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

Need a 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

Need a 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

Need a 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

Need a hint?

Use print(selected_sales) to display the sliced array.