Pandas Cookbook: Practical recipes for scientific computing, time series, and exploratory data analysis using Python, 3rd Ed

Preface
   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…
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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…
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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 team
      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
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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…
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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
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Index