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Using Python Altair for Data Storytelling

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1.What is Data Storytelling?

Getting Started Introduction Thinking for the Audience Communicating a Message Quiz: Data Storytelling

2.Using Altair for Data Storytelling

Introducing Altair Vega and Vega-Lite Encodings Marks Exercise: Draw a Multiline Chart Exercise: Encodings Exercise: Marks Quiz: Using Altair for Data Storytelling

3.The DIKW Pyramid

The Structure of the DIKW Pyramid Turning Data into Information Turning Information into Knowledge Turning Knowledge into Wisdom Exercise: The DIKW Pyramid Quiz: The DIKW Pyramid

4.Introducing the Concept of Data

Data Definition Data Types Data License Data Cleaning Data Enrichment Exercise: Data Types Exercise: Missing Values Exercise: Data Manipulation Quiz: Concept of Data

Project

Visualize Geospatial Data Using Plotly and Mapbox

5.Getting Familiar with Unknown Data

Bar Chart Line Chart Stacked Area Chart Scatter Plot Box Plot Pie Chart Heatmap Geographical Map Exercise: Slopegraph and Dumbbell Chart Exercise: Trellis Chart, Column Chart, and Stacked Column Chart Exercise: Bubble Chart and Hex Bin Chart

Assessment

Assessment 1

Project

Data Visualization Using Plotly.js

6.Introducing the Concept of Information

Information Definition Asking the Right Questions Aggregation Binning Ordering Scales Decluttering Exercise: Concept of Information Quiz: Concept of Information

7.Data Modeling

Regression Kernel Density Estimation

8.Knowledge

The Concept of Knowledge Setting the Context Adding Annotations Crediting Images and Data Sources Adding Title and Subtitle Choosing Colors Choosing Size Adding Interactivity Detecting Lies Reducing Bias Exercise: Concept of Knowledge Quiz: Knowledge

9.Wisdom

The Concept of Wisdom Adding a Call to Action Combining Multiple Charts Publishing the Story on the Web

Assessment

Assessment 2

10.Case Studies

Bike Vendors Small Restaurant

11.Wrap Up

12.Appendix

Installing Altair

Using Python Altair for Data Storytelling

Exercise: Missing Values

Use the concepts you’ve learned so far to solve this exercise.

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Solution

Consider the following DataFrame:

Python 3.10.4

import pandas as pd
import altair as alt
import numpy as np
df = pd.DataFrame({
    'x': [1,2,3,4,1,2,3,4],
    'y': [1, 3,np.nan, 3,2, np.nan, 4, 4],
    'z': ['orange', 'orange', 'orange', 'orange', 'apple', 'apple','apple', 'apple']
}).dropna()
print(df)

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