
Spreadsheet tools are limited when it comes to dealing with listings. Because they are built around worksheets and designed for accounting purposes, they have no data structure. Unlike [databases](/learn/spreadsheet/difference-between-spreadsheet-database), spreadsheet tools can't have data schema and data constraints. As a result, simple data operations like [merging duplicates](/how-to/excel-merge-duplicates) or consolidating listings are not available with spreadsheets.

Nevertheless, Microsoft Excel or Google Sheets are still our go-to applications when we have to quickly load a list of contacts, companies, etc. to perform cleaning, filtering, etc. because they are so easy to use!

Datablist aims to bring database features and data structures while borrowing spreadsheet user-friendliness.

## Datablist glossary

- **Workspace** - A Datablist account is linked to a workspace that is home to all your collections. A workspace can have any number of collections.
- **Collection** - A collection contains items that share a similar data structure. Collections can store lists of sale leads, customers, companies, partners, etc. [See collections documentation](/docs/collections).
- **Item** - An item is a data record. Items inherit their data schema from the collection they belong to.  [See items documentation](/docs/items).


## Datablist layout
![Datablist Main Layout](/docs_images/getting-started/datablist-layout.png#2159x1275)

### Sidebar

From the sidebar, you can access your collections in Datablist. Click on a collection to open it, drag and drop it on another to create a collection folder.
At the top, you see a summary of your profile information. By clicking on your profile, you can go to your company and user details page.

### Collection Header

Once on a collection, a header appears for managing it. Edit its name, icon, properties from this header. [See collections documentation](/docs/collections).


### Data Table

The data table is essential to work with data. It shows your collection data in a spreadsheet-like table. Move between cells with keyboards, use copy/pasting shortcuts, and more.

Press enter to edit a cell or click on the **Expand** icon to open an item in the detail drawer.

Select items by clicking the checkboxes on the left to delete/merge/export the items and to show the action launcher.


### Drawer

![Datablist Drawer](/docs_images/getting-started/datablist-layout-drawer.png#2154x1279)

The drawer is used to edit an item and to configure options before running an action. It's shown over the data table so it's easy to get back to the listing.

### Actions Launcher

![Datablist Actions Launcher](/docs_images/getting-started/datablist-layout-actions-launcher.png#2155x1277)

All items sharing the same data structure inside a collection open several possibilities. One of them is to run actions on your items to interact with third parties APIs. Once at least one item is selected (and you are a registered user), the actions launcher appears to show Datablist's actions catalog.

## Datablist storage basics

Datablist leverages the user's browser storage to store and manage data.

In recent years, modern web browsers have become platforms to build rich interactive applications. Email clients, spreadsheet tools, or text editors are examples of modern applications that have moved to be run inside web browsers. To support this trend, new features and capabilities were created to persistently store data inside a user's browser.


> **To know more**<br/>
> Datablist underlying storage technology is called IndexedDB. [Click here to learn more about it](https://developer.mozilla.org/en-US/docs/Web/API/IndexedDB_API).


Learn more about Datablist, a spreadsheet alternative to manage your listings.

An introduction to Datablist concepts

Spreadsheet tools are limited when it comes to dealing with listings. Because they are built around worksheets and designed for accounting purposes, they have no data structure. Unlike databases, spreadsheet tools can't have data schema and data constraints. As a result, simple data operations like merging duplicates or consolidating listings are not available with spreadsheets.

Nevertheless, Microsoft Excel or Google Sheets are still our go-to applications when we have to quickly load a list of contacts, companies, etc. to perform cleaning, filtering, etc. because they are so easy to use!

Datablist aims to bring database features and data structures while borrowing spreadsheet user-friendliness.

Datablist glossary

Workspace - A Datablist account is linked to a workspace that is home to all your collections. A workspace can have any number of collections.

Collection - A collection contains items that share a similar data structure. Collections can store lists of sale leads, customers, companies, partners, etc. See collections documentation.

Item - An item is a data record. Items inherit their data schema from the collection they belong to.  See items documentation.

Datablist layout

Datablist Main Layout

Sidebar

From the sidebar, you can access your collections in Datablist. Click on a collection to open it, drag and drop it on another to create a collection folder.
At the top, you see a summary of your profile information. By clicking on your profile, you can go to your company and user details page.

Collection Header

Once on a collection, a header appears for managing it. Edit its name, icon, properties from this header. See collections documentation.

Data Table

The data table is essential to work with data. It shows your collection data in a spreadsheet-like table. Move between cells with keyboards, use copy/pasting shortcuts, and more.

Press enter to edit a cell or click on the Expand icon to open an item in the detail drawer.

Select items by clicking the checkboxes on the left to delete/merge/export the items and to show the action launcher.

Drawer

Datablist Drawer

The drawer is used to edit an item and to configure options before running an action. It's shown over the data table so it's easy to get back to the listing.

Actions Launcher

Datablist Actions Launcher

All items sharing the same data structure inside a collection open several possibilities. One of them is to run actions on your items to interact with third parties APIs. Once at least one item is selected (and you are a registered user), the actions launcher appears to show Datablist's actions catalog.

Datablist storage basics

Datablist leverages the user's browser storage to store and manage data.

In recent years, modern web browsers have become platforms to build rich interactive applications. Email clients, spreadsheet tools, or text editors are examples of modern applications that have moved to be run inside web browsers. To support this trend, new features and capabilities were created to persistently store data inside a user's browser.

To know more
Datablist underlying storage technology is called IndexedDB. Click here to learn more about it.


What's Datablist?


In Datablist, your data is structured with collections that contain items. All items inside a collection get their data structure from the [collection properties](/docs/properties).

For example, a collection can hold people's data to manage contacts (for subscribers, leads, or employees listings). Another collection can be used to store websites (for b2b prospects, competitor websites, Venture Capital funds, etc.).

Collections are linked to your account workspace and a workspace can have an unlimited number of collections.


## Create a collection

To create a new collection, click on the **+** button in the sidebar.

When you create a new Collection, it'll be named "Untitled Collection" with an 👋 by default. You can change this title and icon at any time by clicking on the title or the icon.

![New collection](/docs_images/getting-started/datablist-create-collection.png#1402x394)

A new collection has no data structure. To start adding items, you have to create properties. [Create them manually](/docs/properties) or import CSV/Excel files to create properties based on the file columns.

## Organize your data with folders (nested collections)

When you are dealing with too many collections, it's time to organize them. An easy solution is to group them together in a folder. Click and hold a collection in the sidebar, and move it over another one to create a parent folder.

<div class="content-video">
    <video
        autoplay muted loop playsinline controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/docs/collections/datablist-collection-folder.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Drag and drop to create folder</div>
</div>

## Delete a collection

To delete a collection, its properties, and all its items, select "Delete" in the menu available for each collection in the sidebar.

> **Important:** Datablist data structure is hierarchical. **Deleting a collection delete all its items.**

![Delete a collection](/docs_images/getting-started/datablist-delete-collection.png#1410x656)


### Deleting a collection folder.

The "delete" action is also available for collection folders. When deleting a collection folder, **all its collections and items will be deleted.**


Learn how to use collections to store contacts, leads, employees, or any kind of structured data.

Create, edit, delete collections in Datablist

In Datablist, your data is structured with collections that contain items. All items inside a collection get their data structure from the collection properties.For example, a collection can hold people's data to manage contacts (for subscribers, leads, or employees listings). Another collection can be used to store websites (for b2b prospects, competitor websites, Venture Capital funds, etc.).Collections are linked to your account workspace and a workspace can have an unlimited number of collections.Create a collectionTo create a new collection, click on the + button in the sidebar.When you create a new Collection, it'll be named "Untitled Collection" with an 👋 by default. You can change this title and icon at any time by clicking on the title or the icon.New collectionA new collection has no data structure. To start adding items, you have to create properties. Create them manually or import CSV/Excel files to create properties based on the file columns.Organize your data with folders (nested collections)When you are dealing with too many collections, it's time to organize them. An easy solution is to group them together in a folder. Click and hold a collection in the sidebar, and move it over another one to create a parent folder.Delete a collectionTo delete a collection, its properties, and all its items, select "Delete" in the menu available for each collection in the sidebar.Important: Datablist data structure is hierarchical. Deleting a collection delete all its items.Delete a collectionDeleting a collection folder.The "delete" action is also available for collection folders. When deleting a collection folder, all its collections and items will be deleted.


Create Collections


Datablist lets you create flexible data structures for your collections with properties. Properties are used to store data and to sort and filter your items.

A property is defined with the following:
- **Name** - The name to be shown in the [data table](/docs/introduction#data-table) and the [drawer](/docs/introduction#drawer).
- **Data type** - What kind of data will be stored with the property. [Types can be texts, numbers, checkboxes, etc.](#create-a-new-property) Depending on its type, Datablist will perform data validation to ensure data input from users matches the data type.
- **Data constraint** - Unlike spreadsheet tools, it is possible to add data constraints on your property to [ensure unique values](#ensure-unique-values-for-a-property).

## Create a new property

Data structures for collections are flexible. You can create any number of custom properties that will be available to store values for your items.

Different property types are available to store texts, numbers, URLs, dates, etc. Depending on the property type, data validation will be done to ensure data is consistent.

On a new and empty collection, a shortcut button is shown to create a new property. Otherwise, click the **Manage properties** button on your [collection header](/docs/introduction#collection-header).

![Manage Properties](/docs_images/getting-started/datablist-manage-properties.png#1380x646)

![Add new property](/docs_images/getting-started/datablist-add-property.png#1452x684)

![Create new property](/docs_images/getting-started/datablist-new-property.png#1768x922)


### Data Types
#### Text

Simple text field for short and single line text.


#### Long Text

The Long-Text field is perfect for multiline text. Use it to store notes or descriptions. You can edit a long-text field value directly without leaving the [data table](/docs/introduction#data-table). Just press "Enter" to display a large editable text zone.

![Long Text edit in Data Table](/docs_images/getting-started/datablist-long-text.png#1702x882)

#### Email

Store email addresses. Open default mail client on click.

> **Validation:** Must be a valid email address.

#### Checkbox

A Checkbox is great to represent binary data: true/false or yes/no.

True values appear with a green checkmark. Empty and false values appear with an uncheck mark.
Clicking on the checkbox toggles its value.

![Checkbox in Data Table](/docs_images/getting-started/datablist-checkbox.png#1582x896)


#### Url

Clicking on a URL within a URL field will open up that page.

> **Validation:** Must be a valid url address.

#### Number

Data type to store numeric values. Numbers can be written with or without decimals and can be positive or negative.

> **Validation:** Must be a valid number

#### Date

Store date values (Day, Month, Year). When editing a date, a calendar widget is displayed to make it easy to select a specific date.

![Date in Data Table](/docs_images/getting-started/datablist-date.png#1580x912)

#### DateTime

Almost like date but store time in addition to the date (Day, Month, Year - Hour, Minute, Second).

![DateTime in Data Table](/docs_images/getting-started/datablist-datetime.png#1578x892)

#### Phone

Store phone numbers. Shortcut to open a phone client on click.

## Edit or delete property

To edit or delete a property, click the **Manage properties** button on your [collection header](/docs/introduction#collection-header), and click the **Edit or delete property button**.

Property name, data type and data constraint can be edited at any time.

> **Warning:** Deleting a property will delete the item's property's values.

![DateTime in Data Table](/docs_images/getting-started/datablist-edit-property.png#1556x886)

## Ensure unique values for a property

This enables powerful use cases like [prospect lists](/blog/prospect-list-building-growth-hacking) (email or website URL uniqueness), CRM, or user databases (email uniqueness). With this feature, you can combine listings based on a unique identifier.

When a property has a unique constraint, duplicate items will be avoided during [imports](/docs/import-data) and [manual creation](/docs/items#create-items-manually).

![Unique values constraint](/docs_images/getting-started/datablist-unique-values.png#1452x756)

Passport Numbers, Social Security Numbers, Email Addresses, Website URLs, Business Identification numbers, [Product GTIN](https://www.gs1.org/standards/id-keys/gtin), and the likes are good candidates to be defined with a unique constraint.


## Built-in properties

Datablist comes with two built-in properties:

- **Created At** - A DateTime property storing when the item has been created.
- **Updated At** - A DateTime property that tracks item last modification timestamp.

> **Note:** Those values are always tracked for your items, whether you choose to display their properties or not.

To display built-in properties, click on "Add property" and click on the wanted property.

![Add built-in property](/docs_images/getting-started/datablist-add-built-in-property.png#1696x938)

## Reorder properties

Properties are ranked. Their order is used both for [data table](/docs/introduction#data-table) columns display and for the [detail drawer](/docs/introduction#drawer).

To change your property positions, click the **Manage properties** button on your [collection header](/docs/introduction#collection-header) and use your mouse to drag the "Move" icon higher or lower.

![Reorder properties](/docs_images/getting-started/datablist-reorder-properties.png#2880x1800)


## Display or hide properties from data table

All collection properties are visible in the [item detail drawer](/docs/introduction#drawer). However, properties can be hidden from the [data table](/docs/introduction#data-table) to make the listing more readable.

New properties are visible in the data table by default. To hide a property or to show a hidden one, click on the "Manage Properties" buttons in the collection header and click the visibility toggle.

![Property Data Table visibility](/docs_images/getting-started/datablist-hide-property.png#1568x904)


## Change property column width

To resize a column, use your mouse to drag the column border to a new position, resizing it in the process.

![Resize column width](/docs_images/getting-started/datablist-resize-column-width.png#2880x1800)


## Advanced options

### Property Key

When creating a new property, under the "More options" section, a **Key** setting is available. The **key** is a "slug" string to be used in the [API](https://developers.datablist.com). It is autogenerated from the property's name and can be edited only when creating a new property. Once the propriety is created, the key becomes read-only.


Properties

Datablist lets you create flexible data structures for your collections with properties. Properties are used to store data and to sort and filter your items.

A property is defined with the following:

Name - The name to be shown in the data table and the drawer.

Data type - What kind of data will be stored with the property. Types can be texts, numbers, checkboxes, etc. Depending on its type, Datablist will perform data validation to ensure data input from users matches the data type.

Data constraint - Unlike spreadsheet tools, it is possible to add data constraints on your property to ensure unique values.

Create a new property

Data structures for collections are flexible. You can create any number of custom properties that will be available to store values for your items.

Different property types are available to store texts, numbers, URLs, dates, etc. Depending on the property type, data validation will be done to ensure data is consistent.

On a new and empty collection, a shortcut button is shown to create a new property. Otherwise, click the Manage properties button on your collection header.

Manage Properties

Add new property

Create new property

Data Types

Text

Simple text field for short and single line text.

Long Text

The Long-Text field is perfect for multiline text. Use it to store notes or descriptions. You can edit a long-text field value directly without leaving the data table. Just press "Enter" to display a large editable text zone.

Long Text edit in Data Table

Email

Store email addresses. Open default mail client on click.

Validation: Must be a valid email address.

Checkbox

A Checkbox is great to represent binary data: true/false or yes/no.

True values appear with a green checkmark. Empty and false values appear with an uncheck mark.
Clicking on the checkbox toggles its value.

Checkbox in Data Table

Url

Clicking on a URL within a URL field will open up that page.

Validation: Must be a valid url address.

Number

Data type to store numeric values. Numbers can be written with or without decimals and can be positive or negative.

Validation: Must be a valid number

Date

Store date values (Day, Month, Year). When editing a date, a calendar widget is displayed to make it easy to select a specific date.

Date in Data Table

DateTime

Almost like date but store time in addition to the date (Day, Month, Year - Hour, Minute, Second).

DateTime in Data Table

Phone

Store phone numbers. Shortcut to open a phone client on click.

Edit or delete property

To edit or delete a property, click the Manage properties button on your collection header, and click the Edit or delete property button.

Property name, data type and data constraint can be edited at any time.

Warning: Deleting a property will delete the item's property's values.

DateTime in Data Table

Ensure unique values for a property

This enables powerful use cases like prospect lists (email or website URL uniqueness), CRM, or user databases (email uniqueness). With this feature, you can combine listings based on a unique identifier.

When a property has a unique constraint, duplicate items will be avoided during imports and manual creation.

Unique values constraint

Passport Numbers, Social Security Numbers, Email Addresses, Website URLs, Business Identification numbers, Product GTIN, and the likes are good candidates to be defined with a unique constraint.

Built-in properties

Datablist comes with two built-in properties:

Created At - A DateTime property storing when the item has been created.

Updated At - A DateTime property that tracks item last modification timestamp.

Note: Those values are always tracked for your items, whether you choose to display their properties or not.

To display built-in properties, click on "Add property" and click on the wanted property.

Add built-in property

Reorder properties

Properties are ranked. Their order is used both for data table columns display and for the detail drawer.

To change your property positions, click the Manage properties button on your collection header and use your mouse to drag the "Move" icon higher or lower.

Reorder properties

Display or hide properties from data table

All collection properties are visible in the item detail drawer. However, properties can be hidden from the data table to make the listing more readable.

New properties are visible in the data table by default. To hide a property or to show a hidden one, click on the "Manage Properties" buttons in the collection header and click the visibility toggle.

Property Data Table visibility

Change property column width

To resize a column, use your mouse to drag the column border to a new position, resizing it in the process.

Resize column width

Advanced options

Property Key

When creating a new property, under the "More options" section, a Key setting is available. The key is a "slug" string to be used in the API. It is autogenerated from the property's name and can be edited only when creating a new property. Once the propriety is created, the key becomes read-only.


Configure Properties


The most basic concept in Datablist is the **item**. Each item is represented with a row in the [data table](/docs/introduction). An item can represent anything: a contact, a lead, an employee, a website, etc.

Items are either created manually, by API, or by [importing an external file](/docs/import-data).

## Create items manually

Create a new item with the **New** button in the collection header.

> **Warning:** Your collection must have [properties](/docs/properties) before adding items manually.

![New item](/docs_images/getting-started/datablist-new-item.png#1344x588)

It opens the [drawer](/docs/introduction#drawer) on the right. Save your item quickly by pressing the **Enter** key or cancel and close the drawer with the **Esc** key.

![New item drawer](/docs_images/getting-started/datablist-new-item-drawer.png#2263x1416)

> **Keyboard Shortcut**<br/>
> The fastest way to create an item is with the keyboard shortcut **C** which will open up the new item drawer in your current collection.

### Data validation

Before creating an item, Datablist ensures the item's data validates all its [property data types](/docs/properties#data-types) and [data constraints](/docs/properties#ensure-unique-values-for-a-property).

![Property Validation](/docs_images/getting-started/datablist-item-property-validation.png#1476x708)

## Edit an item

To edit an item, you can open the detail view in the drawer or edit it directly in the table view with our spreadsheet-like interface.

**Edit directly**

On a cell, press **Enter** to edit an item and **Enter** or **Tab** to submit the changes. Press **Esc** to cancel them.

<div class="content-video">
    <video
        autoplay muted loop playsinline controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/docs/items/datablist-edit-data-table.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Edit by pressing Enter</div>
</div>


**Edit item in drawer**

Sometimes, having a form view to edit your items is more convenient. For example, if some of your [collection properties](/docs/properties) have been hidden from the data table. In the drawer, all item properties are shown.

![Open Drawer](/docs_images/getting-started/datablist-open-item.png#1322x542)


## Select items and top bar

On the left of the items, you can find a checkbox to select items. When at least one item is selected, buttons appear in a top bar.

Available buttons:
- **Delete items** - Will permanently delete selected items
- **Export items to CSV** - Generate a CSV file with the selected items
- **Merge items** - [Open Datablist Merging Assistant](/docs/merge-items)
- **Unselect** - Cancel selection

![Top bar buttons](/docs_images/getting-started/datablist-items-selected-bar.png#1378x558)

### Quick select

To quickly select items, you can use your mouse by clicking and holding it while moving over other checkboxes. Another solution is to check one checkbox and click on another one while pressing the **Shift** key. All items in between will be selected.

<div class="content-video">
    <video
        autoplay muted loop playsinline controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/docs/items/datablist-select-items.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Quick select items</div>
</div>

> **Tip:** Use the keyboard shortcut **Ctrl + a** to select all items


### Items count on search term or filtered listing

Datablist doesn't calculate the data table count when you use a [search term](#search-and-sort-items) or [filtered conditions](#filter-items) by default (for performance reasons).

But you can ask Datablist to run a count operation by clicking the "Select all" checkbox and then on the "All items selected" label.

