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Data Wrangling with Python
Data Wrangling with Python

Data Wrangling with Python: Creating actionable data from raw sources

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Profile Icon Dr. Tirthajyoti Sarkar Profile Icon Shubhadeep Roychowdhury
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$24.99 $35.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (32 Ratings)
eBook Feb 2019 452 pages 1st Edition
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$24.99 $35.99
Paperback
$43.99
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Arrow left icon
Profile Icon Dr. Tirthajyoti Sarkar Profile Icon Shubhadeep Roychowdhury
Arrow right icon
$24.99 $35.99
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7 (32 Ratings)
eBook Feb 2019 452 pages 1st Edition
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$24.99 $35.99
Paperback
$43.99
Subscription
Free Trial
Renews at $19.99p/m

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Data Wrangling with Python

Chapter 2. Advanced Data Structures and File Handling

Note

Learning Objectives

By the end of this chapter, you will be able to:

  • Compare Python’s advanced data structures

  • Utilize data structures to solve real-world problems

  • Make use of OS file-handling operations

Note

This chapter emphasizes the data structures in Python and the operating system functions that are the foundation of this book.

Summary

In this chapter, we learned what the term data wrangling means. We also got examples from various real-life data science situations where data wrangling is very useful and is used in industry. We moved on to learn about the different built-in data structures that Python has to offer. We got our hands dirty by exploring lists, sets, dictionaries, tuples, and strings. They are the fundamental building blocks in Python data structures, and we need them all the time while working and manipulating data in Python. We did several small hands-on exercises to learn more about them. We finished this chapter with a carefully designed activity, which let us combine a lot of different tricks from all the different data structures into a real-life situation and let us observe the interplay between all of them.

In the next chapter, we will learn about the data structures in Python and utilize them to solve real-world problems.

Advanced Data Structures


We will start this chapter by discussing advanced data structures. We will do that by revisiting lists. We will construct a stack and a queue, explore multiple element membership checking, and throw a bit of functional programming in for good measure. If all of this sounds intimidating, then do not worry. We will get to things step by step, like in the previous chapter, and you will feel confident once you have finished this chapter.

To start this chapter, you have to open an empty notebook. To do that, you can simply input the following command in a shell. It is advised that you first navigate to an empty directory using cd before you enter the command:

docker run -p 8888:8888 -v 'pwd':/notebooks -it rcshubhadeep/packt-data-wrangling-base:latest

Once the Docker container is running, point your browser to http://localhost:8888 and use dw_4_all as the passcode to access the notebook interface.

Iterator

We will start off this topic with lists. However, before we get into...

Basic File Operations in Python


In the previous topic, we investigated a few advanced data structures and also learned neat and useful functional programming methods to manipulate them without side effects. In this topic, we will learn about a few operating system (OS)-level functions in Python. We will concentrate mainly on file-related functions and learn how to open a file, read the data line by line or all at once, and finally how to cleanly close the file we opened. We will apply a few of the techniques we have learned about on a file that we will read to practice our data wrangling skills further.

Exercise 22: File Operations

In this exercise, we will learn about the OS module of Python, and we will also see two very useful ways to write and read environment variables. The power of writing and reading environment variables is often very important while designing and developing data wrangling pipelines.

Note

In fact, one of the factors of the famous 12-factor app design is the very idea...

Summary


In this chapter, we learned about the workings of advanced data structures such as stacks and queues. We implemented and manipulated both stacks and queues. We then focused on different methods of functional programming, including iterators, and combined lists and functions together. After this, we looked at the OS-level functions and the management of environment variables and files. We also examined a clean way to deal with files, and we created our own CSV parser in the last activity.

In the next chapter, we will be dealing with the three most important libraries, namely NumPy, pandas, and matplotlib.

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Key benefits

  • Focus on the basics of data wrangling
  • Study various ways to extract the most out of your data in less time
  • Boost your learning curve with bonus topics like random data generation and data integrity checks

Description

For data to be useful and meaningful, it must be curated and refined. Data Wrangling with Python teaches you the core ideas behind these processes and equips you with knowledge of the most popular tools and techniques in the domain. The book starts with the absolute basics of Python, focusing mainly on data structures. It then delves into the fundamental tools of data wrangling like NumPy and Pandas libraries. You'll explore useful insights into why you should stay away from traditional ways of data cleaning, as done in other languages, and take advantage of the specialized pre-built routines in Python. This combination of Python tips and tricks will also demonstrate how to use the same Python backend and extract/transform data from an array of sources including the Internet, large database vaults, and Excel financial tables. To help you prepare for more challenging scenarios, you'll cover how to handle missing or wrong data, and reformat it based on the requirements from the downstream analytics tool. The book will further help you grasp concepts through real-world examples and datasets. By the end of this book, you will be confident in using a diverse array of sources to extract, clean, transform, and format your data efficiently.

Who is this book for?

Data Wrangling with Python is designed for developers, data analysts, and business analysts who are keen to pursue a career as a full-fledged data scientist or analytics expert. Although, this book is for beginners, prior working knowledge of Python is necessary to easily grasp the concepts covered here. It will also help to have rudimentary knowledge of relational database and SQL.

What you will learn

  • Use and manipulate complex and simple data structures
  • Harness the full potential of DataFrames and numpy.array at run time
  • Perform web scraping with BeautifulSoup4 and html5lib
  • Execute advanced string search and manipulation with RegEX
  • Handle outliers and perform data imputation with Pandas
  • Use descriptive statistics and plotting techniques
  • Practice data wrangling and modeling using data generation techniques

Product Details

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Publication date : Feb 28, 2019
Length: 452 pages
Edition : 1st
Language : English
ISBN-13 : 9781789804249
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Product Details

Publication date : Feb 28, 2019
Length: 452 pages
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Language : English
ISBN-13 : 9781789804249
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Concepts :
Tools :

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Table of Contents

9 Chapters
Introduction to Data Wrangling with Python Chevron down icon Chevron up icon
Advanced Data Structures and File Handling Chevron down icon Chevron up icon
Introduction to NumPy, Pandas, and Matplotlib Chevron down icon Chevron up icon
A Deep Dive into Data Wrangling with Python Chevron down icon Chevron up icon
Getting Comfortable with Different Kinds of Data Sources Chevron down icon Chevron up icon
Learning the Hidden Secrets of Data Wrangling Chevron down icon Chevron up icon
Advanced Web Scraping and Data Gathering Chevron down icon Chevron up icon
RDBMS and SQL Chevron down icon Chevron up icon
Application of Data Wrangling in Real Life Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.7
(32 Ratings)
5 star 28.1%
4 star 34.4%
3 star 21.9%
2 star 6.3%
1 star 9.4%
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Robert Jun 12, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you don't know anything from Python, but you want to become Data Scientist it can be helpful for you.
Amazon Verified review Amazon
Soumya Das May 26, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Effective book on data wrangling
Amazon Verified review Amazon
Jyothi M Rao Sep 19, 2023
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Udemy Verified review Udemy
Charles Quinn Jul 16, 2023
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Sonia Christensen Mar 08, 2023
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