Hello and welcome! In today's lesson, you will be introduced to Plotly Express, a powerful high-level interface for creating interactive plots with Plotly. This lesson will guide you through the basics of visualizing data from the Billboard Christmas Songs dataset. By the end of this lesson, you'll be able to create and customize basic visualizations that reveal interesting trends in holiday music data.

Plotly Express is a concise, high-level API for creating interactive plots in Python. It simplifies data visualization by reducing the amount of code needed. Unlike lower-level Plotly functions, Plotly Express is designed for quick prototyping and data exploration.

The main benefits of Plotly Express include:

• Ease of Use: With minimal code, you can generate complex plots.
• Interactivity: Plots are not just static images; they are interactive and can be easily exported as HTML files.
• Data Exploration: Helps in rapidly gaining insights into datasets by visualizing trends and distributions.

Plotly Express is particularly useful in situations where quick insights are needed without much overhead. For example, when initially exploring a new dataset, such as the Billboard Christmas Songs dataset we're working with today.

Before diving into visualization, it's essential to load and prepare your data. We'll use the Billboard Christmas Songs dataset. This dataset includes information about songs that appeared on the Billboard Hot 100 chart.

Let's load the dataset and ensure our date field (weekid) is in the correct format using Pandas:

Python
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1import pandas as pd
2
3# Load the Billboard Christmas Songs dataset
4df = pd.read_csv('billboard_christmas.csv')
5
6# Convert 'weekid' column to datetime format
7df['weekid'] = pd.to_datetime(df['weekid'])
8
9# Display the first few rows of the dataframe
10print(df.head())

The output will be:

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1      weekid             song      performer  peak_position  year
20 2023-10-01     Jingle Bells  Michael Bublé              1  2023
31 2023-10-08  White Christmas    Bing Crosby              2  2023
42 2023-10-15   Last Christmas          Wham!              3  2023
53 2023-10-22        Mistletoe  Justin Bieber              4  2023
64 2023-10-29    Santa Tell Me  Ariana Grande              5  2023

This output is a simplified display of the dataset's structure, showcasing its columns and a few rows. It ensures our weekid column is properly formatted as datetime, essential for accurate time-based visualizations.

Converting weekid to datetime is crucial for accurate time-based plotting, allowing us to examine trends over the years.

Now that our data is ready, we can create visualizations that reveal trends within the dataset.

Our first visualization is a line chart that displays the number of unique Christmas songs per year on the Billboard Hot 100. This chart helps us understand trends over time.

Python
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1import plotly.express as px
2
3# Aggregate data to get yearly counts of unique songs
4yearly_songs = df.groupby('year')['song'].nunique().reset_index()
5
6# Create a line chart
7fig = px.line(yearly_songs, 
8              x='year', 
9              y='song',
10              title='Christmas Songs on Billboard Hot 100 by Year')

Output:

Next, we have a scatter plot illustrating the peak positions of songs over time, offering insights into song performance throughout the years.

Python
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1# Generate a scatter plot of all the peak positions over time
2fig = px.scatter(df,
3                 x='weekid',
4                 y='peak_position',
5                 color='song',
6                 title='Peak Positions Over Time')
7
8# Hide the legend
9fig.update_layout(
10    yaxis=dict(autorange="reversed"),
11    showlegend=False)

Notes:

• Reversing the Y axis because a lower number is better so we want it near the top
• Hiding the legend because it is noisy. You can hover over the plot to get details instead

Output:

Lastly, a bar chart ranks performers by the number of unique songs that charted, highlighting the most successful artists. Each of these visualizations serves a distinct purpose, and together they provide a comprehensive view of the dataset.

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1# Find top performers
2top_performers = df.groupby('performer')['song'].nunique().sort_values(ascending=False).head(10)
3
4# Create a bar chart
5fig = px.bar(x=top_performers.index,
6             y=top_performers.values,
7             title='Top 10 Christmas Song Performers',
8             color_discrete_sequence=['darkred'])
9
10# Set y-axis ticks to integers
11fig.update_layout(yaxis=dict(dtick=1))

Each of these visualizations serves a distinct purpose and together they provide a comprehensive view of the dataset.

Note: We set the Y axis to have integer ticks to avoid partial values.

Output:

Congratulations! You've learned the fundamentals of using Plotly Express to create engaging and informative visualizations of data. This lesson covered loading data, creating various charts, and customizing their appearances. These skills are foundational for any data enthusiast interested in exploring and conveying complex datasets visually.

Now, it's time to put these concepts to practice through exercises designed to deepen your understanding. Applying what you've learned will enhance your ability to extract meaningful insights from data and effectively communicate your findings. Let's continue this journey in data visualization and analytics!