Murach’s Python Programming (2nd Edition)
Murach’s R for Data AnalysisMurach college books and courseware since 1974
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Murach’s Python for Data Science (2nd Edition)
List price: $59.50
Murach’s Python for Data Science starts by covering everything your students need to hit the ground running when using Python for data science. First, it presents a crash course in using the Pandas and Seaborn libraries for data analysis and visualization. Then, it presents a thorough course in data analysis, including how to use the Scikit-learn library to create statistical models that make predictions. Finally, it presents four real-world case studies that tie all the coursework together.
Now available as a Canvas course!
The Canvas course file contains all the objectives, quizzes, assignments, and slides that you need to run an effective course. It only takes a few clicks to import it into the Canvas LMS. Then, you can customize it for your course when it is available. Learn more.
The text was perfect for my class. It provided a solid foundation for my students in using the Pandas and Seaborn libraries. I really appreciated the four case studies. They were a big help for my students as they illustrated all phases of data analysis and visualization.”
J. Jasperson – Texas A&M University
- About this Book
- Table of Contents
- Courseware
- FAQs
- Corrections
Book description
To present the essential Python skills for data science in a manageable progression and at the right pace, this book is divided into 4 sections.
Section 1: Get off to a fast start
This section gets your students started fast. First, they’ll learn how to use JupyterLab and Notebooks to organize and work with Python code for data science. Then, they’ll learn how to use the Pandas and Seaborn libraries for data analysis and visualization. By the end of this section, they’ll be able to start doing analyses of their own.
Section 2: The critical skills for success on the job
This section presents the descriptive analysis skills that are critical for success on the job. That includes how to:
- Get data from CSV files, Excel files, JSON files, Stata files, and databases
- Clean data by dropping unneeded rows and columns and fixing missing values, data types, and outliers
- Prepare data by adding columns, modifying the data in columns, and combining data frames
- Analyze data by grouping and aggregating the data, using pivot tables, and more
- Analyze time-series data by reindexing, downsampling, and working with rolling windows and running totals
Section 3: An introduction to predictive analysis
This section presents the predictive analysis skills that your students need to create statistical models that make predictions. Although predictive analysis is a large topic that could be an entire course of its own, this section presents the concepts your students need to get started with it. More specifically, it shows your students how to use the Scikit-learn library to create linear regression models to predict numeric values.
Section 4: The case studies
This section presents four complete analyses that show how the skills in this book can be applied to real-world datasets:
- Polling data for the 2016 presidential election
- Wildfire data from the US Forest Service
- US social survey data
- Basketball shot data from the NBA (National Basketball Association)
These in-depth analyses make sure that your students master the professional skills they’re going to need.
The course prerequisites
The book assumes that the students have some programming experience, the kind they would get from any introduction to programming course. Then, chapter 1 presents the Python skills that are required for this book. If your students need to know more than that, they can refer to Murach’s Python Programming (2nd Edition).
What software your students need
The only software that’s needed for this book is the Anaconda distribution of Python. It includes JupyterLab, Pandas, Seaborn, Scikit-learn, and more.
Appendixes A and B show how to download and install this distribution on both Windows and macOS systems. Then, chapter 1 shows how to get started with JupyterLab.
What courses this book can be used for
This book is an optimal primary text for any course on the use of Python for data science. But it also works well as a supplementary text for any course where students need to use Python to analyze large datasets.
Why your students will learn faster and better with our book
Like all our books, this book is designed to make it as easy as possible for your students to learn new skills faster and retain them better. Here are a few of those features:
- All of the information is presented in paired pages, with the essential syntax, guidelines, and examples on the right page and clear explanations on the left page.
- The paired-pages format is ideal for reference when your students need to refresh their memories about how to do something.
- The four analyses presented in section 4 use real-world datasets.
- The hundreds of short examples present usable code for tasks that your students are likely to need for their own analyses.
- The exercises at the end of each chapter provide a way for your students to gain valuable hands-on experience without any extra busywork.
What's new in this edition
- We changed the title from Data Analysis to Data Science because we think the new title better reflects the content of the book.
- We updated this book to the latest data science libraries including Pandas 2.2, Seaborn 0.13.2, and Scikit-learn 1.41. This resulted in many minor code changes to fix errors and silence deprecation warnings.
- We updated appendixes A (Windows) and B (macOS) to show how to use Anaconda to create an environment that uses the exact versions of the libraries that we used in this book. If your students create and use this environment, you can be sure that all of the code in this book will work as described, even if new versions of the libraries become available.
- We now provide all of the data files for this book from murach.com. As a result, you can be sure that these files will be available to your students.
A summary of some minor changes
- We dropped coverage of the inplace parameter because and consistently use assignment instead.
- Seaborn plots no longer accept the ci parameter, so we now show how to use the errorbar parameter instead.
