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:
Run datasource new
From the command line, run:
great_expectations datasource new
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
Choose Pandas
What are you processing your files with? 1. Pandas 2. PySpark : 1
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
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]:
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.
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:
Run datasource new
From the command line, run:
great_expectations --v3-api datasource new
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
Choose Pandas
What are you processing your files with? 1. Pandas 2. PySpark : 1
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
You will be presented with a Jupyter Notebook which will guide you through the steps of creating a Datasource.
Pandas Datasource Example.
Within this notebook, you will have the opportunity to create your own yaml Datasource configuration. The following text walks through an example.
List files in your directory.
Use a utility like
tree
on the command line orglob
to list files, so that you can see how paths and filenames are formatted. Our example will use the following 3 files in thetest_directory/
folder, which is a sibling of thegreat_expectations/
folder in our project directory:- my_ge_project |- great_expectations |- test_directory |- abe_20201119_200.csv |- alex_20201212_300.csv |- will_20201008_100.csv
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 anInferredAssetFilesystemDataConnector
and aPandasExecutionEngine
.Note: The
base_directory
path needs to be specified either as an absolute path or relative to thegreat_expectations/
directory.datasource_name = "my_file_datasource" config = f""" name: {datasource_name} class_name: Datasource execution_engine: class_name: PandasExecutionEngine data_connectors: my_data_connector: datasource_name: {datasource_name} class_name: InferredAssetFilesystemDataConnector base_directory: ../test_directory/ default_regex: group_names: data_asset_name pattern: (.*) """
You can modify the group names and regex pattern to take into account the naming structure of the CSV files in the directory, e.g.
group_names: - name - timestamp - size pattern: (.+)_(\\d+)_(\\d+)\\.csv
Note: The
InferredAssetS3DataConnector
used in this example is closely related to theConfiguredAssetS3DataConnector
with some key differences. More information can be found in How to choose which DataConnector to use.Test your config using ``context.test_yaml_config``.
context.test_yaml_config( yaml_config=config )
When executed,
test_yaml_config
will instantiate the component and run through aself_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 : InferredAssetFilesystemDataConnector 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): []
This means all has gone well and you can proceed with configuring your new Datasource. If something about your configuration wasn’t set up correctly,
test_yaml_config
will raise an error.Note: 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.
- 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 configuration. The following method will save the new Datasource to your
great_expectations.yml
:sanitize_yaml_and_save_datasource(context, config, overwrite_existing=False)
Note: This will output a warning if a Datasource with the same name already exists. Use
overwrite_existing=True
to force overwriting.
Additional Notes¶
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 thegreat_expectations datasource new
command is executed.
For the V3 (Batch Request) API, relative path locations (e.g. for the
base_directory
) should be specified from the perspective of thegreat_expectations/
directory.