This recipe is valuable if computational time is of utmost importance. It shows the performance improvement of .iat and .at over .iloc and .loc when using scalar selection.
- Preface
- Pandas Foundations
- Introduction
- Dissecting the anatomy of a DataFrame
- Accessing the main DataFrame components
- Understanding data types
- Selecting a single column of data as a Series
- Calling Series methods
- Working with operators on a Series
- Chaining Series methods together
- Making the index meaningful
- Renaming row and column names
- Creating and deleting columns
- Essential DataFrame Operations
- Introduction
- 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
- Comparing missing values
- Transposing the direction of a DataFrame operation
- Determining college campus diversity
- Beginning Data Analysis
- Selecting Subsets of Data
- Boolean Indexing
- Introduction
- 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
- Masking DataFrame rows
- Selecting with booleans, integer location, and labels
- Index Alignment
- Grouping for Aggregation, Filtration, and Transformation
- Introduction
- Defining an aggregation
- 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
- Restructuring Data into a Tidy Form
- Introduction
- 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 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
- Combining Pandas Objects
- Time Series Analysis
- Introduction
- 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
- Visualization with Matplotlib, Pandas, and Seaborn
- Introduction
- Getting started with 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
Getting ready
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