![Count all items](/docs_images/getting-started/items/datablist-count-selected-items.png#1416x708)


## Delete items

To delete one or several items, select them with the left checkbox and use the **Delete items** top bar button.

> **Warning:** While it's possible to undo delete item operations while on the page, deleted items are **not** moved to trash but are permanently deleted. Please export your data before clearing a large amount of data.


Learn how to create new item and manage existing data in your Datablist collection.

Add, edit and delete items in Datablist

The most basic concept in Datablist is the item. Each item is represented with a row in the data table. An item can represent anything: a contact, a lead, an employee, a website, etc.Items are either created manually, by API, or by importing an external file.Create items manuallyCreate a new item with the New button in the collection header.Warning: Your collection must have properties before adding items manually.New itemIt opens the drawer on the right. Save your item quickly by pressing the Enter key or cancel and close the drawer with the Esc key.New item drawerKeyboard Shortcut
The fastest way to create an item is with the keyboard shortcut C which will open up the new item drawer in your current collection.Data validationBefore creating an item, Datablist ensures the item's data validates all its property data types and data constraints.Property ValidationEdit an itemTo edit an item, you can open the detail view in the drawer or edit it directly in the table view with our spreadsheet-like interface.Edit directlyOn a cell, press Enter to edit an item and Enter or Tab to submit the changes. Press Esc to cancel them.Edit item in drawerSometimes, having a form view to edit your items is more convenient. For example, if some of your collection properties have been hidden from the data table. In the drawer, all item properties are shown.Open DrawerSelect items and top barOn the left of the items, you can find a checkbox to select items. When at least one item is selected, buttons appear in a top bar.Available buttons:Delete items - Will permanently delete selected itemsExport items to CSV - Generate a CSV file with the selected itemsMerge items - Open Datablist Merging AssistantUnselect - Cancel selectionTop bar buttonsQuick selectTo quickly select items, you can use your mouse by clicking and holding it while moving over other checkboxes. Another solution is to check one checkbox and click on another one while pressing the Shift key. All items in between will be selected.Tip: Use the keyboard shortcut Ctrl + a to select all itemsItems count on search term or filtered listingDatablist doesn't calculate the data table count when you use a search term or filtered conditions by default (for performance reasons).But you can ask Datablist to run a count operation by clicking the "Select all" checkbox and then on the "All items selected" label.Count all itemsDelete itemsTo delete one or several items, select them with the left checkbox and use the Delete items top bar button.Warning: While it's possible to undo delete item operations while on the page, deleted items are not moved to trash but are permanently deleted. Please export your data before clearing a large amount of data.


Add and manage items


Datablist provides powerful features to browse your items. Among them:

- [Full-text search](#full-text-search)
- [Sort items](#sort-items)
- [Filter items](#filter-items)

## Full-text search

Searching through your items 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**.

![Full-text search](/docs_images/getting-started/filtering/datablist-full-search.png#1956x650)

## Sort items

Items can be sorted on any visible property by clicking on the property column. Property values can be sorted in ascending or descending order.

Sort items using the drop-down menu available on each column.

![Sort items by property](/docs_images/getting-started/filtering/datablist-items-sort.png#1827x736)

Or using the "Manage Order" button.

![Sort items using the Order By menu](/docs_images/getting-started/filtering/datablist-items-sort-2.png#1412x905)

The first time you sort by a property, Datablist asks you to index your property data. Just click "Add an index". Datablist will process all the data from the selected property and create an internal index. This index will be then used to rank your items.

This index will be automatically updated when you add new items or update existing ones.

![Indexing Data](/docs_images/getting-started/filtering/datablist-sort-indexing.png#1600x818)

An arrow is shown next to the column name when a column is sorted.

![Sort results](/docs_images/getting-started/filtering/datablist-sort-results.png#1854x822)


### Change sorting direction

To the change sorting direction, click again on the "Sort by XX" menu item.

![Change sorting direction](/docs_images/getting-started/filtering/datablist-change-sorting.png#1404x788)

Or from the "Manage Ordering" menu.

![Change direction from "Manage Ordering" menu](/docs_images/getting-started/filtering/datablist-change-sorting-2.png#1482x684)

### Remove sorting

To remove any sorting condition and switch back to the default sorting, click on "Remove sorting" from the column menu.

![Remove sorting](/docs_images/getting-started/filtering/datablist-remove-sorting.png#1256x608)

Or click on "Default" in the "Manage Ordering" menu.

![Order by default rank](/docs_images/getting-started/filtering/datablist-default-sorting.png#1170x630)


> **Notes**<br />
> Order by default is a rank by newly added items.


## Filter items

[Full-text search](#search-and-sort-items) looks for a term in all available properties. To filter your data on specific properties, use **Filter Conditions**.

Filtering is useful to:
- Find items that meet certain conditions
- Have a clean listing by excluding items
- [Filter a CSV file to extract specific rows](/csv-editor)

Filtering works by creating filter conditions on one or several properties.

### Add a filter condition

Add a filter condition by clicking the "Change filters" button in the [collection header](/docs/introduction#collection-header).

![Open Filters modal](/docs_images/getting-started/filtering/datablist-add-filter-condition.png#2206x649)

A filter condition is made of 3 elements:

- **A Property** - Any property in your collection.
- **An Operator** - The filter operator specifies how to compare the data with your filter value. Depending on the property data type, operators can be: *contains*, *is*, *is empty*, *is not empty*, numerical operators, date comparators, etc.
- **A Value** - The value to compare your item's property data against.

> **Note**<br />
> Operators "**is empty**" and "**is not empty**" do not require a value.

**Click the "Apply" button to run your filter condition against your collection items.**

![Filters modal](/docs_images/getting-started/filtering/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.

#### RegEx filtering examples

You can use a Regular Expression to filter your items. Text values that match against the Regular Expression are returned.

> **Note**: Datablist adds the `i` (case insensitive) flag to the Regular Expression.

For example, to find texts containing at least one digit, you can use the RegEx `\d`.

![RegEx Filter modal](/docs_images/getting-started/filtering/filtering-regex.png#1984x1170)

![RegEx Results](/docs_images/getting-started/filtering/filtering-regex-results.png#1666x1012)

Here are some RegEx examples:

- Match texts with at least `5` words: `(?:\w+(?:\s|$)){5,}`
- Match valid email addresses: `^([a-zA-Z0-9._%-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})$`
- Match invalid email addresses: `^(?!([a-zA-Z0-9._%-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})).*$`
- Match valid URLs: `^(?:http(s)?:\/\/)?[\w.-]+(?:\.[\w\.-]+)+[\w\-\._~:/?#[\]@!\$&'\*\+,;=.]+$`
- Match invalid URLs: `^(?!(?:http(s)?:\/\/)?[\w.-]+(?:\.[\w\.-]+)+[\w\-\._~:/?#[\]@!\$&'\*\+,;=.]+).*$`

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

#### Relative datetime filtering

Relative Filtering works by comparing the date to the current day.

You define three parts:

- `Next` or `Last`
- A number
- A duration term: hours, days, months, years

For example: "Last 2 days".

![DateTime absolute filtering](/docs_images/getting-started/filtering/datetime/datablist-datetime-absolute-filtering.png#1894x998)

![DateTime relative filtering](/docs_images/getting-started/filtering/datetime/datablist-datetime-relative-filtering.png#1872x890)


### 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`


### Combining several filter conditions

Filter conditions work in **AND** or **OR** mode.

In **AND** mode, only items matching **all** filter conditions are displayed in your data table.
In **OR** mode, items matching at least one filter condition are displayed in your data table.

Use the "Add another filter" to add another filter condition.

![Combine several filter conditions](/docs_images/getting-started/filtering/datablist-combine-filter-condition-2.png#1914x856)

Filter conditions can be grouped into "Filter Groups". Filter Groups let you combine **AND** and **OR** modes.

For example, you can combine two filter groups with a **AND** mode. And inside each filter group, use **OR** mode.

![Using filter groups](/docs_images/getting-started/filtering/filter-groups.png#1882x1360)




> **Feedback Wanted!** <br />
> Do you need more complex filtering? [Please share your use case with us](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link).


### Remove filter conditions

The filtering button is renamed "Filtered" and the icon is full when the collection is filtered.

![DataTable with filter conditions](/docs_images/getting-started/filtering/datablist-with-filters-ui-change.png#1720x476)

To remove a filter condition, open the filter modal, click the "trash can" button, and submit with the **Apply** button.

![Remove a filter condition](/docs_images/getting-started/filtering/datablist-remove-filter-condition.png#1862x1050)

### Saved Filters

With Saved Filters, you can save your current filters for quick access. This is useful to preserve complex filters that you frequently use. Or to create **Segments** of your data.

This simple feature will boost your productivity and help you manage your collections.

**Saved Filters** are accessible in the down arrow next to the [filters button](#add-a-filter-condition). **And they are shared with your team members**.

When you have at least one filter condition, a "Save filters" action is available to save them.

> **Notes**<br/>
> Saved Filters only store ["filters"](#filter-items) and not the [full-text search](#full-text-search) term.

![Create Saved Filter](/docs_images/getting-started/filtering/create-saved-filters.png#1602x570)

A save filter takes a name, and will automatically attach your current filter conditions to it.

![Name Saved Filter](/docs_images/getting-started/filtering/name-saved-filter.png#1452x1060)

Once created, or after you select previously created "Saved Filter", the menu will change to display 3 actions:

- **Stop filtering on "X"** - This action clears the current filtering conditions. The Saved Filter is not edited. All the filtering conditions from the Saved Filter will be used once again when you select it.
- **Rename "X"** - To change the name on your Saved Filter. The name is only used to list them in the menu.
- **Delete "X"** - Delete the Saved Filter. This action cannot be undone.

![Saved Filter Actions](/docs_images/getting-started/filtering/saved-filter-actions.png#1412x738)

To edit the filtering conditions of a Saved Filter, open the filtering modal and add, edit, or remove conditions. Any change will display a "Edit saved filter" checkbox next to the "Apply" button.



![Edit Saved Filter](/docs_images/getting-started/filtering/edit-saved-filter.png#1974x882)


### Filtering on built-in properties

Datablist comes with built-in properties (createdAt, updatedAt) which can be filtered as any other property. To be able to create filter conditions on your built-in properties, you must add them to your collection properties. [See our documentation on built-in properties](/docs/properties#built-in-properties).

### Filtering and data export

The export button in the [collection header](/docs/introduction#collection-header) exports the full collection items. To export only your filtered items, use the **Select all** checkbox and click the "Export items to CSV/Excel" button.

![Export filtered items](/docs_images/getting-started/items/datablist-export-filtered-items.png#1766x546)




Learn how to create search and filter your Datablist collections

Search and Filter items

Datablist provides powerful features to browse your items. Among them:

Full-text search

Sort items

Filter items

Full-text search

Searching through your items 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.

Full-text search

Sort items

Items can be sorted on any visible property by clicking on the property column. Property values can be sorted in ascending or descending order.

Sort items using the drop-down menu available on each column.

Sort items by property

Or using the "Manage Order" button.

Sort items using the Order By menu

The first time you sort by a property, Datablist asks you to index your property data. Just click "Add an index". Datablist will process all the data from the selected property and create an internal index. This index will be then used to rank your items.

This index will be automatically updated when you add new items or update existing ones.

Indexing Data

An arrow is shown next to the column name when a column is sorted.

Sort results

Change sorting direction

To the change sorting direction, click again on the "Sort by XX" menu item.

Change sorting direction

Or from the "Manage Ordering" menu.

Change direction from "Manage Ordering" menu

Remove sorting

To remove any sorting condition and switch back to the default sorting, click on "Remove sorting" from the column menu.

Remove sorting

Or click on "Default" in the "Manage Ordering" menu.

Order by default rank

Notes
Order by default is a rank by newly added items.

Filter items

Full-text search looks for a term in all available properties. To filter your data on specific properties, use Filter Conditions.

Filtering is useful to:

Find items that meet certain conditions

Have a clean listing by excluding items

Filter a CSV file to extract specific rows

Filtering works by creating filter conditions on one or several properties.

Add a filter condition

Add a filter condition by clicking the "Change filters" button in the collection header.

Open Filters modal

A filter condition is made of 3 elements:

A Property - Any property in your collection.

An Operator - The filter operator specifies how to compare the data with your filter value. Depending on the property data type, operators can be: contains, is, is empty, is not empty, numerical operators, date comparators, etc.

A Value - The value to compare your item's property data against.

Note
Operators "is empty" and "is not empty" do not require a value.

Click the "Apply" button to run your filter condition against your collection items.

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)

"   john  " (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

"    " (spaces) match

"" match

is not empty - Opposite of "is empty".

regexp - Insensitive Regex matching. See below.

RegEx filtering examples

You can use a Regular Expression to filter your items. Text values that match against the Regular Expression are returned.

Note: Datablist adds the i (case insensitive) flag to the Regular Expression.

For example, to find texts containing at least one digit, you can use the RegEx \d.

RegEx Filter modal

RegEx Results

Here are some RegEx examples:

Match texts with at least 5 words: (?:\w+(?:\s|$)){5,}

Match valid email addresses: ^(\[a-zA-Z0-9.\_%-]+@\[a-zA-Z0-9.-]+\\.\[a-zA-Z]{2,})$

Match invalid email addresses: ^(?!(\[a-zA-Z0-9.\_%-]+@\[a-zA-Z0-9.-]+\\.\[a-zA-Z]{2,})).\*$

Match valid URLs: ^(?:http(s)?:\\/\\/)?\[\w.-]+(?:\\.\[\w\\.-]+)+\[\w\\-\\.\_~:/?#\[\\]@!\\$&'\$\$\\\*\\+,;=.]+$

Match invalid URLs: ^(?!(?:http(s)?:\\/\\/)?\[\w.-]+(?:\\.\[\w\\.-]+)+\[\w\\-\\.\_~:/?#\[\\]@!\\$&'\$\$\\\*\\+,;=.]+).\*$

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

Relative datetime filtering

Relative Filtering works by comparing the date to the current day.

You define three parts:

Next or Last

A number

A duration term: hours, days, months, years

For example: "Last 2 days".

DateTime absolute filtering

DateTime relative filtering

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

Combining several filter conditions

Filter conditions work in AND or OR mode.

In AND mode, only items matching all filter conditions are displayed in your data table.
In OR mode, items matching at least one filter condition are displayed in your data table.

Use the "Add another filter" to add another filter condition.

Combine several filter conditions

Filter conditions can be grouped into "Filter Groups". Filter Groups let you combine AND and OR modes.

For example, you can combine two filter groups with a AND mode. And inside each filter group, use OR mode.

Using filter groups

Feedback Wanted!&#x20;
Do you need more complex filtering? Please share your use case with us.

Remove filter conditions

The filtering button is renamed "Filtered" and the icon is full when the collection is filtered.

DataTable with filter conditions

To remove a filter condition, open the filter modal, click the "trash can" button, and submit with the Apply button.

Remove a filter condition

Saved Filters

With Saved Filters, you can save your current filters for quick access. This is useful to preserve complex filters that you frequently use. Or to create Segments of your data.

This simple feature will boost your productivity and help you manage your collections.

Saved Filters are accessible in the down arrow next to the filters button. And they are shared with your team members.

When you have at least one filter condition, a "Save filters" action is available to save them.

Notes
Saved Filters only store "filters" and not the full-text search term.

Create Saved Filter

A save filter takes a name, and will automatically attach your current filter conditions to it.

Name Saved Filter

Once created, or after you select previously created "Saved Filter", the menu will change to display 3 actions:

Stop filtering on "X" - This action clears the current filtering conditions. The Saved Filter is not edited. All the filtering conditions from the Saved Filter will be used once again when you select it.

Rename "X" - To change the name on your Saved Filter. The name is only used to list them in the menu.

Delete "X" - Delete the Saved Filter. This action cannot be undone.

Saved Filter Actions

To edit the filtering conditions of a Saved Filter, open the filtering modal and add, edit, or remove conditions. Any change will display a "Edit saved filter" checkbox next to the "Apply" button.

Edit Saved Filter

Filtering on built-in properties

Datablist comes with built-in properties (createdAt, updatedAt) which can be filtered as any other property. To be able to create filter conditions on your built-in properties, you must add them to your collection properties. See our documentation on built-in properties.

Filtering and data export

The export button in the collection header exports the full collection items. To export only your filtered items, use the Select all checkbox and click the "Export items to CSV/Excel" button.

Export filtered items



Datablist has three ways to import data:
- [Using external files](#import-from-a-csv-or-excel-file)
- With [copy/pasting](#import-with-copypasting) from spreadsheet tools or tabular data
- Or using [Datablist API](https://developers.datablist.com)

## Import from a CSV or Excel file

Datablist is a great [CSV viewer and editor](/csv-editor). Use CSV and Excel files to import data into your collection. Select the collection where your data will be imported or [create a new collection](/docs/collections#create-a-collection) if needed.

![Import CSV/Excel file](/docs_images/import-export/datablist-import-csv-excel.png#1966x924)

On the first step, click the load zone to select a file from your computer, or drag and drop a file to get started. Both CSV and Excel files are accepted:
- CSV files must have a `.csv` file extension.
- Excel files must have one of the following file extensions: `.xlsx`, `.xls`, `.xlsm`, `.xlsb`, `.ods`.

> **Notes on importing an Excel file:**<br/>
> - Cells with formulas are imported with computed values
> - Empty rows are skipped. If headers are on the second row, it will work.

![Drag and Drop file](/docs_images/import-export/datablist-import-drag-and-drop.png#2177x1186)


### Create Properties on empty collection

When starting with an empty collection, the File Import assistant lists the columns from your file.

![Columns Import](/docs_images/import-export/datablist-import-create-properties.png#2126x1060)

Datablist analyses the data from your file to detect each column's data type. **Property types are guessed based on the first 100 items read on the file.** For example:

- Columns with valid emails (such as john.smith@example.com) are defined as **Email**
- Columns with full URLs (with HTTP, HTTPS or FTP protocol) are defined as **URLs**
- Columns with text longer than 70 characters are defined as **Long Text**
- Columns with valid [ISO 8601](https://en.wikipedia.org/wiki/ISO_8601) DateTime formats are defined as **DateTime**
- Columns with `Yes`, `No`, `1`, `0`, `True`, `False`, `Checked` are detected as **Checkboxes**
- Columns with valid numbers are detected as **Number**

To change the Data Type or to import column as another name, click the Data Type button.

![Edit Data Type](/docs_images/import-export/datablist-import-create-properties-edit.png#2126x1064)


### Map columns if collection has properties

On an existing collection with at least one property, you will be asked to map the file columns with your collection properties. When listing your file columns, Datablist will try to auto map the columns with existing collection properties.

> Notes: The auto-mapping algorithm works by removing all non-alphanumeric characters and comparing columns with property names in lower case

You can map one of your columns with the built-in **createdAt** property to set the items created timestamps. To do so, [add the built-in property to your collection](/docs/properties#built-in-properties). Then during the import process, map your file column to the property.

![Map Property](/docs_images/import-export/datablist-import-map.png#1928x1184)

If a column doesn't have a matching property, you can create new properties directly based on the file columns. To create a new property to match your file column, click the **+** button next to the property mapping select.

![Auto Create New Property](/docs_images/import-export/datablist-import-new-property.png#1296x372)

A modal will open to create a property. The name is set based on the column and a property type is suggested.


#### Property Settings

Property settings are accessible during the File Import process:

- Property name
- Property type
- **Do not allow duplicate values** - When one of your collection properties has a ["Unique Values option"](/docs/properties#ensure-unique-values-for-a-property), a **Merging Option** step will be shown. [Please check the bulk updates documentation page to know more.](/docs/bulk-update-data)

![New Property Settings](/docs_images/import-export/datablist-import-new-property-modal.png#1544x848)



### Converting to data types

After creating or mapping your columns, Datablist will parse and convert your lines according to the property data types.

- **Checkbox** - `Yes`, `No`, `1`, `0`, `True`, `False`, `Checked` (case insensitive) are converted to checkbox values
- **DateTime** - Must respect the [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601). If not, data might be converted to some weird dates.
- **Number** - Numeric values must be written with numerical digits. Ex: `100` or `300.49` are valid, `one hundred` is **not** valid. **Decimal must use a dot separator.**

If imported data can't be converted to data type, data is passed as a Text. Review your data to detect any error and then click **Import items** to run the import process.

