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Version: 0.14.13

How to connect to data on a filesystem using Pandas

This guide will help you connect to your data stored on a filesystem using pandas. This will allow you to ValidateThe act of applying an Expectation Suite to a Batch. and explore your data.

Prerequisites: This how-to guide assumes you have:
  • Completed the Getting Started Tutorial
  • Have a working installation of Great Expectations
  • Have access to data on a filesystem


1. Choose how to run the code in this guide

Get an environment to run the code in this guide. Please choose an option below.

If you use the Great Expectations CLICommand Line Interface, run this command to automatically generate a pre-configured Jupyter Notebook. Then you can follow along in the YAML-based workflow below:

great_expectations datasource new

2. Instantiate your project's DataContext

Import these necessary packages and modules.

from ruamel import yaml

import great_expectations as ge
from great_expectations.core.batch import BatchRequest, RuntimeBatchRequest

Load your Data ContextThe primary entry point for a Great Expectations deployment, with configurations and methods for all supporting components. into memory using the get_context() method.

context = ge.get_context()

3. Configure your Datasource

Using this example configuration add in the path to a directory that contains some of your data:

datasource_yaml = f"""
name: taxi_datasource
class_name: Datasource
module_name: great_expectations.datasource
module_name: great_expectations.execution_engine
class_name: PandasExecutionEngine
class_name: RuntimeDataConnector
- default_identifier_name
class_name: InferredAssetFilesystemDataConnector
base_directory: <PATH_TO_YOUR_DATA_HERE>
- data_asset_name
pattern: (.*)

Run this code to test your configuration.


If you specified a directory containing CSV files you will see them listed as Available data_asset_names in the output of test_yaml_config().

Feel free to adjust your configuration and re-run test_yaml_config() as needed.

4. Save the Datasource configuration to your DataContext

Save the configuration into your DataContext by using the add_datasource() function.


5. Test your new Datasource

Verify your new DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems. by loading data from it into a ValidatorUsed to run an Expectation Suite against data. using a Batch RequestProvided to a Datasource in order to create a Batch..

Add the path to your CSV in the path key under runtime_parameters in your BatchRequest.

batch_request = RuntimeBatchRequest(
data_asset_name="<YOUR_MEANINGFUL_NAME>", # This can be anything that identifies this data_asset for you
runtime_parameters={"path": "<PATH_TO_YOUR_DATA_HERE>"}, # Add your path here.
batch_identifiers={"default_identifier_name": "default_identifier"},

Then load data into the Validator.

expectation_suite_name="test_suite", overwrite_existing=True
validator = context.get_validator(
batch_request=batch_request, expectation_suite_name="test_suite"

🚀🚀 Congratulations! 🚀🚀 You successfully connected Great Expectations with your data.

Additional Notes

If you are working with nonstandard CSVs, read one of these guides:

To view the full scripts used in this page, see them on GitHub:

Next Steps

Now that you've connected to your data, you'll want to work on these core skills: