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Mastering pandas

Mastering pandas

By : Kumar
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Mastering pandas

Mastering pandas

By: Kumar

Overview of this book

pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process.
Table of Contents (21 chapters)
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1
Section 1: Overview of Data Analysis and pandas
4
Section 2: Data Structures and I/O in pandas
7
Section 3: Mastering Different Data Operations in pandas
12
Section 4: Going a Step Beyond with pandas

A Tour of Statistics with pandas and NumPy

In this chapter, we'll take a brief tour of classical statistics (also called the frequentist approach) and show you how we can use pandas together with the numpy and stats packages, such as scipy.stats and statsmodels, to conduct statistical analysis. We will also learn how to write the calculations behind these statistics from scratch in Python. This chapter and the following ones are not intended to be primers on statistics; they just serve as an illustration of using pandas along with the stats and numpy packages. In the next chapter, we will examine an alternative approach to the classical view—that is, Bayesian statistics.

In this chapter, we will cover the following topics:

  • Descriptive statistics versus inferential statistics
  • Measures of central tendency and variability
  • Hypothesis testing – the null and alternative...

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