What this book covers
Chapter 1, Anticipating Data Cleaning Issues when Importing Tabular Data into pandas, explores tools for loading CSV files, Excel files, relational database tables, SAS, SPSS, and Stata files, and R files into pandas DataFrames.
Chapter 2, Anticipating Data Cleaning Issues when Importing HTML and JSON into pandas, discusses techniques for reading and normalizing JSON data, and for web scraping.
Chapter 3, Taking the Measure of Your Data, introduces common techniques for navigating around a DataFrame, selecting columns and rows, and generating summary statistics.
Chapter 4, Identifying Missing Values and Outliers in Subsets of Data, explores a wide range of strategies to identify missing values and outliers across a whole DataFrame and by selected groups.
Chapter 5, Using Visualizations for the Identification of Unexpected Values, demonstrates the use of matplotlib and seaborn tools to visualize how key variables are distributed, including with histograms, boxplots, scatter plots, line plots, and violin plots.
Chapter 6, Cleaning and Exploring Data with Series Operations, discusses updating pandas series with scalars, arithmetic operations, and conditional statements based on the values of one or more series.
Chapter 7, Fixing Messy Data when Aggregating, demonstrates multiple approaches to aggregating data by group, and discusses when to choose one approach over the others.
Chapter 8, Addressing Data Issues when Combining DataFrames, examines different strategies for concatenating and merging data, and how to anticipate common data challenges when combining data.
Chapter 9, Tidying and Reshaping Data, introduces several strategies for de-duplicating, stacking, melting, and pivoting data.
Chapter 10, User-Defined Functions and Classes to Automate Data Cleaning, examines how to turn many of the techniques from the first nine chapters into reusable code.