![Text to data types parsing](/docs_images/import-export/datablist-import-convertion.png#2118x1252)


### Run the import

Once ready, run the import to create the items. When the import is finished, a report is available with the number of items created.

![Import Report](/docs_images/import-export/datablist-import-complete.png#1554x888)


> **Note** <br />
> Loading a big CSV file takes time. Expect 5 minutes to load a 1 million lines CSV file. To cancel the loading process before the end, click the "Stop Import" button. It will stop the import process.


## Import with copy/pasting

To help you import data from any spreadsheet tools to Datablist, you can use copy/pasting. This feature allows you to import data from Google Sheets, Microsoft Excel, Apple Numbers, etc.

![Copy Google Sheets rows](/docs_images/import-export/datablist-copy-pasting-from-google-sheets.png#2022x1410)

In Datablist, select the collection where your data will be imported or [create a new collection](/docs/collections#create-a-collection) if needed. Then paste your data by selecting "Edit -> Paste" in your browser menu bar or using the **Ctrl + v** keyboard shortcut. A modal will open to configure the data import.

First, if your pasted data include headers/columns, set "**First row contains headers**" to true. The first row will be skipped and used to map or create properties.

![Paste on Datablist](/docs_images/import-export/datablist-import-copy-pasting.png#2230x1220)

If your collection has properties, map the columns with them. If you need to create new properties that match the columns, click the **+** button.

![Create a property](/docs_images/import-export/datablist-pasting-auto-property.png#1448x590)

When your pasted data contains headers, a property will be automatically created using your header name. [You will be able to edit it later.](/docs/properties#edit-or-delete-property)

For data without headers, clicking the **+** button will show an input to enter a property name.

![Create a property without header](/docs_images/import-export/datablist-paste-unknown-property.png#1682x740)

Once ready, click **Import** to run the import process.


> **Note:** Pasting data containing a single line will overwrite selected cell text


## Import Limits

Anonymous users have a 10,000 items import limit. [Create a free account](https://app.datablist.com/auth/signup) to remove the limit.

For registered users, import works with CSV and Excel files up to 1,5 million lines (1 million in [free plan](/pricing)).


Learn how to import data from Microsoft Excel or spreadsheet tools into your Datablist collection.

Importing Data into Datablist

Datablist has three ways to import data:

Using external files

With copy/pasting from spreadsheet tools or tabular data

Or using Datablist API

Import from a CSV or Excel file

Datablist is a great CSV viewer and editor. Use CSV and Excel files to import data into your collection. Select the collection where your data will be imported or create a new collection if needed.

Import CSV/Excel file

On the first step, click the load zone to select a file from your computer, or drag and drop a file to get started. Both CSV and Excel files are accepted:

CSV files must have a .csv file extension.

Excel files must have one of the following file extensions: .xlsx, .xls, .xlsm, .xlsb, .ods.

Notes on importing an Excel file:

Cells with formulas are imported with computed values

Empty rows are skipped. If headers are on the second row, it will work.

Drag and Drop file

Create Properties on empty collection

When starting with an empty collection, the File Import assistant lists the columns from your file.

Columns Import

Datablist analyses the data from your file to detect each column's data type. Property types are guessed based on the first 100 items read on the file. For example:

Columns with valid emails (such as john.smith@example.com) are defined as Email

Columns with full URLs (with HTTP, HTTPS or FTP protocol) are defined as URLs

Columns with text longer than 70 characters are defined as Long Text

Columns with valid ISO 8601 DateTime formats are defined as DateTime

Columns with Yes, No, 1, 0, True, False, Checked are detected as Checkboxes

Columns with valid numbers are detected as Number

To change the Data Type or to import column as another name, click the Data Type button.

Edit Data Type

Map columns if collection has properties

On an existing collection with at least one property, you will be asked to map the file columns with your collection properties. When listing your file columns, Datablist will try to auto map the columns with existing collection properties.

Notes: The auto-mapping algorithm works by removing all non-alphanumeric characters and comparing columns with property names in lower case

You can map one of your columns with the built-in createdAt property to set the items created timestamps. To do so, add the built-in property to your collection. Then during the import process, map your file column to the property.

Map Property

If a column doesn't have a matching property, you can create new properties directly based on the file columns. To create a new property to match your file column, click the + button next to the property mapping select.

Auto Create New Property

A modal will open to create a property. The name is set based on the column and a property type is suggested.

Property Settings

Property settings are accessible during the File Import process:

Property name

Property type

Do not allow duplicate values - When one of your collection properties has a "Unique Values option", a Merging Option step will be shown. Please check the bulk updates documentation page to know more.

New Property Settings

Converting to data types

After creating or mapping your columns, Datablist will parse and convert your lines according to the property data types.

Checkbox - Yes, No, 1, 0, True, False, Checked (case insensitive) are converted to checkbox values

DateTime - Must respect the ISO 8601 format. If not, data might be converted to some weird dates.

Number - Numeric values must be written with numerical digits. Ex: 100 or 300.49 are valid, one hundred is not valid. Decimal must use a dot separator.

If imported data can't be converted to data type, data is passed as a Text. Review your data to detect any error and then click Import items to run the import process.

Text to data types parsing

Run the import

Once ready, run the import to create the items. When the import is finished, a report is available with the number of items created.

Import Report

Note&#x20;
Loading a big CSV file takes time. Expect 5 minutes to load a 1 million lines CSV file. To cancel the loading process before the end, click the "Stop Import" button. It will stop the import process.

Import with copy/pasting

To help you import data from any spreadsheet tools to Datablist, you can use copy/pasting. This feature allows you to import data from Google Sheets, Microsoft Excel, Apple Numbers, etc.

Copy Google Sheets rows

In Datablist, select the collection where your data will be imported or create a new collection if needed. Then paste your data by selecting "Edit -> Paste" in your browser menu bar or using the Ctrl + v keyboard shortcut. A modal will open to configure the data import.

First, if your pasted data include headers/columns, set "First row contains headers" to true. The first row will be skipped and used to map or create properties.

Paste on Datablist

If your collection has properties, map the columns with them. If you need to create new properties that match the columns, click the + button.

Create a property

When your pasted data contains headers, a property will be automatically created using your header name. You will be able to edit it later.

For data without headers, clicking the + button will show an input to enter a property name.

Create a property without header

Once ready, click Import to run the import process.

Note: Pasting data containing a single line will overwrite selected cell text

Import Limits

Anonymous users have a 10,000 items import limit. Create a free account to remove the limit.

For registered users, import works with CSV and Excel files up to 1,5 million lines (1 million in free plan).


Importing Data


**Unlike spreadsheet tools, it's easy to update your items with an external file in Datablist.** Thanks to data constraints, you can define a unique constraint on one of your properties so it acts as a unique identifier for your items.

## Define a unique identifier

To perform bulk updates, start with defining a unique identifier. Select one of your properties that must have unique values.

For example, on a people listing, email addresses or phone numbers are great as they are unique for each person. For companies listing, you can use their website URLs, etc. Any unique identifier is good.

Then, tell Datablist to use the property as a unique identifier by enabling the "Do not allow duplicate values" option. Open the [Manage Properties](/docs/properties#edit-or-delete-property) modal and [edit your property](/docs/properties#ensure-unique-values-for-a-property) so Datablist ensures it has unique values.

![Define Identifier](/docs_images/import-export/bulk-updates/datablist-bulk-update-create-identifier.png#1840x806)

Once your property is configured as unique, a **Key** symbol will appear in the property's column in the [Data Table](/docs/introduction#data-table).

![Check unique setting](/docs_images/import-export/bulk-updates/datablist-check-unique-property.png#1202x399)

### Remove duplicates

Configuring a property with a unique constraint ensures no duplicates can be added afterward but it doesn't ensure your existing items are unique.

To avoid any bad surprises, check for duplicates on your identifier.

Hopefully, Datablist provides a simple tool to find and merge duplicates. Click the "Duplicates" on your [collection header](/docs/introduction#datablist-layout).

![Duplicates Finder](/docs_images/import-export/bulk-updates/datablist-remove-duplicates.png#1016x325)

Then, choose "Selected Properties".

![Select your identifier property](/docs_images/import-export/bulk-updates/datablist-remove-duplicates-step1.png#1536x689)

And select your identifier property.

![Select your identifier property](/docs_images/import-export/bulk-updates/datablist-remove-duplicates-step2.png#1524x625)

👉 If duplicates are found, merge them.

## Bulk update items

Now you have a unique identifier property, simply import a CSV or Excel file containing a column to be matched with it. All file rows that match with collection items will update items and rows without matching items will create new items.

If your collection looks like:

```
 Name        | Email                
 John        | john@gmail.com       
 Frank       | frank@yahoo.com      
```

And Email is your unique identifier, you can upload a file to edit the Names, or to add new properties to your collection.

For example, importing a file with:

```
 Name            | Email                
 John Doe        | john@gmail.com       
 Frank Ciname    | frank@yahoo.com      
```

Will update the values of the property **Name**.

And importing a file with:

```
 Email                |   Company
 john@gmail.com       |   Google
 frank@yahoo.com      |   IBM
```

Will add a new property to your collection and add the company values to your existing items.

### Mapping identifier

When mapping your file columns with the collection properties, please check that a **Key** icon appears on your identifier column.

![Mapping](/docs_images/import-export/bulk-updates/datablist-bulk-upload-map.png#1466x844)


### Merging options

In the last step, you will be asked to select a merging option.

- **Skip** - If an item already exists with the same property, skip the file entry.
- **Soft Merge** - If an item exists with the same property, update empty item properties and don't update properties with data.
- **Hard Merge** - If data exists for the item property, update it.

![Merging Options](/docs_images/import-export/bulk-updates/all-merging-options.png#1752x1240)


> **Warning:** Always back up your data before performing any data operation.


Update your whole collection using and external file by defining a unique identifier for your collection.

Perform bulk updates using an external file (CSV or Excel)

Unlike spreadsheet tools, it's easy to update your items with an external file in Datablist. Thanks to data constraints, you can define a unique constraint on one of your properties so it acts as a unique identifier for your items.Define a unique identifierTo perform bulk updates, start with defining a unique identifier. Select one of your properties that must have unique values.For example, on a people listing, email addresses or phone numbers are great as they are unique for each person. For companies listing, you can use their website URLs, etc. Any unique identifier is good.Then, tell Datablist to use the property as a unique identifier by enabling the "Do not allow duplicate values" option. Open the Manage Properties modal and edit your property so Datablist ensures it has unique values.Define IdentifierOnce your property is configured as unique, a Key symbol will appear in the property's column in the Data Table.Check unique settingRemove duplicatesConfiguring a property with a unique constraint ensures no duplicates can be added afterward but it doesn't ensure your existing items are unique.To avoid any bad surprises, check for duplicates on your identifier.Hopefully, Datablist provides a simple tool to find and merge duplicates. Click the "Duplicates" on your collection header.Duplicates FinderThen, choose "Selected Properties".Select your identifier propertyAnd select your identifier property.Select your identifier property👉 If duplicates are found, merge them.Bulk update itemsNow you have a unique identifier property, simply import a CSV or Excel file containing a column to be matched with it. All file rows that match with collection items will update items and rows without matching items will create new items.If your collection looks like:And Email is your unique identifier, you can upload a file to edit the Names, or to add new properties to your collection.For example, importing a file with:Will update the values of the property Name.And importing a file with:Will add a new property to your collection and add the company values to your existing items.Mapping identifierWhen mapping your file columns with the collection properties, please check that a Key icon appears on your identifier column.MappingMerging optionsIn the last step, you will be asked to select a merging option.Skip - If an item already exists with the same property, skip the file entry.Soft Merge - If an item exists with the same property, update empty item properties and don't update properties with data.Hard Merge - If data exists for the item property, update it.Merging OptionsWarning: Always back up your data before performing any data operation.


Bulk update items


## Export data table

To download a file containing your data table items, simply click **Export** in your collection header.

> **Note:** Exported data represents your current data table items. If your collection is filtered -for example with a [search term](/docs/search-filter-items#full-text-search)- the export file will contain your filtered items.

![Export Button](/docs_images/import-export/export/datablist-export-button.png#1274x560)

A modal will open to select the Export format and the properties to export.

[CSV](/learn/csv/csv-uses) and Microsoft Excel (.xlsx) formats are available. When at least one of your properties is [hidden from the Data Table](/docs/properties#display-or-hide-properties-from-data-table), an option is shown to export only visible properties or all your properties.

For [CSV format](/learn/csv/csv-uses) exports, an option to select the column delimiter is available. **Comma** and **Semicolon** delimiters are available. Exported CSV files use UTF-8 for encoding.

![Export Modal](/docs_images/import-export/export/datablist-export-modal-with-delimiter.png#1552x844)


You can open the generated file with Microsoft Excel, Google Sheets, Apple Numbers, or any [CSV editors](/csv-editor).

## Export selected items

To export only some items from your collection, [select items](/docs/items#select-items-and-top-bar) and click the "Export items to CSV/Excel" button in the top bar.

The export modal will open to perform an export with your selected items.

![Export Selected Items](/docs_images/import-export/export/datablist-selected-items.png#1569x875)

## Export with copy/pasting

Another simple way to export items or cells is to use copy/pasting. Select items or cells and click "Edit -> Copy" in your browser or use the keyword shortcut `Ctrl + c` to copy the data in your clipboard.

Clipboard data is compatible with any spreadsheet tools, [data editors](/data-editor), or text editors.

## Data Types export formats

When exporting data, Datablist converts all data types to string values:

- **Checkbox** data is exported with `TRUE` and `FALSE` values.
- **DateTime** data is exported using [ISO 8601 format](https://en.wikipedia.org/wiki/ISO_8601)


Learn how to export your data as CSV or Excel files in Datablist. Export all your collection items or only selected items.

Exporting your data

Export data table

To download a file containing your data table items, simply click Export in your collection header.

Note: Exported data represents your current data table items. If your collection is filtered -for example with a search term- the export file will contain your filtered items.

Export Button

A modal will open to select the Export format and the properties to export.

CSV and Microsoft Excel (.xlsx) formats are available. When at least one of your properties is hidden from the Data Table, an option is shown to export only visible properties or all your properties.

For CSV format exports, an option to select the column delimiter is available. Comma and Semicolon delimiters are available. Exported CSV files use UTF-8 for encoding.

Export Modal

You can open the generated file with Microsoft Excel, Google Sheets, Apple Numbers, or any CSV editors.

Export selected items

To export only some items from your collection, select items and click the "Export items to CSV/Excel" button in the top bar.

The export modal will open to perform an export with your selected items.

Export Selected Items

Export with copy/pasting

Another simple way to export items or cells is to use copy/pasting. Select items or cells and click "Edit -> Copy" in your browser or use the keyword shortcut Ctrl + c to copy the data in your clipboard.

Clipboard data is compatible with any spreadsheet tools, data editors, or text editors.

Data Types export formats

When exporting data, Datablist converts all data types to string values:

Checkbox data is exported with TRUE and FALSE values.

DateTime data is exported using ISO 8601 format


Exporting Data

Dealing with duplicates is an exhausting task with spreadsheet tools. Learn how to find duplicate items in your collection and how to merge or remove the duplicates.

