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Mastering pandas

Mastering pandas

By : Kumar
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Mastering pandas

Mastering pandas

By: 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)
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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

pandas-datareader

We can use pandas to not only read data from local CSV or text files but also from various popular remote data sources such as Yahoo Finance, World Bank, and so on. Without any support from pandas, this would have been tedious and we would have to resort to web scraping. This simple and powerful functionality is provided through the pandas-datareader.

It provides us with a direct way of connecting through various data sources from the comfort of the pandas ecosystem without having to delve into the complexity of HTML/JavaScript code where data is enmeshed. These data sources can be accessed by providing the source name and data code. Only a subset of the data can be obtained.

Let's delve deeper and see how we can use it:

  1. Install pandas-datareader through pip using the following command:
pip install pandas-datareader  

You can also install it through conda...

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