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.

What you will learn

Speed up your data analysis by importing data into pandas
Keep relevant data points by selecting subsets of your data
Create a high-quality dataset by cleaning data and fixing missing values
Compute actionable analytics with grouping and aggregation in pandas
Master time series data analysis in pandas
Make powerful reports in pandas using Jupyter notebooks

Who this book is for

This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book.

Introduction to pandas and Data Analysis
Installation of pandas and Supporting Software
Using NumPy and Data Structures with pandas
I/Os of Different Data Formats with pandas
Indexing and Selecting in pandas
Grouping, Merging, and Reshaping Data in pandas
Special Data Operations in pandas
Time Series and Plotting Using Matplotlib
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

About the Author

Ashish Kumar is seasoned data science professional and a published author with a social bent of mind. An IIT Madras grad and a Young India Fellow, he has 6+ years of experience in implementing and deploying Data Science & ML solutions for challenging industry problems in both hands-on and managerial roles. NLP, IoT Analytics, R Shiny development, Customer & Product Analytics are some of his core expertise and is fluent in Python and R. When not crunching data, he sneaks to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling startups.

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