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Version: 0.14.13

How to connect to in-memory data in a Pandas dataframe

This guide will help you connect to your data that is an in-memory Pandas dataframe. 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 in a Pandas dataframe


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.

import pandas as pd
from ruamel import yaml

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

Load your DataContext into memory using the get_context() method.

context = ge.get_context()

3. Configure your Datasource

Using this example configuration we configure a RuntimeDataConnector as part of our DatasourceProvides a standard API for accessing and interacting with data from a wide variety of source systems., which will take in our in-memory frame.:

datasource_yaml = f"""
name: example_datasource
class_name: Datasource
module_name: great_expectations.datasource
module_name: great_expectations.execution_engine
class_name: PandasExecutionEngine
class_name: RuntimeDataConnector
- default_identifier_name

Run this code to test your configuration.


Note: Since the Datasource does not have data passed-in until later, the output will show that no data_asset_names are currently available. This is to be expected.

4. Save the Datasource configuration to your DataContext

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


6. Test your new Datasource

Verify your new Datasource by loading data from it into a Validator using a RuntimeBatchRequest.

The dataframe we are using in this example looks like the following

Please feel free to substitute your data.

df = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7, 8, 9]], columns=["a", "b", "c"])

Add the variable containing your dataframe (df in this example) to the batch_data key under runtime_parameters in your RuntimeBatchRequest.

batch_request = RuntimeBatchRequest(
data_asset_name="<YOUR_MEANINGFUL_NAME>", # This can be anything that identifies this data_asset for you
runtime_parameters={"batch_data": df}, # df is your dataframe
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

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: