Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

With so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python. You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model. By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.

Preface

About the Book

1. Introduction to Visualization with Python – Basic and Customized Plotting

Introduction

Handling Data with pandas DataFrame

Plotting with pandas and seaborn

Tweaking Plot Parameters

Summary

Free Chapter

2. Static Visualization – Global Patterns and Summary Statistics

Introduction

Creating Plots that Present Global Patterns in Data

Creating Plots That Present Summary Statistics of Your Data

Summary

3. From Static to Interactive Visualization

Introduction

Static versus Interactive Visualization

Applications of Interactive Data Visualizations

Getting Started with Interactive Data Visualizations

Summary

4. Interactive Visualization of Data across Strata

Introduction

Interactive Scatter Plots

Other Interactive Plots in altair

Summary

5. Interactive Visualization of Data across Time

Introduction

Temporal Data

Types of Temporal Data

Understanding the Relation between Temporal Data and Time‑Series Data

Examples of Domains That Use Temporal Data

Visualization of Temporal Data

Choosing the Right Aggregation Level for Temporal Data

Resampling in Temporal Data

Interactive Temporal Visualization

Summary

6. Interactive Visualization of Geographical Data

Introduction

Choropleth Maps

Plots on Geographical Maps

Summary

7. Avoiding Common Pitfalls to Create Interactive Visualizations

Introduction

Data Formatting and Interpretation

Data Visualization

Cheat Sheet for the Visualization Process

Summary

Appendix

1. Introduction to Visualization with Python – Basic and Customized Plotting

2. Static Visualization – Global Patterns and Summary Statistics

3. From Static to Interactive Visualization

4. Interactive Visualization of Data across Strata

5. Interactive Visualization of Data across Time

6. Interactive Visualizations of Data across Geographical Regions

7. Avoiding Common Pitfalls to Create Interactive Visualizations

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5. Interactive Visualization of Data across Time

Activity 5: Create an Interactive Temporal Visualization using RangeTool and Aggregator

Import required libraries:

from bokeh.io import show
from bokeh.layouts import column
from bokeh.models import ColumnDataSource, RangeTool
from bokeh.plotting import figure
from bokeh.io import push_notebook, show, output_notebook
from pathlib import Path
import pandas as pd
import numpy as np
from ipywidgets import interact
%matplotlib inline

Setup the output to Jupyter Notebook:

DATA_PATH = Path(“../datasets/chap5_data/”)
output_notebook()

Create a DataFrame microsoft_df and parse the date column:

microsoft_df = pd.read_csv(DATA_PATH / “microsoft_stock.csv”, parse_dates=[‘date’])

Set the index as date:
```
microsoft_df.index = microsoft_df.date
```
Create date numpy array and source as ColumnDataSource. We will use these to draw line plot:
```
dates = np.array(microsoft_df...
```

Interactive Data Visualization with Python - Second Edition

By : Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Interactive Data Visualization with Python - Second Edition

By: Abha Belorkar, Sharath Chandra Guntuku, Shubhangi Hora, Anshu Kumar

Overview of this book

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5. Interactive Visualization of Data across Time

Activity 5: Create an Interactive Temporal Visualization using RangeTool and Aggregator