great_expectations | Always know what to expect from your data | Data Visualization library

config = """
name: my_fancy_checkpoint
config_version: 1
class_name: Checkpoint
run_name_template: "%Y-%M-foo-bar-template-$VAR"
validations:
  - batch_request:
      datasource_name: my_datasource
      data_connector_name: my_special_data_connector  

from great_expectations.data_context.types.base import DataContextConfig, DatasourceConfig, S3StoreBackendDefaults

data_context_config = DataContextConfig(
    datasources={
        "sql_warehouse": DatasourceConfig(
            class_name="Datasour  

import os
from pathlib import Path

from great_expectations.data_context import BaseDataContext
from great_expectations.data_context.types.base import (
    DataContextConfig,
)
from prefect import Flow, Parameter, task
from prefect.tasks.great_expec  

"""
This is a template for creating custom TableExpectations.
For detailed instructions on how to use it, please see:
    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_table_expectations
  

"""
This is a template for creating custom MulticolumnMapExpectations.
For detailed instructions on how to use it, please see:
    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_multicolum  

"""
This is a template for creating custom ColumnPairMapExpectations.
For detailed instructions on how to use it, please see:
    https://docs.greatexpectations.io/docs/guides/expectations/creating_custom_expectations/how_to_create_custom_column_pair  

            self.engine = sa.create_engine(connection_string, **kwargs)

import sqlalchemy as sa

make_url = import_make_url()
except ImportError:
        sa = None

/your/virtualenv/bin/pyth

df['V'] = df['B'] == df.groupby('A')['B'].transform('first')

>>> df
   A  B      V
0  1  a   True
1  1  a   True
2  2  a   True
3  3  b   True
4  1  a   True
5  2  b  False
6  3  c  False
7  1  d  False

from azure.storage.filedatalake import DataLakeFileClient

with open('/tmp/gregs_expectations.json', 'r') as file:
    data = file.read()

file = DataLakeFileClient.from_connection_string("my_connection_string",