np.ones() for one-filled arrays in NumPy - Mini Project: Build & Apply

Create and Use One-Filled Arrays with np.ones()

📖 Scenario: Imagine you are helping a factory manager who wants to track the status of machines. Each machine is either working (1) or not working (0). To start, the manager wants to create a list showing all machines are working.

🎯 Goal: You will create a one-filled array using np.ones() to represent all machines working. Then, you will set a threshold for the number of machines and finally print the array.

📋 What You'll Learn

Create a numpy array of ones with a specific size

Create a variable to hold the number of machines

Use np.ones() with the number of machines

Print the resulting array

💡 Why This Matters

🌍 Real World

One-filled arrays are useful to represent active or working states in machines, sensors, or other systems.

💼 Career

Data scientists often initialize arrays with ones to set default values or masks before processing data.

1

Create a numpy array of ones

Import numpy as np and create a numpy array called machines filled with ones of size 5 using np.ones().

NumPy

import numpy as np
# Create a numpy array called machines filled with ones of size 5
# Your code here

Hint

Use np.ones(5) to create an array of five ones.

2

Set the number of machines

Create a variable called num_machines and set it to 5.

NumPy

import numpy as np
machines = np.ones(5)
# Create a variable num_machines and set it to 5
# Your code here

Hint

Just write num_machines = 5.

3

Create the one-filled array using the variable

Update the machines array to use np.ones() with the size from num_machines.

NumPy

import numpy as np
machines = np.ones(5)
num_machines = 5
# Update machines to be np.ones(num_machines)
# Your code here

Hint

Use the variable num_machines inside np.ones().

4

Print the one-filled array

Print the machines array to show the one-filled array.

NumPy

import numpy as np
num_machines = 5
machines = np.ones(num_machines)
# Print the machines array
# Your code here

Hint

Use print(machines) to display the array.

Practice

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

easy

A. Creates an array filled with ones

B. Creates an array filled with zeros

C. Creates an array filled with random numbers

D. Creates an empty array

Solution

1. Step 1: Understand the function purpose

   np.ones() is designed to create arrays filled with the number one.

2. Step 2: Compare with other array creation functions

   Functions like np.zeros() create arrays filled with zeros, but np.ones() specifically fills with ones.

3. Final Answer:

   Creates an array filled with ones -> Option A

4. Quick Check:

   np.ones() = array of ones [OK]

Hint: Remember: ones means all elements are 1 [OK]

Common Mistakes:

• Confusing np.ones() with np.zeros()
• Thinking it creates random values
• Assuming it creates an empty array

2. Which of the following is the correct syntax to create a 2x3 array of ones with integer type using np.ones()?

easy

A. np.ones((2, 3), dtype=int)

B. np.ones([2, 3], type=int)

C. np.ones(2, 3, dtype='int')

D. np.ones(2, 3, int)

Solution

1. Step 1: Check the shape argument format

   The shape must be given as a tuple, so (2, 3) is correct.

2. Step 2: Check the dtype argument syntax

   The correct keyword is dtype, and the value can be int or 'int'.

3. Final Answer:

   np.ones((2, 3), dtype=int) -> Option A

4. Quick Check:

   Shape as tuple and dtype keyword correct [OK]

Hint: Use tuple for shape and 'dtype' keyword [OK]

Common Mistakes:

• Using list instead of tuple for shape
• Using 'type' instead of 'dtype'
• Passing shape as separate arguments

3. What is the output of the following code?

import numpy as np
arr = np.ones((2, 2), dtype=float)
print(arr)

medium

A. [[1 1]
[1 1]]

B. [[1. 1.]
[1. 1.]]

C. [[1 1 1]
[1 1 1]]

D. [[0. 0.]
[0. 0.]]

Solution

1. Step 1: Understand the shape and dtype

   The shape is (2, 2), so the array has 2 rows and 2 columns. The dtype is float, so elements are floats.

2. Step 2: Predict the printed output

   Each element is 1.0, printed as 1. with a decimal point. So the output is a 2x2 array of 1.0 values.

3. Final Answer:

   [[1. 1.] [1. 1.]] -> Option B

4. Quick Check:

   Shape (2,2) and float type means 1.0 values [OK]

Hint: Float dtype shows decimal points in output [OK]

Common Mistakes:

• Confusing integer and float output formats
• Misreading shape as (2,3)
• Expecting zeros instead of ones

4. Identify the error in this code snippet:

import numpy as np
arr = np.ones(3, 4)
print(arr)

medium

A. print() cannot display numpy arrays

B. Missing dtype argument

C. np.ones() cannot create 2D arrays

D. Shape must be a tuple, so use np.ones((3, 4))

Solution

1. Step 1: Check the shape argument

   The code passes two separate arguments 3 and 4, but np.ones() expects a single shape argument as a tuple.

2. Step 2: Correct the shape argument

   Use np.ones((3, 4)) to create a 3 by 4 array of ones.

3. Final Answer:

   Shape must be a tuple, so use np.ones((3, 4)) -> Option D

4. Quick Check:

   Shape argument must be tuple [OK]

Hint: Shape is always a tuple in np.ones() [OK]

Common Mistakes:

• Passing shape as separate arguments
• Forgetting parentheses around shape tuple
• Thinking dtype is mandatory

5. You want to create a 3D mask array of shape (2, 3, 4) filled with ones of boolean type using np.ones(). Which code snippet correctly does this?

hard

A. np.ones((2, 3, 4), type=bool)

B. np.ones([2, 3, 4], type=bool)

C. np.ones((2, 3, 4), dtype=bool)

D. np.ones(2, 3, 4, dtype=bool)

Solution

1. Step 1: Confirm the shape argument format

   The shape must be a tuple (2, 3, 4) to create a 3D array.

2. Step 2: Use correct dtype keyword and value

   The dtype keyword must be dtype and the value bool to create a boolean array.

3. Final Answer:

   np.ones((2, 3, 4), dtype=bool) -> Option C

4. Quick Check:

   Tuple shape and dtype=bool correct [OK]

Hint: Use tuple shape and dtype=bool for boolean arrays [OK]

Common Mistakes:

• Using list instead of tuple for shape
• Using 'type' instead of 'dtype'
• Passing shape as separate arguments