
Excel files are widely used to store listing data. To manage lists of customers or companies, or to clean products catalogs, Microsoft Excel is an easy-to-use spreadsheet tool to manage data. But like any spreadsheet tool, it's not possible to add data constraints to forbid duplicate information. When several people edit the same Excel file or when data get added frequently, the likelihood of finding duplicate values increases.

Hopefully, it's possible to process the data afterward to remove or merge duplicate values.

Removing duplicates is easy using Microsoft Excel (Menu "Data" -> "Remove Duplicates") but merging duplicates without losing information gets tricky. When merging duplicate rows, you want to remove similar rows and merge complementary rows when possible. Finally, conflicting values should be flagged to be processed manually.

Datablist is an easy-to-use online tool to manipulate data. It's a perfect tool to find and merge duplicate values in an Excel file. Datablist [Duplicates Finder](/features/duplicates-remover) automatically finds and merges complementary duplicates. And conflicting items are skipped for manual action. **[You can use Datablist directly online for free without even signing up.](https://app.datablist.com)**

> **Why not use Excel to merge duplicates?** <br/>
> Microsoft Excel is a very powerful application and has a "Remove Duplicates" feature. However, "Remove Duplicates" keeps the first "matching" row and deletes any other duplicate rows. If two rows are complementary, it leads to a loss of data.
> Merging duplicates should not be complicated. Spreadsheet tools are great but they are generic and they don't focus on data manipulation. Datablist targets structured data listing and provides easy-to-use data manipulation features.

In the following guide, you will learn to find and merge duplicates from an Excel file without losing information.
To illustrate this guide, we'll use an Excel file containing Contacts data with 4 fields: `First Name`, `Last Name`, `Email`, `Job Title` and we will find and merge all contacts based on their email address.

> [Click here to download the tutorial Excel file.](https://storage.googleapis.com/public_datablist/website/guides/excel-merge-duplicates/Book1.xlsx)

 The process can be summed up with the following steps:

1. Load your Excel file
2. Find Duplicates
3. Automatically merge duplicates with no conflict
4. Manually merge remaining duplicates


## Step 1: Load Excel file

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

The first step is to import your Excel file into [Datablist](https://app.datablit.com). To do so, create a collection that will hold your Excel listing data. Use the "Import CSV/Excel" assistant to load your file.

![Create a collection to hold your data](/howto_images/merge-duplicates-excel/new-collection.png#1851x1011)

### Create properties and map with Excel columns

A Datablist collection is like a spreadsheet but with a data structure. And spreadsheet columns are called "Properties" in Datablist. If a property is defined as `Email`, Datablist will ensure only valid email addresses are filled out. Same with dates, checkboxes, etc.

Once your Excel file is loaded, you need to select which columns will be imported and map them with an existing collection property or create new properties.

Datablist auto detects common data types by analyzing the first rows to find patterns.

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    <div class="text-center">Import Excel file</div>
</div>

> **Important**
> - Cells with formulas will be computed during import.
> - Your data must be in the file first worksheet


If your data is scattered across different worksheets or files, please load them all in one Datablist collection and map them with the same properties.


## Step 2: Find duplicates in the Excel file

With your Excel file data now imported, it's time to use Datablist [Duplicates Finder](/features/duplicates-remover) to find and merge duplicates.

In the collection data listing, click the "Duplicates" button in the header (top right).

Two modes are available:

- **All Properties** - In this mode, Datablist looks for items with similar values for all properties. Two items would be considered similar when all of their properties match.
- **Selected Properties** - For this second mode, you will select the properties to be used for similarity check. Two items would be considered similar when they have similar values on all the selected properties.

In this example, the `email` property is enough to identify a contact, so you can select the **Selected Properties** mode with the **email** property.

> The duplicate analysis is a non-destructive action. Until you decide to merge automatically or manually your items, items are not impacted.


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            <source src="https://storage.googleapis.com/public_datablist/website/guides/excel-merge-duplicates/find-duplicates-excel.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Find duplicate items on one or more property/ies</div>
</div>


## Step 3: Bulk merge duplicate items with no conflict

Once all duplicate rows are listed, it's time to merge them! The process is done in two steps:

- First, use Datablist "smart merge" feature to **automatically merge non-conflicting items.**
- Then, use the "Merging Assistant" for the **remaining** duplicate values.

