bulk) flags emails from disposable services like @mailinator.com or @10minutemail.com. This helps you clean up your list and avoid contacting temporary or fake addresses.

### How long does it take to validate a large list of emails?
Processing time depends on the size of your list, but Datablist is optimized for fast, bulk validation. For most lists, you’ll have results within minutes.

Determine if email addresses are business or from personal email provider

If you’ve got a long list of email addresses, manually sorting out business from personal emails can feel like finding a needle in a haystack. 
But here’s the good news: Datablist’s Free Email Validator can do this for you in bulk, with just a few clicks.
It’s a simple, fast way to identify whether an email is linked to a business domain or a personal email provider. For free!
We’ve all seen personal email addresses like john.doe@gmail.com or sarah@yahoo.com. These are free, generic email providers, and people use them for everyday communication. Business emails, on the other hand, usually have a custom domain like info@companyname.com or jane@startup.com.
If you’re sending marketing campaigns or segmenting contacts, this distinction can help you tailor your approach. For example, business emails may require a more formal message than personal ones.
How to identify business email addresses from personal ones from a CSV file
Step 1: Import Your Email List
First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.
Note: The &ampquot;Free Email Validator&ampquot; enrichment is free and unlimited. You just need to create a free account.

Create a new collection in Datablist, click on the &ampquot;+&ampquot; button, and select &ampquot;Import CSV/Excel&ampquot;. Datablist opens CSV files and Excel files.
Import Email List
Step 2: Run the Free Email Validator Enrichment
Once your email list is uploaded, use the &ampquot;Free Email Validator&ampquot;(/enrichments/free-email-validator-bulk) to know if each email address belongs to a personal email provider or a business domain.
Click on the &ampquot;Enrich&ampquot; button in the header.
Click Enrich
Then select &ampquot;Free Email Validator&ampquot;.
Select Free Email Validator
Choose the property containing the email addresses from your collection.
Map your email property
Configure the output options to store the results. This will include whether the email is valid and the &ampquot;Business Email&ampquot; boolean information.
Configure outputs
Step 3: Review the Results
Datablist processes the email list and returns the following:
Valid Email - Confirms whether the email address is valid based on its syntax and MX records.
Error Status - The reason why the email address is invalid. Empty if valid.
Business Email - Return True if the domain is not from a generic email provider (gmail, yahoo, etc.)

Examples of Business Email analysis
Here are some examples:
john.doe@gmail.com will be tagged as a personal email
info@companyname.com will be marked as a business email
random@disposable.com will be marked as invalid with an error message in the Error Status. The Business Email property will be marked as False.

FAQ
Why should you care about checking if an email belongs to a company?
Understanding whether an email is personal or business can help you target your messages more effectively. For example, a personalized approach might be better for personal email users, while business addresses may require a more formal tone.
Is Datablist’s Free Email Validator really free?
Yes! The Free Email Validator is available to all Datablist users without any cost. You can upload as many email addresses as you need and run the validator as often as necessary.
How does Datablist determine if an email is personal or business?
Datablist uses MX records to check the email’s provider. MX records reveal the mail servers associated with the email domain, which allows Datablist to identify whether the email is handled by a personal provider (like Gmail or Yahoo) or a custom domain typically used for business purposes.
Can I use Datablist for large email lists?
Absolutely! Datablist is designed for bulk processing. Whether you have a few dozen or thousands of email addresses in a CSV file, Datablist can handle the list efficiently, providing results quickly.
What other information does the Free Email Validator provide?
In addition to identifying whether an email is personal or business, the validator checks for the email’s validity. This includes flagging invalid addresses and identifying disposable or temporary email services.
What file formats can I upload for validation?
You can upload email lists in CSV or Excel format. Just drag and drop your file into Datablist, and the platform will automatically process the data.
Can Datablist help me remove fake or temporary emails?
Yes! Datablist’s Free Email Validator flags emails from disposable services like @mailinator.com or @10minutemail.com. This helps you clean up your list and avoid contacting temporary or fake addresses.
How long does it take to validate a large list of emails?
Processing time depends on the size of your list, but Datablist is optimized for fast, bulk validation. For most lists, you’ll have results within minutes.


Lead Deduplication, or <a href="/features/duplicates-remover">lead deduping</a>, is the process of finding, and merging duplicate records to have a clean list of unique entries.
A lead represents a person or a company. A duplicate lead is when there are multiple records for the same person or company.
Duplicate leads appear when you have several lead sources that pour into a single lead list. Examples of lead sources are lead magnets, webinars, newsletters, or manual entries.
👉 Check our <a href="/how-to/merge-duplicate-leads-without-losing-data">guide to remove duplicate leads from your list.</a>
The main steps for lead deduplication typically include:
<ol>
<li>
Data Collection: Gather the leads from various sources, such as online forms, CRM systems, marketing automation platforms, and manual entry. The best format to extract data from all sources is the <a href="/learn/csv/csv-meaning">CSV format</a>. Use our <a href="/csv-editor">CSV Editor</a> to import and consolidate all your files.
</li>
<li>
Data Cleansing: Cleanse and standardize the leads by removing any irrelevant or incomplete entries, correcting formatting issues, and normalizing data fields to ensure consistency.
</li>
<li>
Define Duplicate Scoring Algorithm: This involves setting what constitutes a duplicate. For lead deduplication, you want to define your matching criteria on unique identifiers: email address, phone number, or contact name. For company leads the best identifier is the company website. Then, you can select how to compare values between leads. Do you want an exact match on names, or use fuzzy matching to match duplicate leads with slight changes.
</li>
<li>
Duplicate Identification: Run the deduplication algorithm on your leads. Duplicate groups are created with leads having a high similarity score.
</li>
<li>
Data Consolidation: Merge or update the duplicate records to create a single, consolidated, and accurate lead entry. This may involve merging contact details, activity history, and any associated information. <a href="/features/combine-csv-files">Data Consolidation</a> can be done automatically using rules or manually for conflicting leads.
</li>
</ol>
👉 Check our <a href="/how-to/merge-duplicate-leads-without-losing-data">free tool for lead deduplication.</a>
<img src="/learn_images/marketing/leads-deduplication/leads-deduplication.png#1078x1056" alt="Datablist Lead Deduplication Tool">
👉 Check our <a href="/how-to/merge-duplicate-leads-without-losing-data">free tool for lead deduplication.</a>


**Lead Deduplication, or [lead deduping](/features/duplicates-remover), is the process of finding, and merging duplicate records to have a clean list of unique entries.**

A lead represents a person or a company. A duplicate lead is when there are multiple records for the same person or company.

Duplicate leads appear when you have several lead sources that pour into a single lead list. Examples of lead sources are lead magnets, webinars, newsletters, or manual entries.

👉 Check our [guide to remove duplicate leads from your list.](/how-to/merge-duplicate-leads-without-losing-data)

The main steps for lead deduplication typically include:

1. Data Collection: Gather the leads from various sources, such as online forms, CRM systems, marketing automation platforms, and manual entry. The best format to extract data from all sources is the [CSV format](/learn/csv/csv-meaning). Use our [CSV Editor](/csv-editor) to import and consolidate all your files.

1. Data Cleansing: Cleanse and standardize the leads by removing any irrelevant or incomplete entries, correcting formatting issues, and normalizing data fields to ensure consistency.

1. Define Duplicate Scoring Algorithm: This involves setting what constitutes a duplicate. For lead deduplication, you want to define your matching criteria on unique identifiers: email address, phone number, or contact name. For company leads the best identifier is the company website. Then, you can select how to compare values between leads. Do you want an exact match on names, or use fuzzy matching to match duplicate leads with slight changes. 

1. Duplicate Identification: Run the deduplication algorithm on your leads. Duplicate groups are created with leads having a high similarity score.

1. Data Consolidation: Merge or update the duplicate records to create a single, consolidated, and accurate lead entry. This may involve merging contact details, activity history, and any associated information. [Data Consolidation](/features/combine-csv-files) can be done automatically using rules or manually for conflicting leads.

👉 Check our [free tool for lead deduplication.](/how-to/merge-duplicate-leads-without-losing-data)

![Datablist Lead Deduplication Tool](/learn_images/marketing/leads-deduplication/leads-deduplication.png#1078x1056)

👉 Check our [free tool for lead deduplication.](/how-to/merge-duplicate-leads-without-losing-data)

When to dedupe leads?

Lead Deduplication, or lead deduping, is the process of finding, and merging duplicate records to have a clean list of unique entries.
A lead represents a person or a company. A duplicate lead is when there are multiple records for the same person or company.
Duplicate leads appear when you have several lead sources that pour into a single lead list. Examples of lead sources are lead magnets, webinars, newsletters, or manual entries.
👉 Check our guide to remove duplicate leads from your list.
The main steps for lead deduplication typically include:
Data Collection: Gather the leads from various sources, such as online forms, CRM systems, marketing automation platforms, and manual entry. The best format to extract data from all sources is the CSV format. Use our CSV Editor to import and consolidate all your files.

Data Cleansing: Cleanse and standardize the leads by removing any irrelevant or incomplete entries, correcting formatting issues, and normalizing data fields to ensure consistency.

Define Duplicate Scoring Algorithm: This involves setting what constitutes a duplicate. For lead deduplication, you want to define your matching criteria on unique identifiers: email address, phone number, or contact name. For company leads the best identifier is the company website. Then, you can select how to compare values between leads. Do you want an exact match on names, or use fuzzy matching to match duplicate leads with slight changes. 

Duplicate Identification: Run the deduplication algorithm on your leads. Duplicate groups are created with leads having a high similarity score.

Data Consolidation: Merge or update the duplicate records to create a single, consolidated, and accurate lead entry. This may involve merging contact details, activity history, and any associated information. Data Consolidation can be done automatically using rules or manually for conflicting leads.


👉 Check our free tool for lead deduplication.
Datablist Lead Deduplication Tool
👉 Check our free tool for lead deduplication.


Ever wondered which email providers are behind the email addresses in your contact list? Knowing this information can be incredibly useful, whether you’re segmenting business emails from personal ones or checking the reliability of email domains. One effective way to uncover this is by looking at the MX (Mail Exchange) records associated with the email domains.
The good news? Datablist’s <a href="/enrichments/free-email-validator-bulk">Free Email Validator</a> enrichment can help you do this in just a few steps!
<h2 id="user-content-what-are-mx-records"><a href="#what-are-mx-records" aria-hidden="true" tabindex="-1"></a>What Are MX Records?</h2>
MX records are part of a domain's DNS (Domain Name System) settings. They tell email servers where to deliver emails sent to that domain. By querying the MX records, you can identify which email service (provider) is handling the email for a particular domain.
For example, when you send an email to <code>someone@gmail.com</code>, the MX records will show that <code>Google</code>’s servers manage the email. Similarly, business domains have their own mail servers or use third-party email providers like Outlook or G Suite.
<h2 id="user-content-how-to-get-email-providers-using-mx-records"><a href="#how-to-get-email-providers-using-mx-records" aria-hidden="true" tabindex="-1"></a>How to Get Email Providers Using MX Records</h2>
Getting the email provider through MX records might sound technical, but Datablist makes it simple. With the <a href="/enrichments/free-email-validator-bulk">Free Email Validator</a> enrichment, you can easily get the email provider from the MX records for all email addresses in your list.
<h3 id="user-content-step-1-import-your-email-list"><a href="#step-1-import-your-email-list" aria-hidden="true" tabindex="-1"></a>Step 1: Import Your Email List</h3>
First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.
<blockquote>
Note: The "Free Email Validator" enrichment is free and unlimited. You just need to create a free account.
</blockquote>
Create a new collection in Datablist, click on the "+" button, and select "Import CSV/Excel". <a href="/csv-editor">Datablist opens CSV files</a> and Excel files.
<img src="/learn_images/marketing/mx-providers/create-collection-cleaned.png#2598x1177" alt="Import Email List">
<h3 id="user-content-step-2-run-the-free-email-validator-enrichment"><a href="#step-2-run-the-free-email-validator-enrichment" aria-hidden="true" tabindex="-1"></a>Step 2: Run the Free Email Validator Enrichment</h3>
Once your email list is uploaded, use the Free Email Validator to check the email providers.
Click on the "Enrich" button in the header.
<img src="/learn_images/marketing/mx-providers/enrich-free-email-validation.png#2627x1236" alt="Click Enrich">
Then select "<a href="/enrichments/free-email-validator-bulk">Free Email Validator</a>".
<img src="/learn_images/marketing/mx-providers/enrichment-free-email-validation.png#2235x1304" alt="Select Free Email Validator">
Choose the property containing the email addresses from your collection.
<img src="/learn_images/marketing/mx-providers/map-properties.png#2185x1339" alt="Map your email property">
Configure the output options to store the results. This will include whether the email is valid and the email provider information.
<img src="/learn_images/marketing/mx-providers/mx-records-outputs.png#2881x1343" alt="Configure outputs">
<h3 id="user-content-step-3-review-the-results"><a href="#step-3-review-the-results" aria-hidden="true" tabindex="-1"></a>Step 3: Review the Results</h3>
Datablist processes the email list and return the following:
<ul>
<li>Valid Email - Confirms whether the email address is valid based on its syntax and MX records.</li>
<li>Error Status - The reason why the email address is invalid. Empty if valid.</li>
<li>MX Provider - Shows which provider manages the email domain (e.g., Gmail, Yahoo, Outlook, etc.).</li>
</ul>
<h2 id="user-content-examples-of-email-provider-identification"><a href="#examples-of-email-provider-identification" aria-hidden="true" tabindex="-1"></a>Examples of Email Provider Identification</h2>
Let’s say you upload a list with these email addresses:
<ul>
<li>john.doe@gmail.com</li>
<li>sales@company.com</li>
<li>contact@disposablemail.com</li>
</ul>
After running the Free Email Validator, Datablist will show:
<ul>
<li>Google as the provider for john.doe@gmail.com.</li>
<li>Custom/Business Provider for sales@company.com (e.g., might show Microsoft’s servers if they use Outlook).</li>
<li>Email invalid and an Error Status saying the email is disposable. For disposable domains, Datablist doesn't check the MX records.</li>
</ul>
<h2 id="user-content-list-of-mx-providers"><a href="#list-of-mx-providers" aria-hidden="true" tabindex="-1"></a>List of MX Providers</h2>
The "Free Email Validator" enrichment returns the name of the email provider based on the MX Records.
Here is the list of built-in providers:
<ul>
<li>google</li>
<li>microsoft</li>
<li>zoho</li>
<li>yahoo</li>
<li>safenames</li>
<li>ovh</li>
<li>amazon</li>
<li>gandi</li>
<li>ionos</li>
<li>mimecast</li>
<li>godaddy</li>
<li>yandex</li>
<li>apple</li>
<li>proofpoint</li>
<li>mailgun</li>
<li>protonmail</li>
<li>mail.ru</li>
<li>barracuda</li>
<li>namecheap</li>
</ul>
<blockquote>
Note: If the MX Records don't match one of those providers, the full MX Record is returned.
</blockquote>
<h2 id="user-content-faq"><a href="#faq" aria-hidden="true" tabindex="-1"></a>FAQ</h2>
<h3 id="user-content-why-check-email-providers"><a href="#why-check-email-providers" aria-hidden="true" tabindex="-1"></a>Why Check Email Providers?</h3>
There are several reasons you might want to know the email providers for the addresses in your list:
<ul>
<li>Segmentation - Separate business emails from generic email providers (like Gmail, Yahoo) to target them differently.</li>
<li>Provider Reliability - Some providers are known for strong spam filtering, while others might be linked to higher bounce rates.</li>
<li>Security - Identify disposable or suspicious email providers that might be linked to fake accounts.</li>
<li>Campaign Optimization - Tailor your email marketing campaigns based on the provider’s deliverability and engagement trends.</li>
</ul>

Ever wondered which email providers are behind the email addresses in your contact list? Knowing this information can be incredibly useful, whether you’re segmenting business emails from personal ones or checking the reliability of email domains. **One effective way to uncover this is by looking at the MX (Mail Exchange) records associated with the email domains.**

The good news? Datablist’s [Free Email Validator](/enrichments/free-email-validator-bulk) enrichment can help you do this in just a few steps!

