Data Visualisation Using Plot

What is Pandas DataFrame.plot?

Pandas provides convenient built-in plotting, which is great for quick, exploratory visualisations. However, its plotting features are more limited compared to dedicated libraries like Matplotlib, Seaborn, or Plotly.

• Creates visualisations directly from DataFrames or Series.
• df.plot(...) is just a wrapper around Matplotlib.
• It creates a Matplotlib Axes object (a chart), plots the data, and returns it.
• Matplotlib itself is the underlying engine that actually draws the chart.
• plt.show() is a Matplotlib command → renders the plot to screen/output.
• Automatically uses column names for labels, legends, and axes.

Syntax

DataFrame.plot(kind="line", x=None, y=None, **kwargs)

• kind → type of plot ("line", "bar", "hist", etc.).
• x → column name to use as x-axis.
• y → column(s) to plot on y-axis.
• **kwargs → passed to Matplotlib (e.g., figsize, title, xlabel, ylabel).

Since df.plot() always returns a Matplotlib Axes, you can keep customising (colors, labels, legends) with standard Matplotlib commands:

ax = df.plot(y="Sales", kind="line", color="red")ax.set_ylabel("Pounds")ax.set_title("Sales Trend")

Plot Types (kind)

Example data

Example DataFrame that is used in the chart examples below.

import pandas as pdimport matplotlib.pyplot as pltimport numpy as np​# Sample datasetdf = pd.DataFrame({    "Product": ["A", "B", "C", "D", "E"],    "Sales": [200, 120, 340, 300, 150],    "Profit": [50, 20, 80, 70, 25],    "Age": [23, 45, 36, 50, 29],    "Height": [160, 170, 175, 180, 165],    "Weight": [55, 70, 80, 90, 60]})

Line Plot (default)

df.plot(y="Sales", kind="line", title="Line Plot - Sales")plt.show()

If you pass multiple y-columns, they’ll all be plotted together:

df.plot(y=["Sales", "Profit"], kind="line")

Bar Plot

df.plot(x="Product", y="Sales", kind="bar", title="Bar Plot - Sales")plt.show()

Horizontal Bar Plot

df.plot(x="Product", y="Sales", kind="barh", title="Horizontal Bar Plot - Sales")plt.show()

Histogram

df["Age"].plot(kind="hist", bins=5, edgecolor="black", title="Histogram - Age")plt.show()

Box Plot

df[["Sales", "Profit"]].plot(kind="box", title="Box Plot - Sales vs Profit")plt.show()

KDE / Density Plot

df["Age"].plot(kind="kde", title="KDE Plot - Age")plt.show()

Note, for KDE charts, you also need to install the SciPy module as it uses it under the hood : pip install scipy.

Area Plot

df.plot(y=["Sales", "Profit"], kind="area", alpha=0.5, title="Area Plot - Sales & Profit")plt.show()

Scatter Plot

df.plot(kind="scatter", x="Height", y="Weight", title="Scatter Plot - Height vs Weight")plt.show()

Pie Chart

df.set_index("Product")["Sales"].plot(kind="pie", autopct="%.1f%%", title="Pie Chart - Sales") # removes y-labelplt.ylabel("") plt.show()