One particular flavor of messy data appears whenever the column names contain multiple different variables themselves. A common example of this scenario occurs when age and sex are concatenated together. To tidy datasets like this, we must manipulate the columns with the pandas str accessor, an attribute that contains additional methods for string processing.
- 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
Tidying when multiple variables are stored as column names
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