pyjanitor | Clean APIs for data

 by   pyjanitor-devs Python Version: 0.27.0 License: MIT

kandi X-RAY | pyjanitor Summary

kandi X-RAY | pyjanitor Summary

pyjanitor is a Python library typically used in Data Science, Pandas applications. pyjanitor 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 pyjanitor' or download it from GitHub, PyPI.

Clean APIs for data cleaning. Python implementation of R package Janitor

            kandi-support Support

              pyjanitor has a medium active ecosystem.
              It has 1149 star(s) with 160 fork(s). There are 19 watchers for this library.
              There were 1 major release(s) in the last 6 months.
              There are 105 open issues and 424 have been closed. On average issues are closed in 70 days. There are 7 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of pyjanitor is 0.27.0

            kandi-Quality Quality

              pyjanitor has 0 bugs and 0 code smells.

            kandi-Security Security

              pyjanitor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              pyjanitor code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              pyjanitor 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

              pyjanitor 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 pyjanitor and discovered the below as its top functions. This is intended to give you an instant insight into pyjanitor implemented functionality, and help decide if they suit your requirements.
            • Returns a pandas DataFrame containing all cells in the given path
            • Return a dataframe of xlsx rows
            • Check if value is of expected type
            • Convert an object to a dictionary
            • Read a sheet from a sheet
            • Rename column names
            • Stripunderscores
            • Change column name
            • Convert camel case to snake_case
            • Dispatches column selection
            • Select column names
            • Check if df_columns is string or categorical dtype
            • Read requirements file
            • Mark a function as deprecated
            • Rename keyword arguments
            • Generate a long description
            • Convert stock symbol
            • Check if the URL is connected to the internet
            • Get symbol name
            • Count the number of functions in a file
            • Return all submodules
            • Factorize columns
            • Check if a column is present in the dataframe
            Get all kandi verified functions for this library.

            pyjanitor Key Features

            No Key Features are available at this moment for pyjanitor.

            pyjanitor Examples and Code Snippets

            Change pandas dataframe format from wide to long similar to pd.melt
            Pythondot img1Lines of Code : 12dot img1License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            ( df.pivot_longer(index=None, 
                              names_to=("id", "source", "panel"), 
                              names_pattern=[r"^id", r"^source", r"^panel"])
               .query("id != ''")
                id source panel
            0  123     de    nq
            1  145     de    nq
            Split row by two columns and keep other columns the same
            Pythondot img2Lines of Code : 52dot img2License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            (df.pivot_longer(index = ['col1', 'col2', 'Unique_Col'], 
                             names_to = ['Name_new', 'Gender_new', 'Age_new'],  
                             names_pattern = ['Name', 'Gender', 'Age'],
                             sort_by_appearance = True)
            Wide date to long data conversion in Python Pandas using column names
            Pythondot img3Lines of Code : 35dot img3License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df.columns = ["_".join(entry.split("_")[::-1]) 
                          if entry.endswith(("count", "date"))
                          else entry
                          for entry in df.columns]
                            ["count", "dat
            Merge pandas DataFrames by partial match in datetime column
            Pythondot img4Lines of Code : 23dot img4License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df_booking.rename(columns={'DepartureTime':'DepartureTime_booking', 'Destination':'Destination_booking'}, inplace=True)
            df_booking = df_booking.merge(df_info, on='IDs')
            df_actual.rename(columns={'DepartureTime':'DepartureTime_actual', 'De
            How to check splitted values of a column without changing the dataframe?
            Pythondot img5Lines of Code : 4dot img5License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            deconcat = df['MyCol'].str.split(sep, expand=True)
            Preserve original column names
            Pythondot img6Lines of Code : 4dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            df_dct = {'df': santandar_data, 'original_names': santandar_data.columns}
            df_dct['df'] = df_dct['df'].clean_names(...)

            Community Discussions


            Merge pandas DataFrames by partial match in datetime column
            Asked 2020-Feb-09 at 12:24

            Hello good people of stackoverflow. I can't quite grasp the solution here, so please, help me out. Please, keep in mind that I'm quite a beginner at python, so please, keep it as simple as you can.

            My company provides employees with transportation to and from work. There is a system in place that tracks when employee got on the bus and which bus the person got onto. Also we receive data from transportation company with information where and when employees were supposed to go as per planning(every employee books the spot in advance). Sometimes people don't book places, sometimes they get onto the wrong bus(not the route they booked) or at the wrong time. My goal is to find such people and provide a report.

            Here is the sample of the data we receive from the transportation company



            Answered 2020-Feb-09 at 12:24

            You can do as follows.

            In the code below, the first df is named as df_booking,the second df is named as df_actual & the SQL database as df_info.


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


            No vulnerabilities reported

            Install pyjanitor

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


            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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            pip install pyjanitor

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