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Data Literacy With Python

You're reading from   Data Literacy With Python A Comprehensive Guide to Understanding and Analyzing Data with Python

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Product type Paperback
Published in Jul 2024
Publisher Mercury_Learning
ISBN-13 9781836640097
Length 271 pages
Edition 1st Edition
Languages
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Authors (2):
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Mercury Learning and Information Mercury Learning and Information
Author Profile Icon Mercury Learning and Information
Mercury Learning and Information
Oswald Campesato Oswald Campesato
Author Profile Icon Oswald Campesato
Oswald Campesato
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Table of Contents (9) Chapters Close

Preface
1. Chapter 1: Working With Data FREE CHAPTER 2. Chapter 2: Outlier and Anomaly Detection 3. Chapter 3: Cleaning Datasets 4. Chapter 4: Introduction to Statistics 5. Chapter 5: Matplotlib and Seaborn 6. Index
Appendix A: Introduction to Python 1. Appendix B: Introduction to Pandas

SEABORN DATASET NAMES

Listing 5.30 displays the contents dataset_names.py that displays the Seaborn built-in datasets, one of which we will use in a subsequent section in order to render a heat map in Seaborn.

Listing 5.30: dataset_names.py

import seaborn as sns

names = sns.get_dataset_names()
for name in names:
  print("name:",name)

Listing 5.30 contains an import statement, followed by initializing the variable names with a list of the dataset names in Seaborn. Then a simple loop iterates through the values in the names variable and prints their values. The output from Listing 5.30 is here:

name: anagrams
name: anscombe
name: attention
name: brain_networks
name: car_crashes
name: diamonds
name: dots
name: exercise
name: flights
name: fmri
name: gammas
name: geyser
name: iris
name: mpg
name: penguins
name: planets
name: taxis
name: tips
name: titanic

The three-line code sample in the next section shows you how to display the rows in the built-in “tips” dataset...

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