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

HTML

HTML is the popular file format for creating and wrapping web elements and pages. Sometimes, tabular data is stored in a file. In such cases, the read_html method is directly used to read such data. This function parses table elements from HTML files and reads the tables as DataFrames:

pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html') 

You can find all of the table elements containing a particular match word by using the following code:

 match = 'Malta National Bank' 
df_list = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html', match=match) 

A DataFrame can be converted into an HTML table element so that it can be placed into an HTML file like so:

data=pd.read_csv('http://bit.ly/2cLzoxH')
print(data.to_html())

We get the following output:

HTML table element created from a DataFrame

A selected...