Pandas Cookbook 3rd Edition by William Ayd ISBN 1836205872 9781836205876 by William Ayd | Matthew Harrison instant download after payment.
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Product details:
ISBN 10: 1836205872
ISBN 13: 9781836205876
Author: William Ayd
From fundamental techniques to advanced strategies for handling big data, visualization, and more, this book equips you with skills to excel in real-world data analysis projects. Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free
Key Features
This book targets features in pandas 2.x and beyond
Practical, easy to implement recipes for quick solutions to common problems in data using pandas
Master the fundamentals of pandas to quickly begin exploring any dataset
Book Description
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.
What you will learn
The pandas type system and how to best navigate it
Import/export DataFrames to/from common data formats
Data exploration in pandas through dozens of practice problems
Grouping, aggregation, transformation, reshaping, and filtering data
Merge data from different sources through pandas SQL-like operations
Leverage the robust pandas time series functionality in advanced analyses
Scale pandas operations to get the most out of your system
The large ecosystem that pandas can coordinate with and supplement
Who this book is for
This book is for Python developers, data scientists, engineers, and analysts. pandas is the ideal tool for manipulating structured data with Python and this book provides ample instruction and examples. Not only does it cover the basics required to be proficient, but it goes into the details of idiomatic
Pandas Cookbook 3rd Table of contents:
- Who this book is for
- What this book covers
- To get the most out of this book
- What you need for this book
- Running a Jupyter notebook
- Conventions
- Assumptions for every recipe
- Dataset descriptions
- Sections
- How to do it
- How it works
- There’s more…
- Get in touch
- Reviews
- Leave a Review!
- Making the Most Out of This Book – Get to Know Your Free Benefits
- pandas Foundations
- Importing pandas
- Series
- How to do it
- DataFrame
- How to do it
- Index
- How to do it
- Series attributes
- How to do it
- DataFrame attributes
- How to do it
- Selection and Assignment
- Basic selection from a Series
- How to do it
- There’s more…
- Basic selection from a DataFrame
- How to do it
- There’s more…
- Position-based selection of a Series
- How to do it
- Position-based selection of a DataFrame
- How to do it
- There’s more…
- Label-based selection from a Series
- How to do it
- There’s more…
- Label-based selection from a DataFrame
- How to do it
- Mixing position-based and label-based selection
- How to do it
- There’s more…
- DataFrame.filter
- How to do it
- Selection by data type
- How to do it
- Selection/filtering via Boolean arrays
- How to do it
- There’s more…
- Selection with a MultiIndex – A single level
- How to do it
- Selection with a MultiIndex – Multiple levels
- How to do it
- There’s more…
- Selection with a MultiIndex – a DataFrame
- How to do it
- Item assignment with .loc and .iloc
- How to do it
- There’s more…
- DataFrame column assignment
- How to do it
- There’s more…
- Data Types
- Integral types
- How to do it
- There’s more…
- Floating point types
- How to do it
- There’s more…
- Boolean types
- How to do it
- String types
- How to do it
- Missing value handling
- How to do it
- There’s more…
- Categorical types
- How to do it
- There’s more…
- Temporal types – datetime
- How to do it
- There’s more…
- Temporal types – timedelta
- How to do it
- There’s more…
- Temporal PyArrow types
- How to do it
- PyArrow List types
- How to do it
- There’s more…
- PyArrow decimal types
- How to do it
- There’s more…
- NumPy type system, the object type, and pitfalls
- How to do it
- There’s more…
- The pandas I/O System
- CSV – basic reading/writing
- How to do it
- There’s more…
- CSV – strategies for reading large files
- How to do it
- There’s more...
- Microsoft Excel – basic reading/writing
- How to do it
- Microsoft Excel – finding tables in non-default locations
- How to do it
- There’s more…
- Microsoft Excel – hierarchical data
- How to do it
- SQL using SQLAlchemy
- How to do it
- SQL using ADBC
- How to do it
- There’s more…
- Apache Parquet
- How to do it
- JSON
- How to do it
- There’s more...
- HTML
- How to do it
- Pickle
- How to do it
- Third-party I/O libraries
- Algorithms and How to Apply Them
- Basic pd.Series arithmetic
- How to do it
- There’s more…
- Basic pd.DataFrame arithmetic
- How it works
- Aggregations
- How to do it
- There’s more…
- Transformations
- How to do it
- There’s more…
- Map
- How to do it
- There’s more…
- Apply
- How to do it
- Summary statistics
- How to do it
- Binning algorithms
- How to do it
- One-hot encoding with pd.get_dummies
- How to do it
- Chaining with .pipe
- How to do it
- Selecting the lowest-budget movies from the top 100
- How to do it
- There’s more…
- Calculating a trailing stop order price
- How to do it
- There’s more…
- Finding the baseball players best at…
- How to do it
- There’s more…
- Understanding which position scores the most per tea
- How to do it
- There’s more…
- Visualization
- Creating charts from aggregated data
- How to do it
- There’s more…
- Plotting distributions of non-aggregated data
- How to do it
- Further plot customization with Matplotlib
- How to do it
- Exploring scatter plots
- How to do it
- There’s more…
- Exploring categorical data
- How to do it
- Exploring continuous data
- How to do it
- Using seaborn for advanced plots
- How to do it
- Reshaping DataFrames
- Concatenating pd.DataFrame objects
- How to do it
- There’s more…
- Merging DataFrames with pd.merge
- How to do it
- There’s more…
- Joining DataFrames with pd.DataFrame.join
- How to do it
- Reshaping with pd.DataFrame.stack and pd.DataFrame.unstack
- How to do it
- Reshaping with pd.DataFrame.melt
- How to do it
- Reshaping with pd.wide_to_long
- How to do it
- Reshaping with pd.DataFrame.pivot and pd.pivot_table
- How to do it
- Reshaping with pd.DataFrame.explode
- How to do it
- There’s more…
- Transposing with pd.DataFrame.T
- How to do it
- Group By
- Group by basics
- How to do it
- There’s more…
- Grouping and calculating multiple columns
- How to do it
- There’s more…
- Group by apply
- How to do it
- Window operations
- How to do it
- There’s more…
- Selecting the highest rated movies by year
- How to do it
- Comparing the best hitter in baseball across years
- How to do it
- Temporal Data Types and Algorithms
- Timezone handling
- How to do it
- DateOffsets
- How to do it
- There’s more…
- Datetime selection
- How to do it
- There’s more…
- Resampling
- How to do it
- There’s more…
- Aggregating weekly crime and traffic accidents
- How to do it
- Calculating year-over-year changes in crime by category
- How to do it
- Accurately measuring sensor-collected events with missing values
- How to do it
- There’s more…
- General Usage and Performance Tips
- Avoid dtype=object
- How to do it
- Be cognizant of data sizes
- How to do it
- Use vectorized functions instead of loops
- How to do it
- Avoid mutating data
- How to do it
- There’s more…
- Dictionary-encode low cardinality data
- How to do it
- Test-driven development features
- How it works
- There’s more…
- 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
- Other Books You May Enjoy
- Index
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Tags: William Ayd, Cookbook, Pandas