Click "Merge non-conflicting duplicates" to run the "smart merge". It works by merging records with similar or complementary values.

For example:

```
email            |     First Name   |    Last Name
james@gmail.com  |     James
james@gmail.com  |                  |     Bond
```

Will be merged into:

```
email            |     First Name   |    Last Name
james@gmail.com  |     James        |     Bond
```

![Bulk merge complementary duplicate values](/howto_images/merge-duplicates-excel/auto-merge-non-conflicts.png#2265x1270)


## Step 4: Manually merge conflicting items

Automatic merging is great for heavy lifting. If after running it, duplicate items remain, it means they are conflicting. Conflicts occur when at least two items have different values for a property. To merge them, you need to select which value to keep.

To do so, you can directly edit the items from the listing or launch Datablist Merging assistant. The assistant selects a primary item by looking at the item with the most data. Then, you can select property values from any other item to be merged into the primary item. At the end of the process, all items are deleted except for the primary item.

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    </video>
    <div class="text-center">Manually merge remaining duplicates</div>
</div>


## Export results in CSV or Excel format

Well done! **Your data is deduped!**

To export the collection to be used by another application, click "Export" in the collection header and select CSV or Microsoft Excel.

![Export in Microsoft Excel format](/howto_images/merge-duplicates-excel/export-excel.png#1412x670)


How to merge duplicate rows in an Excel file without losing data?

Load Excel file

Find duplicates

Automatically merge duplicates

Manually merge conflicting items


Email lists are the start of any email campaign and newsletter management. However, a list can get messy from multiple merges or with user spammy behavior.

The benefits for cleaning your email list are:

- **Improve deliverability** - Every email provider uses your sender score to determine where to route your emails. To avoid being labeled as SPAM and successfully reach your user, the first step is to avoid sending emails to non-existing addresses. A bad sender score is hard to revert, so better spend some time up-front and clean your list from unreachable email addresses.
- **Save money** - Usually, you pay for each email you send. First, pruning your email list to remove duplicates and invalid addresses will save you money. Then, you need to remove all disposable email addresses that will never be read to keep only real emails.
- **Detect and fix typos** - After the cleaning process, wrong email addresses will be flagged. Manually, you can find simple typos in names or domains and fix them.

Email list cleaning is an important part of any digital business and must be done regularly. **Datablist is a perfect data tool to perform this cleaning process.** Using this step by step guide, you will learn how to:
- [Remove duplicate emails](#step-1-remove-duplicate-emails)
- [Check email addresses syntax](#check-email-syntax)
- [Check if emails are from disposable providers](#check-disposable-providers)
- [Ensure email domains exist](#check-mx-records)

Datablist can be used without registration to [view and edit CSV files](/csv-editor). However, the email verification service that we'll use later require an account.

👉  [Create your account for free](https://app.datablist.com/auth/signup) 👈.

> **How does it compare to paid email cleaning services?** <br/>
> If you search for email verification services on Google, you'll find hundreds (thousands?) of them. Almost all of them charge a fee per email address. Datablist includes an email verification service and it is free.
> It is great and enough for simple email verifications. However, if you need deeper analysis or if you have to perform verifications on hundreds of thousands of emails, please use a paid email cleaning service.



## Step 1: Import email addresses

### Create a collection

The first step in the email cleaning process is to create a collection on Datablist in which you will pour your email addresses.

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


In [Datablist](https://app.datablist.com), click the **+** to create a new collection. Give it a name (and an icon 😍).

### Import your lists of email addresses

Now you have a collection, it's time to import your email lists: whether you have only one email list or several that you want to merge!

Datablist offers two options to import your data:
- With CSV files
- Using copy/pasting from a spreadsheet

#### Option 1: Import from CSV files

The [CSV format](/faq/csv/csv-meaning) is a simple standard to transfer tabular data between software applications. Every newsletter tool and digital marketing solution offer exports of your contacts in CSV files. [CSV files](/csv-editor) are first-class citizens in Datablist.

> See also our [How to join CSV files by a unique identifier](/how-to/join-csv-files-unique-identifier-or-column) and [How to remove CSV duplicates](/how-to/remove-csv-duplicates) guides.

