Step 1: Build Your First Histogram

In this Code Lab, you'll explore customer purchasing behavior using real-world e-commerce data. You'll begin by building a basic histogram, which groups continuous numeric values into bins to show the distribution of purchase prices.

Histograms are a foundational chart type for understanding frequency and spread—especially helpful when analyzing how often certain purchase amounts occur.

You’ll be using Matplotlib to create your first histogram based on purchase price.

What You’ll Learn in This Step

• Build a basic histogram using Matplotlib
• Render the plot inline with plt.show()
• Understand the role of bins and frequency in visualizing continuous data

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: step-one.ipynb in the workspace.

A histogram is ideal for showing how frequently values occur within specific ranges. In this task, you'll create your first histogram to visualize customer purchase prices.

Histograms differ from bar charts:

• Bar charts visualize categories.
• Histograms visualize continuous numeric ranges.

In Matplotlib, you'll use the plt.hist() function to generate histograms easily.

import matplotlib.pyplot as plt

plt.hist(data)
plt.show()

You'll work with the transactions.csv file, located in the /data directory. This dataset has already been loaded for you as a Pandas DataFrame called df. It contains customer purchase data from an e-commerce platform.

Step 2: Control Binning and Chart Shape

In this step, you'll learn how to adjust binning parameters in Matplotlib histograms. Binning controls how your continuous data is divided into ranges, which can change the shape and interpretability of your chart.

By changing the number of bins or using cumulative options, you can fine-tune how purchase price frequencies are displayed for clearer insights.

What You’ll Learn in This Step

• Control histogram shape using the bins parameter
• Generate cumulative histograms
• Create histograms for different numeric columns

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 2-step-two.ipynb in the workspace.

Adjust the Number of Bins in Your Histogram

The number of bins determines how the data range is divided into intervals. More bins can reveal detailed patterns, while fewer bins can simplify trends.

Matplotlib allows you to control binning using the bins parameter inside plt.hist().

import matplotlib.pyplot as plt

plt.hist(data, bins=20)
plt.show()

A cumulative histogram shows the running total of values up to each bin, making it easy to see how data accumulates across the range.

By combining cumulative=True and density=True, you'll normalize the values and display cumulative proportions rather than raw counts.

import matplotlib.pyplot as plt

plt.hist(data, bins=20, density=True, cumulative=True)
plt.show()

Histograms aren't limited to just prices—you can visualize any numeric column. In this task, you'll create a histogram based on the quantity column, which shows how many items were purchased per transaction.

By setting fewer bins, you'll simplify the view to highlight overall purchase volume trends.

import matplotlib.pyplot as plt

plt.hist(data, bins=5)
plt.show()

You’ll now create a histogram of product quantities purchased.

Step 3: Add Labels and Titles for Clarity

In this step, you'll enhance your charts by adding labels and titles. Labels help your audience quickly understand what the axes represent, while titles provide essential context for your visualization.

These small additions dramatically increase the readability and professionalism of your charts.

What You’ll Learn in This Step

• Add X-axis and Y-axis labels using plt.xlabel() and plt.ylabel()
• Add chart titles using plt.title()
• Rotate tick labels to improve label readability

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 3-step-two.ipynb in the workspace.

Add Axis Labels and a Title to Your Histogram

While your histogram shows data distribution, it’s missing important context without descriptive labels. You can add text to the x-axis, y-axis, and top of the chart using matplotlib.pyplot functions.

plt.xlabel("x-Axis Label")
plt.ylabel("y-Axis Label")
plt.title("Chart Title")

Rotate X-Axis Tick Labels for Better Readability

When category or numeric labels overlap, rotating them can improves readability. This is especially helpful when dealing with long or closely packed labels.

You can rotate tick labels using plt.xticks() and its rotation parameter.

plt.xticks(rotation=45)

Step 4: Style and Export Your Histogram

In this step, you’ll enhance your histograms visually by applying color, transparency, and edge styling. Clear, attractive charts are easier to present and interpret.

You’ll also practice exporting your chart as an image using plt.savefig() for use in reports or presentations.

What You’ll Learn in This Step

• Apply color, edge color, and transparency with Matplotlib styling parameters
• Export charts as PNG image files using plt.savefig()

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 4-step-four.ipynb in the workspace.

Styling improves both the clarity and appearance of your visualizations. Matplotlib allows you to easily customize color, edge lines, and transparency using optional parameters in plt.hist().

plt.hist(data, color="skyblue", edgecolor="black", alpha=0.7)

Once your chart looks polished, you may want to save it as an image for reports or presentations. Matplotlib allows you to export plots using plt.savefig().

plt.savefig("filename.png")
plt.savefig("folder/subfolder/filename.png")

Step 5: Overlay Multiple Histograms and Add Legends

In this step, you’ll combine multiple histograms on the same plot. Overlaying two distributions allows you to compare related data directly. You’ll also learn how to add legends to clearly label each dataset on the chart.