Find, remove or merge duplicates

Dealing with duplicates is an exhausting task with spreadsheet tools. Datablist provides a built-in deduplication feature to find duplicate values in your collections. Duplicates can then be removed or merged (automatically or using a merging assistant).To find the duplicates in a collection, click on the "Duplicates Finder" button in your collection header.Note:
Check Importing Data to learn how to load your datasets into Datablist.Duplicates FinderSelect duplicate check methodThe first step is to select how to compare items to find duplicate values. Two methods are available:All Properties - Look for items with similar values for all properties. Two items are considered similar when all of their properties match.Selected Properties & Multi Collection - With this mode, you can select the properties to be used for similarity check and select other collections to be included in the analysis. Two items are considered similar when they have similar values on the selected properties. Different algorithms are available to detect duplicate items with small variationsMost of the time, Selected Properties & Multi Collections is the best option. This is perfect to deduplicate contacts based on their email addresses and phone numbers, or companies using their website URLs. Use Selected Properties when you have one or several properties that identify a product, person, company, etc.Find duplicates across several collectionsIf you select "Selected Properties & Multi Collections", you will be able to select other collections. In multi-collection mode, you will need to map the selected properties of your current collection with properties from the others. Datablist will create a single "computed" list with the items from all the collections and run the deduplication algorithm on it. If duplicates are found, you will be able to merge them to create consolidated items, or to remove the duplicate items to keep only one item for each duplicate group.Multiple List DeduplicationConfigure PropertiesNext, if you click on the Selected Properties method, you have to select which properties will be checked.For example, in a collection with people items, you would select the Email property.Example for Selected PropertiesMapping other collection propertiesFor multi-collection deduplication, an extra step is required: the mapping of the properties. For each property you select, you have to select a matching property in each of the collections you have added. This step is mandatory.Multiple List DeduplicationSelect the comparison algorithm and run the deduplication checkThen, a review screen is shown with the duplicate mode and the number of items in your collection. For "Selected Properties & Multi Collections", you can configure the deduplication algorithm to be used on each property.The deduplication analysis is a read-only operation. It will not perform any changes in your collection items until you decide to merge items.Deduplication Algorithm SelectionHow does Datablist deal with empty values?Finding duplicate items is a probabilistic calculation. Datablist creates pairs of items to compare. Then, it computes a "similarity" score for each pair.When the score passes a threshold, the items are put into a duplicate group.The similarity score between two items is the average similarity across all compared properties.For a defined property, when both items have empty values, the similarity is 100%. And if only one item has an empty value, the similarity is set to 50%.For all algorithms except the fuzzy ones, the threshold is set at 80%.For example, for the following two items:The similarity score will be (1+0,5)/2 = 0,75 => 75%.This is less than the threshold of 80%, so they will not be matched.Now, with another property:The similarity score will be (1+1+0,5)/3 = 0,83 => 83%.This time, the result is above the threshold. They will be matched.Notes: When running the Duplicate Finder on just 2 properties, an empty value on one of the properties will prevent a match if other items have value. To be able to set a custom threshold and match below the default 80%, please use fuzzy matching.How does the "Exact" algorithm work?The "Exact" algorithm performs a few changes when it compares two items:It removes spaces before and after the text (but not in between words)It is case insensitive by default. Check the "Case sensitive" option if you need to differentiate by text case.If you have three items such as:And you run the "Exact" algorithm on the properties "First Name", "Last Name" and "Company". The three items will be flagged as duplicates.Because:The algorithm compares the text without case (uppercase or lowercase are the same)The algorithm removes leading and trailing spacesThe similarity average with the empty value for item id 0003 on "Last Name" is still above 80%: (1+1+0.5)/3 => 83%. See how Datablist deals with empty values.How does the "Smart" algorithm work?The "Smart" algorithm goal is to find duplicate items, even with small changes:It is case insensitiveIt removes all spaces and punctuation characters (before, after, between words)It removes symbols (smileys, etc.)It matches words in different ordersIt removes URL protocol for URL comparaisonIf you have three items such as:And you run the "Smart" algorithm on the "Full Name", and "Company Website" properties. The three items will be flagged as duplicates.Because:The algorithm compares texts without symbols, case, punctuation, and word orderThe algorithm remove the protocol (https and http) and trailing slash for URL comparaisonThe similarity average with the empty value for item id 0003 on "Company Website" is still above 80%: (1+1+0.5)/3 => 83%. See how Datablist deals with empty values.How does the Metaphone algorithm work?The "Metaphone" algorithm is a phonetic algorithm. It converts texts to codes to match similar-sounding words. Datablist uses the "Double Metaphone" algorithm.It is built on top of the smart algorithm. It is useful to find duplicate items with typos.If you have three items such as:And you run the "Metaphone" algorithm on the "Full Name", and "Company" properties. The three items will be flagged as duplicates.Because:The algorithm compares texts without case, punctuation, and word order"Filip Dupon", "Philip Dupont", and "philippe Dupond" sound similar"google", "googl", and "gogle" sound similarOnce you have selected the algorithm, click "Run duplicates check" to continue.How do the Fuzzy Matching algorithms work?Datablist implements fuzzy matching for deduplication. Fuzzy matching works by calculating the distance between two items. This distance is then converted to a similarity ratio. Items with a similarity above a configured threshold are considered similar.After selecting "Distance Algorithms" in the Duplicates Finder, a select input lets you choose between two distance algorithms:Levenshtein DistanceJaro-Winkler DistanceAnd a configurable threshold value is shown. The threshold ranges from 50 and 100. 100 is for an exact match. The default value is 80. You can start with a high value and re-run the deduplication algorithm with a lower value.Fuzzy Matching AlgorithmsFuzzy Matching processStep 1: Blocking to reduce the number of comparisonsFuzzy matching compares two records together to get a similarity ratio. Doing a comparison for each record pair in a dataset is computationally intensive.For 200 000 records, it means 200 000\*199 999/2 = 19 999 900 000 pairs.To solve this, Datablist searches for records that could be similar. And discard the pairs that are very different.This is called the blocking step. Datablist analyzes the properties selected for deduplication and looks for the most discriminative one. Then the algorithm creates potential similar pairs using the discriminative property when records have similar values for this property.For properties with exact, smart, Metaphone algorithm, similar values means the values must be the same after being processed by those algorithms.For a property with a fuzzy matching algorithm used, it compares the first three characters of each record property value and creates record pairs with the records sharing the same three characters.Once the pairs of potentially similar records are generated, Datablist Duplicate Finder computes a similarity ratio for each pair.Step 2: Compute a ratio for each propertyDatablist needs a similarity ratio to compare it with the defined threshold.The Jaro-Winkler algorithm returns by default a ratio.However, the Levenshtein algorithm returns a distance value (the number of changes needed to transform a string into another string). Datablist converts the calculated distance to a ratio using this formula:Step 3: Get the average ratio between two itemsA final ratio is calculated using the average for all the properties compared.For example, if you search for duplicate items on two properties: First Name, and Last Name.The similarity between the item "00001" and "00002" is calculated with:Step 4: Compare the similarity with the thresholdThen, the similarity is compared to the defined threshold. If the similarity is greater than or equal to the threshold, the items are marked as similar.Step 5: Group items into Duplicate GroupsFinally, Datablist processes all the duplicate pairs to create duplicate groups.For example, if Item1 and Item2 are similar, and Item2 and Item5 are also similar, then a group will be created with "Item1", "Item2", and "Item5".Duplicates ListingAfter running through all your items for duplicate items, Datablist will list all the duplicate groups found.From this page, two actions are available:Merge duplicates automatically without losing dataMerge conflicting items manuallyThe recommended way to deal with duplicates is to run auto merge duplicates first (1 in the screenshot below) and then pursue with Datablist merging assistant for conflicting items (2 in the screenshot below).Duplicate Item listingAutomatically merge duplicatesTo save you time, Datablist provides an "Auto Merge" algorithm. With "Auto Merge", most of the merging cases are possible. It handles merging non-conflicting items, combining duplicate values into a single item, and dropping duplicate values during the merging. The auto merge is triggered by clicking on the "Auto Merge duplicates when possible" button.The merging non-conflicting items algorithm is always activated. Other merging algorithms need to be switched on to be activated.Merge non-conflicting itemsDatablist is able to merge non-conflicting items automatically without losing any information. It works as follows:If all the duplicate items have the same property values, only one item will be kept and the others will be deleted.If the duplicate items are complementary, the item with the most information will be selected as the primary item and its property values will be filled using other item property values. Then all items except the primary item will be deleted.If duplicate items have conflicted property values, items will be skipped for manual merging (or to be combined with the combining duplicates algorithm, or dropped)For example, the following duplicates:Will be merged into:Combine conflicting propertiesThe Auto Merge algorithm comes with a second option to combine values from duplicates into a single text property. It keeps the data from all the duplicate items and merges them with a delimiter into a single item.One or more properties can be combined and 4 delimiters are available: Line Break, Semicolon, Comma and Space.Combine Duplicates - OptionsFor example, combining the Phone property with a semi-colon:Will be merged into:This is useful when merging a list of leads or companies. If you have two leads with the same email address but different "Notes" and "Phone Number" values, combining them will create a consolidated lead.Note: The algorithm only combines text values. If the property you want to combine is a Checkbox, Number, Datetime, etc. (see full data types list), it will not be combined.Drop conflicting valuesThe last option available when merging duplicate items is to drop conflicting values.Use this option:When the property must be a single value.For example, if you are cleaning a CRM lead list. You will have an external CRM "record id". To re-import your cleaned data back into your CRM, the "record id" must be a single value.When it doesn't make sense to keep several values.For example, if you have a "last activity" property with conflicting values. Date, Checkbox, and Number can't be combined so you can drop conflicting data to keep only one value.Important
Datablist selects one of the duplicate items to be a master item. When conflicts exist to merge items, the value from the master item is kept. The item with the most properties containing data is selected as the master. If two items have the same number of properties with data, the item with the highest rank (last added in Datablist) is selected.For example, configuring the algorithm to drop conflicting values on AccountId:Will be merged into:Algorithm ExplanationThe second item has 4 properties with data (AccountId, email, Last Name, Job Title). It is the master item.First Name, Last Name, and Job Title are merged without conflict.AccountId has a conflict. So the value from the master item is kept.Master Item SelectionWhen merging several items into a single one, Datablist selects a master item and merges the data from the other items into it.Master Item RulesThree rules are available:Most completeLast updatedFirst createdMaster Item Rule: Most CompleteThis is the default rule. The algorithm selects the item with the most properties containing values. If items have the same number of properties with data, it selects the one with the longest text. And when they still have the same text length, it selects the last created.Example 1 with Most Complete:For this first example, the algorithm will select the second item (ItemId: ODFJSDK123aSD). It has a value for the Job Title property, while the first has an empty value.Example 2 with Most Complete:In this second example, both have value for every property, but the first one has a longer Notes value. So the master item rule selects the first item (ItemId: SDFASDCSXCAER)Master Item Rule: Last updatedDatablist keeps two internal values for each item: CreatedAt and UpdatedAt. The UpdatedAt value is automatically updated after a change.The "Last updated" master item rule, keeps the last item updated.Master Item Rule: First createdThe "First created" master item rule, keeps the first item created in Datablist.Manually merge conflicting itemsFor some duplicate items, conflicting values prevent them to be merged without losing data. For example, imagine two items that store contact information with the following data:We know they represent the same person because they have similar email addresses. Unfortunately, they have two different job titles, the name is not the same, and merging them into a single item means choosing which data to keep.This is where to use Datablist Merging Assistant. A "merge" button is available on the left of every duplicates group.Manual MergingIt opens the merging assistant for the items. On this modal, you can select which value to keep for each conflicting property. After merging them, all except one (the master item) will be deleted.Merging AssistantPlease check our Datablist Merging Assistant documentation page to learn more.Edit or delete itemsAnother way to deal with duplicate values is to directly edit an item's data or delete unnecessary items. You can directly perform those actions with the buttons listed on each item.Edit and delete buttonsExport duplicate groupsIn some use cases, you might want to export the list of duplicate items to process them externally. An export button is available to export the duplicate groups to a CSV or Excel file.Export duplicate groupsDuplicate groups are listed one after the other.For example, for two duplicate groups:AndThe export listing will be:Note: Duplicate group export is only available for single-collection deduplication.FAQCan Datablist miss some duplicates?Analysis of large lists of items is time consuming and resource intensive. For example, a list of 1000 items generates 1000\*999/2 = 499 500 unique pairs of items.This is manageable.But a list of 200 000 items generates 200 000\*199 999/2 = 19 999 900 000 pairs. This is not possible to return results in a few seconds.To get results fast, Datablist doesn't compare all items between them. It uses an algorithm to create a list of "potential duplicate pairs". Those "potential duplicate pairs" are then compared with the similarity algorithm. A similarity score is calculated for each. When the similarity score is higher than the threshold, a duplicate group is returned.This algorithm to find "potential duplicate pairs" is enough most of the time. For some rare cases, your items will need more analysis to find all the duplicate groups. This happens when your data doesn't have enough distinct values. Datablist will not be able to discard enough pairs.This case is highlighted with an icon next to the processed items.Incomplete processingWhen this happens, you can process the duplicate groups found. And then restart another deduplication process to find other duplicates.To learn moreRead our guides for step by step tutorials on how to deal with duplicates for CSV and Excel files:Merge duplicate rows in an Excel fileFind and Merge CSV duplicatesOnline Data Matching tool



Find and Replace is a powerful tool to clean data. When you need to replace a string in your data or to normalize values, Find and Replace is a perfect choice. And with the advanced options, it becomes a tool to remove strings from your texts.

Datablist offers a fast and powerful **Find And Replace** feature. Use it on any dataset size, from a few items to hundreds of thousands.

## Find and Replace workflow

### Step 1: Define the pattern and the options

From the [Datablist app](https://app.datablist.com), open the Find and Replace tool from the "Clean" menu.

![Open Find & Replace tool](/docs_images/data-cleaning/find-replace/clean-header-find-replace.png#1502x590)

The first step is to define the pattern to be looked for. And the property to search.

![Find and Replace inputs](/docs_images/data-cleaning/find-replace/find-replace-start.png#2356x1444)

The "Find" pattern is mandatory. All other inputs are optional. With an empty "Replace with" string, the pattern will be removed from your data.

Select if the search must be performed on a specific property or on all your data.

To learn more about the options, check the [Find and Replace Options](#find-and-replace-options) section.


### Step 2: Run the "find" phase

Datablist "Find and Replace" uses a two phases workflow. It first searches for all the occurrences of your entered pattern. And then, after your confirmation, it replaces them.

Click on the "Find" button to run the first phase.

![Search results](/docs_images/data-cleaning/find-replace/find-replace-preview.png#2278x1436)

On the search summary, you will find:

- How many occurrences of the pattern have been found
- How many distinct items have the pattern in their property(ies). If the pattern is found twice in a text, it will count for 1 item.
- The occurrences number per property.
- And a preview of the **replacement** process on a sample of 10 items.


### Step 3: Replace matched terms

At any moment, you can change the search options. Change the search pattern or the options until you get the requested preview results.

Then, run the replace phase by clicking "Replace All".

The replacement process is fast! On hundreds of thousands of items, it will take seconds or a few minutes. The more items to edit, the longer.

The items will be updated directly in the data table. Close the drawer or re-open the "Options" section to start a new Find and Replace process.

![Find & Replace results](/docs_images/data-cleaning/find-replace/find-replace-results.png#1534x852)


## Find and Replace Options

### Match case

By default, searching for "John" will return items that have "john", "John", or "JOHN" in their data. To make your search case-sensitive, and only replace items with "John", check the checkbox "Match case".

### Match using regular expression

When using regular expressions, all options except "Match case" are disabled. Please check the Find and Replace using [Regular Expression](#find-and-replace-using-regular-expression) to see examples.

![Find and Replace with Regular Expression option](/docs_images/data-cleaning/find-replace/find-replace-regex.png#1832x1022)

### Whole words

Searching for "and" will match "Land", "and", "Andor", etc. To only search for "and", check the "whole words" checkbox. This option can also be used with brackets or parenthesis. Search for `(s)` with "Whole words" checked to search and replace all `(s)` occurrences but not the ones belonging to words (like `street(s)`).


### Ignore Accents

Characters with accents are different from their non-accented ancestor. `é` is a different character than `e`, or `ë`. Datablist Find and Replace tool has the option to ignore the differences. Any accented or non-accented character will match any accented or non-accented character.

For example:

- Search for "Ile de R**é**" will match "Ile de Re", "Ile de Ré".
- Search for "Ile de R**e**" will also match "Ile de Re", "Ile de Ré".



## Find and Replace using Regular Expression

### Regular Expression Cheat Sheet

```
| ^  	| Start of string
| $  	| End of string
| \b 	| Word boundary
| \s 	| White space (tab, space, new line, etc.)
| \w 	| Word characters - A word character is a character a-z, A-Z, 0-9, including _ (underscore)
| \d 	| Matches digits from 0 to 9
| + 	| n+ - matches any string that contains at least one n
| * 	| n* - quantifier matches any string that contains zero or more occurrences of n
| ? 	| n? quantifier matches any string that contains zero or one occurrences of n
```

### Remove url scheme

```
^(http|https|ftp):?//(www)?(\.)?
```
And replace with an empty string.


![Remove URL scheme find](/docs_images/data-cleaning/find-replace/remove-url-scheme.png#2276x1442)

![Remove URL scheme preview](/docs_images/data-cleaning/find-replace/remove-url-scheme-results.png#2354x1452)


### Remove letter at the end

Define the letter you want to remove and add `$` to indicate the letter must be at the end.

Here is an example to remove the `/` from url.
```
/$
```

And replace with an empty string.

![Remove last letter](/docs_images/data-cleaning/find-replace/remove-end-letter-regex.png#1748x1464)

![Remove last letter preview](/docs_images/data-cleaning/find-replace/remove-end-letter-results.png#1742x1460)


### Get domain from an email address

```
^(\w|\.|-|\+)*@
```

And replace with an empty string.

![Get domain from email address](/docs_images/data-cleaning/find-replace/get-domain-email-regex.png#1832x1022)

![Get domain from email address preview](/docs_images/data-cleaning/find-replace/get-domain-email-results.png#1756x1456)

### Remove people name titles

`^\s*(mr|mrs|dr|miss|ms|sir|madam|m).?\s`

And replace with an empty string.

![Remove name titles](/docs_images/data-cleaning/find-replace/remove-name-titles.png#1852x1240)

![Remove name titles preview](/docs_images/data-cleaning/find-replace/remove-name-titles-results.png#1850x1234)


### Remove Company name suffix

`,?\s(llc|inc|incorporated|corporation|corp|co|gmbh|ltd).?$`

And replace with an empty string.

![Remove company names suffix](/docs_images/data-cleaning/find-replace/remove-company-name-suffix.png#1676x1232)

![Remove company names suffix preview](/docs_images/data-cleaning/find-replace/remove-company-name-suffix-preview.png#1678x1242)

### Keep only the first word of a sentence (or keep the first name from full name)

```
^(?<first>(\w)*)(.*)
```

And replace with

```
$<first>
```

![Keep first word Regex](/docs_images/data-cleaning/find-replace/keep-first-word-regex.png#2276x1442)

![Keep first word results](/docs_images/data-cleaning/find-replace/keep-first-word-regex-results.png#1894x1460)


### Remove the first word of a sentence (or keep the last name from full name)

```
^(?<first>(\w)+)(\s)+(?<last>.+)
```

And replace with

```
$<last>
```

![Keep last name Regex](/docs_images/data-cleaning/find-replace/keep-last-name-regex.png#2176x1454)

![Keep last name results](/docs_images/data-cleaning/find-replace/keep-last-name-results.png#2154x1444)


### Remove the alias component of email addresses

```
^(?<first>(\w|-|~)*)\+(\w)*(?<last>(@.*))
```

And replace with

```
$<first>$<last>
```

![Remove email alias](/docs_images/data-cleaning/find-replace/detect-alias-menu-reg-ex.png#2210x1164)

![Remove email alias results](/docs_images/data-cleaning/find-replace/detect-alias-menu-reg-ex-preview.png#2226x1158)

Check our full guide on [email list cleaning](/how-to/email-list-cleaning).


### Remove query params from URLs

To remove query params (`?path=string` in a URL such as `https://www.google.com?path=string`) from a list of URLS, use the following RegEx.

```
\?.*$
```

And replace with an empty string


## FAQ

### Why some properties are not available to search in?

Find and Replace is available only for Text and Number properties.



Finding and replacing text within a large data set is complex. Learn how to use Regex, and advanced options to clean your data.

Advanced Find and Replace

Find and Replace is a powerful tool to clean data. When you need to replace a string in your data or to normalize values, Find and Replace is a perfect choice. And with the advanced options, it becomes a tool to remove strings from your texts.Datablist offers a fast and powerful Find And Replace feature. Use it on any dataset size, from a few items to hundreds of thousands.Find and Replace workflowStep 1: Define the pattern and the optionsFrom the Datablist app, open the Find and Replace tool from the "Clean" menu.Open Find & Replace toolThe first step is to define the pattern to be looked for. And the property to search.Find and Replace inputsThe "Find" pattern is mandatory. All other inputs are optional. With an empty "Replace with" string, the pattern will be removed from your data.Select if the search must be performed on a specific property or on all your data.To learn more about the options, check the Find and Replace Options section.Step 2: Run the "find" phaseDatablist "Find and Replace" uses a two phases workflow. It first searches for all the occurrences of your entered pattern. And then, after your confirmation, it replaces them.Click on the "Find" button to run the first phase.Search resultsOn the search summary, you will find:How many occurrences of the pattern have been foundHow many distinct items have the pattern in their property(ies). If the pattern is found twice in a text, it will count for 1 item.The occurrences number per property.And a preview of the replacement process on a sample of 10 items.Step 3: Replace matched termsAt any moment, you can change the search options. Change the search pattern or the options until you get the requested preview results.Then, run the replace phase by clicking "Replace All".The replacement process is fast! On hundreds of thousands of items, it will take seconds or a few minutes. The more items to edit, the longer.The items will be updated directly in the data table. Close the drawer or re-open the "Options" section to start a new Find and Replace process.Find & Replace resultsFind and Replace OptionsMatch caseBy default, searching for "John" will return items that have "john", "John", or "JOHN" in their data. To make your search case-sensitive, and only replace items with "John", check the checkbox "Match case".Match using regular expressionWhen using regular expressions, all options except "Match case" are disabled. Please check the Find and Replace using Regular Expression to see examples.Find and Replace with Regular Expression optionWhole wordsSearching for "and" will match "Land", "and", "Andor", etc. To only search for "and", check the "whole words" checkbox. This option can also be used with brackets or parenthesis. Search for (s) with "Whole words" checked to search and replace all (s) occurrences but not the ones belonging to words (like street(s)).Ignore AccentsCharacters with accents are different from their non-accented ancestor. é is a different character than e, or ë. Datablist Find and Replace tool has the option to ignore the differences. Any accented or non-accented character will match any accented or non-accented character.For example:Search for "Ile de Ré" will match "Ile de Re", "Ile de Ré".Search for "Ile de Re" will also match "Ile de Re", "Ile de Ré".Find and Replace using Regular ExpressionRegular Expression Cheat SheetRemove url schemeAnd replace with an empty string.Remove URL scheme findRemove URL scheme previewRemove letter at the endDefine the letter you want to remove and add $ to indicate the letter must be at the end.Here is an example to remove the / from url.And replace with an empty string.Remove last letterRemove last letter previewGet domain from an email addressAnd replace with an empty string.Get domain from email addressGet domain from email address previewRemove people name titles^\s\*(mr|mrs|dr|miss|ms|sir|madam|m).?\sAnd replace with an empty string.Remove name titlesRemove name titles previewRemove Company name suffix,?\s(llc|inc|incorporated|corporation|corp|co|gmbh|ltd).?$And replace with an empty string.Remove company names suffixRemove company names suffix previewKeep only the first word of a sentence (or keep the first name from full name)And replace withKeep first word RegexKeep first word resultsRemove the first word of a sentence (or keep the last name from full name)And replace withKeep last name RegexKeep last name resultsRemove the alias component of email addressesAnd replace withRemove email aliasRemove email alias resultsCheck our full guide on email list cleaning.Remove query params from URLsTo remove query params (?path=string in a URL such as https://www.google.com?path=string) from a list of URLS, use the following RegEx.And replace with an empty stringFAQWhy some properties are not available to search in?Find and Replace is available only for Text and Number properties.


Find and Replace



## Convert Text to DateTime

Datablist provides an easy to use tool to parse text and convert it as DateTime. This tool can be used to convert text to simple dates as well. For Dates, the hours, minutes, seconds, etc. are set to zero.

Datablist uses format strings from [Unicode Technical Standard](https://www.unicode.org/reports/tr35/tr35-dates.html#Date_Field_Symbol_Table).

Here is a summary of useful DateTime format patterns:

```
d - The day of the month from 1 through 31
dd - The day of the month from 01 through 31
M - The month, from 1 through 12
MM - The month, from 01 through 12
yy - The year from 00 to 99
yyyy - The year as a four-digit number
h - The hour using 12-hour clock from 1 - 12
hh - The hour using 12-hour clock from 01 - 12
H - The hour using 24-hour clock from 0 - 24
HH - The hour using 24-hour clock from 00 - 24
m - The minute from 0 to 59
mm - The minute from 00 to 59
s - The second, from 0 through 59
ss - The second, from 00 through 59
```

> **Notes**<br />
> - Both **Month** and **Minutes** start with the letter **m**. Beware that to differentiate them, we use a uppercase **M** for months, and a lettercase **m** for minutes.
> - D and DD which represent the day of a year (1, 2, ..., 365, 366) are often confused with d and dd which represent the day of a month (1, 2, ..., 31).
> - YY and YYYY which represent the local week-numbering year (44, 01, 00, 17) are often confused with yy and yyyy which represent the calendar year.