## What Are MX Records?

MX records are part of a domain's DNS (Domain Name System) settings. They tell email servers where to deliver emails sent to that domain. By querying the MX records, you can identify which email service (provider) is handling the email for a particular domain.

For example, when you send an email to `someone@gmail.com`, the MX records will show that `Google`’s servers manage the email. Similarly, business domains have their own mail servers or use third-party email providers like Outlook or G Suite.

## How to Get Email Providers Using MX Records

Getting the email provider through MX records might sound technical, but Datablist makes it simple. With the [Free Email Validator](/enrichments/free-email-validator-bulk) enrichment, you can easily get the email provider from the MX records for all email addresses in your list.


### Step 1: Import Your Email List

First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.

> **Note:** The "Free Email Validator" enrichment is **free** and unlimited. You just need to create a free account.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 You need a free Datablist account
</aside>

Create a new collection in Datablist, click on the "+" button, and select "Import CSV/Excel". [Datablist opens CSV files](/csv-editor) and Excel files.

![Import Email List](/learn_images/marketing/mx-providers/create-collection-cleaned.png#2598x1177)


### Step 2: Run the Free Email Validator Enrichment

Once your email list is uploaded, use the Free Email Validator to check the email providers.

Click on the "Enrich" button in the header.

![Click Enrich](/learn_images/marketing/mx-providers/enrich-free-email-validation.png#2627x1236)

Then select "[Free Email Validator](/enrichments/free-email-validator-bulk)".

![Select Free Email Validator](/learn_images/marketing/mx-providers/enrichment-free-email-validation.png#2235x1304)

Choose the property containing the email addresses from your collection.

![Map your email property](/learn_images/marketing/mx-providers/map-properties.png#2185x1339)


Configure the output options to store the results. This will include whether the email is valid and the email provider information.

![Configure outputs](/learn_images/marketing/mx-providers/mx-records-outputs.png#2881x1343)


### Step 3: Review the Results

Datablist processes the email list and return the following:

- **Valid Email** - Confirms whether the email address is valid based on its syntax and MX records.
- **Error Status** - The reason why the email address is invalid. Empty if valid.
- **MX Provider** - Shows which provider manages the email domain (e.g., Gmail, Yahoo, Outlook, etc.).


## Examples of Email Provider Identification

Let’s say you upload a list with these email addresses:

- john.doe@gmail.com
- sales@company.com
- contact@disposablemail.com

After running the Free Email Validator, Datablist will show:

- Google as the provider for john.doe@gmail.com.
- Custom/Business Provider for sales@company.com (e.g., might show Microsoft’s servers if they use Outlook).
- Email invalid and an Error Status saying the email is disposable. For disposable domains, Datablist doesn't check the MX records.


## List of MX Providers

The "Free Email Validator" enrichment returns the name of the email provider based on the MX Records.

Here is the list of built-in providers:

- google
- microsoft
- zoho
- yahoo
- safenames
- ovh
- amazon
- gandi
- ionos
- mimecast
- godaddy
- yandex
- apple
- proofpoint
- mailgun
- protonmail
- mail.ru
- barracuda
- namecheap

> **Note:** If the MX Records don't match one of those providers, the full MX Record is returned.

## FAQ

### Why Check Email Providers?

There are several reasons you might want to know the email providers for the addresses in your list:

- **Segmentation** - Separate business emails from generic email providers (like Gmail, Yahoo) to target them differently.
- **Provider Reliability** - Some providers are known for strong spam filtering, while others might be linked to higher bounce rates.
- **Security** - Identify disposable or suspicious email providers that might be linked to fake accounts.
- **Campaign Optimization** - Tailor your email marketing campaigns based on the provider’s deliverability and engagement trends.

Get email providers for emails

Ever wondered which email providers are behind the email addresses in your contact list? Knowing this information can be incredibly useful, whether you’re segmenting business emails from personal ones or checking the reliability of email domains. One effective way to uncover this is by looking at the MX (Mail Exchange) records associated with the email domains.
The good news? Datablist’s Free Email Validator enrichment can help you do this in just a few steps!
What Are MX Records?
MX records are part of a domain&amp#39;s DNS (Domain Name System) settings. They tell email servers where to deliver emails sent to that domain. By querying the MX records, you can identify which email service (provider) is handling the email for a particular domain.
For example, when you send an email to someone@gmail.com, the MX records will show that Google’s servers manage the email. Similarly, business domains have their own mail servers or use third-party email providers like Outlook or G Suite.
How to Get Email Providers Using MX Records
Getting the email provider through MX records might sound technical, but Datablist makes it simple. With the Free Email Validator enrichment, you can easily get the email provider from the MX records for all email addresses in your list.
Step 1: Import Your Email List
First, you’ll need to upload your email addresses to Datablist. You can either upload a CSV file or copy and paste the email list directly from a spreadsheet.
Note: The &ampquot;Free Email Validator&ampquot; enrichment is free and unlimited. You just need to create a free account.

Create a new collection in Datablist, click on the &ampquot;+&ampquot; button, and select &ampquot;Import CSV/Excel&ampquot;. Datablist opens CSV files and Excel files.
Import Email List
Step 2: Run the Free Email Validator Enrichment
Once your email list is uploaded, use the Free Email Validator to check the email providers.
Click on the &ampquot;Enrich&ampquot; button in the header.
Click Enrich
Then select &ampquot;Free Email Validator&ampquot;.
Select Free Email Validator
Choose the property containing the email addresses from your collection.
Map your email property
Configure the output options to store the results. This will include whether the email is valid and the email provider information.
Configure outputs
Step 3: Review the Results
Datablist processes the email list and return the following:
Valid Email - Confirms whether the email address is valid based on its syntax and MX records.
Error Status - The reason why the email address is invalid. Empty if valid.
MX Provider - Shows which provider manages the email domain (e.g., Gmail, Yahoo, Outlook, etc.).

Examples of Email Provider Identification
Let’s say you upload a list with these email addresses:
john.doe@gmail.com
sales@company.com
contact@disposablemail.com

After running the Free Email Validator, Datablist will show:
Google as the provider for john.doe@gmail.com.
Custom/Business Provider for sales@company.com (e.g., might show Microsoft’s servers if they use Outlook).
Email invalid and an Error Status saying the email is disposable. For disposable domains, Datablist doesn&amp#39;t check the MX records.

List of MX Providers
The &ampquot;Free Email Validator&ampquot; enrichment returns the name of the email provider based on the MX Records.
Here is the list of built-in providers:
google
microsoft
zoho
yahoo
safenames
ovh
amazon
gandi
ionos
mimecast
godaddy
yandex
apple
proofpoint
mailgun
protonmail
mail.ru
barracuda
namecheap

Note: If the MX Records don&amp#39;t match one of those providers, the full MX Record is returned.

FAQ
Why Check Email Providers?
There are several reasons you might want to know the email providers for the addresses in your list:
Segmentation - Separate business emails from generic email providers (like Gmail, Yahoo) to target them differently.
Provider Reliability - Some providers are known for strong spam filtering, while others might be linked to higher bounce rates.
Security - Identify disposable or suspicious email providers that might be linked to fake accounts.
Campaign Optimization - Tailor your email marketing campaigns based on the provider’s deliverability and engagement trends.



When scraping websites for lead generation, finding the "About Us" page offers invaluable insights. Typically, these pages provide critical information including the business's history, values, leadership team, and contact details.
Here are some of the most common URL structures used for "About Us" pages:
<ul>
<li>/about</li>
<li>/about-us</li>
<li>/about_me (for personal sites or blogs)</li>
<li>/aboutus</li>
<li>/company/about</li>
<li>/company</li>
<li>/our-story</li>
<li>/our-company</li>
<li>/who-we-are</li>
<li>/meet-the-team</li>
<li>/about-our-company</li>
<li>/about-company</li>
<li>/aboutus.html</li>
<li>/about/index.html</li>
</ul>
These variations are widely used across different websites and industries.
<h1 id="user-content-how-to-automatically-follow-about-us-pages-when-scraping"><a href="#how-to-automatically-follow-about-us-pages-when-scraping" aria-hidden="true" tabindex="-1"></a>How to automatically follow "About us" pages when scraping?</h1>
With this list of URL paths, it's easy to scrape any website and follow the "About" link in order to get data.
The best examples of data to extract are:
<ul>
<li>Email addresses</li>
<li>LinkedIn Company pages</li>
<li>LinkedIn People profiles</li>
<li>Phone numbers</li>
<li>Social Links (Instagram, Facebook, Youtube, etc.)</li>
<li>Texts that describe what a company does</li>
</ul>
This is what Datablist <a href="/enrichments/scrape-texts-linkedin-email-instagram-links-webpage-bulk">Smart Scraper</a> does!
You can define a list of websites and the Smart Scraper will visit each website to extract data from the defined webpage and the website about page. <a href="/enrichments/scrape-texts-linkedin-email-instagram-links-webpage-bulk">Click here to learn more about the Smart Scraper</a>.
<img src="/learn_images/scraping/about-us-paths/outputs.png#2355x1361" alt="Smart Scraper">

When scraping websites for lead generation, finding the "About Us" page offers invaluable insights. Typically, these pages provide critical information including the business's history, values, leadership team, and contact details.

Here are some of the most common URL structures used for "About Us" pages:

- /about
- /about-us
- /about_me (for personal sites or blogs)
- /aboutus
- /company/about
- /company
- /our-story
- /our-company
- /who-we-are
- /meet-the-team
- /about-our-company
- /about-company
- /aboutus.html
- /about/index.html

These variations are widely used across different websites and industries.

# How to automatically follow "About us" pages when scraping?

With this list of URL paths, it's easy to scrape any website and follow the "About" link in order to get data.

The best examples of data to extract are:

- Email addresses
- LinkedIn Company pages
- LinkedIn People profiles
- Phone numbers
- Social Links (Instagram, Facebook, Youtube, etc.)
- Texts that describe what a company does

This is what Datablist [Smart Scraper](/enrichments/scrape-texts-linkedin-email-instagram-links-webpage-bulk) does!
You can define a list of websites and the Smart Scraper will visit each website to extract data from the defined webpage and the website about page. [Click here to learn more about the Smart Scraper](/enrichments/scrape-texts-linkedin-email-instagram-links-webpage-bulk).

![Smart Scraper](/learn_images/scraping/about-us-paths/outputs.png#2355x1361)

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 No signup required
</aside>

The most common URL paths for "About Us" pages

When scraping websites for lead generation, finding the &ampquot;About Us&ampquot; page offers invaluable insights. Typically, these pages provide critical information including the business&amp#39;s history, values, leadership team, and contact details.
Here are some of the most common URL structures used for &ampquot;About Us&ampquot; pages:
/about
/about-us
/about_me (for personal sites or blogs)
/aboutus
/company/about
/company
/our-story
/our-company
/who-we-are
/meet-the-team
/about-our-company
/about-company
/aboutus.html
/about/index.html

These variations are widely used across different websites and industries.
How to automatically follow &ampquot;About us&ampquot; pages when scraping?
With this list of URL paths, it&amp#39;s easy to scrape any website and follow the &ampquot;About&ampquot; link in order to get data.
The best examples of data to extract are:
Email addresses
LinkedIn Company pages
LinkedIn People profiles
Phone numbers
Social Links (Instagram, Facebook, Youtube, etc.)
Texts that describe what a company does

This is what Datablist Smart Scraper does!
You can define a list of websites and the Smart Scraper will visit each website to extract data from the defined webpage and the website about page. Click here to learn more about the Smart Scraper.
Smart Scraper


In lead generation, for businesses targeting Shopify stores, extracting the country code from a list of Shopify URLs can be a game-changer.
Hopefully, the country code information is quickly available in the source code of the Shopify stores.
In this article, you will learn:
<ul>
<li><a href="#how-to-find-the-country-code-for">To find the country code manually for a Shopify store.</a></li>
<li><a href="#how-to-extract-country-codes-for-a-list-of-shopify-stores---in-bulk">Extract the country codes for a list of Shopify stores in bulk.</a></li>
</ul>
<h2 id="user-content-how-to-find-the-country-code-for-a-shopify-store-manually"><a href="#how-to-find-the-country-code-for-a-shopify-store-manually" aria-hidden="true" tabindex="-1"></a>How to find the country code for a Shopify Store manually</h2>
<img src="/learn_images/scraping/extract-country-codes/view-source-code-shopify.png#2209x1199" alt="Open Shopify source code">
<img src="/learn_images/scraping/extract-country-codes/search-country-code.png#1472x1302" alt="Search &#x22;Shopify.country&#x22;">
<h2 id="user-content-how-to-extract-country-codes-for-a-list-of-shopify-stores---in-bulk"><a href="#how-to-extract-country-codes-for-a-list-of-shopify-stores---in-bulk" aria-hidden="true" tabindex="-1"></a>How to extract country codes for a list of Shopify Stores - In Bulk</h2>
Now, to extract country codes in bulk, you need to automate this process.
To do so, you can use Datablist Bulk Scraper. Datablist is a powerful <a href="/use-cases/lead-list-building">Leads Relationship Management</a> tool.
First, import your list of Shopify stores in Datablist <a href="/csv-editor">CSV Editor</a>.
Then, click on the "Enrich" button in the header.
<img src="/learn_images/scraping/extract-country-codes/import-list-stores.png#2473x1480" alt="Import List of Shopify Stores">
Select the <a href="/enrichments/scrape-urls-css-selectors-regex-bulk">Bulk Scraper</a> enrichment. The Bulk Scraper will open each store using the Shopify URL and extract the country code from the HTML source code.
<img src="/learn_images/scraping/extract-country-codes/bulk-scraper-enrichment.png#2337x1042" alt="Select Bulk Scraper">
In the scraper configuration, scroll to the "Regex #1" setting. And write the following regular expression:
<code>Shopify\.country\s=\s"(\w+)";</code>
<img src="/learn_images/scraping/extract-country-codes/configure-regex.png#2256x1308" alt="Configure Input with Regular Expression">
Then, continue to configure the outputs. Create new properties to store the Regex Text #1.
You can also add the URL Scraping Status output to know when a store is no longer online.
<img src="/learn_images/scraping/extract-country-codes/scraping-outputs.png#2258x1370" alt="Configure Scraping Outputs">
Then run the Bulk Scraper enrichment on your first 10 stores. If the results are good, process the whole list.
To finish, clean unreachable stores to keep only up-to-date Shopify leads.
<img src="/learn_images/scraping/extract-country-codes/shopify-stores-country-code-results.png#1646x1146" alt="Shopify Stores Country Code List">

In lead generation, for businesses targeting Shopify stores, extracting the country code from a list of Shopify URLs can be a game-changer.