In this example, we will use a demo contact CSV file with three columns: `First Name`, `Last Name`, `email`. [Download the demo file here](https://storage.googleapis.com/public_datablist/website/guides/clean-contact-list/datablist-contact-list.csv).

To import your CSV file, click the "Import CSV" button and select your file. Datablist will read the columns and show you a mapping page.

Create a property with the **+** button for every CSV column. On property creation, select the data type for each column. **Select Email for the email address column so Datablist can perform data integrity analysis.**

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    </video>
    <div class="text-center">Create a new collection and import CSV</div>
</div>



#### Option 2: Import with Copy/Pasting

Datablist is compatible with copy/pasting from any spreadsheet. Just select the cells from your spreadsheet, go to your Datablist collection and use the `Edit -> Paste` from your browser or directly the `Ctrl + v` keyboard shortcut.

On pasting, Datablist will show you the columns and rows it has detected. To import a column, map it to an existing property or create a new property.

> **Warning**: Only mapped columns will be imported!

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    <div class="text-center">Copy Pasting From Google Sheets</div>
</div>


### Import other contact lists if needed

If you are building an email collection from several sources, just import all your lists into the same collection. Just ensure all the list are mapped to the same properties.


## Step 2: Find and merge duplicates

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    </video>
    <div class="text-center">Detect and remove duplicates</div>
</div>


It's common to have an email list built over time. The result is several email listings merged into one. **Resulting in duplicates!** If your list stores contact information like *First Name*, *Last Name*, etc. you might have contact information spread over several duplicates rows.

With all your email addresses in one place. The second step is to [remove or merge duplicates entries](/features/duplicates-remover).

Use Datablist "Duplicates Finder" features by clicking on the "Duplicates" button in the collection header (on the right).

In the configuration screen, you need to select how duplicates will be checked:

- **All Properties** - Look for full items similarity: Two items would be considered similar when all of their properties match.
- **Selected properties** - Two items would be considered similar when all of their properties match.

In our example, two contacts are duplicates if they share the same email address. So, pick the **Selected Properties** mode and select the **Email** property.

Run the duplicates check to see a preview of all duplicates found in the collection.

If duplicates are found, three actions are available:
- remove duplicate items
- edit them
- merge (and consolidate) contacts.

To merge duplicate contacts, click on the "Merge Items" button on the left of every duplicate contacts group.

![Merge contacts](/howto_images/clean-emails/merge-duplicates.png#2170x528)

Datablist has a merging tool. On the right, a "Primary Item" is shown and on the left the remaining duplicate contacts are called "Secondary Items". Datablist elects the contact with the most data as "Primary item".

![Eliminate duplicate rows in a CSV](/howto_images/clean-emails/merge-duplicate-rows-modal.png#1908x1162)

When possible, property values from secondary items are auto selected to be merged into the primary items. If several values conflict, you will have to make a decision and select which value to keep.

If the resulting "Primary item" suits you, click the **Merge** button to confirm the merge process. All the secondary items will be deleted to keep only one combined item.

Once all the duplicates have been processed, go back to the collection.


## Step 3: Free email list cleaning

Now you have a collection on Datablist with all the email addresses and no duplicates, it's time to clean it!

### What does the service check?

Datablist has a built-in free email verification service. This free service does 3 verifications:

- Email syntax analysis
- Disposable providers check
- Domain MX records check

#### Email syntax analysis
The first check is to ensure the email conforms to the [IEFT standard](https://datatracker.ietf.org/doc/html/rfc2822#section-3.4.1) and does a complete syntactical analysis.

This analysis will flagged addresses without the at sign (@), with invalid domains, etc.

#### Check disposable providers

The second check is to detect temporary emails. The service looks for domains belonging to Disposable Email Address (DEA) providers such as Mailinator, Temp-Mail, YopMail, etc.

The current database lists about 3000 disposable provider domains and is updated regularly using [this disposable domains list](https://github.com/disposable-email-domains/disposable-email-domains/blob/master/disposable_email_blocklist.conf).

#### Check domain MX records

A valid email address must have a corresponding domain name with configured MX records. Those MX records specify the mail server accepting the email messages for the domain. Missing MX records indicate an invalid email address.

