When NumPy is not fast enough - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the library that helps speed up Python loops.

NumPy

import [1]

Drag options to blanks, or click blank then click option'

Anumba

Bpandas

Cmatplotlib

Dscipy

Attempts:

3 left

💡 Hint

Common Mistakes

Importing pandas instead of numba.

Trying to speed up code without any special library.

The numba library can speed up Python loops by compiling them to fast machine code.

2fill in blank

medium

Complete the code to create a fast function using numba's decorator.

NumPy

@[1]
def fast_sum(arr):
    total = 0
    for x in arr:
        total += x
    return total

Drag options to blanks, or click blank then click option'

Ajit

Bnjit

Cvectorize

Dcompile

Attempts:

3 left

💡 Hint

Common Mistakes

Using @jit which is slower than @njit in some cases.

Using @vectorize which is for element-wise functions.

The @njit decorator from numba compiles the function without Python overhead, making it very fast.

3fill in blank

hard

Fix the error in the code by completing the blank to convert a list to a NumPy array.

NumPy

import numpy as np

my_list = [1, 2, 3, 4]
my_array = np.[1](my_list)
print(my_array)

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Aasarray

Blist

Carray

Dfromlist

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list which does not exist.

Using np.fromlist which is not a valid NumPy function.

The np.array() function converts a list into a NumPy array.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that stores squares of numbers greater than 3.

NumPy

squares = {x: x[1]2 for x in range(1, 6) if x [2] 3}

Drag options to blanks, or click blank then click option'

A**

B>

C<

D*

Attempts:

3 left

💡 Hint

Common Mistakes

Using * instead of ** for power.

Using < instead of > in the condition.

The ** operator is used for exponentiation (power), and > checks if x is greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that stores uppercase keys and values greater than 0.

NumPy

result = { [1]: [2] for k, v in data.items() if v [3] 0}

Drag options to blanks, or click blank then click option'

Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of k.upper().

Using < instead of > in the condition.

The keys are converted to uppercase with k.upper(), values are v, and the condition checks if v > 0.