- In figure 6-10 of chapter 6, we now show how to use the ffill() method instead of the fillna() method.
- Pandas no longer automatically drops categorical columns when you call numeric aggregate functions. As a result, we now select the numeric data before calling aggregate functions.
- Due to changes in the way Pandas groups data, we no longer drop unused categories when grouping by a categorical column, and we explain how the observed parameter works.
What people said about the first edition
“I really appreciated the four case studies. They were a big help for my students as they illustrated all phases of data analysis and visualization.”
— J. Jasperson – Texas A&M University
“In his first at-bat, Scott McCoy smashes this one out of the park! This book is not just informative, it is exciting.”
— Scott Spurlock, Software Engineer, Georgia
“Unlike some other books on data analysis with Python, the explanations of how to perform data analysis are thorough rather than terse or with no explanations.”
— Posted at an online bookseller
“This is a fun book for beginners and experienced data scientists.”
— Posted at an online bookseller
What people say about Murach books
“This is my first exposure to Murach’s books, and I love them. I like the organization of the content, the consistent approach in each book, and the accuracy of the material.”
—Bob L., Michigan
“I really like the paired-pages format of detailed information on the left and quick notes on the right. This helps me to quickly find the information I’m looking for.”
—Roxanne T., Student, Washington
“I can’t praise this book highly enough. The clarity used in picking what to include, when to introduce it, and how to do so is remarkable.”
—Charles Ferguson, Software Developer, Australia
“Another thing I like is the exercises at the end of each chapter. They’re a great way to reinforce the main points of each chapter and force you to get your hands dirty.”
—Hien Luu, SD Forum/Java SIG
“Your book was indispensable to me. The answers were right there at every turn. All the examples made sense, and they all worked!”
—Alan Vogt, ETL Consultant, Massachusetts
“This book covers the perfect amount of description, and it does not make you bored by providing unnecessary details.”
—Posted at an online bookseller
On Murach’s Python Programming: “This is now my third book for Python, and it is the ONLY one that has made me feel comfortable solving problems and reading code. The paired pages approach is fantastic, and it makes learning the syntax, rules, and conventions understandable for me.”
—Posted at an online bookseller
“Your books shine out from the rest—the quality of writing and presentation of information is topnotch, and the consistency of quality across books is impressive.”
—Nolan Tamashiro, Developer
View the table of contents for this book in a PDF: Table of Contents (PDF)
Click on any chapter title to display or hide its content.
Section 1 Get off to a fast start
Chapter 1 Introduction to Python for data science
Introduction to data science
What data science is
The five phases of data analysis and visualization
The IDEs for Python data science
The Python skills that you need for data science
How to install and import the Python modules for data science
How to call and chain methods
The coding basics for Python data science
How to use JupyterLab as your IDE
How to start JupyterLab and work with a Notebook
How to edit and run the cells in a Notebook
How to use the Tab completion and tooltip features
How syntax and runtime errors work
How to use Markdown language
How to get reference information
Two more skills for working with JupyterLab
How to split the screen between two Notebooks
How to use Magic Commands
Introduction to the case studies
The Polling case study
The Forest Fires case study
The Social Survey case study
The Sports Analytics case study
Chapter 2 The Pandas essentials for data analysis
Introduction to the Pandas DataFrame
The DataFrame structure
Two ways to get data into a DataFrame
How to save and restore a DataFrame
How to examine the data
How to display the data in a DataFrame
How to use the attributes of a DataFrame
How to use the info(), nunique(), and describe() methods
How to access the columns and rows
How to access columns
How to access rows
How to access a subset of rows and columns
Another way to access a subset of rows and columns
How to work with the data
How to sort the data
How to use the statistical methods
How to use Python for column arithmetic
How to modify the string data in columns
How to shape the data
How to use indexes
How to pivot the data
How to melt the data
How to analyze the data
How to group the data
How to aggregate the data
How to plot the data
Chapter 3 The Pandas essentials for data visualization
Introduction to data visualization
The Python libraries for data visualization
Long vs. wide data for data visualization
How the Pandas plot() method works by default
The three basic parameters for the Pandas plot() method
How to create 8 types of plots
How to create a line plot or an area plot
How to create a scatter plot
How to create a bar plot
How to create a histogram or a density plot
How to create a box plot or a pie plot
How to enhance a plot
How to improve the appearance of a plot
How to work with subplots
How to use chaining to get the plots you want
Chapter 4 The Seaborn essentials for data visualization
Introduction to Seaborn
The Seaborn methods for plotting
The general methods vs. the specific methods
How to use the basic Seaborn parameters
How to use the Seaborn parameters for working with subplots
How to enhance and save plots
How to set the title, x label, and y label
How to set the ticks, x limits, and y limits
How to set the background style
How to work with subplots
How to save a plot
How to create relational plots
How to create a line plot
How to create a scatter plot
