SciPy vs NumPy relationship - Practice Questions

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the relationship between SciPy and NumPy

Which statement best describes the relationship between SciPy and NumPy?

A SciPy is built on top of NumPy and extends its functionality with additional scientific computing tools.

B NumPy is a subset of SciPy that focuses only on matrix operations.

C NumPy is built on top of SciPy to provide basic array operations.

D SciPy and NumPy are completely independent libraries with no overlap in functionality.

💡 Hint

Think about which library provides the core array data structure and which adds more specialized functions.

NumPy provides the core array data structure and basic operations. SciPy builds on NumPy by adding more advanced scientific functions like optimization, integration, and statistics.

❓ Predict Output

intermediate

Output of NumPy and SciPy array operations

What is the output of the following code?

SciPy

import numpy as np
from scipy import linalg

arr = np.array([[1, 2], [3, 4]])
inv_arr = linalg.inv(arr)
print(np.round(inv_arr, 2))

A [[ 0.5 0. ] [ 0. 0.5]]

B [[ 2. -1. ] [-1.5 0.5]]

C [[-2. 1. ] [ 1.5 -0.5]]

D Raises a TypeError because linalg.inv does not accept NumPy arrays

💡 Hint

Recall that linalg.inv computes the inverse of a matrix.

The inverse of the matrix [[1, 2], [3, 4]] is [[-2, 1], [1.5, -0.5]]. NumPy arrays work perfectly with SciPy's linear algebra functions.

❓ data_output

advanced

Comparing mean calculation with NumPy and SciPy

Given the array data = np.array([1, 2, 3, 4, 5]), what is the output of the following code?

SciPy

import numpy as np
from scipy import stats

data = np.array([1, 2, 3, 4, 5])
mean_np = np.mean(data)
mean_scipy = stats.tmean(data)
print((mean_np, mean_scipy))

A Raises an AttributeError because stats.tmean is deprecated

B (3.0, 2.5)

C (2.5, 3.0)

D (3.0, 3.0)

💡 Hint

Both functions calculate the mean but may have different default behaviors.

Both np.mean and stats.tmean compute the mean of the array. Since no limits are set for tmean, it returns the same mean as np.mean.

❓ visualization

advanced

Plotting with NumPy and SciPy integration

Which option correctly creates a plot of a sine wave using NumPy for data and SciPy for signal processing?

SciPy

import numpy as np
import matplotlib.pyplot as plt
from scipy import signal

x = np.linspace(0, 2 * np.pi, 100)
sine_wave = np.sin(x)
filtered_wave = signal.savgol_filter(sine_wave, 11, 3)
plt.plot(x, sine_wave, label='Original')
plt.plot(x, filtered_wave, label='Filtered')
plt.legend()
plt.show()

A Plots two lines: the original sine wave and a smoothed version using Savitzky-Golay filter.

B Plots only the original sine wave without any filtering.

C Raises a NameError because signal.savgol_filter is not imported.

D Plots a cosine wave instead of sine due to a code mistake.

💡 Hint

Check how signal.savgol_filter is used to smooth data.

The code uses NumPy to create the sine wave data and SciPy's savgol_filter to smooth it. Both lines are plotted with labels.

🔧 Debug

expert

Identifying the error in combining SciPy and NumPy functions

What error does the following code produce?

SciPy

import numpy as np
from scipy import optimize

def f(x):
    return np.sin(x) + np.cos(x)

result = optimize.root(f, 0)
print(result.x)

A TypeError because optimize.root expects a list, not a function.

B No error; prints the root near 0.

C ValueError because the initial guess 0 is invalid.

D AttributeError because np.sin and np.cos are not recognized inside the function.

💡 Hint

Check how optimize.root works with functions and initial guesses.

The code correctly defines a function using NumPy's sin and cos, then finds a root near 0 using SciPy's optimize.root. It runs without error and prints the root.