Hopefully, the country code information is quickly available in the source code of the Shopify stores.

In this article, you will learn:

- [To find the country code manually for a Shopify store.](#how-to-find-the-country-code-for)
- [Extract the country codes for a list of Shopify stores **in bulk**.](#how-to-extract-country-codes-for-a-list-of-shopify-stores---in-bulk)


## How to find the country code for a Shopify Store manually


![Open Shopify source code](/learn_images/scraping/extract-country-codes/view-source-code-shopify.png#2209x1199)

![Search "Shopify.country"](/learn_images/scraping/extract-country-codes/search-country-code.png#1472x1302)


## How to extract country codes for a list of Shopify Stores - In Bulk

Now, to extract country codes in bulk, you need to automate this process.

To do so, you can use Datablist Bulk Scraper. Datablist is a powerful [Leads Relationship Management](/use-cases/lead-list-building) tool.

First, import your list of Shopify stores in Datablist [CSV Editor](/csv-editor).

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/auth/signup" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
 Sign up for free
</aside>

Then, click on the "Enrich" button in the header.

![Import List of Shopify Stores](/learn_images/scraping/extract-country-codes/import-list-stores.png#2473x1480)

Select the [**Bulk Scraper**](/enrichments/scrape-urls-css-selectors-regex-bulk) enrichment. The Bulk Scraper will open each store using the Shopify URL and extract the country code from the HTML source code.

![Select Bulk Scraper](/learn_images/scraping/extract-country-codes/bulk-scraper-enrichment.png#2337x1042)

In the scraper configuration, scroll to the "Regex #1" setting. And write the following regular expression:

```Shopify\.country\s=\s"(\w+)";```

![Configure Input with Regular Expression](/learn_images/scraping/extract-country-codes/configure-regex.png#2256x1308)

Then, continue to configure the outputs. Create new properties to store the **Regex Text #1**.

You can also add the **URL Scraping Status** output to know when a store is no longer online.

![Configure Scraping Outputs](/learn_images/scraping/extract-country-codes/scraping-outputs.png#2258x1370)

Then run the Bulk Scraper enrichment on your first 10 stores. If the results are good, process the whole list.

To finish, clean unreachable stores to keep only up-to-date Shopify leads.

![Shopify Stores Country Code List](/learn_images/scraping/extract-country-codes/shopify-stores-country-code-results.png#1646x1146)

Extracy country codes from Shopify stores

In lead generation, for businesses targeting Shopify stores, extracting the country code from a list of Shopify URLs can be a game-changer.
Hopefully, the country code information is quickly available in the source code of the Shopify stores.
In this article, you will learn:
To find the country code manually for a Shopify store.
Extract the country codes for a list of Shopify stores in bulk.

How to find the country code for a Shopify Store manually
Open Shopify source code
Search &ampquot;Shopify.country&ampquot;
How to extract country codes for a list of Shopify Stores - In Bulk
Now, to extract country codes in bulk, you need to automate this process.
To do so, you can use Datablist Bulk Scraper. Datablist is a powerful Leads Relationship Management tool.
First, import your list of Shopify stores in Datablist CSV Editor.
Then, click on the &ampquot;Enrich&ampquot; button in the header.
Import List of Shopify Stores
Select the Bulk Scraper enrichment. The Bulk Scraper will open each store using the Shopify URL and extract the country code from the HTML source code.
Select Bulk Scraper
In the scraper configuration, scroll to the &ampquot;Regex #1&ampquot; setting. And write the following regular expression:
Shopify\.country\s=\s&ampquot;(\w+)&ampquot;;
Configure Input with Regular Expression
Then, continue to configure the outputs. Create new properties to store the Regex Text #1.
You can also add the URL Scraping Status output to know when a store is no longer online.
Configure Scraping Outputs
Then run the Bulk Scraper enrichment on your first 10 stores. If the results are good, process the whole list.
To finish, clean unreachable stores to keep only up-to-date Shopify leads.
Shopify Stores Country Code List


Here is a quick JavaScript snippet to extract all URLs from a webpage fast with Google Chrome Developer Tools. No Browser Extension is required!
The JavaScript code generates a list of URLs in CSV format with the anchor texts, and a boolean to know if the URL is internal or external to the current website. Just copy-paste the results into Google Sheets or with the Datablist <a href="/csv-editor">CSV editor</a>.
<blockquote>
Notes: The code works also with Firefox, Safari, etc.
</blockquote>
<h2 id="user-content-step-1-run-javascript-code-in-google-chrome-developer-tools"><a href="#step-1-run-javascript-code-in-google-chrome-developer-tools" aria-hidden="true" tabindex="-1"></a>Step 1: Run JavaScript code in Google Chrome Developer Tools</h2>
<ul>
<li>Open Google Chrome Developer Tools with <code>Cmd + Opt + i</code> (Mac) or <code>F12</code> (Windows). And click on the <code>Console</code> tab.</li>
<li>Copy-Paste the following JavaScript code and press <code>Enter</code>.</li>
</ul>
<pre><code>const results = [
 ['Url', 'Anchor Text', 'External']
];
var urls = document.getElementsByTagName('a');
for (urlIndex in urls) {
 const url = urls[urlIndex]
 const externalLink = url.host !== window.location.host
 if(url.href &#x26;&#x26; url.href.indexOf('://')!==-1) results.push([url.href, url.text, externalLink]) // url.rel
}
const csvContent = results.map((line)=>{
 return line.map((cell)=>{
 if(typeof(cell)==='boolean') return cell ? 'TRUE': 'FALSE'
 if(!cell) return ''
 let value = cell.replace(/[\f\n\v]*\n\s*/g, "\n").replace(/[\t\f ]+/g, ' ');
 value = value.replace(/\t/g, ' ').trim();
 return `"${value}"`
 }).join('\t')
}).join("\n");
console.log(csvContent)
</code></pre>
<img src="/learn_images/scraping/extract-urls-webpage/copy-js-code.png#1938x694" alt="Open Google Developer Tools">
<h2 id="user-content-step-2-copy-paste-exported-urls-into-a-csv-file-or-spreadsheet-tools"><a href="#step-2-copy-paste-exported-urls-into-a-csv-file-or-spreadsheet-tools" aria-hidden="true" tabindex="-1"></a>Step 2: Copy-paste exported URLs into a CSV file or spreadsheet tools</h2>
The JavaScript code will extract all URLs from the webpage with the following values:
<ul>
<li>URL - The link URL</li>
<li>Anchor Text - The label associated with the link. Called "Anchor Text".</li>
<li>External - A boolean (<code>TRUE</code>, <code>FALSE</code>)</li>
</ul>
The result is printed on the console using <a href="/learn/csv/csv-meaning">CSV format</a>. You can copy-paste the result into spreadsheet tools or with our <a href="/csv-editor">online CSV editor</a>. Or create a new text file with your text editor and save the file with a <code>.csv</code> extension.
Google automatically adds a <code>Copy</code> button when you get a lot of results.
<img src="/learn_images/scraping/extract-urls-webpage/copy-results.png#1790x664" alt="Open Google Results">
<h2 id="user-content-step-3-filter-csv-data-to-get-relevant-links"><a href="#step-3-filter-csv-data-to-get-relevant-links" aria-hidden="true" tabindex="-1"></a>Step 3: Filter CSV data to get relevant links</h2>
To filter quickly your links, open Datablist <a href="/csv-editor">CSV editor</a>. Create a collection and paste your CSV data. Check the <a href="#example-with-google-results">Google</a> and <a href="#example-with-amazon-results">Amazon</a> examples below for a more step-by-step guide.
<img src="/learn_images/scraping/extract-urls-webpage/create-collection.png#1064x668" alt="Create new collection">
Select 'First row contains header' and click on the "+" button for each column to import them.
<img src="/learn_images/scraping/extract-urls-webpage/paste-results.png#2537x1346" alt="Paste results">
Then you will be able to use text search or filter on a specific property.
You can use Datablist to <a href="/features/combine-csv-files">consolidate all your CSV data</a>. And Datablist comes with an algorithm for <a href="/features/duplicates-remover">deduplication</a> to have a clean list or URLs.
<h2 id="user-content-example-with-google-results"><a href="#example-with-google-results" aria-hidden="true" tabindex="-1"></a>Example with Google Results</h2>
This is a step-by-step example with the Google results page. We want to extract all external links from a Google search result.
<h3 id="user-content-step-1-search-for-a-google-term-that-you-want-to-extract-links"><a href="#step-1-search-for-a-google-term-that-you-want-to-extract-links" aria-hidden="true" tabindex="-1"></a>Step 1: Search for a Google term that you want to extract links.</h3>
A nice tip is to add the <code>&#x26;num=100</code> parameter to the URL to force Google into showing 100 results per page.
<img src="/learn_images/scraping/extract-urls-webpage/google/google-num-param.png#1696x458" alt="Force 100 results">
<img src="/learn_images/scraping/extract-urls-webpage/google/step-1-google-results.png#1934x1300" alt="Open Google Results">
<h3 id="user-content-step-2-copy-paste-the-code-and-run-it"><a href="#step-2-copy-paste-the-code-and-run-it" aria-hidden="true" tabindex="-1"></a>Step 2: Copy-paste the code and run it</h3>
Open the <code>console</code> in Google Developer Tools, paste the JavaScript code and press <code>Enter</code>.
<img src="/learn_images/scraping/extract-urls-webpage/google/step2-copy-paste-code.png#2532x1235" alt="Copy-paste code">
Copy the results. Select the line and <code>ctrl + c</code> or use the <code>Copy</code> button when Chrome shows it.
<img src="/learn_images/scraping/extract-urls-webpage/google/step3-run-code.png#2532x1344" alt="Run JavaScript code">
<h3 id="user-content-step-3-filter-results"><a href="#step-3-filter-results" aria-hidden="true" tabindex="-1"></a>Step 3: Filter results</h3>
Open Datablist <a href="/csv-editor">CSV editor</a>, create a collection, and paste the copied URLs.
<blockquote>
Note: Don't forget to select the option <code>First row contains headers</code> and to click the "+" button as shown.
</blockquote>
<img src="/learn_images/scraping/extract-urls-webpage/google/step4-create-collection-datablist.png#2537x1346" alt="Copy-Paste results in Datablist">
Once imported. Click "Filter on property" in the "External" column.
<img src="/learn_images/scraping/extract-urls-webpage/google/step5-filter-1.png#2760x1750" alt="Create Filters">
And filter items with <code>External</code> to <code>TRUE</code> and <code>Url</code> that does not contain <code>google</code>. Check our guide on <a href="/how-to/clean-web-scraping-data-online">how to clean your scraped data</a>
<img src="/learn_images/scraping/extract-urls-webpage/google/step5-filter-2.png#2533x1355" alt="Filter URLs outside Google">
And here are your external links:
<img src="/learn_images/scraping/extract-urls-webpage/google/step6-results.png#2371x1432" alt="Google Links results">
<h2 id="user-content-example-with-amazon-results"><a href="#example-with-amazon-results" aria-hidden="true" tabindex="-1"></a>Example with Amazon Results</h2>
This is a step-by-step example with the Amazon products page. We want to extract all links from this page.
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-speakers-results.png#1926x1588" alt="Amazon products listing">
Open the Chrome Developer Tools:
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-open-developer-tools.png#1930x1650" alt="Open Chrome Developer Tools">
Copy-paste the JavaScript code:
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-copy-paste-code.png#2162x1830" alt="Copy-Paste code">
And run it by pressing <code>Enter</code>:
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-run-code.png#2162x1830" alt="Run code">
With Datablist <a href="/csv-editor">CSV editor</a>, create a collection.
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-create-collection.png#2185x1098" alt="Create collection">
Paste your scrapped URLs:
<img src="/learn_images/scraping/extract-urls-webpage/amazon/import-from-pasting.png#2195x1202" alt="Paste scrapped URLs">
Your scrapped URLs are listed on the tool. Filter the links, <a href="/features/duplicates-remover">deduplicate the results</a>, and <a href="/how-to/clean-web-scraping-data-online">clean your scraped data</a>.
<img src="/learn_images/scraping/extract-urls-webpage/amazon/amazon-results.png#2643x1366" alt="Scrapped Amazon URLs">


Here is a quick JavaScript snippet to extract all URLs from a webpage fast with Google Chrome Developer Tools. No Browser Extension is required!

**The JavaScript code generates a list of URLs in CSV format with the anchor texts, and a boolean to know if the URL is internal or external to the current website. Just copy-paste the results into Google Sheets or with the Datablist [CSV editor](/csv-editor).**

> **Notes**: The code works also with Firefox, Safari, etc.

## Step 1: Run JavaScript code in Google Chrome Developer Tools

- Open Google Chrome Developer Tools with `Cmd + Opt + i` (Mac) or `F12` (Windows). And click on the `Console` tab.
- Copy-Paste the following JavaScript code and press `Enter`.

