4.md

[*] Stats & Aggregation

[*] Statistical Methods

>>> arr = np.array([[0, 72, 3],
...                 [1, 3, -60],
...                 [-3, -2, 4]])
>>> print(arr.min())
-60
>>> print(arr.max())
72
>>> 
>>> print(repr(arr.min(axis=0)))
array([ -3,  -2, -60])
>>> print(repr(arr.max(axis=-1)))
array([72,  3,  4])
>>> 



>>> arr = np.array([[0, 72, 3],
...                 [1, 3, -60],
...                 [-3, -2, 4]])
>>> print(np.mean(arr))
2.0
>>> print(np.var(arr))
977.3333333333334
>>> print(np.median(arr))
1.0
>>> print(repr(np.median(arr, axis=-1)))
array([ 3.,  1., -2.])
>>> 

[*] Agregation

1. Summation

• Adding values row/column wise.

>>> column = 0
>>> row = 1
>>> arr = np.array([[0, 72, 3],
...                 [1, 3, -60],
...                 [-3, -2, 4]])
>>> print(repr(np.sum(arr, axis=0)))
array([ -2,  73, -53])
>>> print(repr(np.sum(arr, axis=column)))
array([ -2,  73, -53])
>>> print(repr(np.sum(arr, axis=row)))
array([ 75, -56,  -1])
>>> 

# for cumulative sum
>>> print(repr(np.cumsum(arr, axis=column)))
array([[  0,  72,   3],
       [  1,  75, -57],
       [ -2,  73, -53]])
>>> print(repr(np.cumsum(arr, axis=row)))
array([[  0,  72,  75],
       [  1,   4, -56],
       [ -3,  -5,  -1]])

2. Concatenation

• Concatenating 2 numpy arrays.

>>> arr1 = np.array([[0, 72, 3],
...                  [1, 3, -60],
...                  [-3, -2, 4]])
>>> arr2 = np.array([[-15, 6, 1],
...                  [8, 9, -4],
...                  [5, -21, 18]])
>>> print(repr(np.concatenate([arr1, arr2])))
array([[  0,  72,   3],
       [  1,   3, -60],
       [ -3,  -2,   4],
       [-15,   6,   1],
       [  8,   9,  -4],
       [  5, -21,  18]])
>>> print(repr(np.concatenate([arr1, arr2], axis=1)))
array([[  0,  72,   3, -15,   6,   1],
       [  1,   3, -60,   8,   9,  -4],
       [ -3,  -2,   4,   5, -21,  18]])
>>> print(repr(np.concatenate([arr2, arr1], axis=1)))
array([[-15,   6,   1,   0,  72,   3],
       [  8,   9,  -4,   1,   3, -60],
       [  5, -21,  18,  -3,  -2,   4]])