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Practice

Aggregation Functions (sum, mean, std, etc.)

NumPy provides built-in functions that make it easy to summarize and analyze data in arrays.

With just one line of code, you can calculate totals, averages, minimums, maximums, and more.


Common Aggregation Functions​

  • np.sum(): total of all values
  • np.mean(): average value
  • np.min(): smallest value
  • np.max(): largest value
  • np.std(): standard deviation
  • np.median(): middle value (helpful for distributions)

Axis Support​

Aggregation functions can summarize an entire array or operate along a specific axis for row-wise or column-wise results.

  • axis=0: column-wise
  • axis=1: row-wise
Aggregation Functions
arr = np.array([[1, 2], [3, 4]])

print(arr.sum())           # Sum of all elements
print(arr.sum(axis=0))     # Sum down columns → [4 6]
print(arr.sum(axis=1))     # Sum across rows → [3 7]

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