# CSV file import

The CSV file import tool reads row by row from loaded CSV files, each containing
separate data for a specific node label or a relationship type. It binds the
contents of the parsed row to the specified variable and either merges the data
with the current dataset or drops the database and starts from scratch,
depending on the user's choice.

> **Note**
>
> For larger datasets, it is still recommended to use [LOAD
> CSV](https://memgraph.com/docs/data-migration/csv#load-csv-cypher-clause) as the [best practice for
> importing](https://memgraph.com/docs/data-migration/best-practices). The CSV file import tool is
> convenient for importing smaller amounts of data or for providing code snippets
> of Cypher queries for import, eliminating the struggle of constructing them
> yourself.

![](https://memgraph.com/docs/pages/data-migration/csv/csv_import.png)

### Import files WITH and NO HEADER

The goal of this example is to import two CSV files.

One file contains data we will use to create nodes labeled `:Person` and the
other file will be used to connect those nodes with the `:IS_FRIENDS_WITH`
relationship.

There are also two variations of the files, files with a header and files
without a header.

  
### Download the files

  **CSV files with a header**

- [`people_nodes_wh.csv`](https://memgraph.com/docs/data/load-csv-cypher/one-type-nodes/with-header/people_nodes.csv)

    This file contains the following data:
```plaintext
id,name
100,Daniel
101,Alex
102,Sarah
103,Mia
104,Lucy
```
  - [`people_relationships_wh.csv`](https://memgraph.com/docs/data/load-csv-cypher/one-type-nodes/with-header/people_relationships.csv)

    The file contains the following data:
```plaintext
id_from,id_to
100,101
100,102
100,103
101,103
102,104
```

**CSV files without a header**

- [`people_nodes_nh.csv`](https://memgraph.com/docs/data/load-csv-cypher/one-type-nodes/no-header/people_nodes.csv)

      The file contains the following data:
```plaintext
100,Daniel
101,Alex
102,Sarah
103,Mia
104,Lucy
```

    - [`people_relationships_nh.csv`](https://memgraph.com/docs/data/load-csv-cypher/one-type-nodes/no-header/people_relationships.csv)

      The file contains the following data:
```plaintext
100,101
100,102
100,103
101,103
102,104
```

  
### Upload files

  Upload your files by selecting them from your local filesystem or using a drag and drop method. Once all your files are uploaded, proceed to configure each one separately.

  Let's start by configuring the nodes by clicking on the `people_nodes.csv` file.

  ![](https://memgraph.com/docs/pages/data-migration/csv/file_upload.png)

  If your CSV files use a delimiter other than a comma, you can adjust the default delimiter before uploading the files:

  ![](https://memgraph.com/docs/pages/data-migration/csv/csv-import-delimiter-default.png)

  Alternatively, you can set a specific delimiter for each file after uploading, which is useful if your files use different delimiters:

  ![](https://memgraph.com/docs/pages/data-migration/csv/csv-import-delimiter-single.png)

  
### Configure nodes

  **CSV files with a header**

After clicking on the `+ Add node` button for our CSV file with a header, make sure you select the checkbox on the left side of the interface indicating that the file has a header row. Just below that checkbox, the preview of the detected properties and the first row of your file will be displayed.

      During configuration, start by adding a **Node label** and choose whether to include an index with the label.

      
> **Note**
>
> [Index data](https://memgraph.com/docs/fundamentals/indexes) carefully as indexes do speed up queries but also take up memory.

      Let's add the label `:Person` and leave the index checkbox empty since we currently only have one type of node and using an index won't impact performance.

      Next, add properties. In the dropdown, you can choose from the properties identified by the import tool from the header of the file. Select the ones you need and customize each property by renaming it, defining the type and specifying whether it should be `Unique`, `Required` or `Indexed`.

      Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

      The image below illustrates a possible configuration:
      - Since nodes are identified by the `id` property, the selected checkboxes indicate uniqueness, requirement, and indexing of that property.
      - For the configuration of the `name` property, not all nodes need unique names, but having that property is necessary, so the required checkbox is marked.

