Metadata-Version: 1.1
Name: csvpivot
Version: 1.1
Summary: Pivot tables for CSV files in the terminal.
Home-page: https://github.com/maxharlow/csvpivot
Author: Max Harlow
Author-email: maxharlow@gmail.com
License: Apache
Description: CSV Pivot
        =========
        
        Pivot tables for CSV files in the terminal.
        
        Part of a set of terminal-based CSV tools, also including [CSV Match] (https://github.com/maxharlow/csvmatch) and [CSV Bar] (https://github.com/maxharlow/csvbar).
        
        Tested on Python 2.7 and 3.5.
        
        
        Installing
        ----------
        
            pip install csvpivot
        
        
        Usage
        -----
        
        Say you have a CSV file such as:
        
        ```
        name,country,gender,salary
        Oliver,UK,M,10000
        Jack,UK,M,21000
        Emily,UK,F,32000
        Harry,UK,M,43000
        Adam,France,M,54000
        Paul,France,M,65000
        Louise,France,F,76000
        Alice,France,F,87000
        Emma,Germany,F,98000
        ```
        
        We could then find the average salary in each country:
        
        ```bash
        $ csvpivot test.csv --rows country --values 'mean(salary)'
        
        country,mean(salary)
        France,70500
        Germany,98000
        UK,26500
        ```
        
        It would be useful to find out the maximum and minimum values too though:
        
        ```bash
        $ csvpivot test.csv --rows country --values 'mean(salary)' 'min(salary)' 'max(salary)'
        
        country,mean(salary),min(salary),max(salary)
        France,70500,54000,87000
        Germany,98000,98000,98000
        UK,26500,10000,43000
        ```
        
        As well as `mean`, `min`, and `max`, CSV Pivot also supports `median`, `sum`, `stddev`, `count`, `countuniq`, `concat`, and `concatuniq`. All require numerical values apart from the last two. If numbers contain commas they are interpreted as thousands separators and removed.
        
        Columns are also supported. So we could break down out data by gender:
        
        ```bash
        $ csvpivot test.csv --rows country --values 'mean(salary)' --columns gender
        
        country,mean(salary):F,mean(salary):M
        France,81500,59500
        Germany,98000,
        UK,32000,24666.666666666668
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.5
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Utilities
