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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

Timezone handling

By far, the most common mistakes with temporal data that I come across stem from a misunderstanding of timezones. On the East Coast of the US where I live, I’ve witnessed many users try to read what they think is a date of 2024-01-01 out of a database, yet ironically end up with a date of 2023-12-31 in their analysis. While that is only offset by a day, the effects of that misalignment can greatly skew summaries that group dates into weekly, monthly, quarterly, or yearly buckets.

For those that have been bitten by an issue like that before, you may have already come to realize that the source system you were communicating with probably did give you a timestamp of 2024-01-01 00:00:00, presumed to be at midnight UTC. Somewhere along the line, an analyst on the East Coast of the US where I live may have had that translated into their local time, which is either four hours offset from UTC during daylight saving time, or five hours offset during standard time...

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