NumPy with Matplotlib - Mini Project: Build & Apply

Visualizing Temperature Data with NumPy and Matplotlib

📖 Scenario: You are a weather analyst who wants to visualize temperature changes over a week. You have daily temperature data and want to create a simple line chart to see the trend.

🎯 Goal: Build a Python program that uses NumPy to store temperature data and Matplotlib to plot a line chart showing temperature changes over 7 days.

📋 What You'll Learn

Create a NumPy array with exact temperature values

Create a NumPy array for days of the week

Plot a line chart with days on the x-axis and temperatures on the y-axis

Label the x-axis as 'Day', y-axis as 'Temperature (°C)', and add a title 'Weekly Temperature'

Display the plot

💡 Why This Matters

🌍 Real World

Weather analysts and scientists often use NumPy and Matplotlib to analyze and visualize temperature and climate data.

💼 Career

Data scientists and analysts use these tools daily to create clear visual reports that help decision-makers understand trends.

1

Create temperature data using NumPy

Import numpy as np and create a NumPy array called temperatures with these exact values: 22, 24, 19, 23, 25, 20, 21.

NumPy

# Import numpy and create the temperatures array
# Your code here

Hint

Use np.array([...]) to create the array with the given temperature values.

2

Create days array using NumPy

Create a NumPy array called days with these exact string values representing days of the week: 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'.

NumPy

import numpy as np

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])

# Create the days array with the exact day names
# Your code here

Hint

Use np.array([...]) with the exact day strings inside quotes.

3

Plot the temperature data using Matplotlib

Import matplotlib.pyplot as plt. Use plt.plot(days, temperatures) to create a line chart showing temperature changes over the days.

NumPy

import numpy as np

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])
days = np.array(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])

# Plot the line chart with days on x-axis and temperatures on y-axis
# Your code here

Hint

Use plt.plot() with days as x and temperatures as y.

4

Add labels, title, and display the plot

Add x-axis label 'Day' using plt.xlabel(), y-axis label 'Temperature (°C)' using plt.ylabel(), and title 'Weekly Temperature' using plt.title(). Then display the plot using plt.show().

NumPy

import numpy as np
import matplotlib.pyplot as plt

temperatures = np.array([22, 24, 19, 23, 25, 20, 21])
days = np.array(['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])

plt.plot(days, temperatures)

# Add x-axis label, y-axis label, title, and show the plot
# Your code here

Hint

Use plt.xlabel('Day'), plt.ylabel('Temperature (°C)'), plt.title('Weekly Temperature'), and plt.show().

Practice

1. What is the main purpose of using NumPy together with Matplotlib in data science?

easy

A. To create numerical data and then visualize it with graphs

B. To write text documents and print them

C. To build websites and style them

D. To store large files on the computer

Solution

1. Step 1: Understand NumPy's role

   NumPy helps create and manage numerical data easily.

2. Step 2: Understand Matplotlib's role

   Matplotlib is used to draw graphs to visualize data.

3. Final Answer:

   To create numerical data and then visualize it with graphs -> Option A

4. Quick Check:

   NumPy + Matplotlib = Data + Graphs [OK]

Hint: NumPy handles numbers, Matplotlib draws pictures [OK]

Common Mistakes:

• Confusing Matplotlib with text editing tools
• Thinking NumPy is for website design
• Mixing file storage with data visualization

2. Which of the following code snippets correctly imports NumPy and Matplotlib for use?

easy

A. import np
import matplotlib.pyplot

B. import numpy
import matplotlib as plt

C. from numpy import matplotlib
import pyplot as plt

D. import numpy as np
import matplotlib.pyplot as plt

Solution

1. Step 1: Check NumPy import syntax

   Correct import is 'import numpy as np' to use 'np' as shortcut.

2. Step 2: Check Matplotlib import syntax

   Correct import is 'import matplotlib.pyplot as plt' to use 'plt' for plotting.

3. Final Answer:

   import numpy as np\nimport matplotlib.pyplot as plt -> Option D

4. Quick Check:

   Standard imports = import numpy as np import matplotlib.pyplot as plt [OK]

Hint: Use 'as np' and 'as plt' for easy code [OK]

Common Mistakes:

• Using wrong aliases like 'import matplotlib as plt'
• Forgetting 'pyplot' in Matplotlib import
• Trying to import non-existing modules

3. What will be the output plot type when running this code?

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.show()

medium

A. A line graph showing a sine wave

B. A bar chart with values from 0 to 10

C. A scatter plot with random points

D. No plot will show due to error

Solution

1. Step 1: Understand data creation

   np.linspace creates 100 points from 0 to 10; y is sine of x values.

2. Step 2: Understand plotting command

   plt.plot draws a line graph connecting (x, y) points; plt.show displays it.

3. Final Answer:

   A line graph showing a sine wave -> Option A

4. Quick Check:

   plot(x, sin(x)) = sine wave line [OK]

Hint: plt.plot with np.sin creates smooth wave line [OK]

Common Mistakes:

• Confusing plt.plot with bar or scatter plots
• Expecting random points instead of line
• Missing plt.show() to display plot

4. Identify the error in this code snippet that tries to plot a cosine wave:

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 50)
y = np.cos(x)
plt.plot(x, y)
plt.show

medium

A. np.cos is not a valid NumPy function

B. np.linspace should use 100 points instead of 50

C. Missing parentheses after plt.show to display the plot

D. x and y arrays have different lengths

Solution

1. Step 1: Check plt.show usage

   plt.show is a function and must be called with parentheses: plt.show()

2. Step 2: Verify other parts

   np.linspace and np.cos are correct; x and y lengths match.

3. Final Answer:

   Missing parentheses after plt.show to display the plot -> Option C

4. Quick Check:

   plt.show() needed to display plot [OK]

Hint: Always call plt.show() with parentheses [OK]

Common Mistakes:

• Forgetting parentheses on plt.show
• Thinking np.cos is invalid
• Assuming array length mismatch without checking

5. You want to plot two functions, sine and cosine, on the same graph using NumPy and Matplotlib. Which code correctly does this?

hard

A. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(np.sin(x))
plt.plot(np.cos(x))
plt.show()

B. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x))
plt.plot(x, np.cos(x))
plt.legend(['sin', 'cos'])
plt.show()

C. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.plot(x, np.sin(x), np.cos(x))
plt.show()

D. import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 2*np.pi, 100)
plt.scatter(x, np.sin(x))
plt.scatter(x, np.cos(x))
plt.show()

Solution

1. Step 1: Check plotting both functions

   import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() plots sine and cosine separately with x values, correct usage.

2. Step 2: Check legend and show

   import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() adds legend for clarity and calls plt.show() properly.

3. Final Answer:

   Code that plots sine and cosine with legend and shows plot -> Option B

4. Quick Check:

   Separate plt.plot calls + legend + plt.show() = import numpy as np import matplotlib.pyplot as plt x = np.linspace(0, 2*np.pi, 100) plt.plot(x, np.sin(x)) plt.plot(x, np.cos(x)) plt.legend(['sin', 'cos']) plt.show() [OK]

Hint: Plot each function with x, add legend, call plt.show() [OK]

Common Mistakes:

• Plotting without x values causing wrong x-axis
• Missing legend to identify lines
• Using scatter instead of line plot for smooth curves