```
const results = [
    ['Url', 'Anchor Text', 'External']
];
var urls = document.getElementsByTagName('a');
for (urlIndex in urls) {
    const url = urls[urlIndex]
    const externalLink = url.host !== window.location.host
    if(url.href && url.href.indexOf('://')!==-1) results.push([url.href, url.text, externalLink]) // url.rel
}
const csvContent = results.map((line)=>{
    return line.map((cell)=>{
        if(typeof(cell)==='boolean') return cell ? 'TRUE': 'FALSE'
        if(!cell) return ''
        let value = cell.replace(/[\f\n\v]*\n\s*/g, "\n").replace(/[\t\f ]+/g, ' ');
        value = value.replace(/\t/g, ' ').trim();
        return `"${value}"`
    }).join('\t')
}).join("\n");
console.log(csvContent)
```

![Open Google Developer Tools](/learn_images/scraping/extract-urls-webpage/copy-js-code.png#1938x694)


## Step 2: Copy-paste exported URLs into a CSV file or spreadsheet tools

The JavaScript code will extract all URLs from the webpage with the following values:

- **URL** - The link URL
- **Anchor Text** - The label associated with the link. Called "Anchor Text".
- **External** - A boolean (`TRUE`, `FALSE`)

The result is printed on the console using [CSV format](/learn/csv/csv-meaning). You can copy-paste the result into spreadsheet tools or with our [online CSV editor](/csv-editor). Or create a new text file with your text editor and save the file with a `.csv` extension.

Google automatically adds a `Copy` button when you get a lot of results.

![Open Google Results](/learn_images/scraping/extract-urls-webpage/copy-results.png#1790x664)


## Step 3: Filter CSV data to get relevant links

To filter quickly your links, open Datablist [CSV editor](/csv-editor). Create a collection and paste your CSV data. Check the [Google](#example-with-google-results) and [Amazon](#example-with-amazon-results) examples below for a more step-by-step guide.

![Create new collection](/learn_images/scraping/extract-urls-webpage/create-collection.png#1064x668)

Select 'First row contains header' and click on the "+" button for each column to import them.

![Paste results](/learn_images/scraping/extract-urls-webpage/paste-results.png#2537x1346)

Then you will be able to use text search or filter on a specific property.
You can use Datablist to [consolidate all your CSV data](/features/combine-csv-files). And Datablist comes with an algorithm for [deduplication](/features/duplicates-remover) to have a clean list or URLs.


## Example with Google Results

This is a step-by-step example with the Google results page. We want to extract all external links from a Google search result.

### Step 1: Search for a Google term that you want to extract links.

**A nice tip is to add the `&num=100` parameter to the URL to force Google into showing 100 results per page.**

![Force 100 results](/learn_images/scraping/extract-urls-webpage/google/google-num-param.png#1696x458)

![Open Google Results](/learn_images/scraping/extract-urls-webpage/google/step-1-google-results.png#1934x1300)


### Step 2: Copy-paste the code and run it

Open the `console` in Google Developer Tools, paste the JavaScript code and press `Enter`.

![Copy-paste code](/learn_images/scraping/extract-urls-webpage/google/step2-copy-paste-code.png#2532x1235)

Copy the results. Select the line and `ctrl + c` or use the `Copy` button when Chrome shows it.

![Run JavaScript code](/learn_images/scraping/extract-urls-webpage/google/step3-run-code.png#2532x1344)

### Step 3: Filter results

Open Datablist [CSV editor](/csv-editor), create a collection, and paste the copied URLs.

> **Note**: Don't forget to select the option `First row contains headers` and to click the "+" button as shown.

![Copy-Paste results in Datablist](/learn_images/scraping/extract-urls-webpage/google/step4-create-collection-datablist.png#2537x1346)

Once imported. Click "Filter on property" in the "External" column.

![Create Filters](/learn_images/scraping/extract-urls-webpage/google/step5-filter-1.png#2760x1750)


And filter items with `External` to `TRUE` and `Url` that does not contain `google`. Check our guide on [how to clean your scraped data](/how-to/clean-web-scraping-data-online)

![Filter URLs outside Google](/learn_images/scraping/extract-urls-webpage/google/step5-filter-2.png#2533x1355)

And here are your external links:

![Google Links results](/learn_images/scraping/extract-urls-webpage/google/step6-results.png#2371x1432)

## Example with Amazon Results

This is a step-by-step example with the Amazon products page. We want to extract all links from this page.

![Amazon products listing](/learn_images/scraping/extract-urls-webpage/amazon/amazon-speakers-results.png#1926x1588)

Open the Chrome Developer Tools:

![Open Chrome Developer Tools](/learn_images/scraping/extract-urls-webpage/amazon/amazon-open-developer-tools.png#1930x1650)

Copy-paste the JavaScript code:

![Copy-Paste code](/learn_images/scraping/extract-urls-webpage/amazon/amazon-copy-paste-code.png#2162x1830)

And run it by pressing `Enter`:

![Run code](/learn_images/scraping/extract-urls-webpage/amazon/amazon-run-code.png#2162x1830)

With Datablist [CSV editor](/csv-editor), create a collection.

![Create collection](/learn_images/scraping/extract-urls-webpage/amazon/amazon-create-collection.png#2185x1098)

Paste your scrapped URLs:

![Paste scrapped URLs](/learn_images/scraping/extract-urls-webpage/amazon/import-from-pasting.png#2195x1202)

Your scrapped URLs are listed on the tool. Filter the links, [deduplicate the results](/features/duplicates-remover), and [clean your scraped data](/how-to/clean-web-scraping-data-online).

![Scrapped Amazon URLs](/learn_images/scraping/extract-urls-webpage/amazon/amazon-results.png#2643x1366)








Extracy all URLs from a webpage

Here is a quick JavaScript snippet to extract all URLs from a webpage fast with Google Chrome Developer Tools. No Browser Extension is required!
The JavaScript code generates a list of URLs in CSV format with the anchor texts, and a boolean to know if the URL is internal or external to the current website. Just copy-paste the results into Google Sheets or with the Datablist CSV editor.
Notes: The code works also with Firefox, Safari, etc.

Step 1: Run JavaScript code in Google Chrome Developer Tools
Open Google Chrome Developer Tools with Cmd + Opt + i (Mac) or F12 (Windows). And click on the Console tab.
Copy-Paste the following JavaScript code and press Enter.

const results = [
    [&#39Url&#39, &#39Anchor Text&#39, &#39External&#39]
];
var urls = document.getElementsByTagName(&#39a&#39);
for (urlIndex in urls) {
    const url = urls[urlIndex]
    const externalLink = url.host !== window.location.host
    if(url.href &amp&amp url.href.indexOf(&#39://&#39)!==-1) results.push([url.href, url.text, externalLink]) // url.rel
}
const csvContent = results.map((line)=&gt{
    return line.map((cell)=&gt{
        if(typeof(cell)===&#39boolean&#39) return cell ? &#39TRUE&#39: &#39FALSE&#39
        if(!cell) return &#39&#39
        let value = cell.replace(/[\f\n\v]*\n\s*/g, &quot\n&quot).replace(/[\t\f ]+/g, &#39 &#39);
        value = value.replace(/\t/g, &#39 &#39).trim();
        return `&quot${value}&quot`
    }).join(&#39\t&#39)
}).join(&quot\n&quot);
console.log(csvContent)
Open Google Developer Tools
Step 2: Copy-paste exported URLs into a CSV file or spreadsheet tools
The JavaScript code will extract all URLs from the webpage with the following values:
URL - The link URL
Anchor Text - The label associated with the link. Called &ampquot;Anchor Text&ampquot;.
External - A boolean (TRUE, FALSE)

The result is printed on the console using CSV format. You can copy-paste the result into spreadsheet tools or with our online CSV editor. Or create a new text file with your text editor and save the file with a .csv extension.
Google automatically adds a Copy button when you get a lot of results.
Open Google Results
Step 3: Filter CSV data to get relevant links
To filter quickly your links, open Datablist CSV editor. Create a collection and paste your CSV data. Check the Google and Amazon examples below for a more step-by-step guide.
Create new collection
Select &amp#39;First row contains header&amp#39; and click on the &ampquot;+&ampquot; button for each column to import them.
Paste results
Then you will be able to use text search or filter on a specific property.
You can use Datablist to consolidate all your CSV data. And Datablist comes with an algorithm for deduplication to have a clean list or URLs.
Example with Google Results
This is a step-by-step example with the Google results page. We want to extract all external links from a Google search result.
Step 1: Search for a Google term that you want to extract links.
A nice tip is to add the &ampamp;num=100 parameter to the URL to force Google into showing 100 results per page.
Force 100 results
Open Google Results
Step 2: Copy-paste the code and run it
Open the console in Google Developer Tools, paste the JavaScript code and press Enter.
Copy-paste code
Copy the results. Select the line and ctrl + c or use the Copy button when Chrome shows it.
Run JavaScript code
Step 3: Filter results
Open Datablist CSV editor, create a collection, and paste the copied URLs.
Note: Don&amp#39;t forget to select the option First row contains headers and to click the &ampquot;+&ampquot; button as shown.

Copy-Paste results in Datablist
Once imported. Click &ampquot;Filter on property&ampquot; in the &ampquot;External&ampquot; column.
Create Filters
And filter items with External to TRUE and Url that does not contain google. Check our guide on how to clean your scraped data
Filter URLs outside Google
And here are your external links:
Google Links results
Example with Amazon Results
This is a step-by-step example with the Amazon products page. We want to extract all links from this page.
Amazon products listing
Open the Chrome Developer Tools:
Open Chrome Developer Tools
Copy-paste the JavaScript code:
Copy-Paste code
And run it by pressing Enter:
Run code
With Datablist CSV editor, create a collection.
Create collection
Paste your scrapped URLs:
Paste scrapped URLs
Your scrapped URLs are listed on the tool. Filter the links, deduplicate the results, and clean your scraped data.
Scrapped Amazon URLs


An Impressum is a legally mandated statement in German-speaking countries, required in publications like books, newspapers, websites, and business correspondence. It must include details about the ownership and authorship of the document.
Most websites in German-speaking countries have an Impressum page with their contact information.
Scraping this data is perfect for lead generation. Impressum pages contain valuable data such as:
<ul>
<li>Founder/CEO Name</li>
<li>Job title</li>
<li>Phone Number</li>
<li>Email Address</li>
<li>Postal Address</li>
</ul>
The information is available but not in a structured format. Until now, it was hard to extract automatically that information. But with AI, I'll show you how to extract those contact information in a structured format.
In this guide, I show you how to use Datablist AI Agent to scrape a list of websites and extract any information you want from the Impressum pages.
You can also watch this video to see it in action.
<h2 id="user-content-step-1-import-your-list-of-websites"><a href="#step-1-import-your-list-of-websites" aria-hidden="true" tabindex="-1"></a>Step 1: Import your list of websites</h2>
First, import a CSV/Excel file with a list of websites you want to scrape. Datablist is a lead management tool with superpowers. One of them is the Datablist AI Agent. Our AI Agent understands text and can scrape website to find relevant data.
To import your CSV file, create an empty collection and click import. Or click on the "Start with a CSV/Excel file" button from the home screen.
<img src="/learn_images/scraping/scrape-impressum/create-collection-from-file.png#2270x1361" alt="Import CSV File">
This is my file imported. It contains two columns: the name of the company, and its website.
<img src="/learn_images/scraping/scrape-impressum/list-websites-blur.png#2423x1327" alt="List of German websites to scrape">
<h2 id="user-content-step-2-write-a-prompt-that-works"><a href="#step-2-write-a-prompt-that-works" aria-hidden="true" tabindex="-1"></a>Step 2: Write a prompt that works</h2>
Now, we will ask an AI Agent to visit each website, find the Impressum page link, and then read the Impressum page to extract contact information.
Click on the "Enrich" menu and select "AI Agent".
<img src="/learn_images/scraping/scrape-impressum/ai-agent-enrichment-blur.png#2297x1229" alt="Open AI Agent">
We must write a prompt with the website URL to visit, and the data we want to extract. And we can add some tips to find the Impressum page.
Here is my prompt:
<pre><code>Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
</code></pre>
You can ask for more (or less) data points. The website URL is defined as a placeholder. And Datablist will run a personalized prompt for each line.
To use a variable for the website, enter 2 brackets characters and select the Website property from your collection.
<img src="/learn_images/scraping/scrape-impressum/write-prompt.png#1526x930" alt="Write a prompt">
<h2 id="user-content-step-3-configure-expected-outputs"><a href="#step-3-configure-expected-outputs" aria-hidden="true" tabindex="-1"></a>Step 3: Configure expected outputs</h2>
After the prompt, we need to configure the expected outputs. The Agent uses the output's name and description along with the prompt to understand the mission.
Here, we have:
<ul>
<li>Founder Name - Description: The name of the founder. Empty if not found.</li>
<li>Job Title - Description: The job title of the founder, when available.</li>
<li>Email Address - Description: Email address found on the impressum page.</li>
<li>Phone Number - Description: The company phone number.</li>
</ul>
<img src="/learn_images/scraping/scrape-impressum/expected-outputs.png#1758x1248" alt="Expected outputs">
<h2 id="user-content-step-4-add-outputs-to-the-collection"><a href="#step-4-add-outputs-to-the-collection" aria-hidden="true" tabindex="-1"></a>Step 4: Add outputs to the collection</h2>
Click "Continue to outputs configuration". The expected outputs configured in the previous step appear here.
Select "+" to add a new property (=column) to your collection for each output.
<img src="/learn_images/scraping/scrape-impressum/add-properties.png#2292x1380" alt="Add properties">
You can also create a property to see the error message when the agent is not able to perform the task.
<h2 id="user-content-step-5-run-the-enrichment"><a href="#step-5-run-the-enrichment" aria-hidden="true" tabindex="-1"></a>Step 5: Run the enrichment</h2>
The last step, click on "Instant run" to run the agent.
<img src="/learn_images/scraping/scrape-impressum/results-blur.png#2537x1398" alt="Scraping results">
The "Error Msg" property shows a text when the agent is not able to reach the website or if no Impressum page is found.

An Impressum is a legally mandated statement in German-speaking countries, required in publications like books, newspapers, websites, and business correspondence. It must include details about the ownership and authorship of the document. 

Most websites in German-speaking countries have an Impressum page with their contact information.

**Scraping this data is perfect for lead generation.** Impressum pages contain valuable data such as:

- Founder/CEO Name
- Job title
- Phone Number
- Email Address
- Postal Address

The information is available but not in a structured format. Until now, it was hard to extract automatically that information. **But with AI, I'll show you how to extract those contact information in a structured format.**

In this guide, I show you how to use Datablist AI Agent to scrape a list of websites and extract any information you want from the Impressum pages.

You can also watch this video to see it in action.

<div className="youtube-video-player">
<iframe src="https://www.youtube.com/embed/TulwqVyrcLE?si=fNTUfmfzAhX1CKNf" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div>

## Step 1: Import your list of websites

First, import a CSV/Excel file with a list of websites you want to scrape. Datablist is a lead management tool with superpowers. One of them is the Datablist AI Agent. Our AI Agent understands text and can scrape website to find relevant data.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Open Datablist
 </a>
 </div>
</aside>

To import your CSV file, create an empty collection and click import. Or click on the "Start with a CSV/Excel file" button from the home screen.

![Import CSV File](/learn_images/scraping/scrape-impressum/create-collection-from-file.png#2270x1361)

This is my file imported. It contains two columns: the name of the company, and its website.

![List of German websites to scrape](/learn_images/scraping/scrape-impressum/list-websites-blur.png#2423x1327)


## Step 2: Write a prompt that works

Now, we will ask an AI Agent to visit each website, find the Impressum page link, and then read the Impressum page to extract contact information.

Click on the "Enrich" menu and select "AI Agent".

![Open AI Agent](/learn_images/scraping/scrape-impressum/ai-agent-enrichment-blur.png#2297x1229)

We must write a prompt with the website URL to visit, and the data we want to extract. And we can add some tips to find the Impressum page.

Here is my prompt:

