Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By : Elias Dabbas
Book Image

Interactive Dashboards and Data Apps with Plotly and Dash

By: Elias Dabbas

Overview of this book

Plotly's Dash framework is a life-saver for Python developers who want to develop complete data apps and interactive dashboards without JavaScript, but you'll need to have the right guide to make sure you’re getting the most of it. With the help of this book, you'll be able to explore the functionalities of Dash for visualizing data in different ways. Interactive Dashboards and Data Apps with Plotly and Dash will first give you an overview of the Dash ecosystem, its main packages, and the third-party packages crucial for structuring and building different parts of your apps. You'll learn how to create a basic Dash app and add different features to it. Next, you’ll integrate controls such as dropdowns, checkboxes, sliders, date pickers, and more in the app and then link them to charts and other outputs. Depending on the data you are visualizing, you'll also add several types of charts, including scatter plots, line plots, bar charts, histograms, and maps, as well as explore the options available for customizing them. By the end of this book, you'll have developed the skills you need to create and deploy an interactive dashboard, handle complexities and code refactoring, and understand the process of improving your application.
Table of Contents (18 chapters)
1
Section 1: Building a Dash App
6
Section 2: Adding Functionality to Your App with Real Data
11
Section 3: Taking Your App to the Next Level

Exploring choropleth maps

Choropleth maps are basically colored polygons representing a certain area on a map. Plotly ships with country maps included (as well as US states), and so it is very easy to plot maps if we have information about countries. We already have such information in our dataset. We have country names, as well as country codes, in every row. We also have the year, some metadata about the countries (region, income group, and so on), and all the indicator data. In other words, every data point is connected to a geographical location. So, let's start by choosing a year and an indicator, and see how the values of our chosen indicator vary across countries:

  1. Open the poverty file into a DataFrame and create the year and indicator variables:
    import pandas as pd
    poverty = pd.read_csv('data/poverty.csv')
    year = 2016
    indicator = 'GINI index (World Bank estimate)'
  2. Create a subset of poverty with values from the selected year and containing...