What You’ll Learn in This Step

• Plot multiple histograms on the same axes
• Use ax.hist() with Matplotlib subplots API
• Add a legend using ax.legend()

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 5-step-five.ipynb in the workspace.

Sometimes you'll want to compare two numeric distributions side by side. You can overlay multiple histograms in Matplotlib using the object-oriented API with ax.hist().

For example, by plotting both purchase price and a derived column, you can compare how frequently different transaction totals occur.

fig, ax = plt.subplots()

ax.hist(first_data, bins=20, color="skyblue", alpha=0.7, label="First")
ax.hist(second_data, bins=20, color="orange", alpha=0.7, label="Second")

plt.show()

When displaying multiple datasets on one chart, legends help viewers quickly identify which series belongs to which color. You can add legends using ax.legend() on your Matplotlib axis object.

ax.legend()
ax.legend(loc="upper right")

Step 6: Create a Styled Histogram Using Seaborn

In this step, you’ll shift from Matplotlib to Seaborn, which simplifies many visualization tasks and provides attractive default styling. You’ll also explore density curves, automatic theming, and custom color palettes.

What You’ll Learn in This Step

• Create histograms using Seaborn’s histplot() function
• Apply kernel density estimates (KDE) for smoothed distributions
• Style charts using Seaborn themes and color palettes

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 6-step-six.ipynb in the workspace.

Seaborn simplifies histogram creation while adding automatic styling. Unlike Matplotlib’s imperative style, Seaborn follows a declarative approach, where you pass both the dataset and column name directly.

import seaborn as sns

sns.histplot(data=df, x="column_name")
plt.show()

Seaborn makes it easy to overlay a kernel density estimate (KDE) on top of your histogram. This smooth curve helps visualize the probability distribution of continuous data.

You can also control the statistic calculation using stat="density" and change the bar style with element="step" to improve how density is displayed.

sns.histplot(data=df, x="price", stat="density", kde=True, element="step")
plt.show()

Seaborn includes built-in themes and color palettes that improve chart aesthetics automatically. You can control these globally using sns.set_theme() and sns.color_palette().

sns.set_theme(style="whitegrid")
sns.set_palette("Blues")

Step 7: Group, Style, and Enhance Seaborn Histograms

In this step, you’ll take Seaborn histograms to the next level by grouping data with the hue parameter, controlling legends, and applying accessibility-focused color palettes.

These advanced features allow you to clearly compare grouped distributions and make your charts accessible for all viewers.

What You’ll Learn in This Step

• Group histograms using Seaborn’s hue parameter
• Customize and remove legends
• Apply colorblind-friendly palettes

You'll complete all tasks in a Jupyter notebook rather than in standalone .py files.

Open the Notebook

• Open the file: 7-step-seven.ipynb in the workspace.

Seaborn’s hue parameter allows you to break down a distribution by category, displaying multiple groups within the same histogram.

When applied, Seaborn automatically assigns different colors to each group, making visual comparisons much easier.

sns.histplot(data=df, x="price", hue="category")
plt.show()

Seaborn automatically adds a legend when using the hue parameter. However, you may want to customize or remove the legend depending on your chart layout.

You can remove the default legend by accessing the axis object and calling ax.legend_.remove().

ax = sns.histplot(data=df, x="price", hue="category")
ax.legend_.remove()
plt.show()

After removing the default legend, you can manually re-add one using ax.legend(). This gives you full control over placement and appearance.

The most common approach is to simply call ax.legend() after plotting and specify the location.

ax.legend(loc="upper right")

ax.legend(loc=1)  # same as 'upper right'

 ax.legend(loc='upper right', bbox_to_anchor=(1.2, 1))

Seaborn includes built-in color palettes designed for accessibility. The "colorblind" palette ensures that your charts remain legible for viewers with common forms of color vision deficiency.

You can apply this palette using sns.set_palette() before plotting.

sns.set_palette("colorblind")
sns.histplot(data=df, x="price", hue="category")
plt.show()

You’ve made it to the end of the lab—nice job!

In this lab, you’ve worked through a full progression of histogram techniques including:

• Building histograms with both Matplotlib and Seaborn
• Controlling bin sizes to shape your charts
• Adding titles, axis labels, and rotating tick labels for better readability
• Styling your charts with colors, edges, and transparency
• Exporting charts as image files for reports or presentations
• Overlaying multiple datasets for side-by-side comparisons
• Using Seaborn’s hue parameter to group data by category
• Customizing legends and applying accessibility-friendly color palettes

Want to see everything combined?

Check out the lab-recap.ipynb notebook in your workspace. It pulls together everything you’ve learned into a fully polished, presentation-ready histogram.