After a standalone installation of Python, each library will have to be separately installed. It is a bit of a hassle to ensure version compatibility between newly installed libraries and the associated dependencies. This is where a third-party distribution like Anaconda comes in handy. Anaconda is the most widely used distribution for Python/R, designed for developing scalable data science solutions.
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Mastering pandas
By :
Mastering pandas
By:
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)
Preface
Free Chapter
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
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