1. Introduction

This tutorial aims to guide you through the process of building interactive dashboards using Plotly, a Python graphing library. Plotly allows you to create stunning, interactive, and publication-quality graphs. By the end of this tutorial, you will be able to make and customize interactive plots and dashboards using Plotly.

Prerequisites

• Basic knowledge of Python programming language
• Familiarity with data visualization concepts

2. Step-by-Step Guide

Installing Plotly

Before we start, we need to install Plotly. You can do this using pip:

pip install plotly

Importing Necessary Libraries

We'll be using the plotly library alongside pandas, a Python library for data manipulation and analysis.

import plotly.express as px
import pandas as pd

3. Code Examples

Example 1: Basic Line Plot

Let's start by creating a basic line plot. We'll use px.data.gapminder() which returns a dataset containing information about countries.

# Load the dataset
df = px.data.gapminder()

# Create a line plot
fig = px.line(df.query("country=='Canada'"), x="year", y="pop")

# Show the plot
fig.show()

In the code above, we first load the dataset. Then, we create a line plot where years are on the x-axis and population is on the y-axis.

Example 2: Bar Chart

Now, let's move on to creating a bar chart.

# Load the dataset
df = px.data.tips()

# Create a box plot
fig = px.bar(df, x="day", y="total_bill", color="sex")

# Show the plot
fig.show()

In this case, we load a different dataset and make a bar plot where days are on the x-axis and the total bill is on the y-axis. The color of the bars represents the gender of the person who paid the bill.

4. Summary

In this tutorial, you learned how to create interactive plots and dashboards using Plotly. We created a line plot and a bar chart.

Next Steps

• Explore more types of plots that can be created using Plotly.
• Learn how to customize the appearance of your plots.

Additional Resources

• Plotly Python Graphing Library
• Plotly Express

5. Practice Exercises

Exercise 1: Line Plot

Create a line plot using the gapminder dataset. Display life expectancy on the y-axis.

Exercise 2: Bar Chart

Create a bar chart using the tips dataset. Display total bill on the y-axis and time on the x-axis. Color the bars by the size of the party.

Solutions

Solution to Exercise 1

# Load the dataset
df = px.data.gapminder()

# Create a line plot
fig = px.line(df.query("country=='Canada'"), x="year", y="lifeExp")

# Show the plot
fig.show()

Solution to Exercise 2

# Load the dataset
df = px.data.tips()

# Create a bar chart
fig = px.bar(df, x="time", y="total_bill", color="size")

# Show the plot
fig.show()

Tips for Further Practice

• Try creating other types of plots like scatter plots, histograms, etc.
• Try customizing the appearance of your plots by changing colors, labels, etc.