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Pandas in Action
Boris Paskhaver
  • MEAP began February 2020
  • Publication in Spring 2021 (estimated)
  • ISBN 9781617297434
  • 525 pages (estimated)
  • printed in black & white
Pandas has rapidly become one of Python's most popular data analysis libraries. With pandas you can efficiently sort, analyze, filter and munge almost any type of data. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career.
Table of Contents detailed table of contents

Part 1: Getting Started

1 Introducing Pandas

1.1 Data in the 21st Century

1.2 Introducing pandas

1.2.1 Pandas vs Graphical Spreadsheet Applications

1.2.2 Pandas vs Its Competitors

Summary

2 A Whirlwind Tour of pandas

2.1 Importing a Dataset

2.2 Manipulating a DataFrame

2.3 Counting Values in a Series

2.4 Filtering a Column by One or More Criteria

2.5 Grouping Data

2.6 Summary

Part 2: The Python Ecosystem

3 Python Crash Course

3.1 Simple Data Types

3.1.1 Numbers

3.1.2 Strings

3.1.3 Booleans

3.2 Operators

3.2.1 Mathematical Operators

3.2.2 Equality and Inequality Operators

3.3 Variables

3.4 Functions

3.4.1 Arguments and Return Values

3.4.2 Custom Functions

3.5 Objects and Methods

3.5.1 Attributes

3.5.2 Methods

3.5.3 Additional String Methods

3.6 Lists

3.6.1 Iteration

3.6.2 Lists and Strings

3.7 Tuples

3.8 Dictionaries

3.8.1 Dictionary Iteration

3.9 Sets

3.9.1 Set Operations

3.10 Modules, Classes, and Datetimes

3.11 Summary

4 NumPy Crash Course

4.1 Dimensions

4.2 The ndarray Object

4.2.1 Generating a Numeric Range with the arrange Method

4.2.2 Attributes on a ndarray Object

4.2.3 The reshape Method

4.2.4 The randint Function

4.2.5 The randn Function

4.3 The nan Object

4.4 Summary

Part 3: The Series

5 The Series Object

5.1 Overview of a Series

5.1.1 Modules, Classes and Instances

5.1.2 Populating the Series with Values

5.1.3 Customizing the Index

5.1.4 Creating a Series with Missing Values

5.2 Create a Series from Python Objects

5.2.1 Dictionaries

5.2.2 Tuples

5.2.3 Sets

5.2.4 NumPy Arrays

5.3 Retrieving the First and Last Rows

5.4 Mathematical Operations

5.4.1 Arithmetic Operations

5.4.2 Broadcasting

5.5 Passing the Series to Python’s Built-In Functions

5.6 Coding Challenges / Exercises

5.7 Summary

6 Series Methods

6.1 Importing a Dataset with the read_csv Method

6.2 Sorting a Series

6.2.1 Sorting by Values with the sort_values Method

6.2.2 Sorting by Index with the sort_index Method

6.2.3 Retrieving the Smallest and Largest Values with the nsmallest and nlargest Methods

6.3 Overwriting a Series with the inplace Parameter

6.4 Counting Values with the value_counts Method

6.5 Invoking a Function on Every Series Value with the apply Method

6.6 Coding Challenge: Deriving Insights from a Series

6.6.1 Problem

6.6.2 Solution

6.7 Summary

Part 4: The DataFrame

7 The DataFrame Object

8 Filtering a DataFrame

Part 5: Working with Text Data

9 Working with Text Data

Part 6: Grouping, Aggregating and Merging Data

10 Similarity MultiIndex DataFrames

11 Reshaping and Pivoting

12 The GroupBy Object

13 Merging, Joining and Concatenating

Part 7: Working with Dates And Times

14 Working with Dates and Times

Part 8: Input and Output

15 Imports and Exports

16 Configuring Pandas

Part 9: Visualization

17 Visualization

Appendixes

About the Technology

Anyone who’s used spreadsheet software will find pandas familiar. While its column-based grids might remind you of Excel or Google Sheets, pandas is more flexible and far more powerful. It can efficiently perform operations on millions of rows and be used in tandem with other Python libraries for statistics, machine learning, and more. And best of all, using pandas doesn’t mean sacrificing user productivity or needing to write tons of complex code. It’s clean, intuitive, and fast.

About the book

Pandas in Action makes it easy to dive into Python-based data analysis. You’ll learn to use pandas to automate repetitive spreadsheet functionality and derive insight from data by sorting columns, filtering data subsets, and creating multi-leveled indices. Each chapter is a self-contained tutorial, letting you dip in when you need to troubleshoot tricky problems. Best of all, you won’t be learning from sterile or randomly created data. You’ll start with a variety of datasets that are big, small, incomplete, broken, and messy and learn how to clean and format them for proper analysis.

What's inside

  • Import a CSV, identify issues with its data structures, and convert it to the proper format
  • Sort, filter, pivot, and draw conclusions from a dataset and its subsets
  • Identify trends from text-based and time-based data
  • Organize, group, merge, and join separate datasets
  • Real-world datasets that are easy to download and explore

About the reader

For readers experienced with spreadsheet software who know the basics of Python.

About the author

Boris Paskhaver is a software engineer, Agile consultant, and educator. His six programming courses on Udemy have amassed 236,000 students, with an average course rating of 4.59 out of 5. He first used Python and the pandas library to derive a variety of business insights at the world’s #1 jobs site, Indeed.com.

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