Area Plot Gallery¶
Area plots visualise distributions over time or across categories using filled bands. They're well suited to retail dashboards where the eye needs to land on trend shape and cumulative size at the same time, rather than reading individual values.
Area plots excel at:
- Trend shape: see whether daily revenue is climbing, plateauing, or rolling over
- Channel mix over time: contrast a fast-growing channel against a flat one
- Region-by-region comparisons: parallel trends are easy to read as overlaid bands
import matplotlib.pyplot as plt
import pandas as pd
from openretailscience.plots import area
from openretailscience.plots.styles.graph_utils import set_axis_shorthand
Basic Area Plot¶
The simplest call: pass a single value_col and the DataFrame's index becomes the x-axis. Use this shape when the index already carries the meaning, like a DatetimeIndex of trading days. The filled band makes the direction of travel easier to read than a thin line.
ax = area.plot(
daily_revenue,
value_col="revenue",
eyebrow="Revenue trend",
title="Daily revenue nearly doubled over a two-week stretch",
subtitle="Total daily revenue ($), 14-day window",
y_label="Revenue ($)",
)
set_axis_shorthand(ax.yaxis)
plt.show()
Using x_col¶
When the x-axis values live in a column rather than the index, point x_col at that column. Common when the data comes back from a SQL query that puts the time bucket in its own column instead of the index. Order is preserved as-is, so make sure the column is already sorted the way you want it shown.
ax = area.plot(
monthly_sales,
x_col="month",
value_col="sales",
eyebrow="Monthly performance",
title="Sales climbed steadily from January to a May peak",
subtitle="Total monthly sales ($), January to June",
y_label="Sales ($)",
)
set_axis_shorthand(ax.yaxis)
plt.show()
Multiple Areas with group_col¶
Pass group_col to draw one band per category from a long-format DataFrame. The function pivots the data internally and assigns a colour per group.
ax = area.plot(
category_revenue,
x_col="week",
value_col="revenue",
group_col="product_category",
eyebrow="Category performance",
title="Electronics widens its lead over Apparel and Home each week",
subtitle="Weekly revenue ($) by product category, weeks 1 to 5",
x_label="Week",
y_label="Revenue ($)",
legend_title="Product Category",
move_legend_outside=True,
)
set_axis_shorthand(ax.yaxis)
plt.show()
Multiple Value Columns¶
If the data is already in wide format (one column per series), pass a list to value_col instead of using group_col. The two are mutually exclusive: passing both raises ValueError because the function wouldn't know how to disambiguate. Use this shape when the upstream query gives you each series as its own column.
ax = area.plot(
channel_revenue,
x_col="quarter",
value_col=["Online", "Stores", "Wholesale"],
eyebrow="Channel mix",
title="Online sales grew every quarter while Stores stayed flat",
subtitle="Quarterly revenue ($) by channel, full year",
y_label="Revenue ($)",
legend_title="Sales Channel",
move_legend_outside=True,
)
set_axis_shorthand(ax.yaxis)
plt.show()
Plotting a Pandas Series¶
A Series works directly: the function converts it to a single-column DataFrame and uses the series name as the value column. Useful when the upstream computation (a groupby().sum() for example) already returns a Series and you don't want to wrap it just to plot.
ax = area.plot(
customer_growth,
value_col="Active Customers",
eyebrow="Customer growth",
title="Active customers grew 50% across the seven-week window",
subtitle="Weekly active customer count",
x_label="Week",
y_label="Number of Customers",
)
set_axis_shorthand(ax.yaxis)
plt.show()
Advanced Styling Options¶
Two knobs worth knowing: source_text adds a small attribution at the bottom of the chart for export-ready images, and move_legend_outside=True lifts the legend to the right edge so it never sits on top of the data.
ax = area.plot(
regional_sales,
x_col="month",
value_col=["North", "South", "West"],
eyebrow="Regional sales",
title="All three regions move on parallel upward tracks",
subtitle="Monthly sales ($) by region, January to June",
y_label="Sales ($)",
legend_title="Region",
move_legend_outside=True,
source_text="Source: Internal Sales Database",
)
set_axis_shorthand(ax.yaxis)
plt.show()
End-of-Line Legend¶
Pass legend_style="end_of_line" to suppress the boxed legend and place a coloured series label at the right end of each band instead. The label sits next to the data it describes, so the reader doesn't need to bounce back and forth between a legend swatch and a band. Works best with three to five series, more than that and the labels start to crowd each other.
ax = area.plot(
category_revenue,
x_col="week",
value_col="revenue",
group_col="product_category",
legend_style="end_of_line",
eyebrow="Category performance",
title="End-of-line labels sit beside each band instead of in a separate box",
subtitle="Weekly revenue ($) by product category, weeks 1 to 5",
x_label="Week",
y_label="Revenue ($)",
)
set_axis_shorthand(ax.yaxis)
plt.show()
