================================================================ Strictly Typed Pandas: static type checking of pandas DataFrames

I love Pandas! But in production code I’m always a bit wary when I see:

.. code-block:: python

import pandas as pd

def foo(df: pd.DataFrame) -> pd.DataFrame:
    # do stuff
    return df

Because… How do I know which columns are supposed to be in df?

Using strictly_typed_pandas, we can be more explicit about what these data should look like.

.. code-block:: python

from strictly_typed_pandas import DataSet

class Schema:
    id: int
    name: str

def foo(df: DataSet[Schema]) -> DataSet[Schema]:
    # do stuff
    return df

Where DataSet:

• is a subclass of pd.DataFrame and hence has the same functionality as DataFrame.
• validates whether the data adheres to the provided schema upon its initialization.
• is immutable, so its schema cannot be changed using inplace modifications.

The DataSet[Schema] annotations are compatible with:

• mypy for type checking during linting-time (i.e. while you write your code).
• typeguard (<v3.0) for type checking during run-time (i.e. while you run your unit tests).

To get the most out of strictly_typed_pandas, be sure to:

• set up mypy in your IDE.
• run your unit tests with pytest --stp-typeguard-packages=foo.bar (where foo.bar is your package name).

Installation

.. code-block:: bash

pip install strictly-typed-pandas

Documentation

For example notebooks and API documentation, please see our ReadTheDocs <https://strictly-typed-pandas.readthedocs.io/>_.

FAQ