```
Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
```

You can ask for more (or less) data points. The website URL is defined as a placeholder. And Datablist will run a personalized prompt for each line.

**To use a variable for the website, enter 2 brackets characters and select the Website property from your collection.**

![Write a prompt](/learn_images/scraping/scrape-impressum/write-prompt.png#1526x930)

## Step 3: Configure expected outputs

After the prompt, we need to configure the expected outputs. The Agent uses the output's name and description along with the prompt to understand the mission.

Here, we have:

- **Founder Name** - Description: The name of the founder. Empty if not found.
- **Job Title** - Description: The job title of the founder, when available.
- **Email Address** - Description: Email address found on the impressum page.
- **Phone Number** - Description: The company phone number.

![Expected outputs](/learn_images/scraping/scrape-impressum/expected-outputs.png#1758x1248)


## Step 4: Add outputs to the collection

Click "Continue to outputs configuration". The expected outputs configured in the previous step appear here.

Select "+" to add a new property (=column) to your collection for each output.

![Add properties](/learn_images/scraping/scrape-impressum/add-properties.png#2292x1380)

You can also create a property to see the error message when the agent is not able to perform the task.

## Step 5: Run the enrichment

The last step, click on "Instant run" to run the agent.

![Scraping results](/learn_images/scraping/scrape-impressum/results-blur.png#2537x1398)

The "Error Msg" property shows a text when the agent is not able to reach the website or if no Impressum page is found.

Automatically extract contact information on the Impressum page

An Impressum is a legally mandated statement in German-speaking countries, required in publications like books, newspapers, websites, and business correspondence. It must include details about the ownership and authorship of the document. 
Most websites in German-speaking countries have an Impressum page with their contact information.
Scraping this data is perfect for lead generation. Impressum pages contain valuable data such as:
Founder/CEO Name
Job title
Phone Number
Email Address
Postal Address

The information is available but not in a structured format. Until now, it was hard to extract automatically that information. But with AI, I&amp#39;ll show you how to extract those contact information in a structured format.
In this guide, I show you how to use Datablist AI Agent to scrape a list of websites and extract any information you want from the Impressum pages.
You can also watch this video to see it in action.
Step 1: Import your list of websites
First, import a CSV/Excel file with a list of websites you want to scrape. Datablist is a lead management tool with superpowers. One of them is the Datablist AI Agent. Our AI Agent understands text and can scrape website to find relevant data.
To import your CSV file, create an empty collection and click import. Or click on the &ampquot;Start with a CSV/Excel file&ampquot; button from the home screen.
Import CSV File
This is my file imported. It contains two columns: the name of the company, and its website.
List of German websites to scrape
Step 2: Write a prompt that works
Now, we will ask an AI Agent to visit each website, find the Impressum page link, and then read the Impressum page to extract contact information.
Click on the &ampquot;Enrich&ampquot; menu and select &ampquot;AI Agent&ampquot;.
Open AI Agent
We must write a prompt with the website URL to visit, and the data we want to extract. And we can add some tips to find the Impressum page.
Here is my prompt:
Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
You can ask for more (or less) data points. The website URL is defined as a placeholder. And Datablist will run a personalized prompt for each line.
To use a variable for the website, enter 2 brackets characters and select the Website property from your collection.
Write a prompt
Step 3: Configure expected outputs
After the prompt, we need to configure the expected outputs. The Agent uses the output&amp#39;s name and description along with the prompt to understand the mission.
Here, we have:
Founder Name - Description: The name of the founder. Empty if not found.
Job Title - Description: The job title of the founder, when available.
Email Address - Description: Email address found on the impressum page.
Phone Number - Description: The company phone number.

Expected outputs
Step 4: Add outputs to the collection
Click &ampquot;Continue to outputs configuration&ampquot;. The expected outputs configured in the previous step  appear here.
Select &ampquot;+&ampquot; to add a new property (=column) to your collection for each output.
Add properties
You can also create a property to see the error message when the agent is not able to perform the task.
Step 5: Run the enrichment
The last step, click on &ampquot;Instant run&ampquot; to run the agent.
Scraping results
The &ampquot;Error Msg&ampquot; property shows a text when the agent is not able to reach the website or if no Impressum page is found.


Ein Impressum ist eine gesetzlich vorgeschriebene Erklärung in deutschsprachigen Ländern, die in Publikationen wie Büchern, Zeitungen, Websites und Geschäftskorrespondenz erforderlich ist. Es muss Details über die Eigentümerschaft und Urheberschaft des Dokuments enthalten.
Die meisten Websites in deutschsprachigen Ländern haben eine Impressum-Seite mit ihren Kontaktinformationen.
Das Scrapen dieser Daten ist perfekt für die Lead-Generierung. Impressum-Seiten enthalten wertvolle Daten wie:
<ul>
<li>Namen der Unternehmensleitungs</li>
<li>Berufsbezeichnung</li>
<li>Telefonnummer</li>
<li>E-Mail-Adresse</li>
<li>Postadresse</li>
</ul>
Die Informationen sind verfügbar, aber nicht in einem strukturierten Format. Bis jetzt war es schwierig, diese Informationen automatisch zu extrahieren. Aber mit KI zeige ich dir, wie du diese Kontaktinformationen in einem strukturierten Format extrahieren kannst.
In dieser Anleitung zeige ich dir, wie du den Datablist KI-Agenten nutzen kannst, um eine Liste von Websites zu scrapen und alle gewünschten Informationen von den Impressum-Seiten zu extrahieren.
Du kannst dir auch dieses Video ansehen, um es in Aktion zu sehen.
<h2 id="user-content-schritt-1-importiere-deine-liste-von-websites"><a href="#schritt-1-importiere-deine-liste-von-websites" aria-hidden="true" tabindex="-1"></a>Schritt 1: Importiere deine Liste von Websites</h2>
Zuerst importierst du eine CSV/Excel-Datei mit einer Liste von Websites, die du scrapen möchtest. Datablist ist ein Lead-Management-Tool mit Superkräften. Eine davon ist der Datablist KI-Agent. Unser KI-Agent versteht Text und kann Websites durchsuchen, um relevante Daten zu finden.
Um deine CSV-Datei zu importieren, erstelle eine leere Sammlung und klicke auf Import. Oder klicke auf der Startseite auf die Schaltfläche "Start with a CSV/Excel file".
<img src="/learn_images/scraping/scrape-impressum/create-collection-from-file.png#2270x1361" alt="CSV-Datei importieren">
Dies ist meine importierte Datei. Sie enthält zwei Spalten: den Namen des Unternehmens und seine Website.
<img src="/learn_images/scraping/scrape-impressum/list-websites-blur.png#2423x1327" alt="Liste deutscher Websites zum Scrapen">
<h2 id="user-content-schritt-2-schreibe-einen-funktionierenden-prompt"><a href="#schritt-2-schreibe-einen-funktionierenden-prompt" aria-hidden="true" tabindex="-1"></a>Schritt 2: Schreibe einen funktionierenden Prompt</h2>
Jetzt bitten wir einen KI-Agenten, jede Website zu besuchen, den Link zur Impressum-Seite zu finden und dann die Impressum-Seite zu lesen, um Kontaktinformationen zu extrahieren.
Klicke auf das Menü "Enrich" und wähle "AI Agent".
<img src="/learn_images/scraping/scrape-impressum/ai-agent-enrichment-blur.png#2297x1229" alt="AI Agent öffnen">
Wir müssen einen Prompt mit der zu besuchenden Website-URL und den Daten, die wir extrahieren möchten, schreiben. Und wir können einige Tipps hinzufügen, um die Impressum-Seite zu finden.
Hier ist mein Prompt:
<pre><code>Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
</code></pre>
Du kannst nach mehr (oder weniger) Datenpunkten fragen. Die Website-URL wird als Platzhalter definiert. Und Datablist führt für jede Zeile einen personalisierten Prompt aus.
Um eine Variable für die Website zu verwenden, gib 2 geschweifte Klammern ein und wähle die Website-Eigenschaft aus deiner Sammlung.
<img src="/learn_images/scraping/scrape-impressum/write-prompt.png#1526x930" alt="Einen Prompt schreiben">
<h2 id="user-content-schritt-3-konfiguriere-erwartete-ausgaben"><a href="#schritt-3-konfiguriere-erwartete-ausgaben" aria-hidden="true" tabindex="-1"></a>Schritt 3: Konfiguriere erwartete Ausgaben</h2>
Nach dem Prompt müssen wir die erwarteten Ausgaben konfigurieren. Der Agent verwendet den Namen und die Beschreibung der Ausgabe zusammen mit dem Prompt, um die Aufgabe zu verstehen.
Hier haben wir:
<ul>
<li>Founder Name - Beschreibung: The name of the founder. Empty if not found.</li>
<li>Job Title - Beschreibung: The job title of the founder, when available.</li>
<li>Email Address - Beschreibung: Email address found on the impressum page.</li>
<li>Phone Number - Beschreibung: The company phone number.</li>
</ul>
<img src="/learn_images/scraping/scrape-impressum/expected-outputs.png#1758x1248" alt="Erwartete Ausgaben">
<h2 id="user-content-schritt-4-füge-ausgaben-zur-sammlung-hinzu"><a href="#schritt-4-f%C3%BCge-ausgaben-zur-sammlung-hinzu" aria-hidden="true" tabindex="-1"></a>Schritt 4: Füge Ausgaben zur Sammlung hinzu</h2>
Klicke auf "Continue to outputs configuration". Die im vorherigen Schritt konfigurierten erwarteten Ausgaben erscheinen hier.
Wähle "+" um für jede Ausgabe eine neue Eigenschaft (=Spalte) zu deiner Sammlung hinzuzufügen.
<img src="/learn_images/scraping/scrape-impressum/add-properties.png#2292x1380" alt="Eigenschaften hinzufügen">
Du kannst auch eine Eigenschaft erstellen, um die Fehlermeldung zu sehen, wenn der Agent die Aufgabe nicht ausführen kann.
<h2 id="user-content-schritt-5-führe-die-anreicherung-aus"><a href="#schritt-5-f%C3%BChre-die-anreicherung-aus" aria-hidden="true" tabindex="-1"></a>Schritt 5: Führe die Anreicherung aus</h2>
Der letzte Schritt: Klicke auf "Instant run", um den Agenten auszuführen.
<img src="/learn_images/scraping/scrape-impressum/results-blur.png#2537x1398" alt="Scraping-Ergebnisse">
Die Eigenschaft "Error Msg" zeigt einen Text an, wenn der Agent die Website nicht erreichen kann oder wenn keine Impressum-Seite gefunden wird.

Ein Impressum ist eine gesetzlich vorgeschriebene Erklärung in deutschsprachigen Ländern, die in Publikationen wie Büchern, Zeitungen, Websites und Geschäftskorrespondenz erforderlich ist. Es muss Details über die Eigentümerschaft und Urheberschaft des Dokuments enthalten.

Die meisten Websites in deutschsprachigen Ländern haben eine Impressum-Seite mit ihren Kontaktinformationen.

**Das Scrapen dieser Daten ist perfekt für die Lead-Generierung.** Impressum-Seiten enthalten wertvolle Daten wie:

- Namen der Unternehmensleitungs
- Berufsbezeichnung
- Telefonnummer
- E-Mail-Adresse
- Postadresse

Die Informationen sind verfügbar, aber nicht in einem strukturierten Format. Bis jetzt war es schwierig, diese Informationen automatisch zu extrahieren. **Aber mit KI zeige ich dir, wie du diese Kontaktinformationen in einem strukturierten Format extrahieren kannst.**

In dieser Anleitung zeige ich dir, wie du den Datablist KI-Agenten nutzen kannst, um eine Liste von Websites zu scrapen und alle gewünschten Informationen von den Impressum-Seiten zu extrahieren.

Du kannst dir auch dieses Video ansehen, um es in Aktion zu sehen.

<div className="youtube-video-player">
 <iframe src="https://www.youtube.com/embed/TulwqVyrcLE?si=fNTUfmfzAhX1CKNf" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div>

## Schritt 1: Importiere deine Liste von Websites

Zuerst importierst du eine CSV/Excel-Datei mit einer Liste von Websites, die du scrapen möchtest. Datablist ist ein Lead-Management-Tool mit Superkräften. Eine davon ist der Datablist KI-Agent. Unser KI-Agent versteht Text und kann Websites durchsuchen, um relevante Daten zu finden.

<aside class="text-center mb-5">
 <div class="mb-2">
 <a href="https://app.datablist.com/" className="btn btn-primary btn-large" target="_blank">
 Öffne Datablist
 </a>
 </div>
</aside>

Um deine CSV-Datei zu importieren, erstelle eine leere Sammlung und klicke auf Import. Oder klicke auf der Startseite auf die Schaltfläche "Start with a CSV/Excel file".

![CSV-Datei importieren](/learn_images/scraping/scrape-impressum/create-collection-from-file.png#2270x1361)

Dies ist meine importierte Datei. Sie enthält zwei Spalten: den Namen des Unternehmens und seine Website.

![Liste deutscher Websites zum Scrapen](/learn_images/scraping/scrape-impressum/list-websites-blur.png#2423x1327)


## Schritt 2: Schreibe einen funktionierenden Prompt

Jetzt bitten wir einen KI-Agenten, jede Website zu besuchen, den Link zur Impressum-Seite zu finden und dann die Impressum-Seite zu lesen, um Kontaktinformationen zu extrahieren.

Klicke auf das Menü "Enrich" und wähle "AI Agent".

![AI Agent öffnen](/learn_images/scraping/scrape-impressum/ai-agent-enrichment-blur.png#2297x1229)

Wir müssen einen Prompt mit der zu besuchenden Website-URL und den Daten, die wir extrahieren möchten, schreiben. Und wir können einige Tipps hinzufügen, um die Impressum-Seite zu finden.

Hier ist mein Prompt:

