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Hands-On Exploratory Data Analysis with Python

You're reading from   Hands-On Exploratory Data Analysis with Python Perform EDA techniques to understand, summarize, and investigate your data

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Product type Paperback
Published in Mar 2020
Publisher Packt
ISBN-13 9781789537253
Length 352 pages
Edition 1st Edition
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Authors (2):
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Suresh Kumar Mukhiya Suresh Kumar Mukhiya
Author Profile Icon Suresh Kumar Mukhiya
Suresh Kumar Mukhiya
Usman Ahmed Usman Ahmed
Author Profile Icon Usman Ahmed
Usman Ahmed
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Table of Contents (17) Chapters Close

Preface 1. Section 1: The Fundamentals of EDA
2. Exploratory Data Analysis Fundamentals FREE CHAPTER 3. Visual Aids for EDA 4. EDA with Personal Email 5. Data Transformation 6. Section 2: Descriptive Statistics
7. Descriptive Statistics 8. Grouping Datasets 9. Correlation 10. Time Series Analysis 11. Section 3: Model Development and Evaluation
12. Hypothesis Testing and Regression 13. Model Development and Evaluation 14. EDA on Wine Quality Data Analysis 15. Other Books You May Enjoy Appendix

Data transformation

Although there are a lot of objects returned by the extracted data, we do not need all the items. We will only extract the required fields. Data cleansing is one of the essential steps in the data analysis phase. For our analysis, all we need is data for the following: subject, from, date, to, label, and thread.

Let's look at all the steps involved in data transformation in the following sections.

Data cleansing

Let's create a CSV file with only the required fields. Let's start with the following steps:

  1. Import the csv package:
import csv
  1. Create a CSV file with only the required attributes:
with open('mailbox.csv', 'w') as outputfile:
writer = csv.writer(outputfile...
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