For every email address domain, the service checks the DNS records and looks for the MX ones. If the domain doesn't exist, the email will be flagged as invalid. If the domain exists and doesn't have a valid MX record, it will also be flagged as invalid.

### Perform cleaning on your collection

Performing an email list cleaning on Datablist is simple. Just select the email addresses in your collection and run the "Email Address Validation" action.

Once you have selected "Email Address Validation", a drawer opens on the right with 3 sections: `Settings`, `Input Properties`, and `Output`.

**Settings**

Select "Check for MX-records in email domain" in the settings to analyze the MX records.

**Input Properties**

Select the property from your collection that contains the email address. In this example, the collection has an "Email" property that will be matched.

**Output**

The "Email Address Validation" service returns 2 values:

- Valid Email - A boolean (`true` or `false`) to indicate whether the email address is valid.
- Error status - A text explaining why the email address is invalid when "Valid" Email" is `false`.

> **It's important to map the output properties to the collection to store the results.**

Click the **+** on the right of each output property to add the result properties to the collection.

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    </video>
    <div class="text-center">Verify email addresses for free</div>
</div>


Once the service is done, analyze all invalid emails to detect easy fix typos.

## Step 4: Remove unsubscribed emails

This last step is optional. You might have a dedicated list containing all unsubscribed emails that you want to remove from your main list. If your list with unsubscribed emails has a specific column like:

```
email           |     Unsubscribed
xxx@xxx.com     |     yes
xxx@xxx.com     |     yes
xxx@xxx.com     |     yes
```

It is possible to perform a join operation and add the `Unsubscribed` information to your contact list.

To do so, go to the properties configuration modal and select "Do no allow duplicate values" on the Email property. Because you set the `Email` property to be unique, your data will be merged into your collection resulting in a single list with the `Unsubscribed` property.

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            <source src="https://storage.googleapis.com/public_datablist/website/guides/clean-contact-list/email-uniqueness.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Define unique option for Email property</div>
</div>

Then, simply import your unsubscribed email list in the Datablist collection using a CSV file or copy/pasting.

> Don't forget to **create and map** the "Unsubscribed" column during the import.


## Extra Step: Automatic merge duplicates during import

To prevent and merge duplicates automatically during the import process. Check the "do not allow duplicate values" option on the Email property ([see video on step 4](#step-4-remove-unsubscribed-emails)). With this property option, Datablist will automatically deduplicate and merge your contacts on data import.

During the import stepper, a merging option will be shown to configure how Datablist must deal with duplicates.

![CSV Join - Merging Options](/howto_images/csv/csv-merge-options.png#1587x618)

**The merging option is important when your listing contains contact information in addition to the email address.**

- With `Soft Merge`, if a contact exists with the same email, it **will not** update properties with previous data (contacts already in the collection or the first contact found in the CSV). This is the default setting.
- With `Hard Merge`, if data exists with the same email, it will **update it**.

---

If you have any feedback on this guide or if you have questions, please [contact us](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link).


How to clean an email list for free

Import email addresses

Merge duplicate email addresses

Free email list cleaning

Remove unsubscribed emails


[CSV files](/faq/csv/csv-uses) are everywhere to define structured data for software applications or data sets. Despite being ubiquitous, CSV manipulation is hard and often requires technical knowledge.

For simple manipulations, spreadsheets are enough. However, they are limited when it comes to:

- [Consolidate CSV files using a unique identifier](/how-to/join-csv-files-unique-identifier-or-column)
- Dedupe rows in a CSV

 In this guide, we will focus on the last manipulation: detecting and removing duplicates from CSV files.

 > If you have several CSV files that you want to join together using a unique column, go to the [join csv files](/how-to/join-csv-files-unique-identifier-or-column) guide.


## Merge duplicates from CSV files

You have exported your data from an application in a CSV file and want to get rid of the duplicates? You have concatenated several CSV files into a big one and now need to clean it? Or maybe you have several sources of data no longer in sync and want to merge all those versions into a single listing. Microsoft Excel or Google Sheets will not help you with your problem because they don't allow you to set up a unique constraint on a column.

On a small CSV file, this task can be done manually. On bigger files, it would take hours to process the entries one by one. And you risk human errors.

**What you need is a tool to automatically detect CSV entries with similar values in one or several columns.** Then, once the duplicates are found, you can edit or merge them to consolidate their data and remove the duplicates.