Example of dates and formats:

```
2/5/2023 13:34:11 is M/d/yyyy HH:mm:ss
```

```
February 5, 2023 is MMMM d, yyyy
```

```
Sunday, June 5, 2013 is EEEE, MMMM d, yyyy
```

## Convert Unix Timestamp to DateTime

Another format to represent a DateTime is the Epoch Unix Timestamp. A timestamp is the number of seconds between the Unix Epoch (00:00:00 UTC on 1 January 1970) and a particular date.

Datablist lets you convert Unix Timestamps from a Text or Number property to a DateTime property.

A timestamp usually represents a number of seconds. But now we see values with higher granularity, for example, timestamps in milliseconds.

Datablist automatically detects if the timestamps are in seconds or milliseconds.

![Convert Unix Timestamp to DateTime](/docs_images/data-cleaning/convert-text/convert-timestamp-datetime.png#2436x1390)


## Convert Text to Number

The Text to Number converter searches for digits in the Text and return an Integer or a Float number.

### Normalize separators

Separators for decimals and thousands differ with languages.

Both `1,000.00` and `1.000,00` are valid numbers depending if you are in France or in the US. 

With the Text to Number converted, you define the format and get rid of the ambiguity to have structured number values.

> If your numbers don't have decimals, the Decimal Separator option won't impact the results.

### Extract numbers from text with letters

Running the Text to Number converter on text with both digits and letters will remove the letters to keep only the digits.

`1000 USD` will be converted to `1000`.

If the text contains several numbers, only the first one is kept.

`1000 USD with a discount of 50` will be converted to `1000`.

## Convert Number to Text

The opposite conversion, **Number to Text**, is also available. This is useful when you have to concatenate your numbers with a Text property.

## Convert Text to Checkbox (Boolean)

Use the convert Text to Checkbox tool to create a Boolean property with True and False values.

You only have to set the text values that must be converted to "True". All other values will be "False".

> Notes: <br/>
> - Values will be transformed to lowercase before the process. No need to write the values with case variations. 
> - Spaces are removed both from the data and from the conversion values

![Text to Checkbox](/docs_images/data-cleaning/convert-text/convert-text-to-checkbox.png#1740x1436)


## Convert DateTime to Text

You can convert DateTime values to texts using the same patterns shown for [text to DateTime](#convert-text-to-datetime). You can add extra text that will be kept during the conversion using single quotes.

For example, with the format:

```
'Extra text:' dd-MM-yyyy
```

DateTime values will be converted into text such as `Extra text: 12-12-2022`

![DateTime to Text](/docs_images/data-cleaning/convert-text/convert-datetime-text-format.png#2281x1228)

![DateTime to Text Preview](/docs_images/data-cleaning/convert-text/convert-datetime-text-preview.png#2168x1237)





Convert your text data to Datetime, Number, Checkbox. Manage complex use case with advanced options.

Convert Text to Datetime, Number, Checkbox

Convert Text to DateTimeDatablist provides an easy to use tool to parse text and convert it as DateTime. This tool can be used to convert text to simple dates as well. For Dates, the hours, minutes, seconds, etc. are set to zero.Datablist uses format strings from Unicode Technical Standard.Here is a summary of useful DateTime format patterns:NotesBoth Month and Minutes start with the letter m. Beware that to differentiate them, we use a uppercase M for months, and a lettercase m for minutes.D and DD which represent the day of a year (1, 2, ..., 365, 366) are often confused with d and dd which represent the day of a month (1, 2, ..., 31).YY and YYYY which represent the local week-numbering year (44, 01, 00, 17) are often confused with yy and yyyy which represent the calendar year.Example of dates and formats:Convert Unix Timestamp to DateTimeAnother format to represent a DateTime is the Epoch Unix Timestamp. A timestamp is the number of seconds between the Unix Epoch (00:00:00 UTC on 1 January 1970) and a particular date.Datablist lets you convert Unix Timestamps from a Text or Number property to a DateTime property.A timestamp usually represents a number of seconds. But now we see values with higher granularity, for example, timestamps in milliseconds.Datablist automatically detects if the timestamps are in seconds or milliseconds.Convert Unix Timestamp to DateTimeConvert Text to NumberThe Text to Number converter searches for digits in the Text and return an Integer or a Float number.Normalize separatorsSeparators for decimals and thousands differ with languages.Both 1,000.00 and 1.000,00 are valid numbers depending if you are in France or in the US.With the Text to Number converted, you define the format and get rid of the ambiguity to have structured number values.If your numbers don't have decimals, the Decimal Separator option won't impact the results.Extract numbers from text with lettersRunning the Text to Number converter on text with both digits and letters will remove the letters to keep only the digits.1000 USD will be converted to 1000.If the text contains several numbers, only the first one is kept.1000 USD with a discount of 50 will be converted to 1000.Convert Number to TextThe opposite conversion, Number to Text, is also available. This is useful when you have to concatenate your numbers with a Text property.Convert Text to Checkbox (Boolean)Use the convert Text to Checkbox tool to create a Boolean property with True and False values.You only have to set the text values that must be converted to "True". All other values will be "False".Notes: Values will be transformed to lowercase before the process. No need to write the values with case variations.Spaces are removed both from the data and from the conversion valuesText to CheckboxConvert DateTime to TextYou can convert DateTime values to texts using the same patterns shown for text to DateTime. You can add extra text that will be kept during the conversion using single quotes.For example, with the format:DateTime values will be converted into text such as Extra text: 12-12-2022DateTime to TextDateTime to Text Preview


Text to Datetime, Number, Checkbox


Datablist's Merging Assistant helps you clean up duplicate items and merge them into one master item.

> **Notes:** To find and merge duplicate items, please check our [Find and remove duplicates](/docs/find-duplicates) documentation.

## Merging Assistant

The Merging Assistant is available directly in the top bar once you have at least two items selected ([see how to select items](/docs/items#select-items-and-top-bar)).

![Merging Assistant Button](/docs_images/data-management/merge-items/datablist-merging-assistant-button.png#1542x576)

Clicking on the button opens the **Merging Assistant** for the selected items.

Datablist analyses the items to find the one with the most data and defines it as the primary item. **The goal is to complete the primary item with data from "secondary items" to be able to delete those.**

![Merging Assistant](/docs_images/data-management/merge-items/datablist-merging-assistant.png#1730x1058)

Empty properties in the primary item are automatically filled with secondary item values if available. If several values compete for a property, clicking on a secondary item property's value copies its data into the primary item.

<div class="content-video">
    <video
        autoplay muted loop playsinline controls width="100%" height="auto" class="d-block">
            <source src="https://storage.googleapis.com/public_datablist/website/docs/data-management/datablist-merge-items.mp4" type="video/mp4" />
    </video>
    <div class="text-center">Merging Assistant</div>
</div>


Once done, clicking on "Merge" will delete all secondary items (if not set otherwise) and update the primary item accordingly.


Learn how to merge two or more items into a single consolidated item in Datablist. Merge contacts, leads or products with a no-code interface.

Merge two or more items into one consolidated item

Datablist's Merging Assistant helps you clean up duplicate items and merge them into one master item.Notes: To find and merge duplicate items, please check our Find and remove duplicates documentation.Merging AssistantThe Merging Assistant is available directly in the top bar once you have at least two items selected (see how to select items).Merging Assistant ButtonClicking on the button opens the Merging Assistant for the selected items.Datablist analyses the items to find the one with the most data and defines it as the primary item. The goal is to complete the primary item with data from "secondary items" to be able to delete those.Merging AssistantEmpty properties in the primary item are automatically filled with secondary item values if available. If several values compete for a property, clicking on a secondary item property's value copies its data into the primary item.Once done, clicking on "Merge" will delete all secondary items (if not set otherwise) and update the primary item accordingly.


Merge several items into one


Whether you're a content creator, a data analyst, or a business professional, Datablist Calculations will be your go-to tool for gaining insights and enhancing your decision-making processes.

Calculations provide different types of summarizations of all the values for a given property. It takes the items from the current view, meaning in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

## Perform a calculation


To run a calculation, click on a property column and select "Perform Calculation".

![Calculations Menu](/docs_images/data-cleaning/calculations/calculation.png#1224x786)

Then, select the calculation. The result will be shown directly in the drawer.

![Distinct Values](/docs_images/data-cleaning/calculations/discting_values.png#2126x1402)

Other calculations depend on the property data types such as Text or Number.

## Calculations available for all data types:

- **Count Empty** - How many items with an empty value for the property.
- **Count Filled** - How many items with a value for the property.


## Calculation available for text-based data types

### Characters count
Return the sum of all characters. Leading and trailing spaces are not counted. But spaces in between words are counted. Digits and smileys are counted. Please note that standard emojis count as two characters.


### Words count
Return the number of words found in texts. Words are split using spaces. Compound words such as "rule-breaking" count for 1 word.

- 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").

### Count distinct values

Return facets for a property with how many times each value appears. This is great for aggregation of limited choice values (countries, status, etc.).

![Distinct Values Options](/docs_images/data-cleaning/calculations/distinct_values_options.png#1754x1364)

The distinct values calculation has two settings:

- Splitting Rule: When enabled, each value is split into terms using a separator. Then the terms are counted.
- Case-sensitive: Check this setting to keep the default text case during text comparison. When enabled, `Paris` and `PARIS` would be counted as two distinct values. If disabled, Datablist converts all values to lowercase before doing the aggregation. 

> **Note**: Leading and trailing spaces are removed during the processing.

A limitation is set to a maximum of 10 000 distinct values. When you hit this limit, a warning message is displayed and only the 10000 values with the highest occurence will be displayed.

Distinct values can be exported in Spreadsheet format (with columns and rows), or as a comma-separated list.

Use the "Spreadsheet Format" to paste the results in a new Datablist collection ([see import with copy/pasting]((/docs/import-data#import-with-copypasting) )) or in Microsoft Excel. The comma-separated list format can be used with the "In" and "Not in" filter operators ([see filtering](/docs/search-filter-items#list-of-filter-operators-for-text)).

![Copy distinct values to clipboard](/docs_images/data-cleaning/calculations/export_distinct_values.png#882x576)


## Calculation for number-based data types

- **Min** - Return the lowest value for the property.
- **Max** - Return the highest value for the property.
- **Average** - Return the sum of values divided by the number of non-empty values.
- **Sum of values** - Return the sum of values.


Run calculations on your items to get the number of characters, of words, get facets...

Calculations

Whether you're a content creator, a data analyst, or a business professional, Datablist Calculations will be your go-to tool for gaining insights and enhancing your decision-making processes.Calculations provide different types of summarizations of all the values for a given property. It takes the items from the current view, meaning in this order:If you have selected items in your collection, it will process themIf you have a filter or a full-text search term, it will process the filtered itemsOtherwise, it will process all your collection itemsPerform a calculationTo run a calculation, click on a property column and select "Perform Calculation".Calculations MenuThen, select the calculation. The result will be shown directly in the drawer.Distinct ValuesOther calculations depend on the property data types such as Text or Number.Calculations available for all data types:Count Empty - How many items with an empty value for the property.Count Filled - How many items with a value for the property.Calculation available for text-based data typesCharacters countReturn the sum of all characters. Leading and trailing spaces are not counted. But spaces in between words are counted. Digits and smileys are counted. Please note that standard emojis count as two characters.Words countReturn the number of words found in texts. Words are split using spaces. Compound words such as "rule-breaking" count for 1 word.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:The word count is 9.Spaces before or after a text are ignored, and multi-space characters are ignored.
In the text:The word count is 3 ("datablist", "is", "great").Count distinct valuesReturn facets for a property with how many times each value appears. This is great for aggregation of limited choice values (countries, status, etc.).Distinct Values OptionsThe distinct values calculation has two settings:Splitting Rule: When enabled, each value is split into terms using a separator. Then the terms are counted.Case-sensitive: Check this setting to keep the default text case during text comparison. When enabled, Paris and PARIS would be counted as two distinct values. If disabled, Datablist converts all values to lowercase before doing the aggregation.Note: Leading and trailing spaces are removed during the processing.A limitation is set to a maximum of 10 000 distinct values. When you hit this limit, a warning message is displayed and only the 10000 values with the highest occurence will be displayed.Distinct values can be exported in Spreadsheet format (with columns and rows), or as a comma-separated list.Use the "Spreadsheet Format" to paste the results in a new Datablist collection (see import with copy/pasting) or in Microsoft Excel. The comma-separated list format can be used with the "In" and "Not in" filter operators (see filtering).Copy distinct values to clipboardCalculation for number-based data typesMin - Return the lowest value for the property.Max - Return the highest value for the property.Average - Return the sum of values divided by the number of non-empty values.Sum of values - Return the sum of values.



Datablist comes with a Bulk Edit tool. Currently available actions are:

- [Edit current value](#edit-current-value)
- [Change text case](#change-text-case)
- [Truncate](#truncate)
- [Add a Prefix or a Suffix](#add-a-prefix-or-a-suffix)
- [Remove extra spaces](#remove-extra-spaces)
- [Remove symbols](#remove-symbols)
- [Convert HTML into Plain Text](#convert-html-into-plain-text)
- [Clear the property](#clear-the-property)

## Bulk Edit Process

### Step 1 - Items to edit

The bulk edit feature works by selecting the items to edit in this order:

- If you have selected items in your collection, it will bulk edit them
- If you have a filter or a full-text search term, it will bulk edit the filtered items
- Otherwise, it will bulk edit all your collection items

Click on "Bulk Edit" in the "Edit" menu:

![Bulk Edit](/docs_images/data-edit/bulk-edit/bulk-edit-tool.png#1042x516)

### Step 2 - Select a property to edit

All your collection properties are displayed in the Bulk Edit drawer.

### Step 3 - Select action and options

The actions list depends on the property data type. Some are available for all data types. Others are restricted to `Text` properties.

![Bulk Edit Actions](/docs_images/data-edit/bulk-edit/bulk-edit-actions.png#2078x1448)

### Step 4 - Preview and run

Before editing your items, a preview step is shown. If the preview suits you, click "Edit all" to run the editing process on your items.

If you change a setting (the bulk edit action or its options), the preview step is reinitialized. And another preview will be shown again before editing your data.

![Bulk Edit Actions](/docs_images/data-edit/bulk-edit/bulk-edit-preview.png#1940x1456)



## Edit current value

Replace the current value with a new value.

For a text property, use it to set a text for a property to all your items.
For a number property, use it to change the number value to all your items.


## Truncate

The truncate action is available for `text` properties. It has 3 options:

- **Max length** - The max length allowed. If a text is longer, it will be truncated
- **Don't cut on word** - When checked, will truncate on the first space before the defined length. It means the truncated results length will always be shorter or equal to the original text.
- **Add Ellipsis** - If checked, will add '...' at the end when the text is shortened. The result string will be at max 3 characters longer than `Max length`. **If the text is not shortened, an ellipsis won't be added.


## Add a Prefix or a Suffix

This action is available for `text` properties. It is self-explanatory: it adds a prefix or a suffix to your text.

> Notes: It doesn't add a space. If needed, add a space after the prefix text or before the suffix text.

## Remove symbols

This action is available for `text` properties. It remove non-letter and non-digit characters from your data.

An option to keep punctionation is available ('-', '.', ',', ':', '!', '?', '@', etc.).

Examples with removing punctuation option:

```
France (2022) => France 2022
John 🚀! => John
contact@gmail.com 🙏 => contactgmailcom
```

Examples with keeping punctuation option

```
France (2022) => France (2022)
John 🚀! => John !
contact@gmail.com 🙏 => contact@gmail.com
```

## Remove Extra Spaces

The "Remove Extra Spaces" tool can remove all spaces in a text, or just "extra spaces".

![option to remove all spaces](/docs_images/data-edit/bulk-edit/remove-extra-spaces.png#1722x1030)

Use the "Remove all spaces" option to normalize phone numbers, etc.
For example: `+33 7 78 23 93 23` would be edited to `+33778239323`.

When the "Remove all spaces" is disabled, the tool cleans your texts with the following steps:

- Remove leading and trailing spaces
- Remove extra spaces between words (keep a single space). A space can be a `Tab`, a space, a non-breaking space (nbsp), and any other character that represents a space.
- Remove extra new line (keep a single newline character)

## Change Text Case

For `text` properties. 3 modes are available:

- **Uppercase** - All letters will be converted to their uppercase version. Ex: `john` => `JOHN`
- **Lowercase** - All letters will be converted to their lowercase version. Ex: `API` => `api`
- **Capitalize** - The first letter of all words will be capitalized. Ex: `john is a good man` => `John Is A Good Man`
- **Capitalize only the first word** - Only the first letter of the first word will be capitalized. Ex: `john is a good man` => `John is a good man`
- **Reverse case** - Converts the uppercase caracters to lowercase and lowercase characters to uppercase. Ex: `John` -> `jOHN`

## Convert HTML into Plain Text

Convert HTML to text while keeping some structure. This action removes HTML tags and concatenates the text to generate readable text.

- Bullet lists are converted to text lists with `-`
- New lines are kept

And the action cleans the results to remove any extra spaces.


## Clear the property

Will clear values for all current items for the selected property.

If your items are:

```
First Name   |    Last  Name     |    Job Title
John         |     Doe           |       CEO
Frank        |     Sinatra       |      Singer
```

Clearing on the property `Last Name` will give you:

```
First Name   |    Last  Name     |    Job Title
John         |                   |       CEO
Frank        |                   |      Singer
```

Datablist comes with a Bulk Edit tool. Clean your Text, Number, HTML data at scale.

Bulk Edit

Datablist comes with a Bulk Edit tool. Currently available actions are:Edit current valueChange text caseTruncateAdd a Prefix or a SuffixRemove extra spacesRemove symbolsConvert HTML into Plain TextClear the propertyBulk Edit ProcessStep 1 - Items to editThe bulk edit feature works by selecting the items to edit in this order:If you have selected items in your collection, it will bulk edit themIf you have a filter or a full-text search term, it will bulk edit the filtered itemsOtherwise, it will bulk edit all your collection itemsClick on "Bulk Edit" in the "Edit" menu:Bulk EditStep 2 - Select a property to editAll your collection properties are displayed in the Bulk Edit drawer.Step 3 - Select action and optionsThe actions list depends on the property data type. Some are available for all data types. Others are restricted to Text properties.Bulk Edit ActionsStep 4 - Preview and runBefore editing your items, a preview step is shown. If the preview suits you, click "Edit all" to run the editing process on your items.If you change a setting (the bulk edit action or its options), the preview step is reinitialized. And another preview will be shown again before editing your data.Bulk Edit ActionsEdit current valueReplace the current value with a new value.For a text property, use it to set a text for a property to all your items.
For a number property, use it to change the number value to all your items.TruncateThe truncate action is available for text properties. It has 3 options:Max length - The max length allowed. If a text is longer, it will be truncatedDon't cut on word - When checked, will truncate on the first space before the defined length. It means the truncated results length will always be shorter or equal to the original text.Add Ellipsis - If checked, will add '...' at the end when the text is shortened. The result string will be at max 3 characters longer than Max length. \*\*If the text is not shortened, an ellipsis won't be added.Add a Prefix or a SuffixThis action is available for text properties. It is self-explanatory: it adds a prefix or a suffix to your text.Notes: It doesn't add a space. If needed, add a space after the prefix text or before the suffix text.Remove symbolsThis action is available for text properties. It remove non-letter and non-digit characters from your data.An option to keep punctionation is available ('-', '.', ',', ':', '!', '?', '@', etc.).Examples with removing punctuation option:Examples with keeping punctuation optionRemove Extra SpacesThe "Remove Extra Spaces" tool can remove all spaces in a text, or just "extra spaces".option to remove all spacesUse the "Remove all spaces" option to normalize phone numbers, etc.
For example: +33 7 78 23 93 23 would be edited to +33778239323.When the "Remove all spaces" is disabled, the tool cleans your texts with the following steps:Remove leading and trailing spacesRemove extra spaces between words (keep a single space). A space can be a Tab, a space, a non-breaking space (nbsp), and any other character that represents a space.Remove extra new line (keep a single newline character)Change Text CaseFor text properties. 3 modes are available:Uppercase - All letters will be converted to their uppercase version. Ex: john => JOHNLowercase - All letters will be converted to their lowercase version. Ex: API => apiCapitalize - The first letter of all words will be capitalized. Ex: john is a good man => John Is A Good ManCapitalize only the first word - Only the first letter of the first word will be capitalized. Ex: john is a good man => John is a good manReverse case - Converts the uppercase caracters to lowercase and lowercase characters to uppercase. Ex: John -> jOHNConvert HTML into Plain TextConvert HTML to text while keeping some structure. This action removes HTML tags and concatenates the text to generate readable text.Bullet lists are converted to text lists with -New lines are keptAnd the action cleans the results to remove any extra spaces.Clear the propertyWill clear values for all current items for the selected property.If your items are:Clearing on the property Last Name will give you:



**Datablist Data Extractor is a perfect tool to extract structured information from unstructured text.**

For [data cleaning](/how-to/data-cleaning), the Data Extractor helps you find entities in your CSV texts. When dealing with [scraped data](/how-to/clean-web-scraping-data-online), this tool finds URLs and email addresses in scraped text to build your [lead list](/use-cases/lead-list-building).

