Based on the provided sample which is centered around the use of Plotly for data visualization and creating Dashboards using the Dash library, here's a markdown exercise for you:

Practice Exercises for Plotly and Dashboards

Overview

In this section, you'll delve deeper into the capabilities of Plotly and Dash to generate compelling visualizations and dashboards. Start with a review of the core concepts covered, then move on to the exercises below.

Remember, it's essential to grasp these concepts well, as data visualization is a crucial skill in data analysis and data science.

Core Concepts

Plotly Basics

Plotly is an open-source plotting library that provides a rich set of interactive plots and visualizations. It is especially known for its capabilities to produce interactive web-ready visualizations.

Example:

import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex")
fig.show()

Creating Dashboards using Dash

Dash is a Python framework for building analytical web applications. It's built on top of Plotly, and it seamlessly integrates with data manipulation libraries such as pandas.

Example:

import dash
from dash import dcc, html
import plotly.express as px

app = dash.Dash(__name__)

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex")

app.layout = html.Div(children=[
    html.H1("Dash App"),
    dcc.Graph(figure=fig)
])

if __name__ == '__main__':
    app.run_server(debug=True)

Data Cleaning with Pandas

Ensuring that the data is clean and well-structured is a prerequisite for any visualization task. We can use pandas for data manipulation and cleaning.

Example:

import pandas as pd
df = pd.DataFrame({'A': [1,2,3], 'B': [4,5,6]})
df = df.assign(C = df['A'] + df['B'])

Exercises

To solidify your understanding, try out the following exercises:

1. Use Plotly to create a line plot for any time-series dataset. Add titles, axis labels, and legends to make the visualization self-explanatory.
2. Generate a bar plot using Plotly. Ensure that the bars are color-coded based on a categorical variable.
3. With pandas, load a dataset, inspect its first few rows, check for any missing values, and create a new column based on existing columns.
4. Create a basic dashboard using Dash that showcases two plots side by side.
5. Extend the dashboard by adding a dropdown widget that allows the user to select which variable they want to visualize in one of the plots.

Remember to check out the official Plotly and Dash documentation for any doubts or additional functionalities you'd like to explore.