Aprende Discovering Patterns with Combined Visualizations

Combining multiple visualization techniques is a powerful way to reveal complex relationships and trends in retail data that might remain hidden with single plots.

By overlaying or arranging different types of plots, you can:
- Compare distributions;
- Spot outliers;
- Identify interactions between variables more effectively.

For example:

Overlaying a boxplot with a swarmplot allows you to see both summary statistics and individual data points;
Faceted grids help you compare distributions across groups or categories at a glance.


              12345678910111213141516171819
            
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Sample retail sales data
data = {
    "Product_Category": ["A", "A", "B", "B", "C", "C", "A", "B", "C", "A", "B", "C"],
    "Sales": [250, 300, 200, 220, 330, 310, 270, 210, 340, 260, 230, 320]
}
df = pd.DataFrame(data)

plt.figure(figsize=(8, 6))
sns.boxplot(x="Product_Category", y="Sales", data=df, showfliers=False, width=0.5)
sns.swarmplot(x="Product_Category", y="Sales", data=df, color="black", size=6)
plt.title("Sales by Product Category: Boxplot with Swarmplot Overlay")
plt.ylabel("Sales")
plt.xlabel("Product Category")
plt.tight_layout()
plt.show()


              123456789101112131415161718
            
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt

# Extended sample with Store_Location
data = {
    "Store_Location": ["East", "West", "East", "West", "East", "West", "East", "West", "East", "West", "East", "West"],
    "Sales": [250, 300, 200, 220, 330, 310, 270, 210, 340, 260, 230, 320]
}
df = pd.DataFrame(data)

g = sns.FacetGrid(df, col="Store_Location", height=4, aspect=1)
g.map_dataframe(sns.histplot, x="Sales", bins=5, color="skyblue", edgecolor="black")
g.set_titles(col_template="Store: {col_name}")
g.set_axis_labels("Sales", "Count")
plt.suptitle("Sales Distribution Across Store Locations", y=1.08)
plt.tight_layout()
plt.show()

Combining different visualizations gives you a more complete understanding of retail data. Here’s how these techniques work together:

Overlaying a boxplot and swarmplot:
- Highlights median sales and the spread within each product category;
- Shows the precise distribution and clustering of individual sales points;
- Quickly uncovers outliers or categories with unusual variation.
Faceted grid of histograms:
- Lets you compare sales distributions across multiple store locations side by side;
- Makes it easier to spot regional trends or anomalies.

Using these combined approaches gives you richer, more actionable insights than relying on single plots alone. This supports more informed business decisions and helps you detect patterns that might otherwise remain hidden.

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 4. Capítulo 4

Pregunte a AI

Pregunte lo que quiera o pruebe una de las preguntas sugeridas para comenzar nuestra charla

Suggested prompts:

Can you explain when to use a boxplot with a swarmplot versus a faceted grid?

What other combinations of plots work well for retail data analysis?

Can you suggest ways to interpret the results from these visualizations?

Awesome!

Completion rate improved to 5.56

Desliza para mostrar el menú