The data extractor uses pattern recognition to find:

- [Email Addresses in texts](#extract-email-addresses-from-a-text)
- [URLs in texts](#extract-urls-from-a-text)
- [Domains from email addresses](#extract-the-domain-from-an-email-address)
- [Domains from URLs](#extract-the-domain-from-a-url)
- [Mentions (ex: @name) in texts](#extract-mentions-from-a-text)
- [Tags (ex: #tag) in texts](#extract-tags-from-a-text)

## How to use the Data Extractor

### Step 1: Open the Data Extractor

The Data Extractor tool works by selecting the items to process in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

Datablist Data Extractor is available from the "Edit" menu. Just click on the "Extract url, email, tag, etc." menu item.

![Open Data Extractor tool](/docs_images/data-cleaning/data-extractor/data_extractor_menu.png#1266x710)

### Step 2: Select a property with unstructured text

Then, select the property from your collection you want to extract data from.

![Data Extractor options](/docs_images/data-cleaning/data-extractor/data_extractor.png#1656x1096)

### Step 2: Run the Data Extractor

A dry run lets you get a preview of the extraction on the first 10 items.

![Data Extractors](/docs_images/data-cleaning/data-extractor/data_extractor_select_extractor.png#2356x1410)

If the preview results suit you, click on the "Extract data" to process your items.

> **Notes**: The dry run step is reinitialized each time the data extractor options change.

![Data Extractor Preview](/docs_images/data-cleaning/data-extractor/data_extractor_preview.png#2356x1356)

Once finished, a summary is displayed with the number of items processed. And your data table is updated with the results.

![Data Extractor Results](/docs_images/data-cleaning/data-extractor/data_extractor_results.png#2354x1328)



## Dealing with multiple results

When multiple entities are found in your text, the Data Extractor returns all the entities separated with a comma.

## Extractor descriptions

### Extract the domain from an email address

This extractor takes a property with email addresses and returns the domain with the extension.

Examples:

- contact@datablist.com -> datablist.com
- jean.bond@gmail.uk.co -> jean.bond@gmail.uk.co

> **Note**: If the email address is invalid, it returns an empty value.

### Extract email addresses from a text

This extractor parses a text property to find one or several email addresses.

For example, from a text such as:

```
Please contact us at name@gmail.com for any inquiries about XXX.
Or use the following email address for customer support questions: support@xxx.com
```

The extractor returns: `name@gmail.com,support@xxx.com`

### Extract URLs from a text

This extractor parses a text property to find one or several URLs.

For example, from a text such as:

```
Visit our online documentation at https://docs.datablist.com
```

The extractor returns: `https://docs.datablist.com`

> **Note**: To be valid, the URLs must be absolute, with scheme (https, http, or ftp, etc). A partial URL like `doc.datablist.com` won't be returned.


### Extract the domain from a URL

This extractor takes a property with an URL and returns the domain with the extension.

Examples:

- https://www.datablist.com -> datablist.com
- https://www.google.io/test/path/string.html -> google.io

> **Note**: If the URL is invalid, it returns an empty value.


### Extract mentions from a text

This extractor parses a text and returns the mentions in it. The `@` character is also returned.

Examples:

```
Mum, friend with @pseudo and married with @pseudo2
```

Returns `@pseudo,@pseudo2`

### Extract tags from a text

This extractor parses a text and returns the tags in it. The `#` character is also returned.

Examples:

```
Live in #paris. #workhardplayhard
```

Returns `#paris,#workhardplayhard`




Extract information from unstructured text. Datablist automatically detects URLs, email addresses, mentions, tags, etc.

Extract email, URLs, domain, mentions, tags, etc. from unstructured texts

Datablist Data Extractor is a perfect tool to extract structured information from unstructured text.For data cleaning, the Data Extractor helps you find entities in your CSV texts. When dealing with scraped data, this tool finds URLs and email addresses in scraped text to build your lead list.The data extractor uses pattern recognition to find:Email Addresses in textsURLs in textsDomains from email addressesDomains from URLsMentions (ex: @name) in textsTags (ex: #tag) in textsHow to use the Data ExtractorStep 1: Open the Data ExtractorThe Data Extractor tool works by selecting the items to process in this order:If you have selected items in your collection, it will process themIf you have a filter or a full-text search term, it will process the filtered itemsOtherwise, it will process all your collection itemsDatablist Data Extractor is available from the "Edit" menu. Just click on the "Extract url, email, tag, etc." menu item.Open Data Extractor toolStep 2: Select a property with unstructured textThen, select the property from your collection you want to extract data from.Data Extractor optionsStep 2: Run the Data ExtractorA dry run lets you get a preview of the extraction on the first 10 items.Data ExtractorsIf the preview results suit you, click on the "Extract data" to process your items.Notes: The dry run step is reinitialized each time the data extractor options change.Data Extractor PreviewOnce finished, a summary is displayed with the number of items processed. And your data table is updated with the results.Data Extractor ResultsDealing with multiple resultsWhen multiple entities are found in your text, the Data Extractor returns all the entities separated with a comma.Extractor descriptionsExtract the domain from an email addressThis extractor takes a property with email addresses and returns the domain with the extension.Examples:contact@datablist.com -> datablist.comjean.bond@gmail.uk.co -> jean.bond@gmail.uk.coNote: If the email address is invalid, it returns an empty value.Extract email addresses from a textThis extractor parses a text property to find one or several email addresses.For example, from a text such as:The extractor returns: name@gmail.com,support@xxx.comExtract URLs from a textThis extractor parses a text property to find one or several URLs.For example, from a text such as:The extractor returns: https://docs.datablist.comNote: To be valid, the URLs must be absolute, with scheme (https, http, or ftp, etc). A partial URL like doc.datablist.com won't be returned.Extract the domain from a URLThis extractor takes a property with an URL and returns the domain with the extension.Examples:https://www.datablist.com -> datablist.comhttps://www.google.io/test/path/string.html -> google.ioNote: If the URL is invalid, it returns an empty value.Extract mentions from a textThis extractor parses a text and returns the mentions in it. The @ character is also returned.Examples:Returns @pseudo,@pseudo2Extract tags from a textThis extractor parses a text and returns the tags in it. The # character is also returned.Examples:Returns #paris,#workhardplayhard


Extract email, URLs, etc. from texts


When cleaning data (for example [cleaning scraped data](/how-to/clean-web-scraping-data-online)), separating a property into several properties is a simple yet powerful feature.

Splitting text into properties is perfect to:

- [Split full names into First Name and Last Name](#split-full-names-into-first-name-and-last-name)
- [Split email addresses into name and domain](#split-email-addresses-into-name-and-domain)
- [Split multi values property into single-value properties](#split-multi-values-property-into-single-value-properties)


## Step 1: Open the Split Property tool

Open the "Split Property" tool in the "Edit" menu.

The tool selects the items to process in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

![Open the Split Property tool](/docs_images/data-edit/split-property/open-split-property-menu.png#1310x854)


And select the property you want to split. **Only text-based properties are available.** Checkbox, Datetime, etc. properties are not available.



## Step 2: Split Property options

### Delimiters

![Split Property options](/docs_images/data-edit/split-property/split-property-drawer.png#1760x1196)

Then, select one or more delimiters to use. Common delimiters are available:

- @ (at)
- Comma
- Semicolon
- Dot
- Tab
- Space
- @ (at)

Or you can write custom delimiter characters.

> **Notes**<br/>
> **If you write a multi-characters delimiter, the whole "pattern" will be searched (and not each letter).** If you need to split on multiple delimiters, run the Split Property tool once with a delimiter, and do it again for each delimiter.

![Custom Delimiter](/docs_images/data-edit/split-property/custom-delimiter.png#1532x1062)


### One term per property

By clicking on the "More settings" link, you get an option to group splitted terms in distinct properties.

With this option, Datablist analyses all the distinct terms that need to be generated from the split process. Then, for each item, split terms are moved to their matching new property.

For example, with this initial data:

```
#id         #industries
0001        finance, technology
0002        technology, construction
0003        construction, education
```

And a split process using the "," delimiter. With the "One term per property" option enabled, the result will be:

```
#id         #finance     #technology    #construction      #education
0001        finance      technology
0002                     technology     construction
0003                                    construction       education
```


## Step 3: Data Analysis

After configuring the settings, Datablist analyzes your data to know how many properties need to be created.

![Data Analysis](/docs_images/data-edit/split-property/data-analysis.png#2276x1208)

> **Warning**<br />
> For performance reason, only the first 2000 items are analyzed. You have to be sure they are representative of your other items.


## Step 4: Preview and run

Like many cleaning and editing tools in Datablist, a preview step is shown before running the algorithm.

The preview screen shows a sample of the splitting algorithm on your first items. 

![Preview](/docs_images/data-edit/split-property/split-property-preview.png#2310x1204)

If the results look good, click on "Split property" to run the algorithm on all your current items.


## Examples

### Split Full Names into First Name and Last Name

Some CRMs use `Full Name`, while others split it into `First Name` and `Last Name`. The result is a headache to [consolidate your lead lists](/how-to/merge-duplicate-leads-without-losing-data).

With Datablist [Split Text](/docs/split-text-properties) tool, it's easy to convert your `Full Name` property into `First Name` and `Last Name`. Run the algorithm on your `Full Name` property and use a `Space` delimiter.

![Split Full Name into First Name and Last Name](/docs_images/data-edit/split-property/use-case-full-name.png#2062x1182)

### Split email addresses into name and domain

You can learn a lot on your leads with their email addresses. The email domain is a good example. Does it belong to a corporate website, an email provider, or a disposable email service? To [clean your email list](/how-to/email-list-cleaning), getting the domain address is the first step.

Run the [Split Text](/docs/split-text-properties) algorithm on your `Email` property with the `@` delimiter.

![Split email address to get the domain](/docs_images/data-edit/split-property/use-case-email.png#2254x1204)

### Split multi values property into single-value properties

The Datablist tool to [merge duplicate items](/features/duplicates-remover) merges conflicting data by combining texts into a single property.

Datablist [Split Property](/docs/split-text-properties) algorithm is complementary to [Datablist Duplicates Finder](/features/duplicates-remover). To force a single value in a property (for email addresses, website URLs, etc.), run the algorithm with the same delimiter.

![Split multi-values into several properties](/docs_images/data-edit/split-property/use-case-multi-values.png#2360x886)


### Split text into distinct properties

Use the "One term per property" to create a property for each distinct split value. This feature is useful for segmentation and [data cleaning](/how-to/data-cleaning) or [data matching](/how-to/data-matching-tool).

![Create Property for each distinct term - Preview](/docs_images/data-edit/split-property/use-case-disctinct-terms-preview.png#2354x1242)

![Create Property for each distinct term - Results](/docs_images/data-edit/split-property/use-case-disctinct-terms.png#1792x626)


Datablist has powerful feature to edit data. The Split Property tool takes a property, a delimiter, and split your data into multiple properties.

Split text into different properties

When cleaning data (for example cleaning scraped data), separating a property into several properties is a simple yet powerful feature.Splitting text into properties is perfect to:Split full names into First Name and Last NameSplit email addresses into name and domainSplit multi values property into single-value propertiesStep 1: Open the Split Property toolOpen the "Split Property" tool in the "Edit" menu.The tool selects the items to process in this order:If you have selected items in your collection, it will process themIf you have a filter or a full-text search term, it will process the filtered itemsOtherwise, it will process all your collection itemsOpen the Split Property toolAnd select the property you want to split. Only text-based properties are available. Checkbox, Datetime, etc. properties are not available.Step 2: Split Property optionsDelimitersSplit Property optionsThen, select one or more delimiters to use. Common delimiters are available:@ (at)CommaSemicolonDotTabSpace@ (at)Or you can write custom delimiter characters.Notes
If you write a multi-characters delimiter, the whole "pattern" will be searched (and not each letter). If you need to split on multiple delimiters, run the Split Property tool once with a delimiter, and do it again for each delimiter.Custom DelimiterOne term per propertyBy clicking on the "More settings" link, you get an option to group splitted terms in distinct properties.With this option, Datablist analyses all the distinct terms that need to be generated from the split process. Then, for each item, split terms are moved to their matching new property.For example, with this initial data:And a split process using the "," delimiter. With the "One term per property" option enabled, the result will be:Step 3: Data AnalysisAfter configuring the settings, Datablist analyzes your data to know how many properties need to be created.Data AnalysisWarning
For performance reason, only the first 2000 items are analyzed. You have to be sure they are representative of your other items.Step 4: Preview and runLike many cleaning and editing tools in Datablist, a preview step is shown before running the algorithm.The preview screen shows a sample of the splitting algorithm on your first items.PreviewIf the results look good, click on "Split property" to run the algorithm on all your current items.ExamplesSplit Full Names into First Name and Last NameSome CRMs use Full Name, while others split it into First Name and Last Name. The result is a headache to consolidate your lead lists.With Datablist Split Text tool, it's easy to convert your Full Name property into First Name and Last Name. Run the algorithm on your Full Name property and use a Space delimiter.Split Full Name into First Name and Last NameSplit email addresses into name and domainYou can learn a lot on your leads with their email addresses. The email domain is a good example. Does it belong to a corporate website, an email provider, or a disposable email service? To clean your email list, getting the domain address is the first step.Run the Split Text algorithm on your Email property with the @ delimiter.Split email address to get the domainSplit multi values property into single-value propertiesThe Datablist tool to merge duplicate items merges conflicting data by combining texts into a single property.Datablist Split Property algorithm is complementary to Datablist Duplicates Finder. To force a single value in a property (for email addresses, website URLs, etc.), run the algorithm with the same delimiter.Split multi-values into several propertiesSplit text into distinct propertiesUse the "One term per property" to create a property for each distinct split value. This feature is useful for segmentation and data cleaning or data matching.Create Property for each distinct term - PreviewCreate Property for each distinct term - Results



Merging multiple properties into a single one is easy with Datablist. It will help you during the [cleaning of your data](/how-to/clean-web-scraping-data-online). Or when [consolidating data](/features/combine-csv-files) into a single collection, to merge the properties coming from multiple sources into a master property.

Merging data from multiple properties is perfect to:

- [Consolidate multiple email addresses into a single email address property](#consolidate-multiple-email-addresses-into-a-single-email-address-property)
- [Create a Full Name property using First Name and Last Name](#create-a-full-name-property-using-first-name-and-last-name)
- [Join several address parts into a single Address property](#join-several-address-parts-into-a-single-address-property)


## Step 1: Open the Merge Properties tool

Open the "Merging Properties" tool in the "Edit" menu.

The tool selects the items to process in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

![Open the Merge Properties tool](/docs_images/data-edit/merge-properties/merge-properties-tool-edit-menu.png#1138x598)


## Step 2: Define properties and delimiter

**You can merge as many properties as you want.** And a minimum of two properties is required.

In the `Merge Properties` tool, you will see all your `Text` based properties. Other [data types](/docs/properties#data-types) are not allowed.

Start by selecting a property.

![Select a first property](/docs_images/data-edit/merge-properties/select-first-property.png#1652x994)

Then select your other properties. A new select input appears every time you add a property to merge.

![Select two or more properties](/docs_images/data-edit/merge-properties/select-more-properties-define-delimiter.png#1858x1146)



### Select delimiter

Then, select the delimiter to use. Common delimiters are available:

- Space
- No Delimiter: Texts will be concatenated directly
- Comma
- Semicolon
- Dot
- Tab
- New line

Or you can write custom delimiter characters.

![Select two or more properties](/docs_images/data-edit/merge-properties/custom-delimiter.png#1578x1140)


### Note on the merging algorithm

The `Merge Properties` algorithm skips empty values.

For example, for the following data:

```
First Name        Last Name
Jean              Bond
                  Richie
```

With a comma (`,`) delimiter, the resulting property will contain:

```
Full Name
Jean,Bond
Richie
```

**On the second item, the first property has an empty value so the delimiter is not used.**


## Step 3: Preview and run

A preview step is shown before running the merging algorithm. It runs the algorithm on your first items without saving the results.

A new property will be created to store the merging data. The property is named with a concatenation of the merging properties. You will be able to rename it later.

> **Note:** The merging algorithm runs without updating the source properties. 


![Merging data preview](/docs_images/data-edit/merge-properties/merging-data-preview.png#1988x1428)

If the results look good, click on "Merge Properties" to run the algorithm on all your current items.

To finish up, rename the property using the property settings.


![Edit Property Menu](/docs_images/data-edit/merge-properties/menu-edit-property.png#1214x566)

![Rename Property](/docs_images/data-edit/merge-properties/rename-property.png#1596x980)


## Use Cases:

### Consolidate multiple email addresses into a single email address property

If you are [consolidating several CSV files](/how-to/join-csv-files-unique-identifier-or-column) or collections into a master collection, you can end up with multiple properties storing email values.

The Merge Properties tool is perfect to consolidate all your email addresses into a `Emails` single property.

For items with only one property containing data, the result property with get a single value. If multiple properties can have data for an item, use a comma or semicolon delimiter to merge the email addresses.


### Create a Full Name property using First Name and Last Name

In [leads management](/use-cases/lead-list-building), you are always moving from separated `First Name`, `Last Name` properties and a single `Full Name` property.

Use Datablist Merging Properties with a space delimiter to reconstruct `Full Name` values from `First Name` and `Last Name`.

### Join several address parts into a single Address property

If you are dealing with `Street`, `City`, `Zip Code`, `State`, etc. parts and want a single "Address" column, use the merging algorithm with a comma delimiter to create a single `Address` property.



Combine texts from multiple properties into a single property with the Merge Properties tool.

Merge Properties without losing data

Merging multiple properties into a single one is easy with Datablist. It will help you during the cleaning of your data. Or when consolidating data into a single collection, to merge the properties coming from multiple sources into a master property.Merging data from multiple properties is perfect to:Consolidate multiple email addresses into a single email address propertyCreate a Full Name property using First Name and Last NameJoin several address parts into a single Address propertyStep 1: Open the Merge Properties toolOpen the "Merging Properties" tool in the "Edit" menu.The tool selects the items to process in this order:If you have selected items in your collection, it will process themIf you have a filter or a full-text search term, it will process the filtered itemsOtherwise, it will process all your collection itemsOpen the Merge Properties toolStep 2: Define properties and delimiterYou can merge as many properties as you want. And a minimum of two properties is required.In the Merge Properties tool, you will see all your Text based properties. Other data types are not allowed.Start by selecting a property.Select a first propertyThen select your other properties. A new select input appears every time you add a property to merge.Select two or more propertiesSelect delimiterThen, select the delimiter to use. Common delimiters are available:SpaceNo Delimiter: Texts will be concatenated directlyCommaSemicolonDotTabNew lineOr you can write custom delimiter characters.Select two or more propertiesNote on the merging algorithmThe Merge Properties algorithm skips empty values.For example, for the following data:With a comma (,) delimiter, the resulting property will contain:On the second item, the first property has an empty value so the delimiter is not used.Step 3: Preview and runA preview step is shown before running the merging algorithm. It runs the algorithm on your first items without saving the results.A new property will be created to store the merging data. The property is named with a concatenation of the merging properties. You will be able to rename it later.Note: The merging algorithm runs without updating the source properties.Merging data previewIf the results look good, click on "Merge Properties" to run the algorithm on all your current items.To finish up, rename the property using the property settings.Edit Property MenuRename PropertyUse Cases:Consolidate multiple email addresses into a single email address propertyIf you are consolidating several CSV files or collections into a master collection, you can end up with multiple properties storing email values.The Merge Properties tool is perfect to consolidate all your email addresses into a Emails single property.For items with only one property containing data, the result property with get a single value. If multiple properties can have data for an item, use a comma or semicolon delimiter to merge the email addresses.Create a Full Name property using First Name and Last NameIn leads management, you are always moving from separated First Name, Last Name properties and a single Full Name property.Use Datablist Merging Properties with a space delimiter to reconstruct Full Name values from First Name and Last Name.Join several address parts into a single Address propertyIf you are dealing with Street, City, Zip Code, State, etc. parts and want a single "Address" column, use the merging algorithm with a comma delimiter to create a single Address property.



