book

Pandas Cookbook - Third Edition

Name: Pandas Cookbook - Third Edition
ISBN: 9781836205876

by William Ayd, Matthew Harrison

October 2024

Beginner to intermediate

404 pages

8h 50m

English

Packt Publishing

Read now

Unlock full access

Preface
Who this book is forWhat this book coversTo get the most out of this bookWhat you need for this bookRunning a Jupyter notebookConventionsAssumptions for every recipeDataset descriptionsSectionsHow to do itHow it worksThere’s more…Get in touchReviewsLeave a Review!Making the Most Out of This Book – Get to Know Your Free Benefits
pandas Foundations
Importing pandasSeriesHow to do itDataFrameHow to do itIndexHow to do itSeries attributesHow to do itDataFrame attributesHow to do it
Selection and Assignment
Basic selection from a SeriesHow to do itThere’s more…Basic selection from a DataFrameHow to do itThere’s more…Position-based selection of a SeriesHow to do itPosition-based selection of a DataFrameHow to do itThere’s more…Label-based selection from a SeriesHow to do itThere’s more…Label-based selection from a DataFrameHow to do itMixing position-based and label-based selectionHow to do itThere’s more…DataFrame.filterHow to do itSelection by data typeHow to do itSelection/filtering via Boolean arraysHow to do itThere’s more…Selection with a MultiIndex – A single levelHow to do itSelection with a MultiIndex – Multiple levelsHow to do itThere’s more…Selection with a MultiIndex – a DataFrameHow to do itItem assignment with .loc and .ilocHow to do itThere’s more…DataFrame column assignmentHow to do itThere’s more…
Data Types
Integral typesHow to do itThere’s more…Floating point typesHow to do itThere’s more…Boolean typesHow to do itString typesHow to do itMissing value handlingHow to do itThere’s more…Categorical typesHow to do itThere’s more…Temporal types – datetimeHow to do itThere’s more…Temporal types – timedeltaHow to do itThere’s more…Temporal PyArrow typesHow to do itPyArrow List typesHow to do itThere’s more…PyArrow decimal typesHow to do itThere’s more…NumPy type system, the object type, and pitfallsHow to do itThere’s more…
The pandas I/O System
CSV – basic reading/writingHow to do itThere’s more…CSV – strategies for reading large filesHow to do itThere’s more...Microsoft Excel – basic reading/writingHow to do itMicrosoft Excel – finding tables in non-default locationsHow to do itThere’s more…Microsoft Excel – hierarchical dataHow to do itSQL using SQLAlchemyHow to do itSQL using ADBCHow to do itThere’s more…Apache ParquetHow to do itJSONHow to do itThere’s more...HTMLHow to do itPickleHow to do itThird-party I/O libraries
Algorithms and How to Apply Them
Basic pd.Series arithmeticHow to do itThere’s more…Basic pd.DataFrame arithmeticHow it worksAggregationsHow to do itThere’s more…TransformationsHow to do itThere’s more…MapHow to do itThere’s more…ApplyHow to do itSummary statisticsHow to do itBinning algorithmsHow to do itOne-hot encoding with pd.get_dummiesHow to do itChaining with .pipeHow to do itSelecting the lowest-budget movies from the top 100How to do itThere’s more…Calculating a trailing stop order priceHow to do itThere’s more…Finding the baseball players best at…How to do itThere’s more…Understanding which position scores the most per teaHow to do itThere’s more…
Visualization
Creating charts from aggregated dataHow to do itThere’s more…Plotting distributions of non-aggregated dataHow to do itFurther plot customization with MatplotlibHow to do itExploring scatter plotsHow to do itThere’s more…Exploring categorical dataHow to do itExploring continuous dataHow to do itUsing seaborn for advanced plotsHow to do it
Reshaping DataFrames
Concatenating pd.DataFrame objectsHow to do itThere’s more…Merging DataFrames with pd.mergeHow to do itThere’s more…Joining DataFrames with pd.DataFrame.joinHow to do itReshaping with pd.DataFrame.stack and pd.DataFrame.unstackHow to do itReshaping with pd.DataFrame.meltHow to do itReshaping with pd.wide_to_longHow to do itReshaping with pd.DataFrame.pivot and pd.pivot_tableHow to do itReshaping with pd.DataFrame.explodeHow to do itThere’s more…Transposing with pd.DataFrame.THow to do it
Group By
Group by basicsHow to do itThere’s more…Grouping and calculating multiple columnsHow to do itThere’s more…Group by applyHow to do itWindow operationsHow to do itThere’s more…Selecting the highest rated movies by yearHow to do itComparing the best hitter in baseball across yearsHow to do it
Temporal Data Types and Algorithms
Timezone handlingHow to do itDateOffsetsHow to do itThere’s more…Datetime selectionHow to do itThere’s more…ResamplingHow to do itThere’s more…Aggregating weekly crime and traffic accidentsHow to do itCalculating year-over-year changes in crime by categoryHow to do itAccurately measuring sensor-collected events with missing valuesHow to do itThere’s more…

General Usage and Performance Tips
Avoid dtype=objectHow to do itBe cognizant of data sizesHow to do itUse vectorized functions instead of loopsHow to do itAvoid mutating dataHow to do itThere’s more…Dictionary-encode low cardinality dataHow to do itTest-driven development featuresHow it worksThere’s more…
The pandas Ecosystem
Foundational librariesNumPyPyArrowExploratory data analysisYData ProfilingData validationGreat ExpectationsVisualizationPlotlyPyGWalkerData sciencescikit-learnXGBoostDatabasesDuckDBOther DataFrame librariesIbisDaskPolarscuDF
Other Books You May Enjoy
Index

Content preview from Pandas Cookbook - Third Edition

Index

Symbols

.iloc

item assignment with 38, 39

.loc

item assignment with 38, 39

[] operator

using, disadvantages 14, 15

[] operator, with DataFrame

selecting 16, 17

using, list argument 17

.pipe

for chaining 149-152

advanced plots

seaborn, using 214-223

aggregations 132-134

Apache Parquet

using 105-112

apply 140-142

Arrow Database Connectivity (ADBC)

using, with SQL 101-103

baseball players

data, summarizing to show metrics 159, 160

performance, comparing 290-298

position, finding that scored most runs for team over each season 161, 162

binary 124

binning algorithms 143-148

Boolean arrays

filtering via 30-33

selection via 30-33

Boolean types 50, 51

categorical data

exploring 202-209

type 56-59

chaining

with .pipe 149-152

comma-separated ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.

Read now

Unlock full access

More than 5,000 organizations count on O’Reilly

O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.

Julian F.

Head of Cybersecurity

I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.

Addison B.

Field Engineer

I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.

Amir M.

Data Platform Tech Lead

I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.

Mark W.

Embedded Software Engineer

Publisher Resources

ISBN: 9781836205876

Cloud Computing

Data Engineering

Data Science

AI & ML

Programming Languages

Software Architecture

IT/Ops

Security

Design

Business

Soft Skills

Pandas Cookbook - Third Edition

by William Ayd, Matthew Harrison

Index

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.