Multi-Line Plots

1The “why”: comparing stories on one page

Put a week of Gangtok's maximum and minimum temperatures on the same chart and two insights appear that neither chart alone could give you:

• Comparison — the vertical gap between the lines is the daily temperature range. You can see instantly which day was most extreme.
• Trends — is the week getting warmer or colder? Are the mornings getting chillier while the afternoons stay hot?

Putting related datasets on one chart makes comparisons clear and reveals insights you would miss on separate graphs.

2The “data”: side-by-side storytellers

For multiple lines, your data needs to be organised like this:

• One common X-axis — usually the time component. Both lines share the same days_of_week timeline.
• Multiple Y-axis datasets — each line gets its own list of values. One for max_temperatures, one for min_temperatures. Each list tells the story of one trend.

3The “plotting”: one call per line

Drawing multiple lines is surprisingly simple — you just call plt.plot() once for each line. Each call gets its own Y data, and can carry its own style:

• color='red' for the maximum, suggesting heat.
• color='blue' for the minimum, suggesting cold.
• label="…" — a name tag for the line, which the legend will use later.

4The “legend”: your chart's translator

With more than one line, how does anyone know which is which? That is where the legend steps in. Try switching it off below while both lines are drawn — the chart immediately becomes a guessing game.

import matplotlib.pyplot as plt
days_of_week = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
max_temperatures = [22, 24, 23, 25, 26, 24, 23]
min_temperatures = [15, 16, 15, 17, 18, 16, 15]
plt.plot(days_of_week, max_temperatures, color="red",
         label="Max Temperature (°C)")
plt.plot(days_of_week, min_temperatures, color="blue",
         label="Min Temperature (°C)")
plt.title("Weekly Temperature Trends in Gangtok")
plt.xlabel("Day of the Week")
plt.ylabel("Temperature (°C)")
plt.legend(loc="upper left")
plt.show()

The legend works in two steps, and both are required:

• The magic label= — in each plt.plot() call you attach a name tag to that line.
• The plt.legend() call— made once, after all your lines are plotted. It gathers up every label and draws a neat box showing each line's style next to its name.

5Putting it all together

Here is the complete program that recreates the weekly temperature chart. Run it, then try changing a colour, moving the legend, or adding a third line for the average temperature.

# Program to visualize Max and Min Temperatures for a week in Gangtok
import matplotlib.pyplot as plt

# --- 1. Define our data: days and temperatures ---
days_of_week = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
max_temperatures = [22, 24, 23, 25, 26, 24, 23]
min_temperatures = [15, 16, 15, 17, 18, 16, 15]

# --- 2. Plotting each temperature line ---
plt.plot(days_of_week, max_temperatures,
         color='red', label="Max Temperature (°C)")

plt.plot(days_of_week, min_temperatures,
         color='blue', label="Min Temperature (°C)")

# --- 3. Adding chart labels and title ---
plt.title("Weekly Temperature Trends in Gangtok",
          fontsize=18, color='darkslategray')
plt.xlabel("Day of the Week", fontsize=14, color='gray')
plt.ylabel("Temperature (°C)", fontsize=14, color='gray')

# --- 4. Adding the legend ---
plt.legend(loc="upper left")

# --- 5. Saving, then displaying the chart ---
# savefig() before show(), because show() clears the figure.
plt.savefig("weekly_temperature.png")
plt.show()

6Recap