dfschema 0.0.11

lightweight pandas.DataFrame schema

DFS (aka Dataframe_Schema)

DFS is a lightweight validator for pandas.DataFrame. You can think of it as a jsonschema for dataframe.

Key features:

1. Lightweight: only dependent on pandas and pydantic (which depends only on typing_extensions)
2. Explicit: inspired by JsonSchema, all schemas are stored as json (or yaml) files and can be generated or changed on the fly.
3. Simple: Easy to use, no need to change your workflow and dive into the implementation details.
4. Comprehensive: Summarizes all errors in a single summary exception, checks for distributions, works on subsets of the dataframe
5. Rapid: base schemas can be generated from given dataframe or sql query (using pd.read_sql).
6. Handy: Supports command line interface (with [cli] extra).
7. Extendable: Core idea is to validate dataframes of any type. While now supports only pandas, we'll add abstractions to run same checks on different types of dataframes (CuDF, Dask, SparkDF, etc )

QuickStart

1. Validate DataFrame

Via wrapper

import pandas as pd
import dfschema as dfs


df = pd.DataFrame({
  "a": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
  "b": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
})

schema_pass = {
  "shape": {"min_rows": 10}
}

schema_raise = {
  "shape": {"min_rows": 20}
}


dfs.validate(df, schema_pass)  # won't raise any issues
dfs.validate(df, schema_raise) # will Raise DataFrameSchemaError

Alternatively (v2 optional), you can use the root class, DfSchema:

dfs.DfSchema.from_dict(schema_pass).validate(df)  # won't raise any issues
dfs.DfSchema.from_dict(schema_raise).validate(df)  # will Raise DataFrameSchemaError

2. Generate Schema

dfs.DfSchema.from_df(df)

3. Read and Write Schemas

schema = dfs.DfSchema.from_file('schema.json')
schema.to_file("schema.yml")

4. Using CLI

Note: requires [cli] extra as relies on Typer and click

Validate via CLI

dfschema validate --read_kwargs_json '{delimiter="|"}' FILEPATH SCHEMA_FILEPATH

Supports

• csv
• xlsx
• parquet
• feather

Generate via CLI

dfs generate --format 'yaml' DATA_PATH > schema.yaml

Installation

WIP

Alternatives

• TableScheme
• GreatExpectations. Large and complex package with Html reports, Airflow Operator, connectors, etc. an work on out-of-memory data, SQL databases, parquet, etc
• Pandera - awesome package, great and suitable for type hinting, compatible with hypothesis
  • great talk
• Tensorflow validate
• DTF expectations

Changes

• [[changelog]]

Roadmap

• Add tutorial Notebook
• Support tableschema
• Support Modin models
• Support SQLAlchemy ORM models
• Built-in Airflow Operator?
• Interactive CLI/jupyter for schema generation