Sum and Product on a 2D Array – Detailed Version
In this problem, you are provided with a 2-D array of dimensions X (rows) and Y (columns). Your task is to perform two operations using NumPy:
Sum over axis 0:
The numpy.sum() function computes the sum of array elements along a specified axis.
When applied with axis=0, it calculates the sum for each column.
For example, given the 2-D array:
[[1, 2],
[3, 4]]
The sum along axis 0 is computed as:
For column 0: 1 + 3 = 4
For column 1: 2 + 4 = 6
Resulting in the array: [4, 6].
Product of the Result:
The numpy.prod() function computes the product of array elements along a specified axis.
By applying it to the resulting array [4, 6], we get the product:
4 × 6 = 24
Step-by-Step Explanation:
Input Handling:
The first line of input contains two space-separated integers, representing the number of rows (X) and columns (Y) of the array.
The next X lines each contain Y space-separated integers, forming the 2-D array.
Converting Input to a NumPy Array:
Using list comprehension along with map() and input().split(), we convert the input into a list of lists, which is then converted to a NumPy array using np.array().
Sum Calculation:
We use np.sum(arr, axis=0) to calculate the sum along the columns. This produces a 1-D array containing the sum of each column.
Product Calculation:
We then use np.prod() on the result of the sum to calculate the product of these sums.
Output:
Finally, the computed product is printed.
This detailed solution not only shows how to use the sum and prod functions from NumPy but also explains the significance of the axis parameter and how to process multi-dimensional data effectively.
Code (Detailed Version):
import numpy as np
def solve():
Read the dimensions of the array (number of rows and columns)
n, m = map(int, input().split())
Construct the 2-D array from the input lines and convert it to a NumPy array of integers
arr = np.array([list(map(int, input().split())) for _ in range(n)])
Compute the sum of array elements along axis 0 (column-wise sum)
sum_result = np.sum(arr, axis=0)
Compute the product of the values in the sum_result array
prod_result = np.prod(sum_result)
Print the final product
print(prod_result)
if _name_ == '__main__':
solve()