New Term Entry: NumPy Random Module: .power()

content/numpy/concepts/random-module/terms/power/power.md

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+---
+Title: '.power()'
+Description: 'Return random samples from power distribution as numpy array'
+Subjects: 
+    - 'AI'
+    - 'Data Science'
+    - 'Computer Science'
+Tags:
+    - 'Arrays'
+    - 'Functions'
+    - 'Math'
+    - 'Numpy'useruser
+CatalogContent:
+    - 'learn-python'
+    - 'paths/data-science'
+
+---
+The **`.power()`** function, *aka: Power-Function-Distribution* is part of the NumPy library. Primarily draws samples in [0, 1] from a power distribution with positive exponent and returns them in the form of an array.
+
+This function is commonly used for statistical testing, simulations and generating machine learning synthetic data.
+
+**PS:** New code should use the power method of a Generator instance instead
+
+## Syntax
+
+```pseudo
+random.power(a, size=None)
+```
+
+**Parameters:**
+1.`a`: float or array_like of floats
+    - Parameter of the distribution. Must be non-negative.
+
+2.`size`: int or tuple of ints, optional
+    - Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. If size is `None` (default), a single value is returned if `a` is a scalar. Otherwise, `np.array(a).size` samples are drawn.
+
+**Return value:**
+
+An array ([`ndarray`](https://www.codecademy.com/resources/docs/numpy/ndarray)) of a shape consistent with the size filled with random floats from a power distribution over [0, 1).
+
+**Raises**: Value Error if `a` is negatif or null.
+
+## Example
+
+```py
+>>> import numpy as np
+>>> import random
+
+# If size=None
+
+>>> print(np.random.power(2))
+0.6846237833835251
+
+>>> print(np.random.power(7))
+0.8110137348659924
+
+>>> print(np.random.power(2,3))
+[0.49055308 0.93763919 0.67387802]
+
+# If size is an integer
+
+>>> print(np.random.power(10,10))
+[0.72456875 0.94693609 0.92619849 0.94813071 0.97270994 0.93135905
+ 0.95546733 0.94237131 0.96817561 0.98170604]
+
+>>> print(np.random.power(10,2))
+[0.94107937 0.90562675]
+
+>>> print(np.random.power(1000,3))
+[0.99809396 0.99916794 0.99774856]
+
+#If size is a tuple ()
+
+>>> print(np.random.power(1000,(5,5))) #5*5matrix
+[[0.99880382 0.99934307 0.99996164 0.99815375 0.99837584]
+ [0.99662911 0.99923268 0.9973192  0.99894496 0.99980047]
+ [0.99655904 0.99914458 0.99831727 0.99856803 0.99699759]
+ [0.99924632 0.99945667 0.99825157 0.99897058 0.99888353]
+ [0.99464373 0.99692161 0.99948955 0.99904031 0.99854974]]
+```