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

• 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:

Plot Types (kind)

Example data

Example DataFrame that is used in the chart examples below.

Line Plot (default)

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

Bar Plot

Horizontal Bar Plot

Histogram

Box Plot

KDE / Density Plot

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

Area Plot

Scatter Plot

Pie Chart