How to create categorical plots
How to create a bar plot
How to create a box plot
How to create distribution plots
How to create a histogram
How to create a KDE or ECDF plot
How to enhance a distribution plot
Other techniques for enhancing a plot
How to use other Axes methods to enhance a plot
How to annotate a plot
How to set the color palette
How to enhance a plot that has subplots
How to customize the titles for subplots
How to set the size of a specific plot
Section 2 The critical skills for success on the job
Chapter 5 How to get the data
How to find the data that you want to analyze
Common data sources
How to find and select the data that you want
How to import data into a DataFrame
How to import data directly into a DataFrame
How to download a file to disk before importing it
How to work with a zip file on disk
How to get database data into a DataFrame
How to run queries against a database
How to use a SQL query to import data into a DataFrame
How to work with a Stata file
How to get and explore the metadata of a Stata file
How to build DataFrames for the metadata and the data
How to work with a JSON file
How to download a JSON file to disk
How to open a JSON file in JupyterLab
How to drill down into the data
How to build a DataFrame for the data
Chapter 6 How to clean the data
Introduction to data cleaning
A general plan for cleaning the data
What the info() method can tell you
What the unique values can tell you
What the value counts can tell you
How to simplify the data
How to drop rows based on conditions
How to drop duplicate rows
How to drop columns
How to rename columns
How to find and fix missing values
How to find missing values
How to drop rows with missing values
How to fill missing values
How to fix data type problems
How to find dates and numbers that are imported as objects
How to convert date and time strings to the datetime data type
How to convert object columns to numeric data types
How to work with the category data type
How to replace invalid values and convert a column’s data type
How to fix data problems when you import the data
How find and fix outliers
How to find outliers
How to fix outliers
Chapter 7 How to prepare the data
How to add and modify columns
How to work with datetime columns
How to work with string columns
How to work with numeric columns
How to add a summary column to a DataFrame
How to apply functions and lambda expressions
How to apply functions to rows or columns
How to apply user-defined functions
How lambda expressions work with DataFrames
How to apply lambda expressions
How to work with indexes
How to set and remove an index
How to unstack indexed data
How to combine DataFrames
How to join DataFrames with an inner join
How to join DataFrames with a left or outer join
How to merge DataFrames
How to concatenate DataFrames
The SettingWithCopyWarning
What the warning is telling you
How to handle the warning
Chapter 8 How to analyze the data
How to create and plot long data
How to melt columns to create long data
How to plot melted columns
How to group and aggregate the data
How to group and apply a single aggregate method
How to work with a DataFrameGroupBy object
How to apply multiple aggregate methods
How to create and use pivot tables
How to use the pivot() method
How to use the pivot_table() method
How to work with bins
How to create bins of equal size
How to create bins with equal numbers of unique values
How to plot binned data
More skills for data analysis
How to select the rows with the largest values
How to calculate the percent change
How to rank rows
How to find other methods for analysis
Chapter 9 How to analyze time-series data
How to reindex time-series data
How to generate time periods
How to reindex with datetime indexes
How to reindex with a semi-month index
How a user-defined function can improve a datetime index
How reindexing with an improved index can improve plots
How to resample time-series data
How to use the resample() method
How to use the label and closed parameters when you downsample
How downsampling can improve plots
How to work with rolling windows
The concept of rolling windows
How to create rolling windows
How to plot rolling window data
How to work with running totals
How to create running totals
How to plot running totals
Section 3 An introduction to predictive analysis
Chapter 10 How to make predictions with a linear regression model
Introduction to predictive analysis
Types of predictive models
Introduction to regression analysis
How to find correlations between variables
The Housing dataset
How to identify correlations with a scatter plot
How to identify correlations with a grid of scatter plots
How to identify correlations with r-values
How to identify correlations with a heatmap
How to use Scikit-learn to work with a linear regression
A procedure for creating and using a regression model
The function and methods for linear regression models
How to create, validate, and use a linear regression model
How to plot the predicted data
How to plot the residuals
How to plot regression models with Seaborn
The lmplot() method and some of its parameters
How to plot a simple linear regression
How to plot a logistic regression
How to plot a polynomial regression
How to plot a lowess regression
How to use the residplot() method to plot the residuals
Chapter 11 How to make predictions with a multiple regression model
A simple regression model for a Cars dataset
The Cars dataset
How to create a simple regression model
How to plot the residuals of a simple regression
How to work with a multiple regression model
How to create a multiple regression model
How to plot the residuals of a multiple regression
How to work with categorical variables
How to identify categorical variables
How to review categorical variables
How to create dummy variables
How to rescale the data and check the correlations
