How to configure a Pandas/filesystem Datasource

This guide shows how to connect to a Pandas Datasource such that the data is accessible in the form of files on a local or NFS type of a filesystem.

Steps

Show Docs for V2 (Batch Kwargs) API

Prerequisites: This how-to guide assumes you have already:

To add a filesystem-backed Pandas datasource do the following:

  1. Run datasource new

    From the command line, run:

    great_expectations datasource new
    
  2. Choose “Files on a filesystem (for processing with Pandas or Spark)”

    What data would you like Great Expectations to connect to?
        1. Files on a filesystem (for processing with Pandas or Spark)
        2. Relational database (SQL)
    : 1
    
  3. Choose Pandas

    What are you processing your files with?
        1. Pandas
        2. PySpark
    : 1
    
  4. Specify the directory path for data files

    Enter the path (relative or absolute) of the root directory where the data files are stored.
    : /path/to/directory/containing/your/data/files
    
  5. Give your Datasource a name

    When prompted, provide a custom name for your filesystem-backed Pandas data source, or hit Enter to accept the default.

    Give your new Datasource a short name.
     [my_data_files_dir]:
    

    Great Expectations will now add a new Datasource ‘my_data_files_dir’ to your deployment, by adding this entry to your great_expectations.yml:

    my_data_files_dir:
      data_asset_type:
        class_name: PandasDataset
        module_name: great_expectations.dataset
      batch_kwargs_generators:
        subdir_reader:
          class_name: SubdirReaderBatchKwargsGenerator
          base_directory: /path/to/directory/containing/your/data/files
      class_name: PandasDatasource
    
      Would you like to proceed? [Y/n]:
    
  6. Wait for confirmation

    If all goes well, it will be followed by the message:

    A new datasource 'my_data_files_dir' was added to your project.
    

    If you run into an error, you will see something like:

    Error: Directory '/nonexistent/path/to/directory/containing/your/data/files' does not exist.
    
    Enter the path (relative or absolute) of the root directory where the data files are stored.
    :
    

    In this case, please check your data directory path, permissions, etc. and try again.

  7. Finally, if all goes well and you receive a confirmation on your Terminal screen, you can proceed with exploring the data sets in your new filesystem-backed Pandas data source.

Show Docs for V3 (Batch Request) API

Prerequisites: This how-to guide assumes you have already:

To add a Pandas filesystem datasource, do the following:

  1. Instantiate a DataContext.

    Create a new Jupyter Notebook and instantiate a DataContext by running the following lines:

    import great_expectations as ge
    context = ge.get_context()
    
  2. List files in your directory.

    Use a utility like tree on the command line or glob to list files, so that you can see how paths and filenames are formatted. Our example will use the following 3 files in the test_directory/ folder, which is a sibling of the great_expectations/ folder in our project directory:

  3. Create or copy a yaml config.

    Parameters can be set as strings, or passed in as environment variables. In the following example, a yaml config is configured for a DataSource, with a ConfiguredAssetFilesystemDataConnector and a PandasExecutionEngine. The example yaml config will take the 3 files shown above and create 1 asset named TestAsset, with name, timestamp and size as the group names.

    Note: The ConfiguredAssetFilesystemDataConnector used in this example is closely related to the InferredAssetFilesystemDataConnector with some key differences. More information can be found in How to choose which DataConnector to use.

    Note: The base_directory path needs to be specified either as an absolute path or relative to the great_expectations/ directory.

    config = f"""
            class_name: Datasource
            execution_engine:
              class_name: PandasExecutionEngine
            data_connectors:
              my_data_connector:
                class_name: ConfiguredAssetFilesystemDataConnector
                base_directory: ../test_directory/
                glob_directive: "*.csv"
                assets:
                  MyAsset:
                    pattern: (.+)\\.csv
                    group_names:
                      - filename
            """
    

    A more complex version of the MyAsset regex definition that takes into account the naming structure of the CSV files in the base_directory/ would look like this:

    config = f"""
            ... see the config above ..
                  MyAsset:
                    pattern: (.+)_(\\d+)_(\\d+)\\.csv
                    group_names:
                      - name
                      - timestamp
                      - size
    

    Additional examples of yaml configurations can be found in the following document: How to configure a ConfiguredAssetDataConnector

  4. Run context.test_yaml_config.

    context.test_yaml_config(
        name="my_pandas_datasource",
        yaml_config=config
    )
    

    When executed, test_yaml_config will instantiate the component and run through a self_check procedure to verify that the component works as expected.

    The resulting output will look something like this:

    Attempting to instantiate class from config...
    Instantiating as a Datasource, since class_name is Datasource
    Instantiating class from config without an explicit class_name is dangerous.
    Consider adding an explicit class_name for None
        Successfully instantiated Datasource
    
    Execution engine: PandasExecutionEngine
    Data connectors:
        my_data_connector : ConfiguredAssetFilesystemDataConnector
    
        Available data_asset_names (1 of 1):
            TestAsset (3 of 3): ['abe_20201119_200.csv', 'alex_20201212_300.csv', 'will_20201008_100.csv']
    
        Unmatched data_references (0 of 0): []
    
        Choosing an example data reference...
            Reference chosen: abe_20201119_200.csv
    
            Fetching batch data...
    
            Showing 5 rows
            ...
    

    Pay attention to the “Available data_asset_names” and “Unmatched data_references” output to ensure that the regex pattern you specified matches your desired data files.

  5. Save the config.

    Once you are satisfied with the config of your new Datasource, you can make it a permanent part of your Great Expectations setup: First, create a new entry in the datasources section of your great_expectations/great_expectations.yml with the name of your Datasource (which is my_pandas_datasource in our example). Next, copy the yml snippet from Step 3 into the new entry.

    Note: Please make sure the yml is indented correctly. This will save you from much frustration.

Additional Notes

  1. For the V2 (Batch Kwargs) API, relative path locations (e.g. for the base_directory) should be specified from the perspective of the directory, in which the

    great_expectations datasource new
    

    command is executed.

  2. For the V3 (Batch Request) API, relative path locations (e.g. for the base_directory) should be specified from the perspective of the great_expectations/ directory.

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