Pandas Cookbook, 3rd Edition

Cover
Copyright
Foreword
Contributors
Table of Contents
Preface
Chapter 1: pandas Foundations
	Importing pandas
	Series
	DataFrame
	Index
	Series attributes
	DataFrame attributes
Chapter 2: Selection and Assignment
	Basic selection from a Series
	Basic selection from a DataFrame
	Position-based selection of a Series
	Position-based selection of a DataFrame
	Label-based selection from a Series
	Label-based selection from a DataFrame
	Mixing position-based and label-based selection
	DataFrame.filter
	Selection by data type
	Selection/filtering via Boolean arrays
	Selection with a MultiIndex – A single level
	Selection with a MultiIndex – Multiple levels
	Selection with a MultiIndex – a DataFrame
	Item assignment with .loc and .iloc
	DataFrame column assignment
Chapter 3: Data Types
	Integral types
	Floating point types
	Boolean types
	String types
	Missing value handling
	Categorical types
	Temporal types – datetime
	Temporal types – timedelta
	Temporal PyArrow types
	PyArrow List types
	PyArrow decimal types
	NumPy type system, the object type, and pitfalls
Chapter 4: The pandas I/O System
	CSV – basic reading/writing
	CSV – strategies for reading large files
	Microsoft Excel – basic reading/writing
	Microsoft Excel – finding tables in non-default locations
	Microsoft Excel – hierarchical data
	SQL using SQLAlchemy
	SQL using ADBC
	Apache Parquet
	JSON
	HTML
	Pickle
	Third-party I/O libraries
Chapter 5: Algorithms and How to Apply Them
	Basic pd.Series arithmetic
	Basic pd.DataFrame arithmetic
	Aggregations
	Transformations
	Map
	Apply
	Summary statistics
	Binning algorithms
	One-hot encoding with pd.get_dummies
	Chaining with .pipe
	Selecting the lowest-budget movies from the top 100
	Calculating a trailing stop order price
	Finding the baseball players best at…
	Understanding which position scores the most per team
Chapter 6: Visualization
	Creating charts from aggregated data
	Plotting distributions of non-aggregated data
	Further plot customization with Matplotlib
	Exploring scatter plots
	Exploring categorical data
	Exploring continuous data
	Using seaborn for advanced plots
Chapter 7: Reshaping DataFrames
	Concatenating pd.DataFrame objects
	Merging DataFrames with pd.merge
	Joining DataFrames with pd.DataFrame.join
	Reshaping with pd.DataFrame.stack and pd.DataFrame.unstack
	Reshaping with pd.DataFrame.melt
	Reshaping with pd.wide_to_long
	Reshaping with pd.DataFrame.pivot and pd.pivot_table
	Reshaping with pd.DataFrame.explode
	Transposing with pd.DataFrame.T
Chapter 8: Group By
	Group by basics
	Grouping and calculating multiple columns
	Group by apply
	Window operations
	Selecting the highest rated movies by year
	Comparing the best hitter in baseball across years
Chapter 9: Temporal Data Types and Algorithms
	Timezone handling
	DateOffsets
	Datetime selection
	Resampling
	Aggregating weekly crime and traffic accidents
	Calculating year-over-year changes in crime by category
	Accurately measuring sensor-collected events with missing values
Chapter 10: General Usage and Performance Tips
	Avoid dtype=object
	Be cognizant of data sizes
	Use vectorized functions instead of loops
	Avoid mutating data
	Dictionary-encode low cardinality data
	Test-driven development features
Chapter 11: The pandas Ecosystem
	Foundational libraries
		NumPy
		PyArrow
	Exploratory data analysis
		YData Profiling
	Data validation
		Great Expectations
	Visualization
		Plotly
		PyGWalker
	Data science
		scikit-learn
		XGBoost
	Databases
		DuckDB
	Other DataFrame libraries
		Ibis
		Dask
		Polars
		cuDF
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