دوال التجميع

import numpy as np

a = np.array([3, 7, 2, 9, 1, 6])
print('sum:', a.sum())     # 28
print('mean:', a.mean())   # 4.666...
print('max:', a.max())     # 9

import numpy as np

a = np.array([1, 2, 3, 4, 5])
print(a.sum())       # 15
print(np.cumsum(a))  # [ 1  3  6 10 15]

import numpy as np

a = np.array([2.0, 4.0, 4.0, 4.0, 5.0, 5.0, 7.0, 9.0])
print('mean:', a.mean())           # 5.0
print('std (pop):', a.std())       # 2.0
print('std (sample):', a.std(ddof=1))  # 2.138...

import numpy as np

a = np.array([3, 7, 2, 9, 1, 6])
print('min:', a.min(), 'at index', a.argmin())  # 1 at 4
print('max:', a.max(), 'at index', a.argmax())  # 9 at 3

import numpy as np

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

print(m.sum(axis=0))  # [5 7 9]  -- column sums
print(m.sum(axis=1))  # [ 6 15]  -- row sums
print(m.mean(axis=0)) # [2.5 3.5 4.5]

import numpy as np

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

row_sums = m.sum(axis=1, keepdims=True)
print(row_sums.shape)  # (2, 1)

normed = m / row_sums
print(normed.round(3))
# [[0.167 0.333 0.5  ]
#  [0.267 0.333 0.4  ]]

import numpy as np

a = np.array([1.0, np.nan, 3.0, np.nan, 5.0])
print(a.sum())          # nan
print(np.nansum(a))     # 9.0
print(np.nanmean(a))    # 3.0
print(np.nanmax(a))     # 5.0

import numpy as np

a = np.array([1, 2, 3, 4, 100])  # outlier at 100
print('mean:', a.mean())          # 22.0  -- skewed
print('median:', np.median(a))    # 3.0   -- robust
print(np.percentile(a, [25, 50, 75]))  # [ 2.  3.  4.]

import numpy as np

a = np.array([1, -2, 3, -4])
print(np.any(a < 0))    # True   (some are negative)
print(np.all(a > 0))    # False  (not all are positive)

# Row-wise check on 2D
m = np.array([[1, 2], [-1, 3]])
print(np.all(m > 0, axis=1))  # [ True False]

import numpy as np

a = np.array([3, 0, 7, 0, 2, 0])
print(np.count_nonzero(a))         # 3
print(np.count_nonzero(a > 2))     # 2  (elements 3 and 7)

m = np.array([[0, 1], [1, 1]])
print(np.count_nonzero(m, axis=0)) # [1 2]

import numpy as np

a = np.array([3, 1, 4, 1, 5, 9, 2, 6, 5, 3])
uniq, counts = np.unique(a, return_counts=True)
print(uniq)    # [1 2 3 4 5 6 9]
print(counts)  # [2 1 2 1 2 1 1]