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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

URL and S3

Sometimes, the data is directly available as a URL. In such cases, read_csv can be directly used to read from these URLs:

pd.read_csv('http://bit.ly/2cLzoxH').head()

Alternatively, to work with URLs in order to get data, we can use a couple of Python packages that we haven't used so far, such as .csv and .urllib. It would suffice to know that .csv provides a range of methods for handling .csv files and that urllib is used to navigate to and access information from the URL. Here is how we can do this:

import csv 
import urllib2 
 
url='http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data' 
response=urllib2.urlopen(url) 
cr=csv.reader(response) 
 
for rows in cr: 
   print rows 
 

AWS S3 is a popular file-sharing and storage repository on the web. Many enterprises store their business operations data as files on S3, which needs...

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