Prev Next

ufunc Summations

NumPy's summation ufuncs, such as sum and cumsum, allow for efficient summation operations across arrays or along specified axes.

Key Topics

Basic Summation

The sum function computes the sum of all elements in an array or along a specified axis.

Example

# Basic summation
import numpy as np

arr = np.array([[1, 2, 3], [4, 5, 6]])

sum_total = np.sum(arr)
sum_rows = np.sum(arr, axis=1)
sum_columns = np.sum(arr, axis=0)

print("Total sum:", sum_total)
print("Row-wise sum:", sum_rows)
print("Column-wise sum:", sum_columns)

Output

Total sum: 21
Row-wise sum: [6 15]
Column-wise sum: [5 7 9]

Explanation: The sum ufunc aggregates array elements, either across the entire array or along specified axes (rows or columns).

Cumulative Summation

The cumsum function computes the cumulative sum of array elements, producing an array where each element is the sum of all previous elements.

Example

# Cumulative summation
cumulative_sum = np.cumsum(arr)

print("Cumulative sum:", cumulative_sum)

Output

Cumulative sum: [1 3 6 10 15 21]

Explanation: The cumsum ufunc calculates cumulative sums, which are useful in tracking progressive totals.

Key Takeaways

  • Total Sum: Use sum to compute totals across arrays or axes.
  • Cumulative Sum: Use cumsum for progressive summation of array elements.
  • Applications: Analyze totals, track progressive values, or calculate cumulative metrics.
Prev Next