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Version: 0.15.50

How to connect to data on S3 using Pandas

This guide will help you connect to your data stored on AWS S3 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 an AWS S3 bucket

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 Batch, BatchRequest, RuntimeBatchRequest

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

context = gx.get_context()

3. Configure your Datasource​

Using this example configuration, add in your S3 bucket and path to a directory that contains some of your data:

datasource_yaml = rf"""
name: my_s3_datasource
class_name: Datasource
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: InferredAssetS3DataConnector
        bucket: <your_s3_bucket_here>
        prefix: <bucket_path_to_data>
        default_regex:
            pattern: (.*)\.csv
            group_names:
                - data_asset_name
"""

Run this code to test your configuration.

context.test_yaml_config(datasource_yaml)

If you specified an S3 path 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 S3 path to your CSV in the path key under runtime_parameters in your BatchRequest.

tip

The path you will want to use is your S3 URI, not the URL.

batch_request = RuntimeBatchRequest(
    datasource_name="version-0.15.50 my_s3_datasource",
    data_connector_name="version-0.15.50 default_runtime_data_connector_name",
    data_asset_name="version-0.15.50 <your_meangingful_name>",  # this can be anything that identifies this data_asset for you
    runtime_parameters={"path": "<path_to_your_data_here>"},  # Add your S3 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: