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

 by   pandera-dev Python Version: 0.19.0b0 License: MIT

kandi X-RAY | pandera Summary

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.
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            kandi-support 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 7 major release(s) in the last 6 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 0.19.0b0

            kandi-Quality Quality

              pandera has 0 bugs and 0 code smells.

            kandi-Security 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.

            kandi-License 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.

            kandi-Reuse 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
            Pythondot img1Lines of Code : 34dot img1License : Permissive (MIT)
            copy iconCopy
            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
            Pythondot img2Lines of Code : 2dot img2License : Permissive (Apache-2.0)
            copy iconCopy
            pip install flytekit
            pip install flytekitplugins-great_expectations
              
            Hypothesis 6.x
            Pythondot img3Lines of Code : 0dot img3License : Non-SPDX (NOASSERTION)
            copy iconCopy
            --------------------
            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
            Pythondot img4Lines of Code : 38dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            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

            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:

            1. People named Rob working in aviation cannot earn less than 5000

            2. People named Bob working in facilities cannot earn less than 1000

            3. Whoever works in facilities has to report their salary, while people working in aviation or IT can choose to leave their salary unreported.

            4. 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:

            1. Load the csv file into a pandas DataFrame.
            2. 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.
            3. 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.
            4. Validate the csv DataFrame with its own pandera schema
            5. 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.

            Support

            The official documentation is hosted on ReadTheDocs: https://pandera.readthedocs.io.
            Find more information at:

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            Install
          • PyPI

            pip install pandera

          • CLONE
          • HTTPS

            https://github.com/pandera-dev/pandera.git

          • CLI

            gh repo clone pandera-dev/pandera

          • sshUrl

            git@github.com:pandera-dev/pandera.git

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