NumPy with machine learning libraries - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import NumPy with its common alias.

NumPy

import [1] as np

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Asklearn

Bnumpy

Cpandas

Dmatplotlib

Attempts:

3 left

💡 Hint

Common Mistakes

Importing pandas or sklearn instead of numpy.

Not using the alias np.

NumPy is commonly imported as np to simplify code when using its functions.

2fill in blank

medium

Complete the code to convert a Python list to a NumPy array.

NumPy

arr = np.[1]([1, 2, 3, 4])

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Aarray

Bmatrix

Clist

Dasarray

Attempts:

3 left

💡 Hint

Common Mistakes

Using np.list which does not exist.

Using np.asarray which also works but is not the common first choice here.

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

3fill in blank

hard

Fix the error in the code to import the train_test_split function from scikit-learn.

NumPy

from sklearn.model_selection import [1]

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Atrain_test_split

BtrainSplit

Csplit_train_test

Dtrain_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like train_test or trainSplit.

Misspelling the function name.

The correct function name is train_test_split from sklearn.model_selection.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps words to their lengths only if length is greater than 3.

NumPy

{word: [1] for word in words if len(word) [2] 3}

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Alen(word)

B<

C>

Dword

Attempts:

3 left

💡 Hint

Common Mistakes

Using the word itself as the value instead of its length.

Using the wrong comparison operator like < instead of >.

The dictionary comprehension maps each word to its length using len(word). The condition filters words with length greater than 3 using >.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps uppercase keys to values only if values are positive.

NumPy

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

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Ak.upper()

Bv

C>

Dk.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using k.lower() instead of uppercase.

Using wrong comparison operators like < or ==.

Swapping keys and values.

The comprehension uses k.upper() to convert keys to uppercase, keeps values as v, and filters for values greater than zero using >.