Proportion of Numbers: Positive, Negative, and Zeros in Python

Discover how to transform a simple array into a revealing statistical analysis with Python!

🔮 Problem Statement

The challenge we address is the creation of a Python function that analyzes a numeric array and determines the proportion of positive, negative, and zero numbers it contains. This task, although simple in its conception, lays the foundation for more complex data analysis and understanding the distribution of values in a dataset.

Parameters:

• int[] nums: Array of integers to evaluate.

Return Value:

• float[3]: An array of three positions, where:

  • the first position corresponds to the proportion of positive numbers in the array
  • the second position corresponds to the proportion of zeros in the array
  • the third position corresponds to the proportion of negative numbers in the array

Example:

>>> proportion_in_array([1, 2, 0, -2, -3])
[0.4, 0.2, 0.4]

🧩 Step-by-Step Solution

The solution is built on the idea of iterating over the array and counting the number of elements that meet each condition (positive, zero, negative) and then dividing these counts by the total length of the array. We use list comprehensions to create filtered sublists and then calculate their length to obtain the counts.

First, we define the proportion_in_array function that receives the list of numbers nums as an argument. This function will be the core of our solution.

def proportion_in_array(nums):

Then, we calculate the length of the list nums and store it in the variable length. This value will be crucial for calculating the proportions.

	length = len(nums)

Now, we calculate the proportion of positive numbers. The list comprehension [n for n in nums if n > 0] creates a new list containing only the positive numbers from nums. The len() function calculates the length of this new list, and the division by length gives us the proportion of positives.

	positives = len([n for n in nums if n > 0]) / length

Similarly, we calculate the proportion of zeros and negative numbers, using list comprehensions and the len() function.

	zeros = len([n for n in nums if n == 0]) / length
	negatives = len([n for n in nums if n < 0]) / length

Finally, the function returns a list containing the proportions of positive numbers, zeros, and negative numbers, in that order.

	return [positives, zeros, negatives]

Complete Solution:

def proportion_in_array(nums):
	"level: easy; points: 2; array_strictly_equal: True"
	length = len(nums)
	positives = len([n for n in nums if n > 0]) / length
	zeros = len([n for n in nums if n == 0]) / length
	negatives = len([n for n in nums if n < 0]) / length
	return [positives, zeros, negatives]

🧠 Key Concepts

List comprehension is a fundamental concept in Python, allowing the concise and efficient creation of new lists from existing iterables. In this case, we used it to filter the positive, negative, and zero numbers from the original array, avoiding the need for explicit loops. This technique not only reduces the amount of code but also improves its readability.

The calculation of proportions, essentially a division, is a basic but powerful mathematical and statistical concept. It allows us to normalize the counts of each type of number, allowing us to compare the distribution of values in different arrays, regardless of their size. Dividing by the total length of the array is what converts raw counts into meaningful proportions.

The concept of lists in Python is key, as they are versatile data structures that can contain ordered collections of elements of different types. In this case, the list is used both as input to the function (the array of numbers) and as output (the array of proportions). The flexibility of lists facilitates the manipulation and processing of data in Python.

💫 Final Thoughts

This function, although simple, is a fundamental building block for data analysis. We could extend it to handle different data types (floats, strings) or to calculate other statistics (mean, median, standard deviation). A possible improvement would be to add input validation to ensure that the nums argument is actually a list and that it contains only numbers, preventing unexpected errors. Did you know that the efficiency of list comprehensions in Python often surpasses that of traditional for loops in filtering and transformation tasks? 🤯

I hope this analysis has been helpful. If you liked it, I invite you to explore other articles on my blog where we unravel the mysteries of code and give you the tools to become a programming master. Don’t miss the next article! 🔥