Enforce column names & data types of pandas DataFrames
dataenforce is a Python package used to enforce column names & types of pandas DataFrames using Python 3 type hinting.
It is a common issue in Data Analysis to pass dataframes into functions without a clear idea of which columns are included or not, and as columns are added to or removed from input data, code can break in unexpected ways. With
dataenforce, you can provide a clear interface to your functions and ensure that the input dataframes will have the right format when your code is used.
How to install
Install with pip:
pip install dataenforce
You can also pip install it from the sources, or just import the
How to use
There are two parts in
dataenforce: the type-hinting part, and the validation. You can use type-hinting with the provided class to indicate what shape the input dataframes should have, and the validation decorator to additionally ensure the format is respected in every function call.
Dataset type indicates that we expect a
Column name checking
from dataenforce import Dataset def process_data(data: Dataset["id", "name", "location"]) pass
The code above specifies that
data must be a DataFrame with exactly the 3 mentioned columns. If you want to only specify a subset of columns which is required, you can use an ellipsis:
def process_data(data: Dataset["id", "name", "location", ...]) pass
def process_data(data: Dataset["id": int, "name": object, "latitude": float, "longitude": float]) pass
The code above specifies the column names which must be there, with associated types. A combination of only names & with types is possible:
Dataset["id": int, "name"].
Reusing dataframe formats
As you're likely to use the same column subsets several times in your code, you can define them to reuse & combine them later:
DName = Dataset["id", "name"] DLocation = Dataset["id", "latitude", "longitude"] # Expects columns id, name def process1(data: DName): pass # Expects columns id, name, latitude, longitude, timestamp def process2(data: Dataset[DName, DLocation, "timestamp"]) pass
@validate decorator ensures that input
Datasets have the right format when the function is called, otherwise raises
from dataenforce import Dataset, validate import pandas as pd @validate def process_data(data: Dataset["id", "name"]): pass process_data(pd.DataFrame(dict(id=[1,2], name=["Alice", "Bob"]))) # Works process_data(pd.DataFrame(dict(id=[1,2]))) # Raises a TypeError, column name missing
How to test
pytest as a testing library. If you have
pytest installed, just run
PYTHONPATH="." pytest in the command line while being in the root folder.
- You can use
dataenforceto type-hint the return value of a function, but it is not currently possible to
validateit (it is not included in the checks)
- You can't use
@validateon a function where you use non-base class type-hints as strings (like
def f() -> "MyClass"). Issue related to PEP 563
- This work is at experimental state. It is not production-ready. Please raise issues & send pull requests if you find/solve some bugs
dataenforceis released under the Apache License 2.0, meaning you can freely use the library and redistribute it, provided Copyright is kept
- Dependencies: Pandas & Numpy
- Tested with Python 3.6, 3.7, 3.8
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