Book description
Use the power of pandas to solve most complex scientific computing problems with ease. Revised for pandas 1.x.
Key Features
 This is the first book on pandas 1.x
 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
The pandas library is massive, and it's common for frequent users to be unaware of many of its more impressive features. The official pandas documentation, while thorough, does not contain many useful examples of how to piece together multiple commands as one would do during an actual analysis. This book guides you, as if you were looking over the shoulder of an expert, through situations that you are highly likely to encounter.
This new updated and revised edition provides you with unique, idiomatic, and fun recipes for both fundamental and advanced data manipulation tasks with pandas. Some recipes focus on achieving a deeper understanding of basic principles, or comparing and contrasting two similar operations. Other recipes will dive deep into a particular dataset, uncovering new and unexpected insights along the way. Many advanced recipes combine several different features across the pandas library to generate results.
What you will learn
 Master data exploration in pandas through dozens of practice problems
 Group, aggregate, transform, reshape, and filter data
 Merge data from different sources through pandas SQLlike operations
 Create visualizations via pandas hooks to matplotlib and seaborn
 Use pandas, time series functionality to perform powerful analyses
 Import, clean, and prepare realworld datasets for machine learning
 Create workflows for processing big data that doesn’t fit in memory
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.
Table of contents
 Preface
 Pandas Foundations
 Essential DataFrame Operations
 Creating and Persisting DataFrames
 Beginning Data Analysis
 Exploratory Data Analysis
 Selecting Subsets of Data

Filtering Rows
 Introduction
 Calculating Boolean statistics
 Constructing multiple Boolean conditions
 Filtering with Boolean arrays
 Comparing row filtering and index filtering
 Selecting with unique and sorted indexes
 Translating SQL WHERE clauses
 Improving the readability of Boolean indexing with the query method
 Preserving Series size with the .where method
 Masking DataFrame rows
 Selecting with Booleans, integer location, and labels
 Index Alignment

Grouping for Aggregation, Filtration, and Transformation
 Introduction
 Defining an aggregation
 Grouping and aggregating with multiple columns and functions
 Removing the MultiIndex after grouping
 Grouping with a custom aggregation function
 Customizing aggregating functions with *args and **kwargs
 Examining the groupby object
 Filtering for states with a minority majority
 Transforming through a weight loss bet
 Calculating weighted mean SAT scores per state with apply
 Grouping by continuous variables
 Counting the total number of flights between cities
 Finding the longest streak of ontime flights

Restructuring Data into a Tidy Form
 Introduction
 Tidying variable values as column names with stack
 Tidying variable values as column names with melt
 Stacking multiple groups of variables simultaneously
 Inverting stacked data
 Unstacking after a groupby aggregation
 Replicating pivot_table with a groupby aggregation
 Renaming axis levels for easy reshaping
 Tidying when multiple variables are stored as column names
 Tidying when multiple variables are stored as a single column
 Tidying when two or more values are stored in the same cell
 Tidying when variables are stored in column names and values
 Combining Pandas Objects

Time Series Analysis
 Introduction
 Understanding the difference between Python and pandas date tools
 Slicing time series intelligently
 Filtering columns with time data
 Using methods that only work with a DatetimeIndex
 Counting the number of weekly crimes
 Aggregating weekly crime and traffic accidents separately
 Measuring crime by weekday and year
 Grouping with anonymous functions with a DatetimeIndex
 Grouping by a Timestamp and another column

Visualization with Matplotlib, Pandas, and Seaborn
 Introduction
 Getting started with matplotlib
 Objectoriented guide to matplotlib
 Visualizing data with matplotlib
 Plotting basics with pandas
 Visualizing the flights dataset
 Stacking area charts to discover emerging trends
 Understanding the differences between seaborn and pandas
 Multivariate analysis with seaborn Grids
 Uncovering Simpson's Paradox in the diamonds dataset with seaborn
 Debugging and Testing Pandas
 Other Books You May Enjoy
 Index
Product information
 Title: Pandas 1.x Cookbook  Second Edition
 Author(s):
 Release date: February 2020
 Publisher(s): Packt Publishing
 ISBN: 9781839213106
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