You're reading from Exploratory Data Analysis with Python Cookbook Over 50 recipes to analyze, visualize, and extract insights from structured and unstructured data

Product type Paperback

Published in Jun 2023

Publisher Packt

ISBN-13 9781803231105

Length 382 pages

Edition 1st Edition

Languages

Python

Tools

Matplotlib

Concepts

Data Analysis

Author (1):

Ayodele Oluleye

View More author details

Table of Contents (13) Chapters

Preface

1. Chapter 1: Generating Summary Statistics

2. Chapter 2: Preparing Data for EDA FREE CHAPTER

3. Chapter 3: Visualizing Data in Python

4. Chapter 4: Performing Univariate Analysis in Python

5. Chapter 5: Performing Bivariate Analysis in Python

6. Chapter 6: Performing Multivariate Analysis in Python

7. Chapter 7: Analyzing Time Series Data in Python

8. Chapter 8: Analysing Text Data in Python

9. Chapter 9: Dealing with Outliers and Missing Values

10. Chapter 10: Performing Automated Exploratory Data Analysis in Python

11. Index

Why subscribe?

12. Other Books You May Enjoy

Identifying the standard deviation of a dataset

The standard deviation is derived from the variance and is simply the square root of the variance. The standard deviation is typically more intuitive because it is expressed in the same units as the dataset, for example, kilometers (km). On the other hand, the variance is typically expressed in units larger than the dataset and can be less intuitive, for example, kilometers squared (km2).

To analyze the standard deviation of a dataset, we will use the sd method from the numpy library in Python.

Getting ready

We will work with the COVID-19 cases again for this recipe.

How to do it…

We will compute the standard deviation using the numpy libary:

Import the numpy and pandas libraries:
```
import numpy as np
import pandas as pd
```
Load the .csv into a dataframe using read_csv. Then subset the dataframe to include only relevant columns:
```
covid_data = pd.read_csv("covid-data.csv")
covid_data = covid_data[[...
```