pandera | A light-weight , flexible , and expressive data validation | Validation library

by pandera-dev Python Version: 0.19.3 License: MIT

X-Ray Key Features Code Snippets(4)Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | pandera Summary

pandera is a Python library typically used in Utilities, Validation applications. pandera has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can install using 'pip install pandera' or download it from GitHub, PyPI.

pandera provides a flexible and expressive API for performing data validation on dataframes to make data processing pipelines more readable and robust.

Support

Quality

Security

License

Reuse

Support

pandera has a medium active ecosystem.

It has 1240 star(s) with 94 fork(s). There are 12 watchers for this library.

There were 10 major release(s) in the last 12 months.

There are 95 open issues and 295 have been closed. On average issues are closed in 34 days. There are 4 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pandera is 0.19.3

Quality

pandera has 0 bugs and 0 code smells.

Security

pandera has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pandera code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pandera is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pandera releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

pandera saves you 5652 person hours of effort in developing the same functionality from scratch.

It has 19002 lines of code, 1166 functions and 110 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed pandera and discovered the below as its top functions. This is intended to give you an instant insight into pandera implemented functionality, and help decide if they suit your requirements.

Validate the schema .
Create a dataframe strategy for a pander .
Decorator for checking types .
Initialize the model .
Register a check function .
Decorate a function to check inputs .
Validate the given output .
Creates a hypothesis test for two samples .
Creates a new field .
Decorator to check function inputs .

Get all kandi verified functions for this library.

pandera Key Features

No Key Features are available at this moment for pandera.

pandera Examples and Code Snippets

usage

Python

Lines of Code : 34

License : Permissive (MIT)

Copy

import numpy as np
import xarray as xr
from xarray_schema import DataArraySchema, DatasetSchema

da = xr.DataArray(np.ones(4, dtype='i4'), dims=['x'], name='foo')

schema = DataArraySchema(dtype=np.integer, name='foo', shape=(4, ), dims=['x'])

schem

Ways to Define the Integration

Python

Lines of Code : 2

License : Permissive (Apache-2.0)

Copy

pip install flytekit
pip install flytekitplugins-great_expectations

Hypothesis 6.x

Python

Lines of Code : 0

License : Non-SPDX (NOASSERTION)

Copy

--------------------
Current pull request
--------------------
6.61.0 - 2022-12-11
6.60.1 - 2022-12-11
6.60.0 - 2022-12-04
6.59.0 - 2022-12-02
6.58.2 - 2022-11-30
6.58.1 - 2022-11-26
6.58.0 - 2022-11-19
6.57.1 - 2022-11-14
6.57.0 - 2022-11-14
6.56.4

Pandas dataframe schema validation for combination of columns

Python

Lines of Code : 38

License : Strong Copyleft (CC BY-SA 4.0)

Copy

import pandera as pa

schema = pa.DataFrameSchema(
    columns={
        "x_coord": pa.Column(pa.Float, pa.Check.in_range(0, 100_000)),
        "y_coord": pa.Column(pa.Float, pa.Check.in_range(0, 100_000)),
        "value_A": pa.Column(pa.

Community Discussions

Trending Discussions on pandera

How do I validate a value in a dataframe which is dependent on other value in that specific row?

Pandas dataframe schema validation for combination of columns

QUESTION

How do I validate a value in a dataframe which is dependent on other value in that specific row?

Asked 2021-Dec-21 at 01:54

Suppose I have a .csv which follows this format:

Name, Salary, Department, Mandatory

Rob, 5500, Aviation, Yes

Bob, 1000, Facilities, No

Tom, 6000, IT, Yes

After exporting this to pandas/modin, I'd like to perform row-differentiated checks, where:

People named Rob working in aviation cannot earn less than 5000
People named Bob working in facilities cannot earn less than 1000
Whoever works in facilities has to report their salary, while people working in aviation or IT can choose to leave their salary unreported.
If any check is violated, we store this in a dataframe and pass forward this case to the human resources department for further investigation.

How would you validate this .csv using Pandera?

Sorry if that is a noobish question but I've read the entire Pandera documentation from A to Z and found no straightforward answer to the task at hand.

...

ANSWER

Answered 2021-Dec-21 at 01:54

Depending on which API you're using, you can check out the wide checks for the object-based API or dataframe checks for the class-based API.

Note: the code snippets below aren't tested, but should be going in the right direction

Class-based API:

Source https://stackoverflow.com/questions/70420536

QUESTION

Pandas dataframe schema validation for combination of columns

Asked 2021-Jan-14 at 14:59

I am developing Pandas DataFrame Schema validation code (in python) using pandera and am looking for the best approach to verify a DataFrame has unique values for a combination of columns.

The original data is supplied by others and is in a CSV format. My code loads the CSV into a Pandas DataFrame and then does a pandera DataFrameSchema validate The dataframe has columns for geographic coordinate system using X and Y coordinates. The nature of the data is that each row in the data set should have a unique X,Y coordinate.

The csv file has the general form:
x_coord, y_coord, value_A, value_B
12.1234, 23.2345, 27.23, 32.84
34.3456, 45.4567, 21.12, 22.32
....
....

Using pandera, the only way that I can think of doing this is:

Take a multi-step approach:

Load the csv file into a pandas DataFrame.
Create a pandas single column DataFrame where the column name is (say) 'coords' and the values are generated from the string combination of the csv DataFrame coordinate columns.
Validate the the coords DataFrame with a pandera DataFrameSchema that has a column check for uniqueness in that column using a pandera column with allow_duplicates=False.
Validate the csv DataFrame with its own pandera schema
Combine the schema errors from the two schema validations and raise that as an error.

The approach seems a little clunky, and I am looking for other options that take more advantage of the flexibility in pandera.

Code to implement the above is:

...

ANSWER

Answered 2021-Jan-14 at 14:59

Here you can use wide checks to have access to the entire dataframe in the Check function arg:

Source https://stackoverflow.com/questions/65714703

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pandera

You can install using 'pip install pandera' or download it from GitHub, PyPI.
You can use pandera like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.