Slicing rows and columns in NumPy - Step-by-Step Execution

Concept Flow - Slicing rows and columns

Start with 2D array

↓

Choose row slice: start:end

↓

Choose column slice: start:end

↓

Extract sub-array

↓

Use or display sliced data

We start with a 2D array, select rows by slicing, then select columns by slicing, and get a smaller sub-array.

Execution Sample

NumPy

import numpy as np
arr = np.array([[1,2,3],[4,5,6],[7,8,9]])
slice_rows = arr[0:2, :]
slice_cols = arr[:, 1:3]
slice_rows_cols = arr[0:2, 1:3]

This code slices the first two rows and all columns, then slices all rows and columns 1 to 3, then slices rows 0:2 and columns 1:3 combined.

Execution Table

Step	Operation	Row slice	Column slice	Resulting array
1	Original array	all	all	[[1 2 3] [4 5 6] [7 8 9]]
2	Slice rows 0 to 2	0:2	all	[[1 2 3] [4 5 6]]
3	Slice columns 1 to 3	all	1:3	[[2 3] [5 6] [8 9]]
4	Slice rows 0 to 2 and columns 1 to 3	0:2	1:3	[[2 3] [5 6]]
5	Exit	-	-	Slicing complete

💡 Slicing stops after extracting desired rows and columns.

Variable Tracker

Variable	Start	After Step 2	After Step 3	After Step 4	Final
arr	[[1 2 3] [4 5 6] [7 8 9]]	[[1 2 3] [4 5 6] [7 8 9]]	[[1 2 3] [4 5 6] [7 8 9]]	[[1 2 3] [4 5 6] [7 8 9]]	[[1 2 3] [4 5 6] [7 8 9]]
slice_rows	N/A	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]	[[1 2 3] [4 5 6]]
slice_cols	N/A	N/A	[[2 3] [5 6] [8 9]]	[[2 3] [5 6] [8 9]]	[[2 3] [5 6] [8 9]]
slice_rows_cols	N/A	N/A	N/A	[[2 3] [5 6]]	[[2 3] [5 6]]

Key Moments - 3 Insights

Why does arr[0:2, :] select the first two rows and all columns?

Because 0:2 means rows starting at index 0 up to but not including 2, and ':' means all columns. See execution_table row 2.

What does arr[:, 1:3] do to the array?

It selects all rows (':') and columns from index 1 up to but not including 3. See execution_table row 3.

How do you slice both rows and columns at the same time?

Use arr[row_slice, column_slice], for example arr[0:2, 1:3] slices rows 0 and 1 and columns 1 and 2. See execution_table row 4.

Visual Quiz - 3 Questions

Look at the execution table, what is the result of slicing rows 0 to 2?

A [[1 2 3] [4 5 6]]

B [[2 3] [5 6]]

C [[7 8 9]]

D [[1 2 3] [4 5 6] [7 8 9]]

💡 Hint

Check execution_table row 2 under 'Resulting array'

At which step does the slicing select columns 1 to 3 for all rows?

A Step 2

B Step 3

C Step 4

D Step 1

💡 Hint

Look at execution_table row 3 for column slice 1:3

If you want to slice only the last column for all rows, how would the slicing look?

A arr[0:2, 0:1]

B arr[2:3, :]

C arr[:, 2:3]

D arr[1:3, 1:3]

💡 Hint

Column slice 2:3 means column index 2 only, see how column slices work in execution_table rows 3 and 4

Concept Snapshot

Slicing rows and columns in numpy:
Use arr[row_start:row_end, col_start:col_end]
Row slice selects rows from start up to but not including end
Column slice selects columns similarly
':' means select all rows or columns
Result is a smaller sub-array

Full Transcript

We start with a 2D numpy array. To slice rows, we use arr[start:end, :], where start and end define the row range. To slice columns, we use arr[:, start:end]. Combining both, arr[row_start:row_end, col_start:col_end] extracts a sub-array. The slices include the start index but exclude the end index. This lets us pick parts of the array easily.

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