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Version: 0.15.50

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
  • A working installation of Great Expectations
  • Have access to data on a filesystem

Steps​

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 gx
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 = gx.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
execution_engine:
  module_name: great_expectations.execution_engine
  class_name: PandasExecutionEngine
data_connectors:
    default_runtime_data_connector_name:
        class_name: RuntimeDataConnector
        batch_identifiers:
            - default_identifier_name
    default_inferred_data_connector_name:
        class_name: InferredAssetFilesystemDataConnector
        base_directory: <path_to_your_data_here>
        default_regex:
          group_names:
            - data_asset_name
          pattern: (.*)
"""

Run this code to test your configuration.

context.test_yaml_config(datasource_yaml)

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. 

context.add_datasource(**yaml.load(datasource_yaml))

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(
    datasource_name="version-0.15.50 taxi_datasource",
    data_connector_name="version-0.15.50 default_runtime_data_connector_name",
    data_asset_name="version-0.15.50 <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. 

context.add_or_update_expectation_suite(expectation_suite_name="version-0.15.50 test_suite")
validator = context.get_validator(
    batch_request=batch_request, expectation_suite_name="version-0.15.50 test_suite"
)
print(validator.head())

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

Additional Notes​

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: