Understanding ufunc methods (reduce, accumulate) in NumPy - Practice Questions & Exercises

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of numpy add.reduce on array

What is the output of this code snippet using numpy.add.reduce?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
result = np.add.reduce(arr)
print(result)

A 10

B [1 3 6 10]

C 24

D [1 2 3 4]

💡 Hint

Reduce sums all elements into a single value.

np.add.reduce sums all elements of the array into one number. Here, 1+2+3+4 = 10.

❓ Predict Output

intermediate

Output of numpy multiply.accumulate on array

What is the output of this code snippet using numpy.multiply.accumulate?

NumPy

import numpy as np
arr = np.array([1, 2, 3, 4])
result = np.multiply.accumulate(arr)
print(result)

A [1 3 6 10]

B 24

C [1 2 6 24]

D [1 2 3 4]

💡 Hint

Accumulate multiplies elements step by step.

np.multiply.accumulate returns the cumulative product: [1, 1*2=2, 2*3=6, 6*4=24].

❓ data_output

advanced

Result shape after reduce on 2D array

Given this 2D array, what is the shape of the result after applying np.add.reduce along axis 1?

NumPy

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
result = np.add.reduce(arr, axis=1)
print(result.shape)

A (3,)

B (2,)

C (2, 3)

D (1, 3)

💡 Hint

Reducing along axis 1 sums each row into one value.

Reducing along axis 1 collapses columns, so each row becomes one sum. The result shape is (2,).

🧠 Conceptual

advanced

Difference between reduce and accumulate

Which statement correctly describes the difference between reduce and accumulate ufunc methods?

A <code>reduce</code> returns a single aggregated value; <code>accumulate</code> returns intermediate results as an array.

B <code>reduce</code> returns intermediate results; <code>accumulate</code> returns a single value.

C Both return the same output but differ in speed.

D <code>reduce</code> works only on 1D arrays; <code>accumulate</code> works on any dimension.

💡 Hint

Think about how many values each method returns.

reduce combines all elements into one value. accumulate shows the step-by-step results as an array.

🔧 Debug

expert

Identify error in ufunc accumulate usage

What error will this code raise and why?

NumPy

import numpy as np
arr = np.array([1, 2, 3])
result = np.add.accumulate(arr, axis=1)
print(result)

A No error, prints [1 3 6]

B TypeError: 'int' object is not iterable

C ValueError: operands could not be broadcast together

D AxisError: axis 1 is out of bounds for array of dimension 1

💡 Hint

Check the array dimensions and axis argument.

The array is 1D, so axis=1 is invalid. This causes an AxisError.