Array shapes and dimensions in Data Analysis Python - Mini Project: Build & Apply

Understanding Array Shapes and Dimensions

📖 Scenario: Imagine you work at a small bakery that tracks daily sales of different types of bread. You want to organize this sales data in arrays to better understand the shape and size of your data.

🎯 Goal: You will create arrays representing sales data, check their shapes and dimensions, and print these details to understand how data is structured in arrays.

📋 What You'll Learn

Create a 1D array with daily sales numbers

Create a 2D array with sales of different bread types over several days

Use variables to store array shapes and dimensions

Print the shape and dimension of each array

💡 Why This Matters

🌍 Real World

Arrays are used to store and organize data like sales, temperatures, or images in rows and columns.

💼 Career

Knowing array shapes and dimensions is essential for data scientists and analysts to prepare data for analysis and machine learning.

1

Create a 1D array for daily sales

Import numpy as np and create a 1D array called daily_sales with these exact values: 10, 15, 7, 12, 9.

Data Analysis Python

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

Need a hint?

Use np.array() to create the array with the given list of numbers.

2

Create a 2D array for sales of bread types

Create a 2D array called bread_sales using np.array() with these exact values: [[5, 3, 2], [7, 8, 4], [6, 5, 3]]. Each inner list represents sales of three bread types for one day.

Data Analysis Python

import numpy as np
daily_sales = np.array([10, 15, 7, 12, 9])
# Create the bread_sales 2D array
# Your code here

Need a hint?

Use np.array() with a list of lists to create a 2D array.

3

Get shapes and dimensions of the arrays

Create variables daily_shape and daily_dim to store the shape and number of dimensions of daily_sales. Similarly, create bread_shape and bread_dim for bread_sales. Use the .shape and .ndim attributes.

Data Analysis Python

import numpy as np
daily_sales = np.array([10, 15, 7, 12, 9])
bread_sales = np.array([[5, 3, 2], [7, 8, 4], [6, 5, 3]])
# Get shape and dimension of daily_sales
# Your code here
# Get shape and dimension of bread_sales
# Your code here

Need a hint?

Use array.shape to get the shape and array.ndim to get the number of dimensions.

4

Print the shapes and dimensions

Print the variables daily_shape, daily_dim, bread_shape, and bread_dim each on its own line in this exact order.

Data Analysis Python

import numpy as np
daily_sales = np.array([10, 15, 7, 12, 9])
bread_sales = np.array([[5, 3, 2], [7, 8, 4], [6, 5, 3]])
daily_shape = daily_sales.shape
daily_dim = daily_sales.ndim
bread_shape = bread_sales.shape
bread_dim = bread_sales.ndim
# Print the shapes and dimensions
# Your code here

Need a hint?

Use print() to show each variable on its own line.