      ![](https://memgraph.com/docs/pages/data-migration/csv/people_nodes_with_header.png)

**CSV files without a header**

After clicking on the `+ Add node` button for our CSV file without a header, make sure you unselect the checkbox on the left side of the interface indicating that the file has a header row. Just below that checkbox, the preview of the first row of your file by columns will be displayed.

      During configuration, start by adding a **Node label** and choose whether to include an index with the label.

      
> **Note**
>
> [Index data](https://memgraph.com/docs/fundamentals/indexes) carefully as indexes do speed up queries but also take up memory.

      Let's add the label `:Person` and leave the index checkbox empty since we currently only have one type of node and using an index won't impact
      performance.

      Next, add properties. In the dropdown, you can choose columns identified
      by the import tool along with the previews of the first row for easier
      managing. Select the ones you need and customize each property by renaming
      it, defining the type and specifying whether it should be `Unique`,
      `Required` or `Indexed`.

      Finally, choose how to handle duplicates: `Import everything`, `Merge` or
      `Skip`.

      The image below illustrates a possible configuration:
      - Since nodes are identified by the `id` property, the selected checkboxes
      indicate uniqueness, requirement, and indexing of that property.
      - For the configuration of the `name` property, not all nodes need unique
      names, but having that property is necessary, so the required checkbox is
      marked.

      ![](https://memgraph.com/docs/pages/data-migration/csv/people_nodes_without_header.png)

  

  
### Configure relationships

  Let's now start with configuring the relationships by clicking on the
  `people_relationships.csv` file.

  **CSV files with a header**

After clicking on the `+ Add edge` button for our CSV file with a header,
      make sure you select the checkbox on the left side of the interface
      indicating that the file has a header row. Again, the preview of the
      detected properties and the first row of your file will be displayed.

      During configuration, start by adding an **Edge type**, in our case
      `IS_FRIENDS_WITH`.

      Moving on, let's add the **Node labels** for both the starting and ending
      nodes of the relationship. Since all our nodes have the label `:Person`,
      we'll select the `:Person` label for both.
      It's important to understand the schema of your data at this point to
      choose the appropriate labels.

      After selecting labels, add the extra property condition that specifies
      which node property corresponds to the selected property from the CSV file
      containing relationships.

      There's an option to add edge properties, but since our relationships
      don't have any properties, we can skip that step.

      Once again, finalize your configuration by choosing how to handle
      duplicates: `Import everything`, `Merge` or `Skip`.

      The image below illustrates a possible relationship configuration:

      ![](https://memgraph.com/docs/pages/data-migration/csv/people_relationships_with_header.png)

**CSV files without a header**

After clicking on the `+ Add edge` button for our CSV file without a header,
      make sure you unselect the checkbox on the left side of the interface
      indicating that the file has a header row. Again, the preview of the
      columns and values of the first row of your file will be displayed.

      During configuration, start by adding an Edge type, in our case
      `IS_FRIENDS_WITH`.

      Moving on, let's add the Node labels for both the starting and ending
      nodes of the relationship. Since all our nodes have the label `:Person`,
      we'll select the `:Person` label for both.
      It's important to understand the schema of your data at this point to
      choose the appropriate labels.

      After selecting labels, add the extra property condition that specifies
      which node property corresponds to the selected column from the CSV file
      containing relationships.

      There's an option to add edge properties, but since our relationships
      don't have any properties, we can skip that step.

      Once again, finalize your configuration by choosing how to handle
      duplicates: `Import everything`, `Merge` or `Skip`.

