Customisation of Plots

Pyplot provides a wide range of functions to make charts more informative and readable. A basic plot shows data, but customisation — adding labels, a title, grid lines, or a legend — turns a raw graph into a clear visual story. The textbook introduces these customisation tools through a single, complete example.

The core customisation functions are summarised in Table 4.2 of the NCERT text. Each function serves a specific purpose:

• grid([b, which, axis]) — Configures the grid lines on the plot. The simplest use is grid(True) to turn them on.
• legend(*args, **kwargs) — Places a legend on the axes, essential when multiple data series are plotted.
• savefig(*args, **kwargs) — Saves the current figure to a file (e.g., as a PNG or PDF).
• show(*args, **kw) — Displays all open figures on the screen.
• title(label[, fontdict, loc, pad]) — Sets a title for the axes. You can control its font, location (e.g., 'center', 'left'), and padding.
• xlabel(xlabel[, fontdict, labelpad]) — Sets the label for the x-axis.
• xticks([ticks, labels]) — Gets or sets the tick locations and labels on the x-axis.
• ylabel(ylabel[, fontdict, labelpad]) — Sets the label for the y-axis.
• yticks([ticks, labels]) — Gets or sets the tick locations and labels on the y-axis.

The textbook demonstrates these in Program 4-2, which plots a line chart of date versus temperature. The code is straightforward:

import matplotlib.pyplot as plt

date = ["25/12", "26/12", "27/12"]
temp = [8.5, 10.5, 6.8]

plt.plot(date, temp)
plt.xlabel("Date")          # add the label on x-axis
plt.ylabel("Temperature")   # add the label on y-axis
plt.title("Date wise Temperature")  # add the title to the chart
plt.grid(True)              # add gridlines to the background
plt.yticks(temp)
plt.show()

Notice the order: after plt.plot(), each customisation function is called separately. plt.xlabel("Date") writes "Date" along the horizontal axis. plt.ylabel("Temperature") writes "Temperature" along the vertical axis. plt.title("Date wise Temperature") places a heading above the chart. plt.grid(True) draws light grid lines behind the data, making it easier to read values. Finally, plt.yticks(temp) forces the y-axis tick marks to appear exactly at the temperature values in the list temp — in this case, 8.5, 10.5, and 6.8. …