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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
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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

Data Formatting


In this topic, we will format a given dataset. The main motivations behind formatting data properly are as follows:

  • It helps all the downstream systems to have a single and pre-agreed form of data for each data point, thus avoiding surprises and, in effect, breaking it.

  • To produce a human-readable report from lower-level data that is, most of the time, created for machine consumption.

  • To find errors in data.

There are a few ways to do data formatting in Python. We will begin with the modulus operator.

The % operator

Python gives us the % operator to apply basic formatting on data. To demonstrate this, we will load the data first by reading the CSV file, and then we will apply some basic formatting on it.

Load the data from the CSV file by using the following command:

from csv import DictReader
raw_data = []
with open("combinded_data.csv", "rt") as fd:
    data_rows = DictReader(fd)
    for data in data_rows:
        raw_data.append(dict(data))

Now, we have a list called raw_data that...

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