In this chapter, we will learn how to work with different kinds of dataset formats in pandas. We'll learn how to use the advanced options provided by pandas' imported CSV files. We will also look at how to work with Excel files in pandas, and how to use advanced options of the read_excel method. We'll explore some of the other pandas methods for working with popular data formats, such as HTML, JSON, PICKLE files, SQL, and so on.
Mastering Exploratory Analysis with pandas
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Mastering Exploratory Analysis with pandas
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Overview of this book
<p>The pandas is a Python library that lets you manipulate, transform, and analyze data. It is a popular framework for exploratory data visualization and analyzing datasets and data pipelines based on their properties. </p><p> </p><p>This book will be your practical guide to exploring datasets using pandas. You will start by setting up Python, pandas, and Jupyter Notebooks. You will learn how to use Jupyter Notebooks to run Python code. We then show you how to get data into pandas and do some exploratory analysis, before learning how to manipulate and reshape data using pandas methods. You will also learn how to deal with missing data from your datasets, how to draw charts and plots using pandas and Matplotlib, and how to create some effective visualizations for your audience. Finally, you will wrapup your newly gained pandas knowledge by learning how to import data out of pandas into some popular file formats. </p><p> </p><p>By the end of this book, you will have a better understanding of exploratory analysis and how to build exploratory data pipelines with Python. </p><p></p>
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Current Title:
Mastering Exploratory Analysis with pandas
Table of Contents (6 chapters)
Preface
Free Chapter
Working with Different Kinds of Datasets
Data Selection
Manipulating, Transforming, and Reshaping Data
Visualizing Data Like a Pro
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