Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you …
Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.
In Pandas in Action you will learn:Pandas has rapidly become one of Python's most popular data analysis libraries. 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. You’ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data.
Data analysis with Python doesn’t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It’s a perfect way to up your data game.
Pandas in Action introduces Python-based data analysis using the amazing pandas library. You’ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you’ll find in the real world.
For readers experienced with spreadsheets and basic Python programming.
Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries.
This approachable guide will get you up and running quickly with all the basics you need to analyze your data.Part 1. Core pandas
Chapter 1. Introducing pandas
Chapter 1. Pandas vs. graphical spreadsheet applications
Chapter 1. A tour of pandas
Chapter 1. Counting values in a Series
Chapter 2. The Series object
Chapter 2. Customizing the Series index
Chapter 2. Creating a Series from Python objects
Chapter 2. Mathematical operations
Chapter 2. Arithmetic operations
Chapter 2. Passing the Series to Python’s built-in functions
Chapter 3. Series methods
Chapter 3. Sorting a Series
Chapter 3. Counting values with the value_counts method
Chapter 3. Invoking a function on every Series value with the apply method
Chapter 4. The DataFrame object
Chapter 4. Shared and exclusive attributes of Series and DataFrames
Chapter 4. Sorting a DataFrame
Chapter 4. Selecting rows from a DataFrame
Chapter 4. Extracting values from Series
Chapter 5. Filtering a DataFrame
Chapter 5. Filtering by a single condition
Chapter 5. Filtering by condition
Chapter 5. The drop_duplicates method
Part 2. Applied pandas
Chapter 6. Working with text data
Chapter 6. String slicing
Chapter 6. Splitting strings
Chapter 7. MultiIndex DataFrames
Chapter 7. MultiIndex DataFrames - Scaling up
Chapter 7. Sorting a MultiIndex
Chapter 7. Extracting one or more rows with loc
Chapter 7. Manipulating the Index
Chapter 8. Reshaping and pivoting
Chapter 8. Additional options for pivot tables
Chapter 8. Melting a data set
Chapter 9. The GroupBy object
Chapter 9. Attributes and methods of a GroupBy object
Chapter 9. Grouping by multiple columns
Chapter 10. Merging, joining, and concatenating
Chapter 10. Missing values in concatenated DataFrames
Chapter 10. Merging on index labels
Chapter 11. Working with dates and times
Chapter 11. Storing multiple timestamps in a DatetimeIndex
Chapter 11. Date offsets
Chapter 11. Coding challenge
Chapter 12. Imports and exports
Chapter 12. Exporting a DataFrame to a JSON file
Chapter 12. Exporting Excel workbooks
Chapter 13. Configuring pandas
Chapter 13. Precision
Chapter 14. Visualization