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

Concept Flow - Slicing rows and columns

Start with 2D array

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Choose row slice: start:end

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Choose column slice: start:end

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Extract sub-array

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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.