      The image below illustrates a possible relationship configuration:

      ![](https://memgraph.com/docs/pages/data-migration/csv/people_relationships_without_header.png)

  

  
### Import data

  Once you've configured your files, Memgraph proceeds through four steps to
  import data:

  1. **Validating files**: Ensuring the consistency of data format across all CSV files.
  2. **Uploading to Memgraph**: Uploading files to prepare them for validation.
  3. **Import progress**: Setting up the database, creating constraints
  (selected in the configuration, such as uniqueness and existence), establishing
  indexes and importing nodes and relationships.
  4. **Database cleanup**: Removing the files from Memgraph.

  The import process is complete! The data has been successfully imported into
  Memgraph and now it's time to head over to the `Query execution` window to
  explore your dataset and execute queries.

  ![](https://memgraph.com/docs/pages/data-migration/csv/import_successful.png)

  
### Final result

  Run the following query to see how the imported data looks as a graph:

```
MATCH p=()-[]-() RETURN p;
```
  ![](https://memgraph.com/docs/pages/data-migration/csv/graph_illustration.png)

____

### Import data from a single CSV file

In this example, we'll use the same data from the previous section, but imported
from the same CSV file.

  
### Download the file

  - [`nodes_and_relationships.csv`](https://memgraph.com/docs/data/load-csv-cypher/import-tool-single/nodes_and_relationships.csv)
  is used to create nodes and relationships.

  The file contains four columns, two containing properties `id` and `name` for
  nodes labeled with `:Person` and two containing `id` property of nodes the
  relationship `:IS_FRIENDS_WITH` is connecting.

  

  
### Configure nodes

  After uploading the file, we need to configure it.

  Let's start by configuring nodes. Select the `+ Add node` button and assign
  the label `:Person` to the nodes. After adding the label, choose properties
  from the dropdown from the CSV related to the nodes, in this case, `id` and
  `name`.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/single_people_nodes.png)

  

  - Since `id` is the key property for identifying nodes, it needs to be
  unique, required and optionally indexed.
  - The `name` property is a string property required for a node, but not
  every node needs to have a unique name property.

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  

  
### Configure relationships

  Let's now configure edges connecting the nodes, with relationship type
  `IS_FRIENDS_WITH`.

  After selecting `+ Add Edge`, assign the `IS_FRIENDS_WITH` relationship type.
  We now need to choose the **Start node** and the **End node** label.
  Since relationship type `IS_FRIENDS_WITH` only connects nodes with `:Person`
  label, both our `Start node` label and `End node` label is going to be
  `:Person`.

  Select the properties from the CSV file that corresponds to the id of the person
  relationship is connecting and add edge properties if they exists.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/single_people_relationships.png)

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  

  
### Final result

  After configuring files and successfully importing data into Memgraph,
  navigate to the **Query execution** window and execute the following query to
  visualize the imported data as a graph:

```
MATCH p=()-[]-() RETURN p;
```

  ![](https://memgraph.com/docs/pages/data-migration/csv/graph_illustration.png)

____

### Import data from multiple CSV files

In this example, the same data from the previous example is split across four
files, each file containing nodes of a single label or relationships of a single
type. All files have a header.

  
### Download the files

  - [`people_nodes.csv`](https://memgraph.com/docs/data/load-csv-cypher/multiple-types-nodes/people_nodes.csv) is used to create nodes labeled `:Person`.
 The file contains the following data:
```csv
id,name,age,city
100,Daniel,30,London
101,Alex,15,Paris
102,Sarah,17,London
103,Mia,25,Zagreb
104,Lucy,21,Paris
```
- [`restaurants_nodes.csv`](https://memgraph.com/docs/data/load-csv-cypher/multiple-types-nodes/restaurants_nodes.csv) is used to create nodes labeled `:Restaurants`.
 The file contains the following data:
```csv
id,name,menu
200,Mc Donalds,Fries;BigMac;McChicken;Apple Pie
201,KFC,Fried Chicken;Fries;Chicken Bucket
202,Subway,Ham Sandwich;Turkey Sandwich;Foot-long
203,Dominos,Pepperoni Pizza;Double Dish Pizza;Cheese filled Crust
```

