Slicing rows and columns in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does slicing rows and columns mean in a numpy array?

It means selecting specific parts of the array by choosing certain rows and columns using index ranges or lists.

beginner

How do you select the first 3 rows and first 2 columns of a numpy array named arr?

Use arr[:3, :2]. The first part :3 selects rows 0, 1, 2 and :2 selects columns 0 and 1.

intermediate

What does arr[1:4, 2] select from a numpy array?

It selects rows 1, 2, and 3 from column 2, returning a 1D array of those values.

beginner

How can you select all rows but only the last column of a numpy array arr?

Use arr[:, -1]. The colon means all rows, and -1 means the last column.

intermediate

What happens if you use arr[::2, ::3] on a numpy array?

It selects every 2nd row and every 3rd column, starting from the first row and column.

Which numpy slice selects rows 2 to 4 and columns 1 to 3 from array arr?

A arr[1:3, 2:4]

B arr[1:4, 2:5]

C arr[2:5, 1:4]

D arr[2:4, 1:3]

Slicing in numpy includes the start index but excludes the end index. So rows 2 to 4 means 2:5 and columns 1 to 3 means 1:4.

What does arr[:, 0] return?

A Last row, first column

B First row, all columns

C First row, first column

D All rows, first column

The colon means all rows, and 0 means the first column.

How do you select every other row and every other column from arr?

A arr[::2, ::2]

B arr[1::2, 1::2]

C arr[::1, ::1]

D arr[2::2, 2::2]

The step value 2 in slicing means every other element starting from index 0.

What is the shape of the result of arr[1:3, 2:5] if arr has shape (5, 6)?

A (2, 3)

B (2, 4)

C (3, 4)

D (3, 3)

Rows 1 to 2 (1:3) gives 2 rows, columns 2 to 4 (2:5) gives 3 columns, so shape is (2, 3).

Which slice selects the last two rows and all columns from arr?

A arr[:, -2:]

B arr[-2:, :]

C arr[:2, :]

D arr[-1:, :]

Negative indices count from the end. -2: means last two rows, and colon means all columns.

Explain how to slice a numpy array to select specific rows and columns.

Think about how you pick parts of a table by row and column numbers.

Describe what arr[::2, 1:5:2] does on a numpy array.

Focus on the meaning of the double colons and step values.

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