Datablist offers a powerful and convenient feature that allows you to write and run your own Javascript code. With this feature, you can edit and enrich your data using custom scripts. Scripts can edit any part of an item and can call external APIs.

Save your JavaScript codes in your code library to automate tasks or workflows. Follow the best practices outlined in this documentation to get the most out of this feature and make your data work for you.

## Open the Javascript editor

To use the Javascript editor in Datablist, click on "Run Javascript" in the "Edit" menu.

![Open JavaScript editor](/docs_images/data-edit/run-javascript/run-js-from-menu.png#1532x708)


## Writing JavaScript codes

Once you have opened the Javascript editor, you can start writing your own custom scripts.

![JavaScript Editor](/docs_images/data-edit/run-javascript/javascript-editor-open.png#1650x1240)

Previously saved JavaScript codes are listed in the "Code Library" section. [More on that later.](#save-javascript-codes)


### How to structure code

Your JavaScript code **must** expose a `runOnItem` function. This function will be run on all your current items. It takes one parameter -an object with the item data- and must return **null** or an object with **some or all** of the collection properties.

Here are examples of `runOnItem` functions:

**Valid ✅: Return null on some cases:**

If the function returns `null`, Datablist will ignore the result and continue to the next item. Returning `null` doesn't erase data.

```
function runOnItem(item){
    if(item.propertyA){
        return null
    }
    return {
        propertyB: "test"
    }
}
```

**Valid ✅: Return only some properties:**

You can return a subset of all the properties. Datablist will perform a partial update with only the properties returned.

```
function runOnItem(item){
    // return only the firstName property
    return {
        fullName: `${item.firstName} ${item.lastName}`
    }
}
```



**Invalid ❌: Returning extra properties:**

If you return a property that is not in your collection properties, it will throw an error.

```
function runOnItem(item){
    // return only the firstName property
    return {
        aNonExistingProperty: "value"
    }
}
```

**Invalid ❌: Returning bad data types**

Datablist works with [Data Types](/docs/properties#data-types). DataBlist will return an error when the data returned is not compatible with the property data type.


```
function runOnItem(item){
    return {
        aNumber: "value", // Must return a number
        aDate: 2423 // Must return a date
    }
}
```


### Cache

The `runOnItem` function gives you only the context of an item. The "item data" as object in the parameters, and the result edits the current item.
For more advanced use cases, you can use a cache variable that is kept during a "run process".

Inside your javascript code, you can access and edit a special object named `scriptCache`.

```
// Init counter
scriptCache['counter'] = scriptCache['counter'] || 0;

function runOnItem(item){
    scriptCache['counter'] += 1;
    return {
        anumberproperty: scriptCache['counter']
    }
}
```

![Use a cache](/docs_images/data-edit/run-javascript/javascript-cache.png#1630x1397)

![Use a cache - results](/docs_images/data-edit/run-javascript/javascript-cache-run-finished.png#1794x1123)


> **Important** - The cache object is reset for each run. The cache is also reset after a preview run.

### Console

To debug your code, you can use the classic `console.log("XX)` function. It will appear in your browser console.

![Write to Console](/docs_images/data-edit/run-javascript/console-example.png#2873x1354)

![Write to Console - Results](/docs_images/data-edit/run-javascript/console-example-results.png#2874x1355)



### Synchronous or asynchronous function

You can write synchronous or asynchronous functions. Add `async` before the function statement to use `await` in your JavaScript function.

```
async function runOnItem(item){
    const response = await fetch('https://api.website.com')
    const data = await response.json()
    return {
        companyName: data['name']
    }
}
```

### Call external APIs

Standard users ([click here to upgrade](https://app.datablist.com/account/upgrade)) can call external APIs directly from the JavaScript code.

```
async function runOnItem(item){
    const response = await fetch('https://postman-echo.com/post', {
        method: 'POST',
        mode: 'cors',
        headers: {
            "Content-Type": "application/json"
        },
        body: JSON.stringify({
            "value": "Datablist"
        })
    })
    const data = await response.json()
    return {
        companyName: data['data']['value']
    }
}
```


### Restricted functions

Free users and anonymous users have some restrictions on what can be called from the JavaScript code. Calling external APIs is forbidden.


## Run JavaScript codes

### Define items to be processed

Datablist will process the items in the "current view". It takes the items in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

### First Step: Preview

Datablist will test your code on a sample dataset before running it on all your data. This will help you catch errors and ensure that your code produces the desired results.

![Preview mode](/docs_images/data-edit/run-javascript/js-preview.png#1992x946)

After the preview run, and if it is successful, you will see the results in a table representing your collection.

Use it to iterate on your code.

![Preview result](/docs_images/data-edit/run-javascript/run-js.png#1904x950)

### Second Step: Run code

When ready, run your code to process all your items. Items are automatically updated with the JavaScript code results.

After the run, a summary is displayed with the number of processed items and the number of items edited.

![JavaScript run results](/docs_images/data-edit/run-javascript/run-results.png#1932x950)


## Code Library

Standard users ([click here to upgrade](https://app.datablist.com/account/upgrade)) can save their JavaScript codes in their Code Library.

![Save JavaScript code](/docs_images/data-edit/run-javascript/save-javascript-codes.png#2122x1362)

Once saved, run it directly.

![Quick run from your code library](/docs_images/data-edit/run-javascript/quick-run-saved-code.png#1782x838)


## Code Examples

Here are some examples of Javascript codes.

### Keep a variable between items

```
scriptCache['counter'] = scriptCache['counter'] || 0;

function runOnItem(item){
    scriptCache['counter'] += 1;
    return {
        index: scriptCache['counter']
    }
}
```


### Count the total number of characters for a property

```
scriptCache['total'] = scriptCache['total'] || 0;
function runOnItem(item){
    if(!item.property){
        return null;
    }
    scriptCache['total'] += item.property.length;

    return {
        results: total
    }
}
```

First, create a number property that will store the count values.

![Count characters for a property](/docs_images/data-edit/run-javascript/count-characters.png#2314x954)

After running the script, sort your collection by the number property. The first number is your count.

![Sort to get the result](/docs_images/data-edit/run-javascript/sort-by-total.png#2284x952)






Run JavaScript code

Datablist offers a powerful and convenient feature that allows you to write and run your own Javascript code. With this feature, you can edit and enrich your data using custom scripts. Scripts can edit any part of an item and can call external APIs.Save your JavaScript codes in your code library to automate tasks or workflows. Follow the best practices outlined in this documentation to get the most out of this feature and make your data work for you.Open the Javascript editorTo use the Javascript editor in Datablist, click on "Run Javascript" in the "Edit" menu.Open JavaScript editorWriting JavaScript codesOnce you have opened the Javascript editor, you can start writing your own custom scripts.JavaScript EditorPreviously saved JavaScript codes are listed in the "Code Library" section. More on that later.How to structure codeYour JavaScript code must expose a runOnItem function. This function will be run on all your current items. It takes one parameter -an object with the item data- and must return null or an object with some or all of the collection properties.Here are examples of runOnItem functions:Valid ✅: Return null on some cases:If the function returns null, Datablist will ignore the result and continue to the next item. Returning null doesn't erase data.Valid ✅: Return only some properties:You can return a subset of all the properties. Datablist will perform a partial update with only the properties returned.Invalid ❌: Returning extra properties:If you return a property that is not in your collection properties, it will throw an error.Invalid ❌: Returning bad data typesDatablist works with Data Types. DataBlist will return an error when the data returned is not compatible with the property data type.CacheThe runOnItem function gives you only the context of an item. The "item data" as object in the parameters, and the result edits the current item.
For more advanced use cases, you can use a cache variable that is kept during a "run process".Inside your javascript code, you can access and edit a special object named scriptCache.Use a cacheUse a cache - resultsImportant - The cache object is reset for each run. The cache is also reset after a preview run.ConsoleTo debug your code, you can use the classic console.log("XX) function. It will appear in your browser console.Write to ConsoleWrite to Console - ResultsSynchronous or asynchronous functionYou can write synchronous or asynchronous functions. Add async before the function statement to use await in your JavaScript function.Call external APIsStandard users (click here to upgrade) can call external APIs directly from the JavaScript code.Restricted functionsFree users and anonymous users have some restrictions on what can be called from the JavaScript code. Calling external APIs is forbidden.Run JavaScript codesDefine items to be processedDatablist will process the items in the "current view". It takes the items in this order:If you have selected items in your collection, it will process themIf you have a filter or a full-text search term, it will process the filtered itemsOtherwise, it will process all your collection itemsFirst Step: PreviewDatablist will test your code on a sample dataset before running it on all your data. This will help you catch errors and ensure that your code produces the desired results.Preview modeAfter the preview run, and if it is successful, you will see the results in a table representing your collection.Use it to iterate on your code.Preview resultSecond Step: Run codeWhen ready, run your code to process all your items. Items are automatically updated with the JavaScript code results.After the run, a summary is displayed with the number of processed items and the number of items edited.JavaScript run resultsCode LibraryStandard users (click here to upgrade) can save their JavaScript codes in their Code Library.Save JavaScript codeOnce saved, run it directly.Quick run from your code libraryCode ExamplesHere are some examples of Javascript codes.Keep a variable between itemsCount the total number of characters for a propertyFirst, create a number property that will store the count values.Count characters for a propertyAfter running the script, sort your collection by the number property. The first number is your count.Sort to get the result



Welcome to this documentation page on how to move items between two collections. Whether you're working with contacts, companies, or any kind of data, being able to move items between collections lets you clean and organize your data.

Moving data in a CRM or spreadsheet tool can be a complex process. Datablist provides an easy-to-use and fast tool to move items.


## Create a collection with the same data structure

To avoid losing data, Datablist prevents you to move your items if all the properties from the source collection are not mapped with the destination collection.

An easy way to have a compatible data structure for your source and destination collection is to create your destination collection using the source.

To do so, clone your collection and uncheck "Clone data".

![Clone collection](/docs_images/data-management/move-items/clone_collection.png#1582x828)

With this "Clone data" unchecked, your new collection will have the same data structure.

![Empty collection](/docs_images/data-management/move-items/dont_copy_data.png#1532x928)

## Move items between collections

The "Move items" tool is available with one or more items selected. In the available actions, click on "Move items to another collection".

![Action to move items](/docs_images/data-management/move-items/move_items_button.png#1996x944)

Then, select the destination collection.

![Select destination](/docs_images/data-management/move-items/select_destination.png#1996x1108)

Finally, map your current collection properties (on the left side) with your destination properties (on the right side).

> **Notes:** Datablist will try to auto-map your properties using their names.

![Map properties](/docs_images/data-management/move-items/map_properties.png#2012x1212)


Click "Move items" once ready to move your items. A notification showing the progress is shown.


## FAQ

### How does the moving algorithm work?

Under the hood, Datablist follows this process:
- If your destination collection has a unique property, ensure no conflict exists. See [FAQ question](#what-happens-if-my-destination-collection-has-a-property-with-unique-values).
- Split your items into chunks.
- For each chunk, add the items at the beginning of your destination collection
- Once all chunks have been processed, remove the items from your current collection

### What happens if my destination collection has a property with unique values?

[The `Unique values` option lets you add a data constraint to your data](/docs/properties#ensure-unique-values-for-a-property). This prevents having items with duplicate values on a property.

When you move items into a collection with at least one property a "unique values" constraint, the algorithm checks that it passes the constraint.

If items from your current collection have duplicate values with the destination collection, an error will be shown. And the process will be stopped.

### Can I continue to use Datablist during the moving process?

For big collections with hundreds of thousands of items, the moving process can take some time. In the meanwhile, you can browse your other collections.

But wait until the process is finished before editing data on your source or destination collections.



Move items between collections

Welcome to this documentation page on how to move items between two collections. Whether you're working with contacts, companies, or any kind of data, being able to move items between collections lets you clean and organize your data.

Moving data in a CRM or spreadsheet tool can be a complex process. Datablist provides an easy-to-use and fast tool to move items.

Create a collection with the same data structure

To avoid losing data, Datablist prevents you to move your items if all the properties from the source collection are not mapped with the destination collection.

An easy way to have a compatible data structure for your source and destination collection is to create your destination collection using the source.

To do so, clone your collection and uncheck "Clone data".

Clone collection

With this "Clone data" unchecked, your new collection will have the same data structure.

Empty collection

Move items between collections

The "Move items" tool is available with one or more items selected. In the available actions, click on "Move items to another collection".

Action to move items

Then, select the destination collection.

Select destination

Finally, map your current collection properties (on the left side) with your destination properties (on the right side).

Notes: Datablist will try to auto-map your properties using their names.

Map properties

Click "Move items" once ready to move your items. A notification showing the progress is shown.

FAQ

How does the moving algorithm work?

Under the hood, Datablist follows this process:

If your destination collection has a unique property, ensure no conflict exists. See FAQ question.

Split your items into chunks.

For each chunk, add the items at the beginning of your destination collection

Once all chunks have been processed, remove the items from your current collection

What happens if my destination collection has a property with unique values?

The Unique values option lets you add a data constraint to your data. This prevents having items with duplicate values on a property.

When you move items into a collection with at least one property a "unique values" constraint, the algorithm checks that it passes the constraint.

If items from your current collection have duplicate values with the destination collection, an error will be shown. And the process will be stopped.

Can I continue to use Datablist during the moving process?

For big collections with hundreds of thousands of items, the moving process can take some time. In the meanwhile, you can browse your other collections.

But wait until the process is finished before editing data on your source or destination collections.



Datablist is a web-based data management platform. It provides easy-to-use [online data cleaning](/how-to/data-cleaning) and editing features.

Datablist comes with a catalog of enrichments to enrich domains, email addresses, [translate texts](/how-to/translate-csv-files-online), etc.

To start, click on the "Enrich" button in the menu.

![Open enrichments](/docs_images/enrichments/start-enrichments.png#1166x602)

> **Note**<br />
> Enrichments are only visible to registered users. [Click here](https://app.datablist.com/auth/signup) to create an account for free.

## Enrichment Sections

A drawer with all available enrichments is displayed.

![Enrichments Listing](/docs_images/enrichments/enrichments-listing.png#2356x1390)

Enrichments are categorized into six sections:

- **Companies** - Organizations enrichments
- **People** - Contact and Email enrichments
- **Translations** - [Text translations](/how-to/translate-csv-files-online) using Google Translate, Deepl, etc.
- **Places** - Enrichments for reverse geocoding or address parsing
- **AI** - Enrichments connected with ChatGPT or other machine learning models
- **URLs** - Get metadata or other information from URLs


## How it works

Enrichments take one or several [properties](/docs/properties) as inputs and generate data into output properties.

The data coming from the enrichments will be saved into new properties or existing properties.

When the enrichment runs, Datablist loops on each of your items. For each item:

- Datablist takes the data from the input properties for the item.
- This data is sent to the enrichment service with the setting values.
- The result is saved into the output properties.


## Credits system

Some enrichments are unlimited, and others rely on third-party providers.

For enrichments with third-party providers, some let you set your own API Key, and others work with the Datablist Credits system.

Datablist Credit System lets you run enrichments in "pay-as-you-go". Once your credit balance is zero, you can top-up your balance. [Read our credits documentation to learn more](/docs/credits).

![Enrichment cost](/docs_images/enrichments/credits-cost.png#1414x860)

## Premium Enrichments

In addition to the credits system, some enrichments are restricted to users with a "Standard" plan. Please [upgrade your account](https://app.datablist.com/account/upgrade) to use them.

![Premium Enrichments](/docs_images/enrichments/restricted-enrichments.png#2358x1208)



**[👉 Continue with our documentation on how to run an enrichment.](/docs/run-enrichment)**


## FAQ

### I have an idea for a new enrichment

Great! I love to learn about new use cases and new enrichment. Please contact me using this [contact form](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link). Or just book a meeting on my [Calendly](https://calendly.com/florian-datablist).


### Can I code my own enrichment?

It is not possible at the moment to create custom enrichments. But you can use the "[Run JavaScript](/docs/run-javascript)" feature to code your own data logic. If your enrichment relies on an external API, use "fetch" in your JavaScript code to call your API.






Getting started with enrichments

Datablist is a web-based data management platform. It provides easy-to-use online data cleaning and editing features.

Datablist comes with a catalog of enrichments to enrich domains, email addresses, translate texts, etc.

To start, click on the "Enrich" button in the menu.

Open enrichments

Note
Enrichments are only visible to registered users. Click here to create an account for free.

Enrichment Sections

A drawer with all available enrichments is displayed.

Enrichments Listing

Enrichments are categorized into six sections:

Companies - Organizations enrichments

People - Contact and Email enrichments

Translations - Text translations using Google Translate, Deepl, etc.

Places - Enrichments for reverse geocoding or address parsing

AI - Enrichments connected with ChatGPT or other machine learning models

URLs - Get metadata or other information from URLs

How it works

Enrichments take one or several properties as inputs and generate data into output properties.

The data coming from the enrichments will be saved into new properties or existing properties.

When the enrichment runs, Datablist loops on each of your items. For each item:

Datablist takes the data from the input properties for the item.

This data is sent to the enrichment service with the setting values.

The result is saved into the output properties.

Credits system

Some enrichments are unlimited, and others rely on third-party providers.

For enrichments with third-party providers, some let you set your own API Key, and others work with the Datablist Credits system.

Datablist Credit System lets you run enrichments in "pay-as-you-go". Once your credit balance is zero, you can top-up your balance. Read our credits documentation to learn more.

Enrichment cost

Premium Enrichments

In addition to the credits system, some enrichments are restricted to users with a "Standard" plan. Please upgrade your account to use them.

Premium Enrichments

👉 Continue with our documentation on how to run an enrichment.

FAQ

I have an idea for a new enrichment

Great! I love to learn about new use cases and new enrichment. Please contact me using this contact form. Or just book a meeting on my Calendly.

Can I code my own enrichment?

It is not possible at the moment to create custom enrichments. But you can use the "Run JavaScript" feature to code your own data logic. If your enrichment relies on an external API, use "fetch" in your JavaScript code to call your API.



Once you have selected an enrichment from the [listing](/docs/enrichments), a drawer appears.

## First step: Configure Settings and Inputs

Datablist asks you for some information to configure the enrichment. The first step is about the enrichment settings and input properties.

Enrichment settings are defined values that apply to the whole enrichment process.

![Enrichment Settings and Inputs](/docs_images/enrichments/run-enrichment-settings-inputs.png#1838x1224)

Input Properties are variable data. They are the source of data for the enrichment. For example, with a translation enrichment, the input is the text that must be translated. Each item has a distinct text. So, the input "Text to translate" might be a direct mapping with a property or a custom-built source text using texts from several properties.

![Mapping Property or custom text for inputs](/docs_images/enrichments/map-or-custom-input.png#1402x808)

> **Note**<br />
> The values in the input properties are not edited. The result of the enrichment is saved in the output properties.

Click on "Continue to outputs configuration" to move to the second step.

## Second step: Outputs and run settings

**Outputs**

After configuring your inputs, you have to configure your output properties. Outputs are what is generated by the enrichment. In our example with Google Translate, the output is the translated text. An enrichment can have one or more output properties.

For each output property, you can:

- **Ignore it** - When an enrichment returns several properties, you might skip some.
- **Map the result with an existing property from your collection**
- **Create a new property to store the result** - When selected, Datablist will add a new property at the end of your existing ones. The property will be automatically configured with the suited data type.

