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

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

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

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

Hint

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

Practice

1. What does the shape of a NumPy array represent?

easy

A. The number of elements along each dimension

B. The total number of elements in the array

C. The data type of the elements

D. The memory size of the array

Solution

1. Step 1: Understand the meaning of shape

   The shape of an array is a tuple that shows how many elements are in each dimension.

2. Step 2: Differentiate shape from other properties

   Shape is not the total count, data type, or memory size, but the size per dimension.

3. Final Answer:

   The number of elements along each dimension -> Option A

4. Quick Check:

   Shape = elements per dimension [OK]

Hint: Shape = count of elements per axis [OK]

Common Mistakes:

• Confusing shape with total number of elements
• Mixing shape with data type
• Thinking shape is memory size

2. Which of the following is the correct way to get the shape of a NumPy array arr?

easy

A. arr.shape

B. arr.shape()

C. shape(arr)

D. arr.get_shape()

Solution

1. Step 1: Recall NumPy shape syntax

   In NumPy, shape is an attribute, not a method, so no parentheses are used.

2. Step 2: Check each option

   arr.shape uses arr.shape correctly; others use incorrect syntax or functions.

3. Final Answer:

   arr.shape -> Option A

4. Quick Check:

   Use attribute, no parentheses [OK]

Hint: Use arr.shape without parentheses [OK]

Common Mistakes:

• Adding parentheses after shape
• Using non-existent functions like get_shape()
• Trying to call shape as a function

3. Given the code:

import numpy as np
arr = np.array([[1, 2, 3], [4, 5, 6]])
print(arr.shape)

What is the output?

medium

A. (3, 2)

B. (6,)

C. (2, 3)

D. (2,)

Solution

1. Step 1: Analyze the array structure

   The array has 2 rows and 3 columns, so shape is (2, 3).

2. Step 2: Confirm shape output

   Printing arr.shape returns the tuple (2, 3).

3. Final Answer:

   (2, 3) -> Option C

4. Quick Check:

   Rows=2, Columns=3 -> (2, 3) [OK]

Hint: Count rows then columns for shape [OK]

Common Mistakes:

• Swapping rows and columns in shape
• Thinking shape is total elements
• Confusing shape with array size

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([1, 2, 3])
print(arr.shape())

medium

A. print statement syntax is incorrect

B. arr is not a NumPy array

C. The array is empty

D. shape is an attribute, not a method; parentheses cause error

Solution

1. Step 1: Identify the error source

   The code calls arr.shape() with parentheses, but shape is an attribute.

2. Step 2: Understand attribute vs method

   Attributes do not use parentheses; calling with () causes a TypeError.

3. Final Answer:

   shape is an attribute, not a method; parentheses cause error -> Option D

4. Quick Check:

   Attributes no parentheses [OK]

Hint: Remember: shape is attribute, no () [OK]

Common Mistakes:

• Using parentheses with shape
• Assuming array is empty causes error
• Misreading print syntax

5. You have a 3D NumPy array with shape (4, 3, 2). Which of the following operations will correctly reshape it into a 2D array with 4 rows and 6 columns?

hard

A. arr.reshape(12, 2)

B. arr.reshape(4, 6)

C. arr.reshape(6, 4)

D. arr.reshape(2, 12)

Solution

1. Step 1: Calculate total elements

   Original shape (4, 3, 2) means 4*3*2 = 24 elements total.

2. Step 2: Determine new shape dimensions

   New shape (4, 6) means 4*6 = 24 elements, matching total elements.

3. Step 3: Verify other options

   arr.reshape(12, 2), arr.reshape(6, 4), and arr.reshape(2, 12) do not produce 4 rows and 6 columns.

4. Final Answer:

   arr.reshape(4, 6) -> Option B

5. Quick Check:

   4*6=24 matches total elements [OK]

Hint: New shape must keep total elements same [OK]

Common Mistakes:

• Mixing up rows and columns in reshape
• Choosing shapes with wrong total elements
• Forgetting reshape must keep total size