Pandas Cookbook, Ayd W., Harrison M., 2024

Pandas Cookbook, Ayd W., Harrison M., 2024.
     
   This book contains a huge number of recipes, ranging from very simple to advanced. All recipes strive to be written in clear, concise, and modern idiomatic pandas code. The How it works sections contain extremely detailed descriptions of the intricacies of each step of the recipe. Often, in the There’s more… section, you will get what may seem like an entirely new recipe. This book is densely packed with an extraordinary amount of pandas code.




Selection and Assignment.
In the previous chapter, we looked at how to create a pd.Series and pd.DataFrame, and we also looked at their relationship to the pd. Index. With a foundation in constructors, we now shift focus to the crucial processes of selection and assignment. Selection, also referred to as indexing, is considered a getter; i.e., it is used to retrieve values from a pandas object. Assignment, by contrast, is a setter that is used to update values.

The recipes in this chapter start out by showing you how to retrieve values from pd.Series and pd. DataFrame objects, with ever-increasing complexity. We will eventually introduce the pd. Multiindex, which can be used to select data hierarchically, before finally ending with an introduction to the assignment operators. The pandas API takes great care to reuse many of the same methods for selection and assignment, which ultimately allows you to be very expressive in how you would like to interact with your data.

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.
Other Books You May Enjoy.
Index.



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