![Output property and Run settings](/docs_images/enrichments/outputs-run-settings.png#1776x1322)

![Ignore, Map, or Create output property](/docs_images/enrichments/output-map.png#1460x446)

![New Property](/docs_images/enrichments/output-new-property.png#1606x1050)


**Run settings**

Before running your enrichment, please check the run settings.

- **Save options** - If checked, your settings, input configurations, and output mapping will be saved in your browser. Text options are encrypted. 

- **Test on first 10 items** - If checked, the enrichment will process your first 10 items. Then, you will be able to run the enrichment on your remaining items.

- **Existing Data Rule** - This setting defines how Datablist must deal when you have outputs mapped with existing properties. See below.


Detail for Existing Data Rules:

- **Skip items with existing values in output properties** - Default Setting - When the item already has data in one of the output properties, the item is skipped. For enrichment with credits, no credit is consumed.

- **Update only empty cells** - If an item already has data in an output property, the current value will not be overwritten. **If an item has existing data for all the output properties, it will be skipped.** For enrichment with credits, credits are consumed on each item with at least one empty output property.

- **Overwrite existing data in output properties** - The enrichment will process all items. Existing data in output properties are replaced. For enrichment with credits, credits are consumed on each item.


![Run Settings](/docs_images/enrichments/run-settings.png#1476x510)


## Run on the first 10 items

By default, the "Test on first 10 items" setting is checked. When enabled, Datablist runs the enrichment in 2 phases:

- Phase 1: Run on the first 10 items
- Phase 2: After review of the phase 1, run the enrichment on the remaining items

![Run on the first 10 items' settings](/docs_images/enrichments/run-first-items-step1.png#1912x1220)

![Results after the first run](/docs_images/enrichments/run-first-items-step2.png#1740x1010)

Counters with how many items have been skipped, and processed are available in real time during the processing. The [amount of credits](/docs/credits) used is also visible.

![Run enrichment on remaining items](/docs_images/enrichments/run-first-items-step3.png#1566x1014)

## Enrichment interruption

### Manual interruption

If you feel the enrichment is blocked or if you wish to change your current settings, click the "Stop" button while the enrichment is running.
Datablist will wait for the current processing items to be finished and will stop the enrichment before the end.

![Run enrichment on remaining items](/docs_images/enrichments/stop-enrichment.png#1325x308)

Once stopped, you can change the settings, or just resume the current processing.

### Interruption following an error

When an error occurs, Datablist will stop the process. In this case, your items will be partially processed. 

If the error is fixed (network error, bad settings, etc.) you will be able to run the enrichment on the remaining items.

## Parallel processing

Under the hood, Datablist splits items into chunks and processes them. When it is possible, Datablist will process several chunks of items in parallel. 

When an error occurs, the enrichment process stops. The chunks being processed run until they finish but Datablist prevents the next chunks from being processed.


## FAQ

## Which items from my collection are processed?

Datablist will process the items in the "current view". It takes the items in this order:

- If you have selected items in your collection, it will process them
- If you have a filter or a full-text search term, it will process the filtered items
- Otherwise, it will process all your collection items

### I have an idea for a new enrichment action

Great! I love to learn about new use cases and new enrichment. Please contact me using this [contact form](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link). Or just book a meeting on my [Calendly](https://calendly.com/florian-datablist).


### Can I code my own enrichment action?

It is not possible at the moment to create custom enrichments. But you can use the "[Run JavaScript](/docs/run-javascript)" feature to code your own data logic. If your enrichment relies on an external API, use "fetch" in your JavaScript code to call your API.






Run an enrichment

Once you have selected an enrichment from the listing, a drawer appears.

First step: Configure Settings and Inputs

Datablist asks you for some information to configure the enrichment. The first step is about the enrichment settings and input properties.

Enrichment settings are defined values that apply to the whole enrichment process.

Enrichment Settings and Inputs

Input Properties are variable data. They are the source of data for the enrichment. For example, with a translation enrichment, the input is the text that must be translated. Each item has a distinct text. So, the input "Text to translate" might be a direct mapping with a property or a custom-built source text using texts from several properties.

Mapping Property or custom text for inputs

Note
The values in the input properties are not edited. The result of the enrichment is saved in the output properties.

Click on "Continue to outputs configuration" to move to the second step.

Second step: Outputs and run settings

Outputs

After configuring your inputs, you have to configure your output properties. Outputs are what is generated by the enrichment. In our example with Google Translate, the output is the translated text. An enrichment can have one or more output properties.

For each output property, you can:

Ignore it - When an enrichment returns several properties, you might skip some.

Map the result with an existing property from your collection

Create a new property to store the result - When selected, Datablist will add a new property at the end of your existing ones. The property will be automatically configured with the suited data type.

Output property and Run settings

Ignore, Map, or Create output property

New Property

Run settings

Before running your enrichment, please check the run settings.

Save options - If checked, your settings, input configurations, and output mapping will be saved in your browser. Text options are encrypted.

Test on first 10 items - If checked, the enrichment will process your first 10 items. Then, you will be able to run the enrichment on your remaining items.

Existing Data Rule - This setting defines how Datablist must deal when you have outputs mapped with existing properties. See below.

Detail for Existing Data Rules:

Skip items with existing values in output properties - Default Setting - When the item already has data in one of the output properties, the item is skipped. For enrichment with credits, no credit is consumed.

Update only empty cells - If an item already has data in an output property, the current value will not be overwritten. If an item has existing data for all the output properties, it will be skipped. For enrichment with credits, credits are consumed on each item with at least one empty output property.

Overwrite existing data in output properties - The enrichment will process all items. Existing data in output properties are replaced. For enrichment with credits, credits are consumed on each item.

Run Settings

Run on the first 10 items

By default, the "Test on first 10 items" setting is checked. When enabled, Datablist runs the enrichment in 2 phases:

Phase 1: Run on the first 10 items

Phase 2: After review of the phase 1, run the enrichment on the remaining items

Run on the first 10 items' settings

Results after the first run

Counters with how many items have been skipped, and processed are available in real time during the processing. The amount of credits used is also visible.

Run enrichment on remaining items

Enrichment interruption

Manual interruption

If you feel the enrichment is blocked or if you wish to change your current settings, click the "Stop" button while the enrichment is running.
Datablist will wait for the current processing items to be finished and will stop the enrichment before the end.

Run enrichment on remaining items

Once stopped, you can change the settings, or just resume the current processing.

Interruption following an error

When an error occurs, Datablist will stop the process. In this case, your items will be partially processed.

If the error is fixed (network error, bad settings, etc.) you will be able to run the enrichment on the remaining items.

Parallel processing

Under the hood, Datablist splits items into chunks and processes them. When it is possible, Datablist will process several chunks of items in parallel.

When an error occurs, the enrichment process stops. The chunks being processed run until they finish but Datablist prevents the next chunks from being processed.

FAQ

Which items from my collection are processed?

Datablist will process the items in the "current view". It takes the items in this order:

If you have selected items in your collection, it will process them

If you have a filter or a full-text search term, it will process the filtered items

Otherwise, it will process all your collection items

I have an idea for a new enrichment action

Great! I love to learn about new use cases and new enrichment. Please contact me using this contact form. Or just book a meeting on my Calendly.

Can I code my own enrichment action?

It is not possible at the moment to create custom enrichments. But you can use the "Run JavaScript" feature to code your own data logic. If your enrichment relies on an external API, use "fetch" in your JavaScript code to call your API.




For some enrichments (Google Translate, Deepl, Email Finder, etc.), Datablist relies on third-party APIs. Most third-party APIs are paid services. To work with this, Datablist has a credit system.

All standard plan users get 5000 credits per month. And you can top up your credit balance with extra credits. **Those extra credits don't expire and are used only when you no longer have monthly credits.**

The top-up options are:

- $20 for 20 000 credits
- $50 for 60 000 credits
- $150 for 200 000 credits

## Estimated costs based on credit amount

With the 20$ top-up:

- 1 credit costs $0.001
- 10 credits cost $0.01
- 50 credits cost $0.05
- 1000 credits cost $1


**👉 With the $50 top-up, you have about 15% off.**

- 1000 credits cost ≈$0,833


**👉 With the $150 top-up, you have a 25% discount.**

- 1000 credits cost 0,75$



## FAQ

### How much each enrichment cost?

The cost of each enrichment is visible in the enrichment description and on the [enrichments store](/enrichments). If the enrichment doesn't have cost information, **this means the enrichment is included and unlimited**.

![Enrichment cost](/docs_images/account/credits/enrichment_cost.png#1596x697)

## How do I top up my credit balance?

Datablist accepts only credit cards and Paypal. Payments are processed with Stripe or Paypal through Chargebee.

### I have more questions

No problem, please contact me using this [contact form](https://docs.google.com/forms/d/e/1FAIpQLSeAZfqCjOg-ok7yYAwz4hmKxhPOeLMIwZrLozsro5HTQhrsxw/viewform?usp=sf_link). Or book a meeting on my [Calendly](https://calendly.com/florian-datablist).

Datablist Credits System

For some enrichments (Google Translate, Deepl, Email Finder, etc.), Datablist relies on third-party APIs. Most third-party APIs are paid services. To work with this, Datablist has a credit system.

All standard plan users get 5000 credits per month. And you can top up your credit balance with extra credits. Those extra credits don't expire and are used only when you no longer have monthly credits.

The top-up options are:

$20 for 20 000 credits

$50 for 60 000 credits

$150 for 200 000 credits

Estimated costs based on credit amount

With the 20$ top-up:

1 credit costs $0.001

10 credits cost $0.01

50 credits cost $0.05

1000 credits cost $1

👉 With the $50 top-up, you have about 15% off.

1000 credits cost ≈$0,833

👉 With the $150 top-up, you have a 25% discount.

1000 credits cost 0,75$

FAQ

How much each enrichment cost?

The cost of each enrichment is visible in the enrichment description and on the enrichments store. If the enrichment doesn't have cost information, this means the enrichment is included and unlimited.

Enrichment cost

How do I top up my credit balance?

Datablist accepts only credit cards and Paypal. Payments are processed with Stripe or Paypal through Chargebee.

I have more questions

No problem, please contact me using this contact form. Or book a meeting on my Calendly.



## Changing Password

To change your password while logged in, just go to your "Account -> Profile" page:
[https://app.datablist.com/account/user_profile](https://app.datablist.com/account/user_profile).

On the "Password" section, click on the "Reset Password" button. A mail will be sent with the instructions to define a new password.

![Change Password](/docs_images/account/password/reset_password.png#1946x1220)



## Password Forgotten

To reset your Datablist password after losing access to your account, click on the "Forgot Password?" link available on the Login page: [https://app.datablist.com/auth/login](https://app.datablist.com/auth/login)

![Forgot Password?](/docs_images/account/password/login_page_reset.png#1802x1166)

Then, enter the email address used to create your Datablist account. If an account exists for the given email address, an email will be sent with the instructions to reset your password.

![Reset Password](/docs_images/account/password/reset_password_auth0.png#1826x1282)

Change or Reset your password

Changing Password

To change your password while logged in, just go to your "Account -> Profile" page:
https://app.datablist.com/account/user\_profile.

On the "Password" section, click on the "Reset Password" button. A mail will be sent with the instructions to define a new password.

Change Password

Password Forgotten

To reset your Datablist password after losing access to your account, click on the "Forgot Password?" link available on the Login page: https://app.datablist.com/auth/login

Forgot Password?

Then, enter the email address used to create your Datablist account. If an account exists for the given email address, an email will be sent with the instructions to reset your password.

Reset Password



Datablist provides an Undo / Redo functionality, which allows you to undo or redo changes to your collection, or items.

Undo and Redo actions can be triggered using keyboard shortcuts or with the "History" action in the [Collection Header](/docs/introduction#collection-header).

![History](/docs_images/productivity/datablist-history.png#1464x498)

## Undo

To undo a change, press Ctrl+Z (Windows) or Cmd+Z (Mac). Undo/Redo is currently available for the following changes:

- **Collection name** - Revert changes in the collection name
- **Collection icon** - Revert to previous collection icon
- **New item** - Delete newly created item
- **Item edit** - Revert your changes done through the [drawer](/docs/introduction#drawer) or the [Data Table](/docs/introduction#data-table)
- **Item delete** - Revert deletes for single items or bulk deletes
- **Column resize** - Revert column size changes in the [Data Table](/docs/introduction#data-table)

> **Warning:** Opening another collection clears history and prevents undoing previous changes


## Redo

If you need to redo an action, press Ctrl+Shift+Z (Windows) or Cmd+Shift+Z (Mac).


Learn how to undo any undesired change with Datablist Undo and Redo feature.

Undo and Redo user actions in Datablist

Datablist provides an Undo / Redo functionality, which allows you to undo or redo changes to your collection, or items.

Undo and Redo actions can be triggered using keyboard shortcuts or with the "History" action in the Collection Header.

History

Undo

To undo a change, press Ctrl+Z (Windows) or Cmd+Z (Mac). Undo/Redo is currently available for the following changes:

Collection name - Revert changes in the collection name

Collection icon - Revert to previous collection icon

New item - Delete newly created item

Item edit - Revert your changes done through the drawer or the Data Table

Item delete - Revert deletes for single items or bulk deletes

Column resize - Revert column size changes in the Data Table

Warning: Opening another collection clears history and prevents undoing previous changes

Redo

If you need to redo an action, press Ctrl+Shift+Z (Windows) or Cmd+Shift+Z (Mac).


Undo and Redo


Learn how to use Datablist as quickly as possible with these keyboard shortcuts.

> **Note:** **Cmd/Ctrl** means **Ctrl** on Windows (or Chrome) OS and **Cmd (⌘)** on Mac OS


## General

- Open command bar with **Cmd/Ctrl + k**
- Use **n** to create a new empty collection
- Use **Cmd/Ctrl + z** to [undo an action](/docs/undo-redo)
- Use **Cmd/Ctrl + Shift + z** to [redo an action](/docs/undo-redo)
- Back or Close drawer/modal with **Esc**


<br/>


## On the DataTable

- Use **c** while on a collection's page to create a new item
- Use **Space** to open drawer for current item
- Use **Enter** to commit cell changes
- Use **Esc** to cancel cell changes in edit mode
- Use **Cmd/Ctrl** + **f** to search inside the collection
- Use **Cmd/Ctrl** + **c** to copy selected cells or items
- Use **Cmd/Ctrl** + **v** to paste data


<br/>

**Moving cursor**

- Move cursor right **→**
- Move cursor left **←**
- Move cursor up **↑**
- Move cursor down **↓**
- Use **Tab** to move to next cell (committing changes when pressed while editing)
- Use **Shift** + **Tab** to move to previous cell
- Move cursor to end of row with **Cmd/Ctrl** + **→**
- Move cursor to beginning of row with **Cmd/Ctrl** + **←**
- Move cursor to beginning of row with **Cmd/Ctrl** + **↑**
- Move cursor to end of items with **Cmd/Ctrl** + **↓**

<br/>

**Selecting Items**

- Use **Cmd/Ctrl** + **a** to select all items
- Use **Shift** and move up **↑** or down **↓** to select items
- Select an item checkbox and click on another item checkbox while pressing **Shift** to select all items in between


Learn how to use Datablist as quickly as possible with keyboard shortcuts.

Keyboard Shortcuts

Learn how to use Datablist as quickly as possible with these keyboard shortcuts.Note: Cmd/Ctrl means Ctrl on Windows (or Chrome) OS and Cmd (⌘) on Mac OSGeneralOpen command bar with Cmd/Ctrl + kUse n to create a new empty collectionUse Cmd/Ctrl + z to undo an actionUse Cmd/Ctrl + Shift + z to redo an actionBack or Close drawer/modal with EscOn the DataTableUse c while on a collection's page to create a new itemUse Space to open drawer for current itemUse Enter to commit cell changesUse Esc to cancel cell changes in edit modeUse Cmd/Ctrl + f to search inside the collectionUse Cmd/Ctrl + c to copy selected cells or itemsUse Cmd/Ctrl + v to paste dataMoving cursorMove cursor right →Move cursor left ←Move cursor up ↑Move cursor down ↓Use Tab to move to next cell (committing changes when pressed while editing)Use Shift + Tab to move to previous cellMove cursor to end of row with Cmd/Ctrl + →Move cursor to beginning of row with Cmd/Ctrl + ←Move cursor to beginning of row with Cmd/Ctrl + ↑Move cursor to end of items with Cmd/Ctrl + ↓Selecting ItemsUse Cmd/Ctrl + a to select all itemsUse Shift and move up ↑ or down ↓ to select itemsSelect an item checkbox and click on another item checkbox while pressing Shift to select all items in between




Datablist uses a database inside your web browser to store your data. And this database is synced in the cloud (check [Pricing](/pricing)). This local database is the reason Datablist is fast. The application doesn't fetch data every time from the server.
Sometimes, Datablist can't store data in your web browser. This can happen for several reasons:

- Your hard drive is full. For example, Chrome and Chromium-based browsers allow up to 80% of disk space to be used by web applications.
- A setting in your web browser limits the storage available.
- Your operating system (Windows, MacOs, etc.) has reached a system limit and must be rebooted.


## How to fix it?

- First, check that your hard drive has free space
- Then, try restarting your web browser
- If it doesn't solve the issue, try rebooting your computer
- And if you still have the problem, check your browser data configuration. See below.


## Check Chrome configuration

If the issue persists, check the "site data settings" to know if Datablist is allowed to store data.

Open a new tab and enter `chrome://settings/content/siteData`.

![Default Site Data setting](/docs_images/troubleshooting/chrome-memory-issue.png#2152x1150)

If the default behavior is to not allow sites to save data, you must create an exception for Datablist.

Click on the "Add" button next to the "Allowed to save data on your device" section. And add `[*.]datablist.com`.

![Add an exception for datablist](/docs_images/troubleshooting/chrome-site-data-exception.png#2140x1110)






Browser Memory Error

Datablist uses a database inside your web browser to store your data. And this database is synced in the cloud (check Pricing). This local database is the reason Datablist is fast. The application doesn't fetch data every time from the server.
Sometimes, Datablist can't store data in your web browser. This can happen for several reasons:

Your hard drive is full. For example, Chrome and Chromium-based browsers allow up to 80% of disk space to be used by web applications.

A setting in your web browser limits the storage available.

Your operating system (Windows, MacOs, etc.) has reached a system limit and must be rebooted.

How to fix it?

First, check that your hard drive has free space

Then, try restarting your web browser

If it doesn't solve the issue, try rebooting your computer

And if you still have the problem, check your browser data configuration. See below.

Check Chrome configuration

If the issue persists, check the "site data settings" to know if Datablist is allowed to store data.

Open a new tab and enter chrome://settings/content/siteData.

Default Site Data setting

If the default behavior is to not allow sites to save data, you must create an exception for Datablist.

Click on the "Add" button next to the "Allowed to save data on your device" section. And add \[\*.]datablist.com.

Add an exception for datablist



When exporting a collection into a Microsoft Excel file, you might get an error saying a cell contains more than 32767 characters.

**This error message is raised because Microsoft Excel has a character limit of 32,767 characters in each cell.**

[See Microsoft Excel limitations](https://support.microsoft.com/en-gb/office/excel-specifications-and-limits-1672b34d-7043-467e-8e27-269d656771c3).

![Excel cell limit](/docs_images/troubleshooting/excel-cell-limit.png#1744x1180)



Datablist prevents you from creating an invalid Microsoft Excel file. **Most of the time you get this error because your file was imported incorrectly.** Datablist allows the import of text-based files such as CSV, TSV, etc., and Excel files. Datablist doesn't handle binary files, SQL files, etc.
 
If your data is valid but you have a cell with very long text. You can get around this limitation by exporting your collection into a [CSV file](/csv-editor).








Excel cell limit reached

When exporting a collection into a Microsoft Excel file, you might get an error saying a cell contains more than 32767 characters.

This error message is raised because Microsoft Excel has a character limit of 32,767 characters in each cell.

See Microsoft Excel limitations.

Excel cell limit

Datablist prevents you from creating an invalid Microsoft Excel file. Most of the time you get this error because your file was imported incorrectly. Datablist allows the import of text-based files such as CSV, TSV, etc., and Excel files. Datablist doesn't handle binary files, SQL files, etc.

If your data is valid but you have a cell with very long text. You can get around this limitation by exporting your collection into a CSV file.


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