np.ones() for one-filled arrays in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

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

np.ones() creates a new array filled with ones. You specify the shape (size) of the array, and it returns an array where every element is 1.

beginner

How do you create a 3x3 array filled with ones using np.ones()?

Use np.ones((3, 3)). The argument is a tuple that defines the shape: 3 rows and 3 columns.

beginner

What data type does np.ones() use by default for the array elements?

By default, np.ones() creates an array of floats (floating-point numbers).

intermediate

How can you create an integer array filled with ones using np.ones()?

Use the dtype parameter: np.ones((3, 3), dtype=int) creates a 3x3 array of integer ones.

beginner

Why might you use np.ones() instead of manually creating an array of ones?

np.ones() is fast, simple, and efficient. It avoids loops and manual filling, making your code cleaner and faster.

What does np.ones((2, 4)) produce?

A A 2x4 array filled with zeros

B A 4x2 array filled with ones

C A 2x4 array filled with ones

D A 2x4 array filled with random numbers

np.ones((2, 4)) creates an array with 2 rows and 4 columns, all filled with ones.

How do you specify the data type of the array elements in np.ones()?

A Using the <code>shape</code> parameter

B Using the <code>dtype</code> parameter

C Using the <code>type</code> function after creation

D You cannot specify data type

The dtype parameter lets you set the data type, like dtype=int for integers.

What is the default data type of elements created by np.ones()?

A String

B Boolean

C Integer

D Float

By default, np.ones() creates arrays with float elements.

Which of these is a correct way to create a 1D array of five ones?

A All of the above

B <code>np.ones([5])</code>

C <code>np.ones((5,))</code>

D <code>np.ones(5)</code>

All these forms create a 1D array of length 5 filled with ones.

Why is np.ones() preferred over manually creating an array of ones with loops?

A It is faster and cleaner

B It is slower but easier

C It uses more memory

D It creates random values

np.ones() is optimized for speed and simplicity, avoiding manual loops.

Explain how to create a 2D array filled with ones using np.ones(). Include how to specify the shape and data type.

Think about the shape tuple and the dtype argument.

Describe why using np.ones() is better than creating an array of ones with a for-loop in Python.

Consider speed and code simplicity.

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