reshape() for changing dimensions in NumPy - Mini Project: Build & Apply

Reshape Arrays with numpy.reshape()

📖 Scenario: Imagine you work in a bakery that tracks daily sales of different types of bread. The sales data is collected as a single list of numbers, but you want to organize it into a table showing sales per day and bread type.

🎯 Goal: You will create a numpy array from a list of sales numbers, then reshape it into a 2D array with rows as days and columns as bread types. Finally, you will print the reshaped array to see the organized sales data.

📋 What You'll Learn

Use numpy to create and reshape arrays

Create a 1D numpy array with exact sales data

Define a new shape as a tuple

Use numpy.reshape() to change the array dimensions

Print the reshaped array

💡 Why This Matters

🌍 Real World

Reshaping arrays is useful when you want to organize flat data into tables or grids, like sales data per day and product.

💼 Career

Data scientists often reshape data to prepare it for analysis or visualization, making this skill important for cleaning and structuring data.

1

Create the sales data array

Import numpy as np and create a 1D numpy array called sales with these exact values: 10, 15, 20, 25, 30, 35.

NumPy

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

Hint

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

2

Define the new shape for the array

Create a variable called new_shape and set it to the tuple (2, 3) to represent 2 days and 3 bread types.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
# Define the new shape as (2, 3)
# Your code here

Hint

A tuple is written with parentheses and commas, like (2, 3).

3

Reshape the sales array

Use np.reshape() with the sales array and new_shape to create a new array called reshaped_sales.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
new_shape = (2, 3)
# Reshape the sales array using np.reshape()
# Your code here

Hint

Call np.reshape() with the array first, then the shape tuple.

4

Print the reshaped sales array

Print the variable reshaped_sales to display the sales data organized by days and bread types.

NumPy

import numpy as np
sales = np.array([10, 15, 20, 25, 30, 35])
new_shape = (2, 3)
reshaped_sales = np.reshape(sales, new_shape)
# Print the reshaped_sales array
# Your code here

Hint

Use print(reshaped_sales) to show the array.

Practice

1. What does the reshape() function do in NumPy?

easy

A. Creates a new array with random values

B. Changes the shape of an array without changing its data

C. Sorts the elements of an array

D. Deletes elements from an array

Solution

1. Step 1: Understand the purpose of reshape()

   The reshape() function changes the shape (dimensions) of an existing array but keeps the data intact.

2. Step 2: Compare with other options

   Options A, B, and C describe different functions or actions unrelated to reshaping.

3. Final Answer:

   Changes the shape of an array without changing its data -> Option B

4. Quick Check:

   reshape() = change shape only [OK]

Hint: reshape() changes shape, data stays same [OK]

Common Mistakes:

• Thinking reshape() changes data values
• Confusing reshape() with sorting or deleting
• Assuming reshape() creates new random arrays

2. Which of the following is the correct syntax to reshape a NumPy array arr into 2 rows and 3 columns?

easy

A. arr.reshape(2[3])

B. arr.reshape{2, 3}

C. arr.reshape(2, 3)

D. reshape(arr, 2, 3)

Solution

1. Step 1: Recall correct reshape syntax

   The correct syntax is array.reshape(dim1, dim2, ...) with dimensions as separate arguments or a tuple.

2. Step 2: Evaluate options

   arr.reshape(2, 3) uses correct method call with separate arguments. arr.reshape(2[3]) has invalid syntax (missing comma). arr.reshape{2, 3} uses curly braces which is wrong syntax. reshape(arr, 2, 3) uses a function call without object which is invalid.

3. Final Answer:

   arr.reshape(2, 3) -> Option C

4. Quick Check:

   Use dot and parentheses with dims [OK]

Hint: Use dot and parentheses with dims, no brackets [OK]

Common Mistakes:

• Forgetting comma between dimensions
• Using curly braces instead of parentheses
• Calling reshape as a standalone function

3. What is the output shape of the following code?

import numpy as np
arr = np.arange(12)
new_arr = arr.reshape(3, 4)
print(new_arr.shape)

medium

A. (3, 4)

B. (4, 3)

C. (12,)

D. (2, 6)

Solution

1. Step 1: Understand the original array

   np.arange(12) creates a 1D array with 12 elements from 0 to 11.

2. Step 2: Reshape to (3, 4)

   The reshape changes the array to 3 rows and 4 columns, so the shape becomes (3, 4).

3. Final Answer:

   (3, 4) -> Option A

4. Quick Check:

   reshape(3,4) = (3,4) shape [OK]

Hint: reshape dims become shape tuple [OK]

Common Mistakes:

• Confusing rows and columns order
• Thinking reshape changes total elements
• Expecting original 1D shape after reshape

4. What error will occur when running this code?

import numpy as np
arr = np.arange(10)
new_arr = arr.reshape(3, 4)

medium

A. ValueError: cannot reshape array of size 10 into shape (3,4)

B. TypeError: reshape() takes exactly 1 argument

C. SyntaxError: invalid syntax

D. No error, reshaping works fine

Solution

1. Step 1: Check original array size

   np.arange(10) creates an array with 10 elements.

2. Step 2: Check reshape dimensions

   Trying to reshape into (3, 4) means 12 elements needed, but only 10 exist.

3. Step 3: Identify error type

   This mismatch causes a ValueError about incompatible shape.

4. Final Answer:

   ValueError: cannot reshape array of size 10 into shape (3,4) -> Option A

5. Quick Check:

   Elements must match for reshape [OK]

Hint: Total elements must match for reshape [OK]

Common Mistakes:

• Ignoring total element count mismatch
• Expecting reshape to auto-fill missing elements
• Confusing ValueError with SyntaxError

5. Given a 1D NumPy array arr = np.arange(24), which of the following reshapes it into a 3D array with shape (2, 3, 4) using automatic dimension calculation with -1 in the first position?

hard

A. arr.reshape(2, 3, 4)

B. arr.reshape(2, -1, 4)

C. arr.reshape(2, 3, -1)

D. arr.reshape(-1, 3, 4)

Solution

1. Step 1: Understand total elements and target shape

   The array has 24 elements. The target shape is 3D with dimensions (2, 3, 4), total 24 elements.

2. Step 2: Use -1 for automatic dimension

   Using -1 lets NumPy calculate that dimension automatically. For (2, 3, 4), if we replace one dimension with -1, NumPy calculates it as 24 divided by product of other dims.

3. Step 3: Check each option

   arr.reshape(2, 3, 4) is explicit (2,3,4) - works but no automatic calculation.
   arr.reshape(2, -1, 4): (2, -1, 4) means -1 = 24/(2*4) = 3, shape (2,3,4) correct.
   arr.reshape(-1, 3, 4): (-1, 3, 4) means -1 = 24/(3*4) = 2, shape (2,3,4) correct.
   arr.reshape(2, 3, -1): (2, 3, -1) means -1 = 24/(2*3) = 4, shape (2,3,4) correct.
   All B, C, D produce correct shape, but question asks for reshaping into (2,3,4) using automatic dimension calculation. Only arr.reshape(-1, 3, 4) uses -1 as first dimension, matching question wording.

4. Final Answer:

   arr.reshape(-1, 3, 4) -> Option D

5. Quick Check:

   -1 auto calculates dimension [OK]

Hint: Use -1 for one dimension to auto calculate [OK]

Common Mistakes:

• Using multiple -1 values in reshape
• Not matching total elements with new shape
• Confusing which dimension -1 replaces