Appendices
B
Python essentials
Telling Stories with Data
Preface
Errors and updates
Foundations
1
Telling stories with data
2
Drinking from a fire hose
3
Reproducible workflows
Communication
4
Writing research
5
Graphs, tables, and maps
Acquisition
6
Measurement, censuses, and sampling
7
APIs, scraping, and parsing
8
Experiments and surveys
Preparation
9
Clean, prepare, and test
10
Store and share
11
Exploratory data analysis
Modeling
12
Linear models
13
Generalized linear models
14
Prediction
Applications
15
Causality from observational data
16
Multilevel regression with post-stratification
17
Text as data
18
Concluding remarks
Appendices
A
R essentials
B
Python essentials
C
SQL essentials
D
Datasets
E
R Markdown
F
Papers
G
Production
H
Class activities
I
Cocktails
References
Table of contents
B.1
Introduction
B.2
Anaconda and VS Code
B.3
Getting started
B.4
Using data
B.5
Making graphs
B.6
Exercises
Practice
Quiz
Activity
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Appendices
B
Python essentials
Online Appendix B — Python essentials
Prerequisites
Key concepts and skills
Software and packages
B.1
Introduction
B.2
Anaconda and VS Code
B.3
Getting started
B.4
Using data
B.5
Making graphs
B.6
Exercises
Practice
Quiz
Activity
A
R essentials
C
SQL essentials