Create Arrays with Special Values

easyPROI/ONumPy Array CreationNumpy

Problem Statement

Create NumPy arrays filled with special values (zeros, ones, specific values).

Background

NumPy provides functions to create pre-filled arrays:

np.zeros(shape): Array of zeros
np.ones(shape): Array of ones
np.full(shape, value): Array filled with specific value
np.empty(shape): Uninitialized array

Your Task

Write a function create_special_arrays(rows, cols) that returns a dictionary with:

"zeros": Array of zeros as nested list
"ones": Array of ones as nested list
"fives": Array filled with 5s as nested list

Output Format

Return a dictionary with exactly these three keys.

Example

Input

rows = 2, cols = 3

Output

{'zeros': [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], 'ones': [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]], 'fives': [[5, 5, 5], [5, 5, 5]]}

Reasoning

Inputs: rows $= 2$ and cols $= 3$ fix the shape of every matrix: 2 rows, each holding 3 entries, so each matrix has $2 \times 3 = 6$ entries.
Math: three constant matrices of that shape. The zero matrix has $a_{ij} = 0$ for every row $i$ and column $j$ ; the all-ones matrix has $a_{ij} = 1$ ; the third has $a_{ij} = 5$ . No entry depends on its position, only on which matrix it belongs to.
Output shape: each matrix is written row by row as a list of 2 rows, each row a list of 3 numbers, e.g. [[1.0, 1.0, 1.0], [1.0, 1.0, 1.0]]. The three matrices sit under the keys zeros, ones and fives.
Notice the number types: the zeros and ones appear as real numbers (0.0, 1.0), while the fives appear as whole numbers (5). The printed output must match this exactly.
Swapping the arguments to $3 \times 2$ gives 3 rows of 2 entries each, not the same lists.

Constraints

Use np.zeros(), np.ones(), np.full()
rows and cols will be positive integers

Learn this in

NumPy & Data WranglingFoundations

Concept primerPREVIEW

1. Background Knowledge

NumPy is Python's foundational library for numerical computing, providing efficient n-dimensional arrays (ndarray) that enable vectorized operations—performing computations on entire arrays without explicit loops. Key functions for this problem:

np.zeros(shape): Creates array filled with 0.0 (float64 by default).
np.ones(shape): Creates array filled with 1.0 (float64 by default).
np.full(shape, fill_value): Creates array filled with any specified value (dtype adapts to value type).

Array shape is a tuple (rows, cols) defining dimensions. Nested lists are Python's native representation: [[0.0, 0.0], [0.0, 0.0]] for 2×2 zeros array. Conversion uses array.tolist().

Prerequisites: Basic NumPy import (import numpy as np), understanding shape tuples, dictionary creation.

2. Algorithm Approach

This is a direct mapping problem: three independent array creation operations stored in a dictionary. No search, sorting, or iteration needed—pure array initialization.

Algorithm:
1. Create zeros array: np.zeros((rows, cols))
2. Create ones array: np.ones((rows, cols))
3. Create fives array: np.full((rows, cols), 5)
4. Convert each to nested list:.tolist()
5. Package in dict: {"zeros": z_list, "ones": o_list, "fives": f_list}

Vectorization principle: NumPy fills entire arrays in contiguous memory using optimized C loops, $O(1)$ initialization regardless of size.

3. Step-by-Step Strategy

import numpy as np

def create_special_arrays(rows, cols):
 # Step 1: Create shape tuple
 shape = (rows, cols)

 # Step 2: Generate arrays using required functions
 zeros_arr = np.zeros(shape) # Shape: (rows, cols), all 0.0
 ones_arr = np.ones(shape) # Shape: (rows, cols), all 1.0
 fives_arr = np.full(shape, 5) # Shape: (rows, cols), all 5

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solution.py

Python 3.13

Test results