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توضیحات

Enforce column names & data types of pandas DataFrames

ویژگی	مقدار
سیستم عامل	-
نام فایل	dataenforce-0.1.2
نام	dataenforce
نسخه کتابخانه	0.1.2
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Cedric Canovas
ایمیل نویسنده	dev@canovas.me
آدرس صفحه اصلی	https://github.com/CedricFR/dataenforce
آدرس اینترنتی	https://pypi.org/project/dataenforce/
مجوز	-

# Overview `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 `dataenforce` folder. # 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. ## Type-hinting: `Dataset` The `Dataset` type indicates that we expect a `pandas.DataFrame` ### 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 ``` ### dtype checking ``` 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 ``` ## Enforcing: `@validate` The `@validate` decorator ensures that input `Dataset`s have the right format when the function is called, otherwise raises `TypeError`. ``` 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 `dataenforce` uses `pytest` as a testing library. If you have `pytest` installed, just run `PYTHONPATH="." pytest` in the command line while being in the root folder. # Notes * You can use `dataenforce` to type-hint the return value of a function, but it is not currently possible to `validate` it (it is not included in the checks) * You can't use `@validate` on 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 * `dataenforce` is 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

نحوه نصب

نصب پکیج whl dataenforce-0.1.2:

pip install dataenforce-0.1.2.whl

نصب پکیج tar.gz dataenforce-0.1.2:

pip install dataenforce-0.1.2.tar.gz