```
Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
```

Du kannst nach mehr (oder weniger) Datenpunkten fragen. Die Website-URL wird als Platzhalter definiert. Und Datablist führt für jede Zeile einen personalisierten Prompt aus.

**Um eine Variable für die Website zu verwenden, gib 2 geschweifte Klammern ein und wähle die Website-Eigenschaft aus deiner Sammlung.**

![Einen Prompt schreiben](/learn_images/scraping/scrape-impressum/write-prompt.png#1526x930)

## Schritt 3: Konfiguriere erwartete Ausgaben

Nach dem Prompt müssen wir die erwarteten Ausgaben konfigurieren. Der Agent verwendet den Namen und die Beschreibung der Ausgabe zusammen mit dem Prompt, um die Aufgabe zu verstehen.

Hier haben wir:

- **Founder Name** - Beschreibung: The name of the founder. Empty if not found.
- **Job Title** - Beschreibung: The job title of the founder, when available.
- **Email Address** - Beschreibung: Email address found on the impressum page.
- **Phone Number** - Beschreibung: The company phone number.

![Erwartete Ausgaben](/learn_images/scraping/scrape-impressum/expected-outputs.png#1758x1248)


## Schritt 4: Füge Ausgaben zur Sammlung hinzu

Klicke auf "Continue to outputs configuration". Die im vorherigen Schritt konfigurierten erwarteten Ausgaben erscheinen hier.

Wähle "+" um für jede Ausgabe eine neue Eigenschaft (=Spalte) zu deiner Sammlung hinzuzufügen.

![Eigenschaften hinzufügen](/learn_images/scraping/scrape-impressum/add-properties.png#2292x1380)

Du kannst auch eine Eigenschaft erstellen, um die Fehlermeldung zu sehen, wenn der Agent die Aufgabe nicht ausführen kann.

## Schritt 5: Führe die Anreicherung aus

Der letzte Schritt: Klicke auf "Instant run", um den Agenten auszuführen.

![Scraping-Ergebnisse](/learn_images/scraping/scrape-impressum/results-blur.png#2537x1398)

Die Eigenschaft "Error Msg" zeigt einen Text an, wenn der Agent die Website nicht erreichen kann oder wenn keine Impressum-Seite gefunden wird.

Automatisch Kontaktinformationen auf der Impressum-Seite extrahieren

Ein Impressum ist eine gesetzlich vorgeschriebene Erklärung in deutschsprachigen Ländern, die in Publikationen wie Büchern, Zeitungen, Websites und Geschäftskorrespondenz erforderlich ist. Es muss Details über die Eigentümerschaft und Urheberschaft des Dokuments enthalten.
Die meisten Websites in deutschsprachigen Ländern haben eine Impressum-Seite mit ihren Kontaktinformationen.
Das Scrapen dieser Daten ist perfekt für die Lead-Generierung. Impressum-Seiten enthalten wertvolle Daten wie:
Namen der Unternehmensleitungs
Berufsbezeichnung
Telefonnummer
E-Mail-Adresse
Postadresse

Die Informationen sind verfügbar, aber nicht in einem strukturierten Format. Bis jetzt war es schwierig, diese Informationen automatisch zu extrahieren. Aber mit KI zeige ich dir, wie du diese Kontaktinformationen in einem strukturierten Format extrahieren kannst.
In dieser Anleitung zeige ich dir, wie du den Datablist KI-Agenten nutzen kannst, um eine Liste von Websites zu scrapen und alle gewünschten Informationen von den Impressum-Seiten zu extrahieren.
Du kannst dir auch dieses Video ansehen, um es in Aktion zu sehen.
Schritt 1: Importiere deine Liste von Websites
Zuerst importierst du eine CSV/Excel-Datei mit einer Liste von Websites, die du scrapen möchtest. Datablist ist ein Lead-Management-Tool mit Superkräften. Eine davon ist der Datablist KI-Agent. Unser KI-Agent versteht Text und kann Websites durchsuchen, um relevante Daten zu finden.
Um deine CSV-Datei zu importieren, erstelle eine leere Sammlung und klicke auf Import. Oder klicke auf der Startseite auf die Schaltfläche &ampquot;Start with a CSV/Excel file&ampquot;.
CSV-Datei importieren
Dies ist meine importierte Datei. Sie enthält zwei Spalten: den Namen des Unternehmens und seine Website.
Liste deutscher Websites zum Scrapen
Schritt 2: Schreibe einen funktionierenden Prompt
Jetzt bitten wir einen KI-Agenten, jede Website zu besuchen, den Link zur Impressum-Seite zu finden und dann die Impressum-Seite zu lesen, um Kontaktinformationen zu extrahieren.
Klicke auf das Menü &ampquot;Enrich&ampquot; und wähle &ampquot;AI Agent&ampquot;.
AI Agent öffnen
Wir müssen einen Prompt mit der zu besuchenden Website-URL und den Daten, die wir extrahieren möchten, schreiben. Und wir können einige Tipps hinzufügen, um die Impressum-Seite zu finden.
Hier ist mein Prompt:
Visit {{WEBSITE}} and scrape the impressum page to extract the following information: 
- Founder name
- Job Title
- Email Address
- Phone Number
The Impressum page is usually on the URL /impressum
Du kannst nach mehr (oder weniger) Datenpunkten fragen. Die Website-URL wird als Platzhalter definiert. Und Datablist führt für jede Zeile einen personalisierten Prompt aus.
Um eine Variable für die Website zu verwenden, gib 2 geschweifte Klammern ein und wähle die Website-Eigenschaft aus deiner Sammlung.
Einen Prompt schreiben
Schritt 3: Konfiguriere erwartete Ausgaben
Nach dem Prompt müssen wir die erwarteten Ausgaben konfigurieren. Der Agent verwendet den Namen und die Beschreibung der Ausgabe zusammen mit dem Prompt, um die Aufgabe zu verstehen.
Hier haben wir:
Founder Name - Beschreibung: The name of the founder. Empty if not found.
Job Title - Beschreibung: The job title of the founder, when available.
Email Address - Beschreibung: Email address found on the impressum page.
Phone Number - Beschreibung: The company phone number.

Erwartete Ausgaben
Schritt 4: Füge Ausgaben zur Sammlung hinzu
Klicke auf &ampquot;Continue to outputs configuration&ampquot;. Die im vorherigen Schritt konfigurierten erwarteten Ausgaben erscheinen hier.
Wähle &ampquot;+&ampquot; um für jede Ausgabe eine neue Eigenschaft (=Spalte) zu deiner Sammlung hinzuzufügen.
Eigenschaften hinzufügen
Du kannst auch eine Eigenschaft erstellen, um die Fehlermeldung zu sehen, wenn der Agent die Aufgabe nicht ausführen kann.
Schritt 5: Führe die Anreicherung aus
Der letzte Schritt: Klicke auf &ampquot;Instant run&ampquot;, um den Agenten auszuführen.
Scraping-Ergebnisse
Die Eigenschaft &ampquot;Error Msg&ampquot; zeigt einen Text an, wenn der Agent die Website nicht erreichen kann oder wenn keine Impressum-Seite gefunden wird.


Records deduplication, also known as data deduplication or <a href="/how-to/data-matching-tool">record matching</a>, is the process of identifying and eliminating duplicate records or entries within a database or dataset. It's a critical <a href="/how-to/data-cleaning">data cleansing</a> technique used to ensure data accuracy and consistency.
<h2 id="user-content-how-does-record-deduplication-work"><a href="#how-does-record-deduplication-work" aria-hidden="true" tabindex="-1"></a>How does Record Deduplication work?</h2>
Record deduplication typically involves two steps:
<ul>
<li>Identifying duplicate records or <a href="/how-to/data-matching-tool">Data Matching</a>: The first step is to identify duplicate records in the dataset. This can be done using a variety of methods, such as comparing unique identifiers (e.g., customer IDs, product IDs), or using phonetic/fuzzy algorithms to identify records that are similar to each other.</li>
<li>Removing duplicate records: Once duplicate records have been identified, they can be removed from the dataset. This can be done manually, or using automated tools. When record data is similar or complementary, the merging process is a nondestructive step. When duplicate records have conflicting data, the merging process must offer text concatenation or a rule to drop conflicting data to be able to merge all fields into a single record.</li>
</ul>
<h2 id="user-content-how-to-identify-duplicate-records"><a href="#how-to-identify-duplicate-records" aria-hidden="true" tabindex="-1"></a>How to identify duplicate records?</h2>
There are two main types of data matching:
<ul>
<li>Exact record matching: Exact deduplication identifies and removes records that are exactly the same. This is the simplest type of deduplication.</li>
<li>Field-specific matching: This is a more complex type of deduplication. It requires the use of algorithms to identify similar records by focusing on specific fields like names, addresses, or phone numbers.</li>
</ul>
For field-specific matching, several algorithms exist:
<ul>
<li>Exact matching: Identifies records that have identical values for the specific fields.</li>
<li>Phonetic matching: Algorithm to match values by their pronunciation. Most phonetic algorithms work best with English words. The <a href="https://en.wikipedia.org/wiki/Soundex" target="_blank" rel="nofollow">Soundex</a> and <a href="/learn/data-cleaning/double-metaphone">Double Metaphone</a> are among the best-known phonetic algorithms.</li>
<li>Fuzzy Matching: Uses algorithms to find records that are similar but not necessarily identical, accommodating variations in spelling, formatting, or other data discrepancies. Two famous fuzzy matching algorithms are the <a href="/learn/data-cleaning/fuzzy-matching-levenshtein-distance">Levenshtein Distance</a> and <a href="/learn/data-cleaning/fuzzy-matching-jaro-winkler-distance">Jaro-Winkler Distance</a>.</li>
</ul>
<h2 id="user-content-why-is-record-deduplication-important"><a href="#why-is-record-deduplication-important" aria-hidden="true" tabindex="-1"></a>Why is Record Deduplication Important?</h2>
Data or record deduplication is crucial for several reasons:
<ul>
<li>Data Quality: Removing duplicates improves data accuracy, integrity, and consistency, preventing errors and confusion.</li>
<li>Efficient Operations: It reduces the storage space required, speeds up data retrieval, and optimizes database performance.</li>
<li>Compliance: In industries with regulatory requirements, deduplication helps maintain data integrity and meet compliance standards.</li>
</ul>
<h2 id="user-content-what-tool-is-best-to-perform-records-deduplication"><a href="#what-tool-is-best-to-perform-records-deduplication" aria-hidden="true" tabindex="-1"></a>What tool is best to perform records deduplication?</h2>
Datablist is a <a href="/csv-editor">free csv editor</a> with powerful <a href="/how-to/data-cleaning">data cleansing</a> features. Datablist <a href="/features/duplicates-remover">Duplicates Finder</a> implements Exact, Phonetic, and Fuzzy Matching algorithms to detect duplicate records across your datasets.
<a href="https://app.datablist.com" target="_blank" rel="nofollow">👉 Open Datablist (it's free)</a>
<img src="/learn_images/data-cleaning/deduplication/data-matching.png#1922x1302" alt="Data Matching">
<img src="/learn_images/data-cleaning/deduplication/data-deduplication.png#2160x1370" alt="Data Deduplication">
<a href="https://app.datablist.com" target="_blank" rel="nofollow">👉 Open Datablist (it's free)</a>
<a href="/how-to/remove-csv-duplicates">👉 Read our guide on CSV deduplication.</a>


**Records deduplication**, also known as **data deduplication** or [record matching](/how-to/data-matching-tool), is the process of identifying and eliminating duplicate records or entries within a database or dataset. It's a critical [data cleansing](/how-to/data-cleaning) technique used to ensure data accuracy and consistency.

## How does Record Deduplication work?

Record deduplication typically involves two steps:

- Identifying duplicate records or [**Data Matching**](/how-to/data-matching-tool): The first step is to identify duplicate records in the dataset. This can be done using a variety of methods, such as comparing unique identifiers (e.g., customer IDs, product IDs), or using phonetic/fuzzy algorithms to identify records that are similar to each other.
- **Removing duplicate records**: Once duplicate records have been identified, they can be removed from the dataset. This can be done manually, or using automated tools. When record data is similar or complementary, the merging process is a nondestructive step. When duplicate records have conflicting data, the merging process must offer text concatenation or a rule to drop conflicting data to be able to merge all fields into a single record.

## How to identify duplicate records?

There are two main types of data matching:

- **Exact record matching**: Exact deduplication identifies and removes records that are exactly the same. This is the simplest type of deduplication.
- **Field-specific matching**: This is a more complex type of deduplication. It requires the use of algorithms to identify similar records by focusing on specific fields like names, addresses, or phone numbers. 

For field-specific matching, several algorithms exist:

- **Exact matching**: Identifies records that have identical values for the specific fields.
- **Phonetic matching**: Algorithm to match values by their pronunciation. Most phonetic algorithms work best with English words. The [Soundex](https://en.wikipedia.org/wiki/Soundex) and [Double Metaphone](/learn/data-cleaning/double-metaphone) are among the best-known phonetic algorithms.
- **Fuzzy Matching**: Uses algorithms to find records that are similar but not necessarily identical, accommodating variations in spelling, formatting, or other data discrepancies. Two famous fuzzy matching algorithms are the [Levenshtein Distance](/learn/data-cleaning/fuzzy-matching-levenshtein-distance) and [Jaro-Winkler Distance](/learn/data-cleaning/fuzzy-matching-jaro-winkler-distance).



## Why is Record Deduplication Important?

Data or record deduplication is crucial for several reasons:

- **Data Quality**: Removing duplicates improves data accuracy, integrity, and consistency, preventing errors and confusion.
- **Efficient Operations**: It reduces the storage space required, speeds up data retrieval, and optimizes database performance.
- **Compliance**: In industries with regulatory requirements, deduplication helps maintain data integrity and meet compliance standards.

## What tool is best to perform records deduplication?

Datablist is a [free csv editor](/csv-editor) with powerful [data cleansing](/how-to/data-cleaning) features. Datablist [Duplicates Finder](/features/duplicates-remover) implements Exact, Phonetic, and Fuzzy Matching algorithms to detect duplicate records across your datasets.

[👉 Open Datablist (it's free)](https://app.datablist.com)

![Data Matching](/learn_images/data-cleaning/deduplication/data-matching.png#1922x1302)

![Data Deduplication](/learn_images/data-cleaning/deduplication/data-deduplication.png#2160x1370)

[👉 Open Datablist (it's free)](https://app.datablist.com)

[👉 Read our guide on CSV deduplication.](/how-to/remove-csv-duplicates)

What is deduplication?

Records deduplication, also known as data deduplication or record matching, is the process of identifying and eliminating duplicate records or entries within a database or dataset. It&amp#39;s a critical data cleansing technique used to ensure data accuracy and consistency.
How does Record Deduplication work?
Record deduplication typically involves two steps:
Identifying duplicate records or Data Matching: The first step is to identify duplicate records in the dataset. This can be done using a variety of methods, such as comparing unique identifiers (e.g., customer IDs, product IDs), or using phonetic/fuzzy algorithms to identify records that are similar to each other.
Removing duplicate records: Once duplicate records have been identified, they can be removed from the dataset. This can be done manually, or using automated tools. When record data is similar or complementary, the merging process is a nondestructive step. When duplicate records have conflicting data, the merging process must offer text concatenation or a rule to drop conflicting data to be able to merge all fields into a single record.

How to identify duplicate records?
There are two main types of data matching:
Exact record matching: Exact deduplication identifies and removes records that are exactly the same. This is the simplest type of deduplication.
Field-specific matching: This is a more complex type of deduplication. It requires the use of algorithms to identify similar records by focusing on specific fields like names, addresses, or phone numbers. 

For field-specific matching, several algorithms exist:
Exact matching: Identifies records that have identical values for the specific fields.
Phonetic matching: Algorithm to match values by their pronunciation. Most phonetic algorithms work best with English words. The Soundex and Double Metaphone are among the best-known phonetic algorithms.
Fuzzy Matching: Uses algorithms to find records that are similar but not necessarily identical, accommodating variations in spelling, formatting, or other data discrepancies. Two famous fuzzy matching algorithms are the Levenshtein Distance and Jaro-Winkler Distance.

Why is Record Deduplication Important?
