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]

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

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]

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

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]

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

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]

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

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]

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

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.

Practice

1. What does the slicing notation array[1:3, 0:2] do on a 2D NumPy array?

easy

A. Selects rows 1 and 2, and columns 0 and 1

B. Selects rows 0 to 3 and columns 0 to 2

C. Selects rows 1 to 3 and columns 0 to 2

D. Selects rows 1 and 3, and columns 0 and 2

Solution

1. Step 1: Understand row slicing 1:3

   This means select rows starting at index 1 up to but not including index 3, so rows 1 and 2.

2. Step 2: Understand column slicing 0:2

   This means select columns starting at index 0 up to but not including index 2, so columns 0 and 1.

3. Final Answer:

   Selects rows 1 and 2, and columns 0 and 1 -> Option A

4. Quick Check:

   Slice end is excluded = A [OK]

Hint: Remember end index is excluded in slicing [OK]

Common Mistakes:

• Including the end index in the slice
• Confusing rows and columns order
• Using commas incorrectly in slicing

2. Which of the following is the correct syntax to select all rows and columns 2 to 4 (inclusive start, exclusive end) from a NumPy array named arr?

easy

A. arr[2:4, :]

B. arr[:, 2:5]

C. arr[2:5, :]

D. arr[:, 2:4]

Solution

1. Step 1: Identify rows and columns in slicing

   The first index before comma is rows, second is columns.

2. Step 2: Select all rows and columns 2 to 4

   All rows means :, columns 2 to 4 means 2:5 because end is excluded.

3. Final Answer:

   arr[:, 2:5] -> Option B

4. Quick Check:

   All rows = :, columns 2 to 4 = 2:5 [OK]

Hint: Use ':' for all rows or columns [OK]

Common Mistakes:

• Swapping rows and columns in slicing
• Using wrong end index in slice
• Missing colon for all rows or columns

3. Given the NumPy array:

import numpy as np
arr = np.array([[10, 20, 30, 40],
                [50, 60, 70, 80],
                [90, 100, 110, 120]])
print(arr[0:2, 1:3])

What is the output?

medium

A. [[50 60]
[90 100]]

B. [[10 20 30]
[50 60 70]]

C. [[20 30 40]
[60 70 80]]

D. [[20 30]
[60 70]]

Solution

1. Step 1: Slice rows 0 to 1

   Rows 0 and 1 are selected (indices 0 and 1), so first two rows.

2. Step 2: Slice columns 1 to 2

   Columns 1 and 2 are selected (indices 1 and 2), so second and third columns.

3. Final Answer:

   [[20 30] [60 70]] -> Option D

4. Quick Check:

   Rows 0-1 and columns 1-2 slice = A [OK]

Hint: Remember slicing excludes the end index [OK]

Common Mistakes:

• Including the end index in slice
• Mixing up rows and columns
• Printing the whole array instead of slice

4. What is wrong with this code snippet to select rows 1 to 3 and columns 0 to 2 from a NumPy array data?

slice = data[1:3, 0:2]

medium

A. It selects rows 1 and 2 only, not row 3

B. It causes a syntax error due to variable name 'slice'

C. It selects columns 0, 1, and 2 instead of 0 and 1

D. It selects rows 0 to 3 instead of 1 to 3

Solution

1. Step 1: Examine row slicing 1:3

   In Python/NumPy slicing, the end index is exclusive, so 1:3 selects rows 1 and 2 only, not row 3.

2. Step 2: Column slicing 0:2

   Selects columns 0 and 1 (excludes 2).

3. Final Answer:

   It selects rows 1 and 2 only, not row 3 -> Option A

4. Quick Check:

   End index excluded in slicing = A [OK]

Hint: Slicing excludes the end index [OK]

Common Mistakes:

• Assuming slice includes end index
• Ignoring variable naming conflicts
• Confusing rows and columns in slicing

5. You have a 5x5 NumPy array arr. How do you extract the middle 3 rows and middle 3 columns as a new array?

hard

A. arr[0:3, 0:3]

B. arr[2:5, 2:5]

C. arr[1:4, 1:4]

D. arr[1:3, 1:3]

Solution

1. Step 1: Identify middle rows in 5x5 array

   Rows are indexed 0 to 4. Middle 3 rows are indices 1, 2, 3, so slice 1:4.

2. Step 2: Identify middle columns similarly

   Columns 1, 2, 3 are middle 3 columns, so slice 1:4.

3. Final Answer:

   arr[1:4, 1:4] -> Option C

4. Quick Check:

   Middle 3 rows and columns = 1:4 slice [OK]

Hint: Count indices carefully; middle 3 means 1 to 3 inclusive [OK]

Common Mistakes:

• Using wrong slice end index
• Selecting too few or too many rows/columns
• Confusing zero-based indexing