Book Image

Mastering pandas - Second Edition

By : Ashish Kumar
Book Image

Mastering pandas - Second Edition

By: Ashish 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)
Free Chapter
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

Conducting Bayesian statistical analysis

Conducting a Bayesian statistical analysis involves the following steps:

  1. Specifying a probability model: In this step, we fully describe the model using a probability distribution. Based on the distribution of a sample that we have taken, we try to fit a model to it and attempt to assign probabilities to unknown parameters.
  2. Calculating a posterior distribution: The posterior distribution is a distribution that we calculate in light of observed data. In this case, we will directly apply Bayes' formula. It will be specified as a function of the probability model that we specified in the previous step.

  1. Checking our model: This is a necessary step where we review our model and its outputs before we make inferences. Bayesian inference methods use probability distributions to assign probabilities to possible outcomes.
...