datacompy | Pandas and Spark DataFrame comparison for humans

by capitalone Python Version: 0.13.1 License: Apache-2.0

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

kandi X-RAY | datacompy Summary

datacompy is a Python library typically used in Big Data, Pandas, Spark applications. datacompy has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install datacompy' or download it from GitHub, PyPI.

Pandas and Spark DataFrame comparison for humans

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datacompy has a low active ecosystem.

It has 297 star(s) with 100 fork(s). There are 24 watchers for this library.

There were 3 major release(s) in the last 6 months.

There are 15 open issues and 76 have been closed. On average issues are closed in 187 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of datacompy is 0.13.1

Quality

datacompy has 0 bugs and 0 code smells.

Security

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

datacompy code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

datacompy is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

datacompy releases are available to install and integrate.

Deployable package is available in PyPI.

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

Top functions reviewed by kandi - BETA

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

Generate the report
Sample a column from a column
Count the number of matching rows
Render a template
Returns the number of rows common to the join
Returns a joined dataframe
Generates a SELECT statement for a select statement
Create case - insensitive case statement
Compares two DataFrames
Get the merged columns from the merged dataframe
Generate number of unique ids within join_columns
Merge two DataFrames
Returns a list of all rows of the query
Merge all rows together
Set the Pandas DataFrame
Validate the given index
Returns rows_only_compare_only_compare
Returns the rows that are the same
Returns the rows_only rows only
Returns all rows that are unqable
Returns a list of rows where each row is the same
Generator for decimal comparator
Set the main dataframe

Get all kandi verified functions for this library.

datacompy Key Features

No Key Features are available at this moment for datacompy.

datacompy Examples and Code Snippets

No Code Snippets are available at this moment for datacompy.

Community Discussions

Trending Discussions on datacompy

Issue with pandas dataframe compare using datacompy library

I am trying to build a python GUI Database Comparator using PySimpleGUI i missing something here?

QUESTION

Issue with pandas dataframe compare using datacompy library

Asked 2021-Jun-30 at 14:25

I am trying to compare two data frames using datacompy package. And I am seeing something weird.

...

ANSWER

Answered 2021-Jun-30 at 14:25

The max diff at 1e-16 indicates it’s a difference in the last mantissa bit, or some rounding / cancellation issue like that. This can happen depending on how the differences are computed (it really shouldn’t if the numbers are exactly the same).

You should set rel_tol or abs_tol to avoid such issues − that’s what these parameters are there for.

For example, datacompy.Compare(..., rel_tol=1e-10) means* that numbers a and b will be the considered the same if abs(a / b - 1) is smaller than 10^-10. This relative threshold is big enough to never happen by accident and small enough for most applications.

You can choose whatever threshold works for you. Since all your unit_cost_amt seem to have 5 digits, you could also use abs_tol=1e-6.

_{* It’s typically defined like that, but I didn’t actually read the datacompy docs}

Source https://stackoverflow.com/questions/68196367

QUESTION

I am trying to build a python GUI Database Comparator using PySimpleGUI i missing something here?

Asked 2021-May-08 at 14:23

I am trying to Build a GUI DB Comparator which is divided in two layout window.

Get the DB Detail from user and Test the connection
Get a Excel file which contains the SQL Statement that will be performed on DB and using Datacompy Comparison will be displayed to the user.

Problem I am facing right now is

DB Details should be on top and the Output box should be below them but it's vice versa
While after taking the input ( DB Details ) when i click on DB Test1 button application close's itself without displaying what went wrong ( No Code error, just went off )

I am new to PySimpleGUI, might have made slilly mistakes, Please guide me through the error and statement which i need to re enter.

Here's the code of First layout window :

...

ANSWER

Answered 2021-May-07 at 07:11

I don't know how this code could work. It was giving me message that layout has wrong [] - and maybe because this I didn't have sg.Output in window. But it was still used and all error messages was send to sg.Output and you could not see error.

Correct layout

Source https://stackoverflow.com/questions/67429039

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

Vulnerabilities

No vulnerabilities reported

Install datacompy

You can install using 'pip install datacompy' or download it from GitHub, PyPI.
You can use datacompy 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

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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