During the last 47 years, many customers have asked me how it is that a small publisher in Fresno can make the best programming books. The short answer is that no other publisher works the way we do. Instead of using freelance writers who get no training, we use a small staff of programmers who are trained in our proven writing and teaching methods. Instead of published 40+ books each year, we focus on just a few. Instead of showing pieces or code, we provide complete applications that have been checked and re-checked for accuracy. And instead of rushing books to market, we refuse to let schedule or budget interfere with quality. As I see it, that's the only way to make sure that every book we publish is the best one on its subject. That's why people often tell me that our books are the ones that they look for first whenever they need to learn a new subject.

Why not try this book and see for yourself! Python for Data Analysis contents, Section 1: Get off to a fast start, Use JupyterLab as your IDE so you can work with Python code that's saved in Jupyter Notebooks, Use Pandas to import data into a DataFrame and analyze that data, Use Pandas to create the early data visualizations that can guide your analysis, Use Seaborn to create enhanced data visualizations that are suitable for presentation, Section 2: The critical skills for descriptive analysis, Download and import data from a variety of sites in a variety of formats, including CSV, Excel, database, Stata, and JSON, Clean the data by dropping unneeded rows and columns and by finding and fixing missing values, data type problems, and outliers, Prepare the data by adding columns, modifying the data in columns, applying functions and lambda expressions, and combining DataFrames, Analyze the data by grouping and aggregating the data, using pivot tables, working with bins, and more, Analyze time-series data by reindexing, downsampling, and working with rolling windows and running totals, Section 3: An introduction to predictive analysis, Learn four ways to find the correlations between variables, Use Scikit-learn to create, test, and validate linear regression models.and to predict future results by using the models, Use Seaborn to plot other types of regression models, Create dummy variables from categorical variables and rescale the data to improve your predictions, Use Scikit-learn to select the right variables for multiple regressions, Section 4: Four real-world case studies, See how all of the coding skills work together in the context of real-world data analyses, Run the code for these case studies as you read about them, Download the data analyses used in this book (see the last page of this book for details) Book jacket.