You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Product type Paperback

Published in Sep 2021

Publisher Packt

ISBN-13 9781801071970

Length 620 pages

Edition 1st Edition

Languages

Python

Tools

Excel

Concepts

Data Science

Author (1):

Nathan George

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Table of Contents (30) Chapters

Preface

1. Part I - An Introduction and the Basics

2. Introduction to Data Science FREE CHAPTER

3. Getting Started with Python

4. Part II - Dealing with Data

5. SQL and Built-in File Handling Modules in Python

6. Loading and Wrangling Data with Pandas and NumPy

7. Exploratory Data Analysis and Visualization

8. Data Wrangling Documents and Spreadsheets

9. Web Scraping

10. Part III - Statistics for Data Science

11. Probability, Distributions, and Sampling

12. Statistical Testing for Data Science

13. Part IV - Machine Learning

14. Preparing Data for Machine Learning: Feature Selection, Feature Engineering, and Dimensionality Reduction

15. Machine Learning for Classification

16. Evaluating Machine Learning Classification Models and Sampling for Classification

17. Machine Learning with Regression

18. Optimizing Models and Using AutoML

19. Tree-Based Machine Learning Models

20. Support Vector Machine (SVM) Machine Learning Models

21. Part V - Text Analysis and Reporting

22. Clustering with Machine Learning

23. Working with Text

24. Part VI - Wrapping Up

25. Data Storytelling and Automated Reporting/Dashboarding

26. Ethics and Privacy

27. Staying Up to Date and the Future of Data Science

28. Other Books You May Enjoy

29. Index

Performing EDA with Seaborn and pandas

We already looked at some of the steps of EDA – mainly numeric analysis, with some plotting. There are many other plots we didn't cover in the previous chapter: boxplots, violin plots, correlograms, missing value plots, and more. Let's look at creating some of these plots.

Making boxplots and letter-value plots

First, we'll take a look at the classic boxplot. This was invented in 1970 by the legendary statistician and mathematician John Tukey. The boxplot helps us to quickly see some information about the distribution of a dataset and enables us to compare subsets of the data easily. Just like histograms and bar plots, boxplots are available in the pandas plotting tools. Before we start examining data, let's first load it and transform some columns:

import pandas as pd
df = pd.read_csv('data/itunes_data.csv')
df['Minutes'] = df['Milliseconds'] / (1000 * 60)
df['MB&apos...

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You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Table of Contents (30) Chapters

Performing EDA with Seaborn and pandas

Making boxplots and letter-value plots

Authors (1)

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You're reading from Practical Data Science with Python Learn tools and techniques from hands-on examples to extract insights from data

Table of Contents (30) Chapters

Performing EDA with Seaborn and pandas

Making boxplots and letter-value plots

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Authors (1)

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