Pandas Built-in Plotting

Pandas includes a .plot() method built directly into DataFrames and Series, providing a convenient shortcut for creating visualisations without importing Matplotlib or Seaborn explicitly. Under the hood, Pandas plotting uses Matplotlib as its default backend — so you can customise plots further using Matplotlib functions.

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Why Use Pandas Plotting?

Advantage	Description
Speed	One line of code to go from DataFrame to chart
Convenience	No need to extract columns manually
Integration	Plots directly from the data structure you are already working with
Familiar	Uses Matplotlib syntax for customisation

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Basic Syntax

import pandas as pd
import matplotlib.pyplot as plt

# DataFrame.plot(kind="chart_type", ...)
df.plot(kind="bar", x="category", y="value")
plt.show()

# Or use shortcut methods
df.plot.bar(x="category", y="value")
plt.show()

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Available Plot Types

Kind	Method	Description
"line"	df.plot.line()	Line plot (default)
"bar"	df.plot.bar()	Vertical bar chart
"barh"	df.plot.barh()	Horizontal bar chart
"hist"	df.plot.hist()	Histogram
"box"	df.plot.box()	Box plot
"kde" / "density"	df.plot.kde()	Kernel Density Estimate
"area"	df.plot.area()	Area chart
"scatter"	df.plot.scatter()	Scatter plot
"pie"	df.plot.pie()	Pie chart
"hexbin"	df.plot.hexbin()	Hexagonal binning plot

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Line Plot

import pandas as pd
import matplotlib.pyplot as plt

data = {
    "Month": ["Jan", "Feb", "Mar", "Apr", "May", "Jun"],
    "Revenue": [42, 45, 48, 52, 55, 61],
    "Expenses": [38, 40, 42, 45, 47, 50]
}
df = pd.DataFrame(data)
df = df.set_index("Month")

ax = df.plot(figsize=(10, 5), marker="o", linewidth=2)
ax.set_ylabel("GBP (thousands)")
ax.set_title("Revenue vs Expenses")
ax.spines[["top", "right"]].set_visible(False)
ax.grid(axis="y", alpha=0.3)
plt.tight_layout()
plt.show()

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Bar Chart

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    "Language": ["Python", "R", "SQL", "Julia", "Scala"],
    "Users": [68, 18, 42, 5, 3]
})

ax = df.plot.bar(x="Language", y="Users", color="#4C72B0",
                 legend=False, figsize=(8, 5))
ax.set_ylabel("Usage (%)")
ax.set_title("Data Science Language Popularity")
ax.spines[["top", "right"]].set_visible(False)
plt.xticks(rotation=0)
plt.tight_layout()
plt.show()

Stacked Bar Chart

import pandas as pd
import matplotlib.pyplot as plt

df = pd.DataFrame({
    "Quarter": ["Q1", "Q2", "Q3", "Q4"],
    "Product A": [120, 135, 148, 162],
    "Product B": [98, 115, 132, 141],
    "Product C": [65, 72, 80, 95]
})
df = df.set_index("Quarter")

ax = df.plot.bar(stacked=True, figsize=(8, 5),
                 color=["#4C72B0", "#DD8452", "#55A868"])
ax.set_ylabel("Revenue (GBP thousands)")
ax.set_title("Quarterly Revenue by Product (Stacked)")
ax.legend(title="Product")
plt.tight_layout()
plt.show()

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Histogram

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

np.random.seed(42)
df = pd.DataFrame({
    "Heights": np.random.normal(170, 10, 1000)
})