strictly-typed-pandas 0.1.4

Static type checking of pandas DataFrames

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

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.

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 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 –typeguard-packages=foo.bar (where foo.bar is your package name).

Installation

pip install strictly-typed-pandas

Documentation

For example notebooks and API documentation, please see our ReadTheDocs.

FAQ

How is this different from Dataenforce / Pandera?

The main difference: strictly_typed_pandas works really well with mypy, allowing you to catch many of the errors during linting-time (i.e. while your coding), rather than during run-time.

Why use Python if you want static typing?

There are just so many good packages for data science in Python. Rather than sacrificing all of that by moving to a different language, I’d like to make the Pythonverse a little bit better.

I found a bug! What should I do?

Great! Contact me and I’ll look into it.

I have a great idea to improve strictly_typed_pandas! How can we make this work?

Awesome, drop me a line!