Examples

• pyjanitor Usage Walkthrough
  • Why pyjanitor?
  • What is method chaining?
  • The pyjanitor approach
  • A survey of pyjanitor functions
  • Some things that are different
  • How it works
  • Demo of various DataFrame manipulation tasks using pyjanitor
  • Example analysis of the data
• Examples
  • Guidelines
• row_to_names : Elevates a row to be the column names of a DataFrame.
  • Background
• Processing Anime Data
  • Background
  • One-Shot
  • Multi-Step
• Replacing Bad Values
  • Load Wind Turbine Data
  • Check Mean
  • Check the Mean (again)
  • Alternate method
  • Confirming this produces the same result
• Processing Bird Call Data
  • Background
  • Citations
  • Get Raw Data
  • Original DataFrames
  • Cleaning Data Using Pyjanitor
• Processing Board Game Data
  • Background
  • Data Cleaning
  • Multi-Step
  • Sample Analysis
• Processing Dirty Data
  • Background
  • Load Data
  • Cleaning Column Names
  • Renaming Individual Columns
  • Coalescing Columns
  • Dealing with Excel Dates
• Processing French train data
  • Background
  • The data
  • Preliminaries
  • Call chaining example
  • Step by step through the methods
• Groupby_agg : Shortcut for assigning a groupby-transform to a new column.
  • Background
  • Use grouby_agg to find average price for each item and append column to dataframe
• Inflating and Converting Currency
  • Background
  • Getting and Cleaning the Data
  • Use inflate_currency() to Inflate All Values to 2018$
  • Plot Time Series to Observe Currency Inflation
  • Use convert_currency() to Convert USD to British Pounds
  • Plot Time Series to Observe Currency Conversion
• Tidy Up Web-Scraped Media Franchise Data
  • Background
  • Structural convention
  • Python implementation
• Normalization and Standardization
  • Load data
  • Examine raw data
  • min-max normalization
  • Standardization (z-score)
• Using sort_naturally
• Processing International Teacher Data
  • Background
  • Data Dictionary
  • Data Cleaning
• Transforming columns
  • Introduction
  • Numeric Data
  • String Data