- [`people_relationships.csv`](https://memgraph.com/docs/data/load-csv-cypher/multiple-types-nodes/people_relationships.csv) is used to connect people with the `:IS_FRIENDS_WITH` relationship.
 The file contains the following data:
```csv
first_person,second_person,met_in
100,102,2014
103,101,2021
102,103,2005
101,104,2005
104,100,2018
101,102,2017
100,103,2001
```
-  [`restaurants_relationships.csv`](https://memgraph.com/docs/data/load-csv-cypher/multiple-types-nodes/restaurants_relationships.csv) is used to connect people with restaurants using the `:ATE_AT` relationship.
 The file contains the following data:
```csv
PERSON_ID,REST_ID,liked
100,200,true
103,201,false
104,200,true
101,202,false
101,203,false
101,200,true
102,201,true
```

  
### Data schema

  When configuring files, it's important to understand the schema of the data
  being imported.

  The image below illustrates the structure of the data schema:

  ![](https://memgraph.com/docs/pages/data-migration/csv/data_schema.png)

  
### Configure nodes

  After uploading all four files in the import window, we need to configure each
  one individually. Let's start by configuring the files containing nodes,
  `people_nodes.csv` and `restaurants_nodes.csv`.

  For the **`people_nodes.csv`** file, we'll assign the `:People` label. Since we
  now have more than one type of node labels, we'll add an index to enhance the
  query speed.

  Next, let's add properties. In the dropdown, select all available properties:
  `id`, `name`, `age` and `city`.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/multiple_people_nodes.png)

  

  - Since `id` is the key property for identifying nodes, it needs to be unique,
  required and optionally indexed.
  - The `name` property is a string property
  required for a node, but not every node needs to have a unique name property.
  - The `age` property is an optional integer property that neither has to exist
  nor be unique.
  - The `city` is an optional string property that neither has to exist nor be
  unique as well.

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  For the **`restaurant_nodes.csv`** file, we'll assign the `:Restaurants` label and
  again add an index to the label.

  Next, let's add properties. In the dropdown, select all available properties:
  `id`, `name` and `menu`.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/multiple_restaurant_nodes.png)

  

  - Since `id` is the key property for identifying nodes, it needs to be unique,
  required and optionally indexed.
  - The `name` property is a string property required for a node, but not every
  node needs to have a unique name property.
  - The `menu` is an optional string property that neither has to exist nor be
  unique.

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  

  
### Configure relationships

  Let's now configure the files containing relationships, `people_relationships.csv`
  and `restaurants_relationships.csv`.

  For the **`people_relationships.csv`** file, we'll assign the `IS_FRIENDS_WITH`
  relationship type. We also need to choose the **Start node** and the
  **End node** label.
  For this step, it's important to know the schema of the data. Since
  relationship type `IS_FRIENDS_WITH` only connects nodes with `:People` label,
  both our `Start node` label and `End node` label is going to be `:People`.

  Next, let's add properties. In the dropdown, select the property `met_in`.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/multiple_people_relationships.png)

  

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  For the **`restaurants_relationships.csv`** file, we'll assign the `ATE_AT`
  relationship type. We also need to choose the **Start node** and the **End node**
  label. From our data schema, relationship type `ATE_AT` connects nodes with
  `:People` label to nodes with `:Restaurants` label.

  Next, let's add properties. In the dropdown, select the property `liked`.
  Since values of that property are either `true` or `false`, the type of the
  property can be defined as boolean.

  The image below illustrates a possible configuration:

  ![](https://memgraph.com/docs/pages/data-migration/csv/multiple_restaurants_relationships.png)

  Finally, choose how to handle duplicates: `Import everything`, `Merge` or `Skip`.

  

  
### Final result

  After configuring files and successfully importing data into Memgraph,
  navigate to the Query execution window and execute the following query to
  visualize the imported data as a graph:

```
MATCH p=()-[]-() RETURN p;
```

  ![](https://memgraph.com/docs/pages/data-migration/csv/graph_illustration_1.png)