Packt+ | Advance your knowledge in tech (2025)

Product typeBook

Published inFeb 2020

PublisherPackt

ISBN-139781839213106

Pages pages

Edition2nd Edition

Preface

• Who this book is for
• What this book covers
• To get the most out of this book
• Get in touch

1. Pandas Foundations

• Introduction
• The pandas DataFrame
• DataFrame attributes
• Understanding data types
• Selecting a column
• Calling Series methods
• Series operations
• Chaining Series methods
• Renaming column names
• Creating and deleting columns

2. Essential DataFrame OperationsFREE CHAPTER

• Selecting multiple DataFrame columns
• Selecting columns with methods
• Ordering column names
• Summarizing a DataFrame
• Chaining DataFrame methods
• DataFrame operations
• Comparing missing values
• Transposing the direction of a DataFrame operation
• Determining college campus diversity

3. Creating and Persisting DataFrames

• Creating DataFrames from scratch
• Writing CSV
• Reading large CSV files
• Using Excel files
• Working with ZIP files
• Working with databases
• Reading JSON
• Reading HTML tables

4. Beginning Data Analysis

• Developing a data analysis routine
• Data dictionaries
• Reducing memory by changing data types
• Selecting the smallest of the largest
• Selecting the largest of each group by sorting
• Replicating nlargest with sort_values
• Calculating a trailing stop order price

5. Exploratory Data Analysis

• Summary statistics
• Column types
• Categorical data
• Continuous data
• Comparing continuous values across categories
• Comparing two continuous columns
• Comparing categorical values with categorical values
• Using the pandas profiling library

6. Selecting Subsets of Data

• Selecting Series data
• Selecting DataFrame rows
• Selecting DataFrame rows and columns simultaneously
• Selecting data with both integers and labels
• Slicing lexicographically

7. Filtering Rows

• Calculating Boolean statistics
• Constructing multiple Boolean conditions
• Filtering with Boolean arrays
• Comparing row filtering and index filtering
• Selecting with unique and sorted indexes
• Translating SQL WHERE clauses
• Improving the readability of Boolean indexing with the query method
• Preserving Series size with the .where method
• Masking DataFrame rows
• Selecting with Booleans, integer location, and labels

8. Index Alignment

• Examining the Index object
• Producing Cartesian products
• Exploding indexes
• Filling values with unequal indexes
• Adding columns from different DataFrames
• Highlighting the maximum value from each column
• Replicating idxmax with method chaining
• Finding the most common maximum of columns

9. Grouping for Aggregation, Filtration, and Transformation

• Defining an aggregation
• Grouping and aggregating with multiple columns and functions
• Removing the MultiIndex after grouping
• Grouping with a custom aggregation function
• Customizing aggregating functions with *args and **kwargs
• Examining the groupby object
• Filtering for states with a minority majority
• Transforming through a weight loss bet
• Calculating weighted mean SAT scores per state with apply
• Grouping by continuous variables
• Counting the total number of flights between cities
• Finding the longest streak of on-time flights

10. Restructuring Data into a Tidy Form

• Tidying variable values as column names with stack
• Tidying variable values as column names with melt
• Stacking multiple groups of variables simultaneously
• Inverting stacked data
• Unstacking after a groupby aggregation
• Replicating pivot_table with a groupby aggregation
• Renaming axis levels for easy reshaping
• Tidying when multiple variables are stored as column names
• Tidying when multiple variables are stored as a single column
• Tidying when two or more values are stored in the same cell
• Tidying when variables are stored in column names and values

11. Combining Pandas Objects

• Appending new rows to DataFrames
• Concatenating multiple DataFrames together
• Understanding the differences between concat, join, and merge
• Connecting to SQL databases

12. Time Series Analysis

• Understanding the difference between Python and pandas date tools
• Slicing time series intelligently
• Filtering columns with time data
• Using methods that only work with a DatetimeIndex
• Counting the number of weekly crimes
• Aggregating weekly crime and traffic accidents separately
• Measuring crime by weekday and year
• Grouping with anonymous functions with a DatetimeIndex
• Grouping by a Timestamp and another column

13. Visualization with Matplotlib, Pandas, and Seaborn

• Getting started with matplotlib
• Object-oriented guide to matplotlib
• Visualizing data with matplotlib
• Plotting basics with pandas
• Visualizing the flights dataset
• Stacking area charts to discover emerging trends
• Understanding the differences between seaborn and pandas
• Multivariate analysis with seaborn Grids
• Uncovering Simpson's Paradox in the diamonds dataset with seaborn

14. Debugging and Testing Pandas

• Apply performance
• Improving apply performance with Dask, Pandarell, Swifter, and more
• Inspecting code
• Debugging in Jupyter
• Managing data integrity with Great Expectations
• Using pytest with pandas
• Generating tests with Hypothesis

15. Other Books You May Enjoy

16. Index