How to create a multiple regression that includes dummy variables
How to improve a multiple regression model
How to select the independent variables
How to test different combinations of variables
How to use Scikit-learn to select the variables
How to select the right number of variables
Section 4 The case studies
Chapter 12 The Polling case study
Get and display the data
Import the modules that you will need
Get the data
Display the data
Clean the data
Examine the data
Drop columns and rows
Rename columns
Fix object columns
Fix data
Take an early plot with Pandas
Save the DataFrame
Prepare the data
Add columns for grouping and filtering
Create a new DataFrame in long form
Take an early plot of the long data with Seaborn
Add monthly bins to the DataFrame
Add an average percent column for each month
Save the wide and long DataFrames
Analyze the data
Plot the national and swing state polls
Plot the voter types
Plot the last two months of polling
Plot the gap changes in selected states
More preparation and analysis
Prepare the gap data for the last week of polling
Plot the gap data for the last week of polling
Prepare the weekly gap data for the swing states
Plot the weekly gap data for the swing states
Chapter 13 The Forest Fires case study
Get the data
Connect and query the database
Import the data into a DataFrame
Clean the data
Examine the data
Improve the readability of the data
Drop unnecessary rows
Drop duplicate rows
Convert dates to datetime objects
Check for missing contain dates
Prepare the data
Add fire_month and days_burning columns
Examine the contain_date and days_burning columns
Analyze the data
Analyze the data for California
Two more plots for California fires
Rank the states by total acres burned
Prepare a DataFrame for total acres burned by year within state
Prepare a DataFrame for the top 4 states
Plot the acres burned total by year for the top 4 states
Review the 20 largest fires in California
Use GeoPandas to plot the fires on a map
Use GeoPandas to plot the California map
Use GeoPandas or Seaborn to plot the California fires on a map
Plot the fires in the continental United States
Chapter 14 The Social Survey case study
Introduction to the Social Survey
Build a DataFrame for the metadata
The employment data
Use the codebook and read the data that you want
Prepare the data
Plot the data and reduce the number of categories
Plot the total counts of the responses
Convert the counts to percents and plot them
The work-life balance data
Search the codebook for small question sets
Read and review the work-life data
Plot the responses for the first question
Plot the responses for the second and third questions
How to expand the scope of the analysis
Use the codebook to find related columns
Use the codebook to find follow-up questions
Select the columns for an expanded DataFrame
Bin the data for a column
How to use a hypothesis to guide your analysis
Develop and test a first hypothesis
Develop and test a second hypothesis
Develop and test a third hypothesis
Chapter 15 The Sports Analytics case study
Get the data and build the DataFrame
Get the data
Build the DataFrame
Clean the data
Locate and drop unneeded rows
Locate and drop unneeded columns
Convert the game_date column to datetime data
Prepare the data
Add a column for the season
Add a column for the shot result
Add a column for points made for each shot
Add three summary columns
Plot the summary data
Plot the points per game by season
Plot the averages of shots, shots made, and points per game by season
Plot the shot locations
Plot the shot locations for two games
Plot the shot locations for two seasons
Plot the shot density for one season
Plot the shot density for two seasons
Appendices
Appendix A How to set up Windows for this book
How to download the files for this book
How to install Anaconda
How to use the Anaconda Navigator
How to create the murach environment
How to unzip some data and test your setup
How to use the Anaconda Prompt
Appendix B How to set up macOS for this book
How to download the files for this book
How to install Anaconda
How to use the Anaconda Navigator
How to create the murach environment
How to unzip some data and test your setup
How to use Terminal with an environment
If you aren’t familiar with the supporting materials that we provide for our books, please visit the About our Courseware page to learn what we provide and how each component can work for you and your students.
If you’re already familiar with our supporting materials from other books, here’s a quick summary of the courseware available for this book.
End-of-chapter activities in the book
- Terms list
- Summary bullets
- Exercises
Student download
- All examples presented in the book
- The starting points for the exercises at the end of each chapter
- The solutions to those exercises
- The case studies presented in the book
Appendixes A (Windows) and B (macOS) give your students instructions for downloading these files.
Instructor’s materials
- Instructional objectives
- Test banks
- Projects for student practice and evaluation
- Case studies for more extensive practice and evaluation
- PowerPoint slides
We provide the files for all of these materials in a zip file. After unzipping this file, you can import these materials into any LMS.
We also provide a Canvas course file that contains most of these files. If you’re using the Canvas LMS, you can use this file to import these materials with just a few clicks.
On this page, we’ll be posting answers to the questions that come up most often about this book. So if you have any questions that you haven’t found answered here at our site, please email us. Thanks!
There are no book corrections that we know of at this time. But if you find any, please email us, and we’ll post any corrections that affect the technical accuracy of the book here. Thank you!
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