NumPy with Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of NumPy array plot with Matplotlib

What will be the output plot when running the following code?

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.plot(x, y)
plt.title('Sine Wave')
plt.xlabel('x values')
plt.ylabel('sin(x)')
plt.show()

A A bar chart with bars representing sine values at discrete points

B A scatter plot of points randomly distributed between 0 and 10 on x-axis

C An empty plot with no lines or points

D A line plot showing a sine wave oscillating between -1 and 1 over x from 0 to 10

💡 Hint

Think about what plt.plot does with x and y arrays.

The code creates 100 points from 0 to 10, computes their sine, and plots a continuous line showing the sine wave.

❓ data_output

intermediate

Shape of NumPy array after meshgrid for plotting

Given the code below, what is the shape of X and Y arrays used for contour plotting?

NumPy

import numpy as np
x = np.linspace(-1, 1, 5)
y = np.linspace(-1, 1, 3)
X, Y = np.meshgrid(x, y)
print(X.shape, Y.shape)

A (3, 5) (3, 5)

B (5, 5) (3, 3)

C (5, 3) (5, 3)

D (3, 3) (5, 5)

💡 Hint

meshgrid creates coordinate matrices for vectorized evaluations.

meshgrid with x length 5 and y length 3 creates X and Y arrays with shape (3,5) matching y rows and x columns.

❓ visualization

advanced

Identify the correct heatmap from NumPy data

Which option shows the correct heatmap visualization for the following data array?

NumPy

import numpy as np
import matplotlib.pyplot as plt

data = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
plt.imshow(data, cmap='viridis')
plt.colorbar()
plt.show()

A A 3x3 grid with uniform color and no gradient

B A 3x3 grid with colors ranging from dark purple (low) to yellow (high) increasing from top-left to bottom-right

C A 3x3 grid with colors randomly assigned ignoring data values

D A line plot showing values 1 to 9 on y-axis

💡 Hint

imshow colors cells based on their numeric values using the colormap.

imshow with 'viridis' colors low values dark purple and high values yellow, so the heatmap shows increasing color from 1 to 9.

🔧 Debug

advanced

Error in plotting NumPy array with Matplotlib

What error will this code produce and why?

NumPy

import numpy as np
import matplotlib.pyplot as plt

x = np.array([1, 2, 3])
y = np.array([4, 5])
plt.plot(x, y)
plt.show()

A No error, plots a line connecting points

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: x and y must have same first dimension

D IndexError: index out of bounds

💡 Hint

Check if x and y arrays have the same length.

Matplotlib requires x and y arrays to have the same length to plot points; here y has length 2 but x has length 3.

🚀 Application

expert

Calculate and plot the 2D Gaussian distribution

Which code snippet correctly calculates a 2D Gaussian distribution and plots it as a contour plot?

A

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = (1/(2*np.pi)) * np.exp(-0.5*(X**2 + Y**2))
plt.contour(X, Y, Z)
plt.title('2D Gaussian')
plt.show()

B

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = np.exp(-(X + Y)**2)
plt.contourf(X, Y, Z)
plt.title('2D Gaussian')
plt.show()

C

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = (1/np.sqrt(2*np.pi)) * np.exp(-0.5*(X**2 + Y**2))
plt.contour(X, Y, Z)
plt.title('2D Gaussian')
plt.show()

D

import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-3, 3, 100)
y = np.linspace(-3, 3, 100)
X, Y = np.meshgrid(x, y)
Z = (1/(2*np.pi)) * np.exp(-0.5*(X + Y))
plt.contour(X, Y, Z)
plt.title('2D Gaussian')
plt.show()

💡 Hint

Recall the formula for 2D Gaussian: (1/(2π)) * exp(-0.5*(x² + y²))

Option A correctly implements the 2D Gaussian formula with squared terms inside the exponent and proper normalization factor.