Slicing rows and columns in NumPy - Mini Project: Build & Apply

Slicing rows and columns

📖 Scenario: You work in a small grocery store. You have a list of daily sales data for different products. You want to look at specific parts of this data to understand sales better.

🎯 Goal: Learn how to slice rows and columns from a NumPy array to select specific parts of the sales data.

📋 What You'll Learn

Create a NumPy array with given sales data

Create variables to select specific rows and columns

Use slicing to extract parts of the array

Print the sliced parts to see the result

💡 Why This Matters

🌍 Real World

Slicing rows and columns helps you focus on parts of large datasets, like sales data, to analyze specific time periods or products.

💼 Career

Data scientists and analysts often slice data arrays to prepare data for reports, visualizations, or machine learning.

1

Create the sales data array

Import NumPy as np and create a 2D NumPy array called sales with these exact values: [[10, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120]].

NumPy

# Import numpy and create the sales array
# Your code here

Hint

Use np.array() to create the array with the exact nested list.

2

Set row and column slice variables

Create a variable called row_slice that selects rows 1 and 2 (second and third rows) and a variable called col_slice that selects columns 0 and 1 (first and second columns). Use Python slice notation.

NumPy

import numpy as np
sales = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120]])

# Create row_slice for rows 1 and 2
# Create col_slice for columns 0 and 1
# Your code here

Hint

Remember, slice(start, stop) selects rows or columns from start up to but not including stop.

3

Slice the sales array using the variables

Create a new variable called selected_sales by slicing the sales array using row_slice for rows and col_slice for columns.

NumPy

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

# Slice sales using row_slice and col_slice
# Your code here

Hint

Use sales[row_slice, col_slice] to slice rows and columns at the same time.

4

Print the sliced sales data

Print the variable selected_sales to see the sliced part of the sales data.

NumPy

import numpy as np
sales = np.array([[10, 20, 30], [40, 50, 60], [70, 80, 90], [100, 110, 120]])
row_slice = slice(1, 3)
col_slice = slice(0, 2)
selected_sales = sales[row_slice, col_slice]

# Print selected_sales
# Your code here

Hint

Use print(selected_sales) to display the sliced array.

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