csvkit
  • Installation
    • Users
    • Developers
  • Tutorial
    • 1. Getting started
    • 2. Examining the data
    • 3. Power tools
    • 4. Going elsewhere with your data
  • Command-Line Usage
    • Input
    • Processing
    • Output (and Analysis)
    • Appendices
  • Using as a Python library
    • csvkit
    • csvkit.py2
    • csvkit.py3
    • csvkit.unicsv
    • csvkit.sniffer
  • Contributing to csvkit
    • Principles
    • Process for contributing code
    • Legalese
  • Release process
 
csvkit
  • Docs »
  • Tutorial
  • Edit on GitHub

TutorialΒΆ

The csvkit tutorial walks through processing and analyzing a real dataset:

  • 1. Getting started
    • 1.1. About this tutorial
    • 1.2. Installing csvkit
    • 1.3. Getting the data
    • 1.4. in2csv: the Excel killer
    • 1.5. csvlook: data periscope
    • 1.6. csvcut: data scalpel
    • 1.7. Putting it together with pipes
    • 1.8. Summing up
  • 2. Examining the data
    • 2.1. csvstat: statistics without code
    • 2.2. csvgrep: find the data you need
    • 2.3. csvsort: order matters
    • 2.4. Summing up
  • 3. Power tools
    • 3.1. csvjoin: merging related data
    • 3.2. csvstack: combining subsets
    • 3.3. csvsql and sql2csv: ultimate power
    • 3.4. Summing up
  • 4. Going elsewhere with your data
    • 4.1. csvjson: going online
    • 4.2. csvpy: going into code
    • 4.3. csvformat: for legacy systems
    • 4.4. Summing up
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© Copyright 2014, Christopher Groskopf.

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