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Data Wrangling with Python

You're reading from   Data Wrangling with Python Creating actionable data from raw sources

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Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781789800111
Length 452 pages
Edition 1st Edition
Languages
Tools
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Authors (2):
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Shubhadeep Roychowdhury Shubhadeep Roychowdhury
Author Profile Icon Shubhadeep Roychowdhury
Shubhadeep Roychowdhury
Dr. Tirthajyoti Sarkar Dr. Tirthajyoti Sarkar
Author Profile Icon Dr. Tirthajyoti Sarkar
Dr. Tirthajyoti Sarkar
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Table of Contents (12) Chapters Close

Data Wrangling with Python
Preface
1. Introduction to Data Wrangling with Python FREE CHAPTER 2. Advanced Data Structures and File Handling 3. Introduction to NumPy, Pandas, and Matplotlib 4. A Deep Dive into Data Wrangling with Python 5. Getting Comfortable with Different Kinds of Data Sources 6. Learning the Hidden Secrets of Data Wrangling 7. Advanced Web Scraping and Data Gathering 8. RDBMS and SQL 9. Application of Data Wrangling in Real Life Appendix

Python for Data Wrangling


There is always a debate on whether to perform the wrangling process using an enterprise tool or by using a programming language and associated frameworks. There are many commercial, enterprise-level tools for data formatting and pre-processing that do not involve much coding on the part of the user. These examples include the following:

  • General purpose data analysis platforms such as Microsoft Excel (with add-ins)

  • Statistical discovery package such as JMP (from SAS)

  • Modeling platforms such as RapidMiner

  • Analytics platforms from niche players focusing on data wrangling, such as Trifacta, Paxata, and Alteryx

However, programming languages such as Python provide more flexibility, control, and power compared to these off-the-shelf tools.

As the volume, velocity, and variety (the three Vs of big data) of data undergo rapid changes, it is always a good idea to develop and nurture a significant amount of in-house expertise in data wrangling using fundamental programming frameworks so that an organization is not beholden to the whims and fancies of any enterprise platform for as basic a task as data wrangling:

Figure 1.2: Google trend worldwide over the last Five years

A few of the obvious advantages of using an open source, free programming paradigm such as Python for data wrangling are the following:

  • General purpose open source paradigm putting no restriction on any of the methods you can develop for the specific problem at hand

  • Great ecosystem of fast, optimized, open source libraries, focused on data analytics

  • Growing support to connect Python to every conceivable data source type

  • Easy interface to basic statistical testing and quick visualization libraries to check data quality

  • Seamless interface of the data wrangling output with advanced machine learning models

Python is the most popular language of choice of machine learning and artificial intelligence these days.

You have been reading a chapter from
Data Wrangling with Python
Published in: Feb 2019
Publisher: Packt
ISBN-13: 9781789800111
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