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

Understanding the role of data manipulation skills

In practical situations, we rarely have our data in the format that we want; we usually have different datasets that we want to merge, and often, we need to normalize and clean up the data. For these reasons, data manipulation and preparation will always play a big part in any data visualization process. So, we will be focusing on this in this chapter and throughout the book.

The plan for preparing our dataset is roughly the following:

  1. Explore the different files one by one.
  2. Check the available data and data types and explore how each can help us categorize and analyze the data.
  3. Reshape the data where required.
  4. Combine different DataFrames to add more ways to describe our data.

Let's go through these steps right away.

Exploring the data files

We start by reading in the files in the data folder:

import os
import pandas as pd
pd.options.display.max_columns = None
os.listdir('data&apos...