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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

Bar charts

This is one of the most common types of visualization that almost everyone must have encountered. Bars can be drawn horizontally or vertically to represent categorical variables.

Bar charts are frequently used to distinguish objects between distinct collections in order to track variations over time. In most cases, bar charts are very convenient when the changes are large. In order to learn about bar charts, let's assume a pharmacy in Norway keeps track of the amount of Zoloft sold every month. Zoloft is a medicine prescribed to patients suffering from depression. We can use the calendar Python library to keep track of the months of the year (1 to 12) corresponding to January to December:

  1. Let's import the required libraries:
import numpy as np
import calendar
import matplotlib.pyplot as plt
  1. Set up the data. Remember, the range stopping parameter is exclusive...
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