Q.Collect the minimum and maximum temperature of your city for a month and present it using a histogram plot.

This is a plain theory question that asks for a practical data-visualization task. The answer must explain the concept, show the code, describe the output, and justify the key lines.

Why a histogram for temperature data?

A histogram groups continuous data (like temperature) into bins (intervals) and shows how many days fall into each bin. For a month of daily min/max temperatures, a histogram reveals the distribution — e.g., most days had a max between 30–35°C, or min temperatures were clustered around 20°C. It does not show the day-by-day trend (that would be a line plot); it shows frequency.

Python code using matplotlib

import matplotlib.pyplot as plt
import numpy as np

# Sample data: daily min and max temperatures for a 30-day month
# Replace with your own city's data
min_temps = [18, 20, 19, 21, 22, 20, 19, 18, 17, 21,
             23, 22, 20, 19, 18, 20, 21, 22, 23, 24,
             22, 21, 20, 19, 18, 17, 19, 20, 21, 22]

max_temps = [30, 32, 31, 33, 34, 32, 31, 30, 29, 33,
             35, 34, 32, 31, 30, 32, 33, 34, 35, 36,
             34, 33, 32, 31, 30, 29, 31, 32, 33, 34]

# Plot histograms
plt.hist(min_temps, bins=8, alpha=0.6, label='Min Temp', color='blue')
plt.hist(max_temps, bins=8, alpha=0.6, label='Max Temp', color='red')

# Labels and title
plt.xlabel('Temperature (°C)')
plt.ylabel('Frequency (number of days)')
plt.title('Distribution of Daily Min and Max Temperatures (June)')
plt.legend()

# Show the plot
plt.show()

Expected output (description)

The plot shows two overlapping histograms on the same axes:

• X-axis: Temperature in °C, ranging from about 17°C to 36°C.
• Y-axis: Frequency — how many days had a temperature in each bin.
• Blue bars (min temps) cluster around 18–22°C, with a peak near 20°C. …

Why a histogram is the wrong choice here

Let's think about what a histogram actually does. A histogram takes a single numerical variable and shows how often values fall into different bins (ranges). For example, "how many days had a temperature between 20°C and 25°C?" That's a frequency distribution.

Your data, however, has two variables (min temp and max temp) changing over time (each day of the month). You want to see the trend — how temperatures rise and fall day by day. A histogram cannot show that. It would just tell you "most days had a max around 30°C" but you'd lose all information about which day was which.

What you should actually plot

For daily min and max temperatures over a month, the right chart is a dual-line plot (or a grouped bar chart if you prefer bars). Here's the code:

import matplotlib.pyplot as plt
import numpy as np

# Sample data: 30 days of temperatures (replace with your actual city data)
days = np.arange(1, 31)
min_temps = [18, 19, 17, 20, 21, 19, 18, 22, 23, 21, 20, 19, 18, 17, 20, 
             22, 24, 23, 21, 20, 19, 18, 17, 19, 21, 23, 25, 24, 22, 20]
max_temps = [30, 31, 29, 32, 33, 31, 30, 34, 35, 33, 32, 31, 30, 29, 32, 
             34, 36, 35, 33, 32, 31, 30, 29, 31, 33, 35, 37, 36, 34, 32]

plt.figure(figsize=(12, 6))
plt.plot(days, min_temps, 'b-o', label='Min Temperature', linewidth=2)
plt.plot(days, max_temps, 'r-o', label='Max Temperature', linewidth=2)
plt.xlabel('Day of Month')
plt.ylabel('Temperature (°C)')
plt.title('Daily Min and Max Temperatures for [Your City] — [Month]')
plt.legend()
plt.grid(True, alpha=0.3)
plt.xticks(days)
plt.tight_layout()
plt.show()

What this shows:

• X-axis: day of the month (1 to 30)
• Y-axis: temperature in °C
• Blue line with circles: minimum temperature each day
• Red line with circles: maximum temperature each day
• You can instantly see trends, cold snaps, heat waves, and the daily temperature range

If you absolutely must make a histogram (as the question asks)

If the question forces a histogram, here's how you'd do it — but understand it's answering a different question:

import matplotlib.pyplot as plt
import numpy as np

# Same data as above
min_temps = [18, 19, 17, 20, 21, 19, 18, 22, 23, 21, 20, 19, 18, 17, 20, 
             22, 24, 23, 21, 20, 19, 18, 17, 19, 21, 23, 25, 24, 22, 20]
max_temps = [30, 31, 29, 32, 33, 31, 30, 34, 35, 33, 32, 31, 30, 29, 32, …