Data or record deduplication is crucial for several reasons:
Data Quality: Removing duplicates improves data accuracy, integrity, and consistency, preventing errors and confusion.
Efficient Operations: It reduces the storage space required, speeds up data retrieval, and optimizes database performance.
Compliance: In industries with regulatory requirements, deduplication helps maintain data integrity and meet compliance standards.

What tool is best to perform records deduplication?
Datablist is a free csv editor with powerful data cleansing features. Datablist Duplicates Finder implements Exact, Phonetic, and Fuzzy Matching algorithms to detect duplicate records across your datasets.
👉 Open Datablist (it&amp#39;s free)
Data Matching
Data Deduplication
👉 Open Datablist (it&amp#39;s free)
👉 Read our guide on CSV deduplication.


The Double Metaphone algorithm is a phonetic algorithm that is used to convert words into a phonetic code. The code can then be used to compare words and names that sound similar, even if they are spelled differently. The Double Metaphone algorithm is an improvement over the original Metaphone algorithm and was created by Lawrence Philips in 2000.
<h2 id="user-content-how-does-the-double-metaphone-algorithm-work"><a href="#how-does-the-double-metaphone-algorithm-work" aria-hidden="true" tabindex="-1"></a>How does the Double Metaphone algorithm work?</h2>
The Double Metaphone algorithm works by first removing all non-alphabetic characters from the word. Next, all vowels are removed, unless the word begins with a vowel. The remaining consonants are then processed according to a set of rules. These rules take into account the pronunciation of English consonants, as well as the way that consonants are often combined in English words.
The Double Metaphone algorithm generates two phonetic codes for each word. The first code is called the primary code and is used for most comparisons. The second code is called the secondary code and is used for comparisons where the primary code is not unique.
For example, the word "Smith" would be converted to the primary code "SMT" and the secondary code "XMT" by the Double Metaphone algorithm. This is because the "th" sound can be pronounced as either "S" or "X" in English, so both codes are generated.
<h2 id="user-content-what-are-the-benefits-of-using-the-double-metaphone-algorithm"><a href="#what-are-the-benefits-of-using-the-double-metaphone-algorithm" aria-hidden="true" tabindex="-1"></a>What are the benefits of using the Double Metaphone algorithm?</h2>
The Double Metaphone algorithm has a number of benefits:
<ul>
<li>
Language Agnostic: The Double Metaphone Algorithm works well with a multitude of languages, making it suitable for applications requiring cross-lingual compatibility.
</li>
<li>
Accurate Phonetic Encoding: It generates phonetic encodings that reflect the way words are spoken, enhancing the accuracy of searches and matches in databases and applications. The Double Metaphone algorithm is more accurate than Soundex and Metaphone at matching words that sound similar. This is because the Double Metaphone algorithm takes into account more of the pronunciation of English consonants.
</li>
<li>
Variation Handling: By providing primary and secondary codes, the algorithm accommodates the various ways a word might be pronounced, increasing the chances of capturing relevant results.
</li>
<li>
Improved Spell Correction: The Double Metaphone Algorithm aids in spell correction by identifying words that sound similar but are spelled differently.
</li>
<li>
Speed: The Double Metaphone algorithm is relatively fast, making it suitable for use in applications where speed is important.
</li>
</ul>
<h2 id="user-content-what-are-the-limitations-of-the-double-metaphone-algorithm"><a href="#what-are-the-limitations-of-the-double-metaphone-algorithm" aria-hidden="true" tabindex="-1"></a>What are the limitations of the Double Metaphone algorithm?</h2>
While powerful, the Double Metaphone Algorithm might not be perfectly accurate in all cases, especially for very short words or words borrowed from foreign languages that retain their original pronunciation.
<h2 id="user-content-how-to-use-double-metaphone-algorithm-to-find-duplicate-records"><a href="#how-to-use-double-metaphone-algorithm-to-find-duplicate-records" aria-hidden="true" tabindex="-1"></a>How to use Double Metaphone algorithm to find duplicate records?</h2>
The Double Metaphone algorithm is suited for <a href="/features/duplicates-remover">data deduplication</a>. This algorithm can be used to generate a code for each word in names, addresses, or any text fields. Then, the codes are compared to find duplicate records.
<a href="/">Datablist</a> is a free <a href="/data-editor">data editor</a> with powerful <a href="/how-to/data-cleaning">data-cleaning features</a>. Datablist <a href="/features/duplicates-remover">Duplicates Finder</a> implements the Double Metaphone to detect duplicate records across your datasets.
<img src="/learn_images/data-cleaning/double-metaphone/double-metaphone-deduplication.png#1336x1310" alt="Double Metaphone Algorithm">
<a href="https://app.datablist.com" target="_blank" rel="nofollow">👉 Open Datablist (it's free)</a>


The Double Metaphone algorithm is a phonetic algorithm that is used to convert words into a phonetic code. The code can then be used to compare words and names that sound similar, even if they are spelled differently. The Double Metaphone algorithm is an improvement over the original Metaphone algorithm and was created by Lawrence Philips in 2000.

## How does the Double Metaphone algorithm work?

The Double Metaphone algorithm works by first removing all non-alphabetic characters from the word. Next, all vowels are removed, unless the word begins with a vowel. The remaining consonants are then processed according to a set of rules. These rules take into account the pronunciation of English consonants, as well as the way that consonants are often combined in English words.

The Double Metaphone algorithm generates two phonetic codes for each word. The first code is called the primary code and is used for most comparisons. The second code is called the secondary code and is used for comparisons where the primary code is not unique.

For example, the word "Smith" would be converted to the primary code "SMT" and the secondary code "XMT" by the Double Metaphone algorithm. This is because the "th" sound can be pronounced as either "S" or "X" in English, so both codes are generated.




## What are the benefits of using the Double Metaphone algorithm?

The Double Metaphone algorithm has a number of benefits:

- **Language Agnostic**: The Double Metaphone Algorithm works well with a multitude of languages, making it suitable for applications requiring cross-lingual compatibility.

- **Accurate Phonetic Encoding**: It generates phonetic encodings that reflect the way words are spoken, enhancing the accuracy of searches and matches in databases and applications. The Double Metaphone algorithm is more accurate than Soundex and Metaphone at matching words that sound similar. This is because the Double Metaphone algorithm takes into account more of the pronunciation of English consonants.

- **Variation Handling**: By providing primary and secondary codes, the algorithm accommodates the various ways a word might be pronounced, increasing the chances of capturing relevant results.

- **Improved Spell Correction**: The Double Metaphone Algorithm aids in spell correction by identifying words that sound similar but are spelled differently.

- **Speed**: The Double Metaphone algorithm is relatively fast, making it suitable for use in applications where speed is important.


## What are the limitations of the Double Metaphone algorithm?

While powerful, the Double Metaphone Algorithm might not be perfectly accurate in all cases, especially for very short words or words borrowed from foreign languages that retain their original pronunciation.

## How to use Double Metaphone algorithm to find duplicate records?

The Double Metaphone algorithm is suited for [data deduplication](/features/duplicates-remover). This algorithm can be used to generate a code for each word in names, addresses, or any text fields. Then, the codes are compared to find duplicate records.

[Datablist](/) is a free [data editor](/data-editor) with powerful [data-cleaning features](/how-to/data-cleaning). Datablist [Duplicates Finder](/features/duplicates-remover) implements the Double Metaphone to detect duplicate records across your datasets.

![Double Metaphone Algorithm](/learn_images/data-cleaning/double-metaphone/double-metaphone-deduplication.png#1336x1310)

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What is the Double Metaphone algorithm?

The Double Metaphone algorithm is a phonetic algorithm that is used to convert words into a phonetic code. The code can then be used to compare words and names that sound similar, even if they are spelled differently. The Double Metaphone algorithm is an improvement over the original Metaphone algorithm and was created by Lawrence Philips in 2000.
How does the Double Metaphone algorithm work?
The Double Metaphone algorithm works by first removing all non-alphabetic characters from the word. Next, all vowels are removed, unless the word begins with a vowel. The remaining consonants are then processed according to a set of rules. These rules take into account the pronunciation of English consonants, as well as the way that consonants are often combined in English words.
The Double Metaphone algorithm generates two phonetic codes for each word. The first code is called the primary code and is used for most comparisons. The second code is called the secondary code and is used for comparisons where the primary code is not unique.
For example, the word &ampquot;Smith&ampquot; would be converted to the primary code &ampquot;SMT&ampquot; and the secondary code &ampquot;XMT&ampquot; by the Double Metaphone algorithm. This is because the &ampquot;th&ampquot; sound can be pronounced as either &ampquot;S&ampquot; or &ampquot;X&ampquot; in English, so both codes are generated.
What are the benefits of using the Double Metaphone algorithm?
The Double Metaphone algorithm has a number of benefits:
Language Agnostic: The Double Metaphone Algorithm works well with a multitude of languages, making it suitable for applications requiring cross-lingual compatibility.

Accurate Phonetic Encoding: It generates phonetic encodings that reflect the way words are spoken, enhancing the accuracy of searches and matches in databases and applications. The Double Metaphone algorithm is more accurate than Soundex and Metaphone at matching words that sound similar. This is because the Double Metaphone algorithm takes into account more of the pronunciation of English consonants.

Variation Handling: By providing primary and secondary codes, the algorithm accommodates the various ways a word might be pronounced, increasing the chances of capturing relevant results.

Improved Spell Correction: The Double Metaphone Algorithm aids in spell correction by identifying words that sound similar but are spelled differently.

Speed: The Double Metaphone algorithm is relatively fast, making it suitable for use in applications where speed is important.


What are the limitations of the Double Metaphone algorithm?
While powerful, the Double Metaphone Algorithm might not be perfectly accurate in all cases, especially for very short words or words borrowed from foreign languages that retain their original pronunciation.
How to use Double Metaphone algorithm to find duplicate records?
The Double Metaphone algorithm is suited for data deduplication. This algorithm can be used to generate a code for each word in names, addresses, or any text fields. Then, the codes are compared to find duplicate records.
Datablist is a free data editor with powerful data-cleaning features. Datablist Duplicates Finder implements the Double Metaphone to detect duplicate records across your datasets.
Double Metaphone Algorithm
👉 Open Datablist (it&amp#39;s free)


The Jaro-Winkler Distance is a similarity measure that quantifies the difference between two strings, often used in the context of record linkage, data deduplication, and string matching. It's an extension of the Jaro Distance metric and takes into account prefixes of matching characters.
<h2 id="user-content-how-does-the-jaro-winkler-distance-work"><a href="#how-does-the-jaro-winkler-distance-work" aria-hidden="true" tabindex="-1"></a>How does the Jaro-Winkler Distance work?</h2>
The Jaro-Winkler Distance computes a value between 0 and 1, where 0 indicates no similarity and 1 represents identical strings. It considers the number of matching characters, the number of transpositions (swapped characters), and a scaling factor for common prefix matches. The scaling factor in Jaro-Winkler gives higher weight to prefix similarities, making it especially effective for cases where slight misspellings or prefixes are common.
<h2 id="user-content-why-is-the-jaro-winkler-distance-important"><a href="#why-is-the-jaro-winkler-distance-important" aria-hidden="true" tabindex="-1"></a>Why is the Jaro-Winkler Distance important?</h2>
Jaro-Winkler is particularly useful when dealing with strings that might have minor discrepancies due to typos, abbreviations, or phonetic variations. Its emphasis on matching prefixes makes it robust against cases where two strings are similar but not identical. This makes it valuable for applications like deduplication, spell correction, and name matching.
<h2 id="user-content-how-is-the-jaro-winkler-distance-calculated"><a href="#how-is-the-jaro-winkler-distance-calculated" aria-hidden="true" tabindex="-1"></a>How is the Jaro-Winkler Distance calculated?</h2>
The calculation involves three main steps: computing the Jaro Distance (a value between 0 and 1), calculating the common prefix length, and applying the Jaro-Winkler formula. The formula takes into account the Jaro Distance, and the prefix scaling factor (usually 0.1 or 0.25), and adjusts the similarity score accordingly.
<h2 id="user-content-what-are-the-limitations-of-the-jaro-winkler-distance"><a href="#what-are-the-limitations-of-the-jaro-winkler-distance" aria-hidden="true" tabindex="-1"></a>What are the limitations of the Jaro-Winkler Distance?</h2>
While effective, the Jaro-Winkler Distance may not be ideal for all scenarios. It's sensitive to the choice of scaling factor, which needs to be tuned based on the specific dataset and application. Additionally, it might not capture more complex semantic differences between strings.
<h2 id="user-content-how-to-use-jaro-winkler-distance-to-find-duplicate-records"><a href="#how-to-use-jaro-winkler-distance-to-find-duplicate-records" aria-hidden="true" tabindex="-1"></a>How to use Jaro-Winkler Distance to find duplicate records?</h2>
The Jaro-Winkler distance is suited for <a href="/learn/data-cleaning/fuzzy-matching">Fuzzy Matching</a> and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.
<a href="/">Datablist</a> is a free <a href="/data-editor">data editor</a> with powerful <a href="/how-to/data-cleaning">data-cleaning features</a>. Datablist <a href="/features/duplicates-remover">Duplicates Finder</a> implements the Leventshtein distance to detect duplicate records across your datasets.
<img src="/learn_images/data-cleaning/jaro-winkler/jaro-winkler-dedupe.png#1384x1402" alt="Fuzzy Matching Algorithm">
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The Jaro-Winkler Distance is a similarity measure that quantifies the difference between two strings, often used in the context of record linkage, data deduplication, and string matching. It's an extension of the Jaro Distance metric and takes into account prefixes of matching characters.

## How does the Jaro-Winkler Distance work?

The Jaro-Winkler Distance computes a value between 0 and 1, where 0 indicates no similarity and 1 represents identical strings. It considers the number of matching characters, the number of transpositions (swapped characters), and a scaling factor for common prefix matches. The scaling factor in Jaro-Winkler gives higher weight to prefix similarities, making it especially effective for cases where slight misspellings or prefixes are common.

## Why is the Jaro-Winkler Distance important?

Jaro-Winkler is particularly useful when dealing with strings that might have minor discrepancies due to typos, abbreviations, or phonetic variations. Its emphasis on matching prefixes makes it robust against cases where two strings are similar but not identical. This makes it valuable for applications like deduplication, spell correction, and name matching.

