Slicing rows and columns in NumPy - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to select the first three rows of the array.

NumPy

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
result = arr[1]

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A[:3]

B[3:]

C[1:3]

D[:4]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [3:] which selects rows starting from index 3, not the first three rows.

Using [:4] which selects four rows but the array only has four rows total.

Using [1:3] which selects rows 1 and 2, not the first three rows.

Using [:3] slices the array to include rows from the start up to but not including index 3, which means the first three rows.

2fill in blank

medium

Complete the code to select the second column from all rows.

NumPy

import numpy as np
arr = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90]])
col = arr[1]

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A[1, :]

B[:, 1]

C[:, 2]

D[1, 1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [1, :] which selects the second row, not the second column.

Using [:, 2] which selects the third column, not the second.

Using [1, 1] which selects a single element, not a column.

Using [:, 1] selects all rows (:) and the second column (index 1).

3fill in blank

hard

Fix the error in the code to select rows 1 and 2 and columns 0 and 1.

NumPy

import numpy as np
arr = np.array([[5, 6, 7], [8, 9, 10], [11, 12, 13]])
subset = arr[1]

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A[0:2, 1:3]

B[1:2, 0:1]

C[1:3, 0:2]

D[1, 2, 0, 1]

Attempts:

3 left

💡 Hint

Common Mistakes

Using [1:2, 0:1] which selects only row 1 and column 0.

Using [0:2, 1:3] which selects wrong rows and columns.

Using [1, 2, 0, 1] which is invalid syntax for slicing.

Using [1:3, 0:2] slices rows 1 and 2 (up to but not including 3) and columns 0 and 1.

4fill in blank

hard

Fill both blanks to create a dictionary with words as keys and their lengths if length is greater than 3.

NumPy

words = ['apple', 'bat', 'carrot', 'dog']
lengths = {word: [1] for word in words if [2]

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Alen(word)

Blen(word) > 3

Cword

Dword > 3

Attempts:

3 left

💡 Hint

Common Mistakes

Using word instead of len(word) for the value.

Using word > 3 which compares string to number and causes error.

Using len(word) without a condition, which includes all words.

The dictionary comprehension uses len(word) as value and filters words with length greater than 3.

5fill in blank

hard

Fill all three blanks to create a dictionary with uppercase words as keys, their lengths as values, only if length is greater than 3.

NumPy

words = ['tree', 'cat', 'house', 'dog']
result = [1]: [2] for w in words if [3]

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Aw.upper()

Blen(w)

Clen(w) > 3

Dw.lower()

Attempts:

3 left

💡 Hint

Common Mistakes

Using w.lower() instead of w.upper() for keys.

Using w instead of len(w) for values.

Using wrong condition like w > 3 which causes error.

The dictionary comprehension uses uppercase words as keys, their lengths as values, and filters words longer than 3.