Datablist is great to perform simple data operations that are not possible with spreadsheets. Use it when you are looking for a great [online CSV editor](/csv-editor).

In this guide, we will work with 2 CSV files that contains thousands of entries. We will load them into a single collection and de-duplicate entries based on 1 of the 4 columns.

> To download the tutorial CSV files: [CSV File 1](https://storage.googleapis.com/public_datablist/website/guides/remove-csv-duplicates/Contacts_1.csv) and [CSV File 2](https://storage.googleapis.com/public_datablist/website/guides/remove-csv-duplicates/Contacts_2.csv)

The CSV file contains 4 columns: `First Name`, `Last Name`, `Email`, `Job Title`. We want to merge entries that share the same email address.

The process can be summed up with:

1. Load your CSV files into a Datablist collection
2. Create properties matching CSV columns
3. Select the properties to perform the duplicates check
4. Merge or delete the duplicates

## Step 1: Load your CSV files into a Datablist collection

<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>
    <span class="orange-text">No signup required</span>
</aside>

### Create a new collection

The first step in our process is to load the CSV file into Datablist. [Open Datablist (No signup required)](https://app.datablist.com) to get started.

To create a new collection, click the "New collection" button with the **+**. Once the collection is created, give it a name and an icon.

Then click on the **Import CSV** button.

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    <div class="text-center">Create a new collection</div>
</div>


### Create properties for your CSV file

With the CSV file now loaded, you can create properties for your collection using the CSV column names. Datablist lists every column found in the CSV so you can create a property for each of them.

CSV data is not typed. When reading a CSV file, everything is a text. To provide better filtering and sorting features, Datablist tries to detect data types by looking at the first 100 lines. For example, when it finds only numbers, it set the property data type to number. Same with date, email, checkboxes (true or false values), etc.

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    </video>
    <div class="text-center">Create collection properties</div>
</div>


### Review and import

In the review step, the CSV rows will be listed directly from the CSV file. Be sure the data is well formatted and is consistent. Then click the "Import items" button and you are done! 💪

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    <div class="text-center">Review, import and upload the second CSV file</div>
</div>


### Do it again for your other CSV files

Now you have a collection with properties configured, perform the "Import CSV/Excel" process to import your other CSV files or Excel files into the same collection.


## Step 2: Find duplicates

After loading the CSV files, the second step is to search for duplicate values. In the collection data listing, click the "Duplicates" button in the header (top right).

Two modes are available:

- **All Properties** - In this mode, Datablist looks for items with similar values for all properties. Two items would be considered similar when all of their properties match.
- **Selected Properties** - For this second mode, you will select the properties to be used for similarity check. Two items would be considered similar when they have similar values on all the selected properties.

Here, the `email` property is enough to identify a contact, so you can select the **Selected Properties** mode with the **email** property.

Once the analysis is done, Datablist lists all your duplicate items based on the `email` property. For each items with one or more duplicates, you can:

- **Edit the item** - Use values from incomplete items to merge the data into a single item.
- **[Merge duplicates](#step-3-merge-duplicates)** - Merge values from secondary items to a selected primary item
- **Delete the extra items** - When duplicates don't bring value, just remove the duplicates

<div class="content-video">
    <video muted playsinline controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/remove-csv-duplicates/videos/duplicates-analysis.mp4" type="video/mp4" />
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    <div class="text-center">Find and remove duplicates</div>
</div>

## Step 3: Merge duplicates

Usually, you want to merge all your CSV duplicate rows into a single item and consolidate the data. Thus, merging duplicates without losing data in the process.

To merge duplicates, click on the "Merge Items" button on the left of every duplicates group.

![Merge duplicates](/howto_images/remove-duplicates/merge-duplicates.png#2170x528)

It opens a merging tool. On the right, a "Primary Item" is shown and on the left the remaining duplicate items are called "Secondary Items".
Datablist elects the item with the most data as "Primary item".

![Eliminate duplicate rows in a CSV](/howto_images/remove-duplicates/merge-duplicate-rows-modal.png#1908x1162)

When possible, property values from secondary items are auto selected to be merged into the primary items. If several values conflict, you will have to make a decision and select which value to keep.