## How is the Jaro-Winkler Distance calculated?

The calculation involves three main steps: computing the Jaro Distance (a value between 0 and 1), calculating the common prefix length, and applying the Jaro-Winkler formula. The formula takes into account the Jaro Distance, and the prefix scaling factor (usually 0.1 or 0.25), and adjusts the similarity score accordingly.

## What are the limitations of the Jaro-Winkler Distance?

While effective, the Jaro-Winkler Distance may not be ideal for all scenarios. It's sensitive to the choice of scaling factor, which needs to be tuned based on the specific dataset and application. Additionally, it might not capture more complex semantic differences between strings.

## How to use Jaro-Winkler Distance to find duplicate records?

The Jaro-Winkler distance is suited for [Fuzzy Matching](/learn/data-cleaning/fuzzy-matching) and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.

[Datablist](/) is a free [data editor](/data-editor) with powerful [data-cleaning features](/how-to/data-cleaning). Datablist [Duplicates Finder](/features/duplicates-remover) implements the Leventshtein distance to detect duplicate records across your datasets.

![Fuzzy Matching Algorithm](/learn_images/data-cleaning/jaro-winkler/jaro-winkler-dedupe.png#1384x1402)

[👉 Open Datablist (it's free)](https://app.datablist.com)



What is the Jaro-Winkler Distance?

The Jaro-Winkler Distance is a similarity measure that quantifies the difference between two strings, often used in the context of record linkage, data deduplication, and string matching. It&amp#39;s an extension of the Jaro Distance metric and takes into account prefixes of matching characters.
How does the Jaro-Winkler Distance work?
The Jaro-Winkler Distance computes a value between 0 and 1, where 0 indicates no similarity and 1 represents identical strings. It considers the number of matching characters, the number of transpositions (swapped characters), and a scaling factor for common prefix matches. The scaling factor in Jaro-Winkler gives higher weight to prefix similarities, making it especially effective for cases where slight misspellings or prefixes are common.
Why is the Jaro-Winkler Distance important?
Jaro-Winkler is particularly useful when dealing with strings that might have minor discrepancies due to typos, abbreviations, or phonetic variations. Its emphasis on matching prefixes makes it robust against cases where two strings are similar but not identical. This makes it valuable for applications like deduplication, spell correction, and name matching.
How is the Jaro-Winkler Distance calculated?
The calculation involves three main steps: computing the Jaro Distance (a value between 0 and 1), calculating the common prefix length, and applying the Jaro-Winkler formula. The formula takes into account the Jaro Distance, and the prefix scaling factor (usually 0.1 or 0.25), and adjusts the similarity score accordingly.
What are the limitations of the Jaro-Winkler Distance?
While effective, the Jaro-Winkler Distance may not be ideal for all scenarios. It&amp#39;s sensitive to the choice of scaling factor, which needs to be tuned based on the specific dataset and application. Additionally, it might not capture more complex semantic differences between strings.
How to use Jaro-Winkler Distance to find duplicate records?
The Jaro-Winkler distance is suited for Fuzzy Matching and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.
Datablist is a free data editor with powerful data-cleaning features. Datablist Duplicates Finder implements the Leventshtein distance to detect duplicate records across your datasets.
Fuzzy Matching Algorithm
👉 Open Datablist (it&amp#39;s free)


Levenshtein Distance, often referred to as edit distance, is a fundamental concept in string comparison and analysis. It quantifies the difference or similarity between two strings by calculating the minimum number of single-character edits required to transform one string into another. These edits can be in the form of insertions, deletions, or substitutions of individual characters.
<h2 id="user-content-how-does-levenshtein-distance-work"><a href="#how-does-levenshtein-distance-work" aria-hidden="true" tabindex="-1"></a>How does Levenshtein Distance work?</h2>
Imagine comparing the words "kitten" and "sitting." The Levenshtein Distance between these words is 3, as three operations are needed to change "kitten" into "sitting": substitute 'k' with 's,' insert 'i' after 'k,' and substitute 'e' with 'g.' This distance value provides a numerical measure of the similarity between the two words, considering variations like typos, misspellings, and other minor changes.
<h2 id="user-content-why-is-levenshtein-distance-important"><a href="#why-is-levenshtein-distance-important" aria-hidden="true" tabindex="-1"></a>Why is Levenshtein Distance important?</h2>
Levenshtein Distance has various practical applications. It serves as the basis for spell checkers, suggesting corrections for misspelled words. It's integral in fuzzy string matching, where data may have slight variations.
In bioinformatics, it helps analyze DNA sequences. By offering a systematic way to measure string differences, Levenshtein Distance plays a vital role in studying genetic variations and pattern recognition.
<h2 id="user-content-what-are-the-limitations-of-levenshtein-distance"><a href="#what-are-the-limitations-of-levenshtein-distance" aria-hidden="true" tabindex="-1"></a>What are the limitations of Levenshtein Distance?</h2>
While effective, Levenshtein Distance may not capture complex semantic differences between strings. Long strings can lead to larger computation times and memory usage. For more nuanced comparisons, other similarity metrics, like Jaccard similarity or cosine similarity, might be more suitable.
<h2 id="user-content-how-to-use-levenshtein-distance-to-find-duplicate-records"><a href="#how-to-use-levenshtein-distance-to-find-duplicate-records" aria-hidden="true" tabindex="-1"></a>How to use Levenshtein Distance to find duplicate records?</h2>
The Levenshtein distance is suited for <a href="/learn/data-cleaning/fuzzy-matching">Fuzzy Matching</a> and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.
<a href="/">Datablist</a> is a free <a href="/data-editor">data editor</a> with powerful <a href="/how-to/data-cleaning">data-cleaning features</a>. Datablist <a href="/features/duplicates-remover">Duplicates Finder</a> implements the Leventshtein distance to detect duplicate records across your datasets.
<img src="/learn_images/data-cleaning/excel-fuzzy-matching/excel-fuzzy-matching.png#1384x1402" alt="Fuzzy Matching Algorithm">
<a href="https://app.datablist.com" target="_blank" rel="nofollow">👉 Open Datablist (it's free)</a>


Levenshtein Distance, often referred to as edit distance, is a fundamental concept in string comparison and analysis. It quantifies the difference or similarity between two strings by calculating the minimum number of single-character edits required to transform one string into another. These edits can be in the form of insertions, deletions, or substitutions of individual characters.

## How does Levenshtein Distance work?

Imagine comparing the words "kitten" and "sitting." The Levenshtein Distance between these words is 3, as three operations are needed to change "kitten" into "sitting": substitute 'k' with 's,' insert 'i' after 'k,' and substitute 'e' with 'g.' This distance value provides a numerical measure of the similarity between the two words, considering variations like typos, misspellings, and other minor changes.

## Why is Levenshtein Distance important?

Levenshtein Distance has various practical applications. It serves as the basis for spell checkers, suggesting corrections for misspelled words. It's integral in fuzzy string matching, where data may have slight variations.

In bioinformatics, it helps analyze DNA sequences. By offering a systematic way to measure string differences, Levenshtein Distance plays a vital role in studying genetic variations and pattern recognition.

## What are the limitations of Levenshtein Distance?

While effective, Levenshtein Distance may not capture complex semantic differences between strings. Long strings can lead to larger computation times and memory usage. For more nuanced comparisons, other similarity metrics, like Jaccard similarity or cosine similarity, might be more suitable.

## How to use Levenshtein Distance to find duplicate records?

The Levenshtein distance is suited for [Fuzzy Matching](/learn/data-cleaning/fuzzy-matching) and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.

[Datablist](/) is a free [data editor](/data-editor) with powerful [data-cleaning features](/how-to/data-cleaning). Datablist [Duplicates Finder](/features/duplicates-remover) implements the Leventshtein distance to detect duplicate records across your datasets.

![Fuzzy Matching Algorithm](/learn_images/data-cleaning/excel-fuzzy-matching/excel-fuzzy-matching.png#1384x1402)

[👉 Open Datablist (it's free)](https://app.datablist.com)



What is the Levenshtein Distance?

Levenshtein Distance, often referred to as edit distance, is a fundamental concept in string comparison and analysis. It quantifies the difference or similarity between two strings by calculating the minimum number of single-character edits required to transform one string into another. These edits can be in the form of insertions, deletions, or substitutions of individual characters.
How does Levenshtein Distance work?
Imagine comparing the words &ampquot;kitten&ampquot; and &ampquot;sitting.&ampquot; The Levenshtein Distance between these words is 3, as three operations are needed to change &ampquot;kitten&ampquot; into &ampquot;sitting&ampquot;: substitute &amp#39;k&amp#39; with &amp#39;s,&amp#39; insert &amp#39;i&amp#39; after &amp#39;k,&amp#39; and substitute &amp#39;e&amp#39; with &amp#39;g.&amp#39; This distance value provides a numerical measure of the similarity between the two words, considering variations like typos, misspellings, and other minor changes.
Why is Levenshtein Distance important?
Levenshtein Distance has various practical applications. It serves as the basis for spell checkers, suggesting corrections for misspelled words. It&amp#39;s integral in fuzzy string matching, where data may have slight variations.
In bioinformatics, it helps analyze DNA sequences. By offering a systematic way to measure string differences, Levenshtein Distance plays a vital role in studying genetic variations and pattern recognition.
What are the limitations of Levenshtein Distance?
While effective, Levenshtein Distance may not capture complex semantic differences between strings. Long strings can lead to larger computation times and memory usage. For more nuanced comparisons, other similarity metrics, like Jaccard similarity or cosine similarity, might be more suitable.
How to use Levenshtein Distance to find duplicate records?
The Levenshtein distance is suited for Fuzzy Matching and data deduplication. This algorithm can be used to compute similarity in names, addresses, or any text fields. Using a threshold value, records with a similarity distance above the threshold are considered as duplicate records.
Datablist is a free data editor with powerful data-cleaning features. Datablist Duplicates Finder implements the Leventshtein distance to detect duplicate records across your datasets.
Fuzzy Matching Algorithm
👉 Open Datablist (it&amp#39;s free)


Fuzzy matching is a sophisticated data comparison technique used to identify similarities between strings or records, even when they're not an exact match. Unlike traditional exact matching, which requires two strings to be identical to consider them a match, fuzzy matching takes into account variations, errors, and discrepancies in the data. This makes it highly effective in scenarios where data might be slightly different due to typos, abbreviations, misspellings, or other inconsistencies.
<h2 id="user-content-how-does-fuzzy-matching-work"><a href="#how-does-fuzzy-matching-work" aria-hidden="true" tabindex="-1"></a>How does Fuzzy Matching Work?</h2>
Fuzzy matching employs distance algorithms to compute a similarity score between two strings. This score quantifies how closely the strings resemble each other. Various methods are used to calculate this score, such as Levenshtein distance, Jaro-Winkler distance, and others. These algorithms analyze the characters, their sequence, and their positions to determine the level of similarity.
<h2 id="user-content-how-to-use-fuzzy-matching-for-data-deduplication"><a href="#how-to-use-fuzzy-matching-for-data-deduplication" aria-hidden="true" tabindex="-1"></a>How to use Fuzzy Matching for data deduplication?</h2>
Fuzzy matching is commonly used for data deduplication, where duplicate records in databases need to be identified and resolved.
<a href="/">Datablist</a> is a free <a href="/data-editor">data editor</a> with powerful <a href="/how-to/data-cleaning">data-cleaning</a> features. Datablist <a href="/features/duplicates-remover">Duplicates Finder</a> implements fuzzy matching algorithms to detect duplicate records across your datasets.
<a href="/how-to/remove-csv-duplicates">👉 Read our guide on CSV deduplication.</a>
<img src="/learn_images/data-cleaning/excel-fuzzy-matching/excel-fuzzy-matching.png#1384x1402" alt="Fuzzy Matching Algorithm">
<a href="https://app.datablist.com" target="_blank" rel="nofollow">👉 Open Datablist (it's free)</a>


Fuzzy matching is a sophisticated data comparison technique used to identify similarities between strings or records, even when they're not an exact match. Unlike traditional exact matching, which requires two strings to be identical to consider them a match, fuzzy matching takes into account variations, errors, and discrepancies in the data. This makes it highly effective in scenarios where data might be slightly different due to typos, abbreviations, misspellings, or other inconsistencies.

## How does Fuzzy Matching Work?

Fuzzy matching employs distance algorithms to compute a similarity score between two strings. This score quantifies how closely the strings resemble each other. Various methods are used to calculate this score, such as Levenshtein distance, Jaro-Winkler distance, and others. These algorithms analyze the characters, their sequence, and their positions to determine the level of similarity.

## How to use Fuzzy Matching for data deduplication?

Fuzzy matching is commonly used for data deduplication, where duplicate records in databases need to be identified and resolved. 

[Datablist](/) is a free [data editor](/data-editor) with powerful [data-cleaning](/how-to/data-cleaning) features. Datablist [Duplicates Finder](/features/duplicates-remover) implements fuzzy matching algorithms to detect duplicate records across your datasets.

[👉 Read our guide on CSV deduplication.](/how-to/remove-csv-duplicates)

![Fuzzy Matching Algorithm](/learn_images/data-cleaning/excel-fuzzy-matching/excel-fuzzy-matching.png#1384x1402)

[👉 Open Datablist (it's free)](https://app.datablist.com)





What tools to dedupe using fuzzy matching?

Fuzzy matching is a sophisticated data comparison technique used to identify similarities between strings or records, even when they&amp#39;re not an exact match. Unlike traditional exact matching, which requires two strings to be identical to consider them a match, fuzzy matching takes into account variations, errors, and discrepancies in the data. This makes it highly effective in scenarios where data might be slightly different due to typos, abbreviations, misspellings, or other inconsistencies.
How does Fuzzy Matching Work?
Fuzzy matching employs distance algorithms to compute a similarity score between two strings. This score quantifies how closely the strings resemble each other. Various methods are used to calculate this score, such as Levenshtein distance, Jaro-Winkler distance, and others. These algorithms analyze the characters, their sequence, and their positions to determine the level of similarity.
How to use Fuzzy Matching for data deduplication?
Fuzzy matching is commonly used for data deduplication, where duplicate records in databases need to be identified and resolved. 
Datablist is a free data editor with powerful data-cleaning features. Datablist Duplicates Finder implements fuzzy matching algorithms to detect duplicate records across your datasets.
👉 Read our guide on CSV deduplication.
Fuzzy Matching Algorithm
👉 Open Datablist (it&amp#39;s free)


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