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  • Book Overview & Buying Pandas Cookbook
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Pandas Cookbook

Pandas Cookbook - Third Edition

By : William Ayd, Matthew Harrison
4.9 (10)
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Pandas Cookbook

Pandas Cookbook

4.9 (10)
By: William Ayd, Matthew Harrison
Buy this Book

Overview of this book

Unlock the full power of pandas 2.x with this hands-on cookbook, designed for Python developers, data analysts, and data scientists who need fast, efficient solutions for real-world data challenges. This book provides practical, ready-to-use recipes to streamline your workflow. With step-by-step guidance, you'll master data wrangling, visualization, performance optimization, and scalable data analysis using pandas’ most powerful features. From importing and merging large datasets to advanced time series analysis and SQL-like operations, this cookbook equips you with the tools to analyze, manipulate, and visualize data like a pro. Learn how to boost efficiency, optimize memory usage, and seamlessly integrate pandas with NumPy, PyArrow, and databases. This book will help you transform raw data into actionable insights with ease. *Email sign-up and proof of purchase required
Table of Contents (14 chapters)
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Preface
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Preface
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Who this book is for
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What this book covers
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To get the most out of this book
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Sections
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Get in touch
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Reviews
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Making the Most Out of This Book – Get to Know Your Free Benefits
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1
pandas Foundations
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pandas Foundations
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Importing pandas
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Series
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DataFrame
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Index
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Series attributes
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DataFrame attributes
2
Selection and Assignment
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Selection and Assignment
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Basic selection from a Series
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Basic selection from a DataFrame
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Position-based selection of a Series
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Position-based selection of a DataFrame
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Label-based selection from a Series
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Label-based selection from a DataFrame
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Mixing position-based and label-based selection
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DataFrame.filter
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Selection by data type
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Selection/filtering via Boolean arrays
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Selection with a MultiIndex – A single level
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Selection with a MultiIndex – Multiple levels
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Selection with a MultiIndex – a DataFrame
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Item assignment with .loc and .iloc
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DataFrame column assignment
3
Data Types
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Data Types
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Integral types
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Floating point types
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Boolean types
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String types
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Missing value handling
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Categorical types
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Temporal types – datetime
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Temporal types – timedelta
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Temporal PyArrow types
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PyArrow List types
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PyArrow decimal types
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NumPy type system, the object type, and pitfalls
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The pandas I/O System
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The pandas I/O System
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CSV – basic reading/writing
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CSV – strategies for reading large files
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Microsoft Excel – basic reading/writing
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Microsoft Excel – finding tables in non-default locations
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Microsoft Excel – hierarchical data
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SQL using SQLAlchemy
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SQL using ADBC
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Apache Parquet
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JSON
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HTML
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Pickle
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Third-party I/O libraries
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Algorithms and How to Apply Them
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Algorithms and How to Apply Them
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Basic pd.Series arithmetic
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Basic pd.DataFrame arithmetic
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Aggregations
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Transformations
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Map
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Apply
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Summary statistics
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Binning algorithms
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One-hot encoding with pd.get_dummies
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Chaining with .pipe
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Selecting the lowest-budget movies from the top 100
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Calculating a trailing stop order price
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Finding the baseball players best at…
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Understanding which position scores the most per tea
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Visualization
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Visualization
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Creating charts from aggregated data
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Plotting distributions of non-aggregated data
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Further plot customization with Matplotlib
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Exploring scatter plots
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Exploring categorical data
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Exploring continuous data
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Using seaborn for advanced plots
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Reshaping DataFrames
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Reshaping DataFrames
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Concatenating pd.DataFrame objects
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Merging DataFrames with pd.merge
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Joining DataFrames with pd.DataFrame.join
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Reshaping with pd.DataFrame.stack and pd.DataFrame.unstack
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Reshaping with pd.DataFrame.melt
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Reshaping with pd.wide_to_long
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Reshaping with pd.DataFrame.pivot and pd.pivot_table
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Reshaping with pd.DataFrame.explode
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Transposing with pd.DataFrame.T
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Group By
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Group By
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Group by basics
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Grouping and calculating multiple columns
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Group by apply
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Window operations
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Selecting the highest rated movies by year
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Comparing the best hitter in baseball across years
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Temporal Data Types and Algorithms
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Temporal Data Types and Algorithms
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Timezone handling
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DateOffsets
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Datetime selection
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Resampling
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Aggregating weekly crime and traffic accidents
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Calculating year-over-year changes in crime by category
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Accurately measuring sensor-collected events with missing values
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General Usage and Performance Tips
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General Usage and Performance Tips
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Avoid dtype=object
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Be cognizant of data sizes
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Use vectorized functions instead of loops
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Avoid mutating data
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Dictionary-encode low cardinality data
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Test-driven development features
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The pandas Ecosystem
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The pandas Ecosystem
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Foundational libraries
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Exploratory data analysis
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Data validation
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Visualization
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Data science
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Databases
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Other DataFrame libraries
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Other Books You May Enjoy
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Other Books You May Enjoy
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Index
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Index

The pandas I/O System

So far, we have been creating our pd.Series and pd.DataFrame objects inline with data. While this is helpful for establishing a theoretical foundation, very rarely would a user do this in production code. Instead, users would use the pandas I/O functions to read/write data from/to various formats.

I/O, which is short for input/output, generally refers to the process of reading from and writing to common data formats like CSV, Microsoft Excel, JSON, etc. There is, of course, not just one format for data storage, and many of these options represent trade-offs between performance, storage size, third-party integration, accessibility, and/or ubiquity. Some formats assume well-structured, stringently defined data (SQL being arguably the most extreme), whereas other formats can be used to represent semi-structured data that is not restricted to being two-dimensional (JSON being great example).

The fact that pandas can interact with so many of these data formats...

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