If the resulting "Primary item" suits you, click the **Merge** button to confirm the merge process. All the secondary items will be deleted to keep only one combined item.


## Export to CSV if needed

Congrats, you have successfully deduped your CSV files! If you need to use the result in another tool, click on the "Export" button to export the collection as another CSV file.

![CSV Export](/howto_images/remove-duplicates/export-csv.png#918x330)


---

If you have any questions, please [contact us](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link).


How to remove CSV duplicates

Load CSV files

Merge duplicates


The CSV file format is a standard to exchange structured data between companies or different software applications. It is text-based and uses punctuation delimiters to separate columns.

Here is an example of CSV file data:

```text
firstName, lastName, email
Joe, Vaughn, vaugh@hotmail.com
Bob, Cunnighan, bobc@gmail.com
```

[CSV files](/faq/csv/csv-uses) can be exported from most tools dealing with data, including CRM, Order Management System, Spreadsheets (Google Sheets or Microsoft Excel), and financial products. In the future, we might have [unified data models](/blog/common-data-model-vs-cloud-information-model) to transfer structured items between applications. In the meantime, we must rely on CSV files.

When it comes to manipulating CSV files, the go-to solution is spreadsheet. Loading a CSV file into Google Sheets or Microsoft Excel is straightforward. However, those tools are limited when it comes to two simple operations:

- Joining CSV files by a unique column
- [Deduplicate and merge CSV entries on a column unique constraint](/how-to/remove-csv-duplicates)

When [performing a join operation](/faq/csv/difference-join-merge), we are using a common column to combine data from several sources. **Spreadsheets cannot define a unique constraint on a column**, therefore provide limited support for joining CSV or deduplication.

This guide contains 2 parts:
- [Join CSV files on a common column with Datablist](#solution-1-join-csv-files-on-a-common-column-with-datablist)
- [Join CSV files using Google Sheet (or Microsoft Excel)](#solution-2-join-csv-files-using-google-sheet-or-microsoft-excel)

In this tutorial, we use 2 demo CSV files:
- [Master CSV: A CSV with `First Name, Last Name, Email`](https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/FirstNameLastNameEmail.csv)
- [A secondary CSV with `Email, Job Title`](https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/EmailJobTitle.csv)


## Solution 1: Join CSV files on a common column with Datablist

<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>
    <span class="orange-text">No signup required</span>
</aside>

Data manipulation is simple with Datablist. Let's see how you can join CSV files by a unique identifier. [Open Datablist (No signup required)](https://app.datablist.com) to get started.


### Step1: Load your first CSV file

The first step is to create a collection to pour in all your CSV data. Click the **+** button in the sidebar to create a new collection.

Once your collection is created, go to the "Import CSV" section.

<div class="content-video">
    <video autoplay muted loop playsinline width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/create-collection-csv-join.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Create a new collection</div>
</div>

> Note: Your CSV file first line must contain column names.

Drag and drop a CSV file or click to select a file from your computer. Once the file is loaded, check that the number of lines and columns preview displayed is correct before moving to the next step.

Map your CSV columns with collection properties or create new properties.

The last step is to click on the "Import" button to launch the import process. Your first CSV file is imported!

<div class="content-video">
    <video controls muted playsinline width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/import-csv.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Import your first CSV</div>
</div>

### Step2: Define which column to use as a unique identifier

Now that you have imported your first CSV file, you can define a "unique values" constraint on a collection property. With this information, Datablist will merge new CSV imports to respect the constraint. Go to the columns configuration and edit the property that will be your unique identifier. Check the "Unique Values" attribute and save.

<div class="content-video">
    <video muted loop controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/unique-values.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Add unique values constraint on a property</div>
</div>


### Step3: Load one or more CSV files

Once you have set a unique constraint on a collection property, import your other CSV files one by one in the existing collection. Create new collection properties if needed during the CSV mapping step.

When your collection has items and has a unique constraint, you can select a merging option during a CSV file import.

![Merging Mode](/howto_images/csv/csv-join-merging-mode.png#2740x1367)

Select how data would be merged into the collection:

- Soft Merge: If data exists for the item property, **don't** update it. This is the default setting.
- Hard Merge: If data exists for the item property, **update it**

>  The "Skip item" option skips the row when an entry with the same identifier value is found in the collection, it **must not** be selected in order to join CSV files.

