Chapter
Chapter 1: Pandas Foundations
Dissecting the anatomy of a DataFrame
Accessing the main DataFrame components
Selecting a single column of data as a Series
Working with operators on a Series
Chaining Series methods together
Making the index meaningful
Renaming row and column names
Creating and deleting columns
Essential Chapter 2: DataFrame Operations
Selecting multiple DataFrame columns
Selecting columns with methods
Ordering column names sensibly
Operating on the entire DataFrame
Chaining DataFrame methods together
Working with operators on a DataFrame
Transposing the direction of a DataFrame operation
Determining college campus diversity
Chapter 3: Beginning Data Analysis
Developing a data analysis routine
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
Chapter 4: Selecting Subsets of Data
Selecting DataFrame rows and columns simultaneously
Selecting data with both integers and labels
Speeding up scalar selection
Slicing lexicographically
Chapter 5: Boolean Indexing
Calculating boolean statistics
Constructing multiple boolean conditions
Filtering with boolean indexing
Replicating boolean indexing with index selection
Selecting with unique and sorted indexes
Gaining perspective on stock prices
Translating SQL WHERE clauses
Determining the normality of stock market returns
Improving readability of boolean indexing with the query method
Preserving Series with the where method
Selecting with booleans, integer location, and labels
Chapter 6: Index Alignment
Examining the Index object
Producing Cartesian products
Filling values with unequal indexes
Appending columns from different DataFrames
Highlighting the maximum value from each column
Replicating idxmax with method chaining
Finding the most common maximum
Chapter 7: Grouping for Aggregation, Filtration, and Transformation
Grouping and aggregating with multiple columns and functions
Removing the MultiIndex after grouping
Customizing an 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
Chapter 8: 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
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 column values
Tidying when two or more values are stored in the same cell
Tidying when variables are stored in column names and values
Tidying when multiple observational units are stored in the same table
Chapter 9: Combining Pandas Objects
Appending new rows to DataFrames
Concatenating multiple DataFrames together
Comparing President Trump's and Obama's approval ratings
Understanding the differences between concat, join, and merge
Connecting to SQL databases
Chapter 10: Time Series Analysis
Understanding the difference between Python and pandas date tools
Slicing time series intelligently
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
Finding the last time crime was 20% lower with merge_asof
Chapter 11: 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
Doing multivariate analysis with seaborn Grids
Uncovering Simpson's paradox in the diamonds dataset with seaborn
Pandas Cookbook
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