Reduction functions in NumPy aggregate the elements of an array into a single value. These functions are useful for tasks such as calculating statistics, finding minimum or maximum values, and computing sums or products.
Common Reduction Functions
Sum: Calculates the sum of all elements in an array.
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import numpy as np
arr = np.array([1, 2, 3, 4, 5])
result = np.sum(arr)Product: Calculates the product of all elements in an array.
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result = np.prod(arr)Minimum: Finds the minimum value in an array.
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result = np.min(arr)Maximum: Finds the maximum value in an array.
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result = np.max(arr)Mean: Calculates the average value of the elements in an array.
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result = np.mean(arr)Median: Finds the median value of the elements in an array.
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result = np.median(arr)Standard Deviation: Calculates the standard deviation of the elements in an array.
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result = np.std(arr)Variance: Calculates the variance of the elements in an array.
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result = np.var(arr)Axis-Wise Reductions
For multidimensional arrays, reduction functions can be applied along specific axes. This allows you to calculate statistics for rows, columns, or other dimensions.
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arr2d = np.array([[1, 2, 3], [4, 5, 6]])
# Sum along the rows
row_sum = np.sum(arr2d, axis=0)
# Sum along the columns
col_sum = np.sum(arr2d, axis=1)
Custom Reduction Functions
You can also create custom reduction functions using NumPy’s ufunc objects. These functions can be applied element-wise to arrays and combined with other reduction functions.
Python
def my_func(x):
return x**2
result = np.sum(my_func(arr))
By effectively using reduction functions, you can efficiently analyze and summarize data in NumPy, gaining valuable insights into your datasets.