![CSV Join - Merging Options](/howto_images/csv/csv-merge-options.png#1587x618)

### Step 4: Export to CSV if needed

Congratulations! 🎉 You've successfully combined CSV files using a common column! If you need to use the result in another tool, click on the "Export" button to export the collection as another CSV file.

![CSV Export](/howto_images/csv/export-to-csv.png#1303x306)


### Full step by step video to join CSV files with Datablist

In the video below, the property "unique values" settings is set directly during the property creation.


<div class="content-video">
    <video controls muted playsinline width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/csv-files-join-datablist.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Join CSV files on a unique column with Datablist</div>
</div>


## Solution 2: Join CSV files using Google Sheet (or Microsoft Excel)

Spreadsheet tools have limited support for merging CSV files with a common column. However, a cell formula can find a row in another table that matches a value. When applied to each row of a table, it can search in another table and return the value from any column in the matched row.

The formula is `VLOOKUP` and is available on [Microsoft Excel](https://support.office.com/en-us/article/VLOOKUP-function-0bbc8083-26fe-4963-8ab8-93a18ad188a1) and [Google Sheet](https://support.google.com/docs/answer/3093318?hl=en).

> **Limitations**
> - With spreadsheets, **one CSV file is used as a master table and must contain all the possible values** for the join column.
> - In all the secondary tables, the join column must be the first column.


### Step 1: Load your CSV files

> In this tutorial, we use Google Sheets (the formula VLOOKUP is similar in Microsoft Excel).

Among your CSVs, choose the one with the most values to be used as the master table. The other will be called secondary CSV files.

First load your master CSV file with `File -> Import` and select your CSV (go to the `Upload` tab to use a CSV file from your computer).

In `Import Location`, select `Insert new sheet(s)`.

![Load a CSV file in Google Sheets](/howto_images/csv/google-sheets-import-csv.png#2030x1678)

Perform this operation for your secondary CSV file(s). **Each CSV file must be uploaded to a dedicated table in the the spreadsheet.**

![CSV files imported in Google Sheets](/howto_images/csv/google-sheets-import-csv-2.png#1932x1590)


### Step 2: Create new columns in the master sheet

The table with your master CSV is your master table and it will receive values from other tables.
In this master table, create new columns to store data from your other tables.

For this tutorial, we want to have the `Job Title` data from our secondary table in our master table, so we add an empty `Job Title` column.

![New Job Title Column](/howto_images/csv/google-sheets-import-csv-3.png#1932x1590)


### Step 3: Move your unique column as the first column in your secondary tables

The `VLOOKUP` formula works by searching the first column in the table it searches in. In all your **secondary** tables, (**no need to do it on the master table**), move the column to use as join column in the first position.

![Identifier Column must be the first column](/howto_images/csv/google-sheets-import-csv-4.png#1932x1590)


### Step 3: Use the VLOOKUP formula

The final step is to use the `VLOOKUP` formula to find rows in the other tables and to display a column from the matched row.

The formula takes 3 arguments:

- search_key - The value to search for. It will be the unique identifier row's value.;
- range - The range to consider for the search. **The first column in the range is searched for the key specified in search_key.** Define your whole secondary table as the range (see video).
- index - The column index in the row to be returned. The first column in the range is numbered 1.
- is_sorted - [TRUE by default] - Indicates whether the column to be searched (the first column of the specified range) is sorted. **FALSE is recommended in most cases.** If set to True and your data is not sorted, your results will be wrong!

```
VLOOKUP(search_key, range, index, [is_sorted])
```

See the video below to understand how to use `VLOOKUP` to join data on a unique column:

<div class="content-video">
    <video controls muted playsinline width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/guides/csv/join-csv/google-sheets-join-csv-vlookup.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Join CSV files on unique column with Google Sheets</div>
</div>

[Read more about the VLOOKUP formula on the Google Sheets documentation](https://support.google.com/docs/answer/3093318?hl=en).

Repeat the operation for every other column from your secondary tables 💪.


How to join CSV files by a unique identifier

Join CSV files with a common column with Datablist

Join CSV files with Google Sheets (or Microsoft Excel)

Guides

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How to remove CSV duplicates

How to join CSV files by a unique identifier