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.



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