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You're reading from Mastering pandas A complete guide to pandas, from installation to advanced data analysis techniques

Product type Paperback

Published in Oct 2019

Publisher

ISBN-13 9781789343236

Length 674 pages

Edition 2nd Edition

Languages

Python

Tools

Pandas

Concepts

Data Analysis

Author (1):

Ashish Kumar

View More author details

Table of Contents (21) Chapters

Preface

1. Section 1: Overview of Data Analysis and pandas FREE CHAPTER

2. Introduction to pandas and Data Analysis

3. Installation of pandas and Supporting Software

4. Section 2: Data Structures and I/O in pandas

5. Using NumPy and Data Structures with pandas

6. I/Os of Different Data Formats with pandas

7. Section 3: Mastering Different Data Operations in pandas

8. Indexing and Selecting in pandas

9. Grouping, Merging, and Reshaping Data in pandas

10. Special Data Operations in pandas

11. Time Series and Plotting Using Matplotlib

12. Section 4: Going a Step Beyond with pandas

13. Making Powerful Reports In Jupyter Using pandas

14. A Tour of Statistics with pandas and NumPy

15. A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates

16. Data Case Studies Using pandas

17. The pandas Library Architecture

18. pandas Compared with Other Tools

19. A Brief Tour of Machine Learning

20. Other Books You May Enjoy

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Using NumPy and Data Structures with pandas

This chapter is one of the most important ones in this book. We will now begin to dive into the nitty-gritty of pandas. We start by taking a tour of NumPy ndarrays, a data structure not in pandas but NumPy. Knowledge of NumPy ndarrays is useful as they are the building blocks on which pandas DataFrames have been built. One key benefit of NumPy arrays is that they execute what is known as vectorized operations, which are operations that require traversing/looping on a Python array and are much faster.

In this chapter, I will present the material via numerous examples using Jupyter.

The topics we will cover in this chapter include a tour of the numpy.ndarray data structure, the pandas.Series one-dimensional (1D) pandas data structure, the pandas.DataFrame two-dimensional (2D) pandas tabular data structure, and the pandas.Panel three-dimensional...

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Authors (1)

Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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