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Mastering pandas. - Second Edition

You're reading from  Mastering pandas. - Second Edition

Product type Book
Published in Oct 2019
Publisher
ISBN-13 9781789343236
Pages 674 pages
Edition 2nd Edition
Languages
Author (1):
Ashish Kumar Ashish Kumar
Profile icon Ashish Kumar

Table of Contents (21) Chapters

Preface Section 1: Overview of Data Analysis and pandas
Introduction to pandas and Data Analysis Installation of pandas and Supporting Software Section 2: Data Structures and I/O in pandas
Using NumPy and Data Structures with pandas I/Os of Different Data Formats with pandas Section 3: Mastering Different Data Operations in pandas
Indexing and Selecting in pandas Grouping, Merging, and Reshaping Data in pandas Special Data Operations in pandas Time Series and Plotting Using Matplotlib Section 4: Going a Step Beyond with pandas
Making Powerful Reports In Jupyter Using pandas A Tour of Statistics with pandas and NumPy A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates Data Case Studies Using pandas The pandas Library Architecture pandas Compared with Other Tools A Brief Tour of Machine Learning Other Books You May Enjoy

The mathematical framework for Bayesian statistics

Bayesian methods are an alternative way of making a statistical inference. We will first look at Bayes' theorem, the fundamental equation from which all Bayesian inference is derived.

A few definitions regarding probability are in order before we begin:

  • A,B: These are events that can occur with a certain probability.
  • P(A) and P(B): This is the probability of the occurrence of a particular event.
  • P(A|B): This is the probability of A happening, given that B has occurred. This is known as a conditional probability.
  • P(AB) = P(A and B): This is the probability of A and B occurring together.

We begin with the following basic assumption:

P(AB) = P(B) * P(A|B)

The preceding equation shows the relation of the joint probability of P(AB) to the conditional probability P(A|B) and what is known as the marginal probability, P(B). If...

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