Pandas Area Plots: Cumulative Trends, Stacked Areas and Options

Use DataFrame.plot.area() (or df.plot(kind='area')) to visualise cumulative totals, part-to-whole contributions, and multi-series volume over time. Learn how to control stacked vs unstacked areas, apply transparency with alpha, customise color palettes, manage secondary axes, and export high-resolution chart images.

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Create a basic stacked area plot 🔝

By default, DataFrame.plot.area() stacks numeric columns on top of each other (stacked=True). Each line represents the cumulative sum of the preceding layers. All values should normally be positive for meaningful stacking.

import pandas as pd
import matplotlib.pyplot as plt

my_dict = {
    "Sale": [80, 90, 75, 80, 85, 80, 75],
    "Cost": [50, 40, 55, 45, 65, 60, 55],
    "Profit": [8, 12, 13, 10, 9, 12, 13]
}
df = pd.DataFrame(data=my_dict)
print(df)

# Plot default stacked area chart
df.plot.area(title="Area Plot: Sales, Cost and Profit", figsize=(7, 4))
plt.show()

Expected console output:

   Sale  Cost  Profit
0    80    50       8
1    90    40      12
2    75    55      13
3    80    45      10
4    85    65       9
5    80    60      12
6    75    55      13

Pandas DataFrame stacked area chart — Default stacked area chart with Sales, Cost, and Profit

Compare stacked vs unstacked areas with alpha transparency 🔝

To plot areas starting from the common zero baseline instead of stacking them, pass stacked=False. When unstacking, always supply an alpha value (e.g. alpha=0.4) so that foreground layers do not hide background layers.

# Unstacked area chart with transparency
df.plot.area(stacked=False, alpha=0.4, title="Unstacked Area Comparison", figsize=(7, 4))
plt.show()

Customise colors, line styles, and dimensions 🔝

Customise your chart using options for dimensions (figsize), font size (fontsize), line styles (style), and explicit color palettes (color):

# Custom RGB color tuples per series
my_colors = [(0.9, 0.4, 0.3), (0.1, 0.8, 0.1), (0.1, 0.5, 0.9)]
df.plot.area(title="Custom Palette Area Plot", color=my_colors, figsize=(6, 3))
plt.show()

Font size option — fontsize=20 (custom axis label scale)

Custom color palette — color=my_colors (custom series palette)

Area plot boundary style 1 — Boundary line style (:)

Area plot boundary style 2 — Boundary line style (.-)

Configure gridlines and legend display 🔝

Toggle gridlines using grid=True or grid=False to assist readers in estimating cumulative values at a glance.

Dual-scale comparison with secondary_y 🔝

When comparing measures of vastly different orders of magnitude, use secondary_y=True with mark_right=True to establish a dedicated right-hand Y-axis.

secondary_y=False (single compressed axis)

mark_right=True (labels right axis series in legend)

Logarithmic scaling on X-axis with logx 🔝

If horizontal categories or time intervals grow exponentially, apply logarithmic scaling with logx=True.

logx=True option — logx=True logarithmic scaling along X-axis

Rotate axis labels with rot 🔝

When dates or long category names crowd the horizontal axis, rotate them with rot=45 or rot=90.

rot=45 tick rotation — rot=45 (angled tick labels)

rot=180 tick rotation — rot=180 (demonstration)

Save charts to file using savefig() 🔝

Save your generated area chart to a high-resolution image using plt.savefig() before calling plt.show():

df.plot.area(title="Sales Performance", figsize=(7, 4))
plt.tight_layout()
plt.savefig("sales_area_plot.png", dpi=300)
print("Saved chart as sales_area_plot.png")
plt.close()

Practical workflow: quarterly department expense tracking 🔝

Here is an end-to-end practical workflow: track cumulative operating expenses across three departments over four quarters and validate calculations with assertions.

import pandas as pd
import matplotlib.pyplot as plt

dept_exp = pd.DataFrame({
    "Quarter": ["Q1", "Q2", "Q3", "Q4"],
    "Engineering": [45000, 52000, 58000, 64000],
    "Marketing": [20000, 28000, 35000, 42000],
    "Operations": [15000, 16000, 17500, 19000]
})

# Assert data integrity
assert len(dept_exp) == 4
total_expenses = dept_exp[["Engineering", "Marketing", "Operations"]].sum().sum()
assert total_expenses == 411500
print("Total Operating Expenses:", total_expenses)

# Plot stacked area with custom Quarter x-axis
ax = dept_exp.plot.area(
    x="Quarter",
    y=["Engineering", "Marketing", "Operations"],
    title="Annual Cumulative Department Expenses",
    figsize=(7, 4),
    grid=True
)
ax.set_ylabel("Expenditure ($)")
plt.show()

Expected console output:

Total Operating Expenses: 411500

Exercise: multi-channel revenue accumulation 🔝

Create a DataFrame with 4 months (Jan, Feb, Mar, Apr) and three revenue streams: Hardware ([12000, 15000, 18000, 22000]), Software ([8000, 9500, 11000, 14000]), and Services ([5000, 6000, 7500, 9000]). Plot a stacked area chart with Month on the X-axis, enable gridlines, title the plot "Q1-Q2 Revenue Streams", and calculate total revenue across all streams.

Exercise solution 🔝

import pandas as pd
import matplotlib.pyplot as plt

streams = pd.DataFrame({
    "Month": ["Jan", "Feb", "Mar", "Apr"],
    "Hardware": [12000, 15000, 18000, 22000],
    "Software": [8000, 9500, 11000, 14000],
    "Services": [5000, 6000, 7500, 9000]
})

total_rev = streams[["Hardware", "Software", "Services"]].sum().sum()
assert total_rev == 137000
print("Total Revenue:", total_rev)

streams.plot.area(
    x="Month",
    y=["Hardware", "Software", "Services"],
    title="Q1-Q2 Revenue Streams",
    grid=True,
    figsize=(6, 3)
)
plt.show()

Expected console output:

Total Revenue: 137000

Practice in Google Colab 🔝

Open in Google Colab View on GitHub
Run the area chart examples interactively, test stacked vs unstacked alpha configurations, and verify the practice exercise directly in Google Colab. All sample datasets are embedded directly in the notebook.

Continue exploring related charting and analysis techniques:

Pandas Plot Overview — The full guide to Pandas charting formats and settings.
Pandas Line Charts — Compare individual metric trajectories over time or categories.
Pandas Bar Charts — Display category totals, grouped bars, and stacked proportions.
Pandas Scatter Plots — Visualise correlation and spread across two numerical variables.
Pandas Pie Charts — Show proportional distributions and market shares.
Pandas Plotting Exercises — Hands-on challenges to test your visualization skills.
SQL GROUP BY Aggregations — Aggregate database records by categories or time periods before plotting.
Pandas Section Home — Complete index of all Pandas pillars, tutorials, and workflows.

Reference: Pandas DataFrame.plot.area official documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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