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typedframe

Typed wrappers over pandas DataFrames with schema validation.

TypedDataFrame is a lightweight wrapper over pandas DataFrame that provides runtime schema validation and can be used to establish strong data contracts between interfaces in your Python code.

The goal of the library is to reveal and make explicit all unclear or forgotten assumptions about your DataFrame.

Check the Official Documentation.

Quickstart

Install typedframe library:

pip install typedframe

Assume an overly simplified preprocessing code like this:

def preprocess(df: pd.DataFrame) -> pd.DataFrame:
    df = df.copy()
    c1_min, c1_max = df['col1'].min(), df['col1'].max()
    df['col1'] = 0 if c1_min == c1_max else (df['col1'] - c1_min) / (c1_max - c1_min)
    df['month'] = df['date'].dt.month
    df['comment'] = df['comment'].str.lower()
    return df

To add typedframe schema support for this transformation we will define two schema classes - for the input and for the output:

import numpy as np
from typedframe import TypedDataFrame, DATE_TIME_DTYPE

class MyRawData(TypedDataFrame):
    schema = {
        'col1': np.float64,
        'date': DATE_TIME_DTYPE,
        'comment': str,
    }


class PreprocessedData(MyRawData):
    schema = {
        'month': np.int8
    }

Then let's modify the preprocess function to take a typed wrapper MyRawData as input and return PreprocessedData:

def preprocess(data: MyRawData) -> PreprocessedData:
    df = data.df.copy()
    c1_min, c1_max = df['col1'].min(), df['col1'].max()
    df['col1'] = 0 if c1_min == c1_max else (df['col1'] - c1_min) / (c1_max - c1_min)
    df['month'] = df['date'].dt.month
    df['comment'] = df['comment'].str.lower()
    return PreprocessedData.convert(df)

As you can see the actual DataFrame can be accessed via the .df attribute of the Typed DataFrame.

Now clients of the preprocess function can easily check what are the inputs and outputs without the need to look at its internals. And if there are some unforseen changes in the data an exception will be thrown before the actual function will be invoked.

Let's check:

import pandas as pd

df = pd.DataFrame({
  'col1': [0.1, 0.2],
  'date': ['2021-01-01', '2022-01-01'],
  'comment': ['foo', 'bar']
})
df.date = pd.to_datetime(df.date)

bad_df = pd.DataFrame({
  'col1': [1, 2],
  'comment': ['foo', 'bar']
})

df2 = preprocess(MyRawData(df))
df3 = preprocess(MyRawData(bad_df))

The first call was successful. But when we've tried to pass a wrong dataframe as input we've got the following error:

AssertionError: Dataframe doesn't match schema
Actual: {'col1': dtype('int64'), 'comment': dtype('O')}
Expected: {'col1': <class 'numpy.float64'>, 'date': dtype('<M8[ns]'), 'comment': <class 'object'>}
Difference: {('col1', <class 'numpy.float64'>), ('date', dtype('<M8[ns]'))}

Supported versions

Tested on the following versions:

Python: 3.9

numpy: 1.20, 1.21, 1.22

pandas: 1.2, 1.3, 1.4

Manually test in your environment

git clone git@github.com:areshytko/typedframe.git
cd typedframe
pip install -r requirements.txt
pytest

Releases

v0.7.0

New Functionality

• NaNs in categoricals are not allowed and cause an assertion. Motivation: Explicit use of pd.Categorical(df.col, categories=[MyTypedFrame.schema['col']]) conversion can introduce such NaNs and bypass the type check. See the pd.Categorical documentation.

v0.6.1

New Functionality

• updated docstrings

Breaking changes