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Mastering pandas

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

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Product type Paperback
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Length 674 pages
Edition 2nd Edition
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Tools
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Author (1):
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Ashish Kumar Ashish Kumar
Author Profile Icon Ashish Kumar
Ashish Kumar
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Toc

Table of Contents (21) Chapters Close

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

Probability distributions

In this section, we will briefly examine the properties of various probability distributions. Many of these distributions are used for Bayesian analysis, and so a brief synopsis is needed before we can proceed. We will also illustrate how to generate and display these distributions using matplotlib. In order to avoid repeating the import statements for every code snippet in each section, we will be presenting the following standard set of Python code imports that need to be run before any of the code snippets mentioned in the following command. You only need to run these imports once per session. The imports are as follows:

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import colors
import matplotlib.pyplot as plt
import matplotlib
%matplotlib inline
  
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