In [1]:

    

import numpy as np


    

In [2]:

    

a = np.array([0, 1, 2])
b = np.array([3, 0, 6])
c = np.array([1, 2, 3])


    

In [3]:

    

print(np.maximum.reduce([a, b, c]))


    

    




[3 2 6]

In [4]:

    

print(np.maximum(np.maximum(a, b), c))


    

    




[3 2 6]

In [5]:

    

print(np.fmax.reduce([a, b, c]))


    

    




[3 2 6]

In [6]:

    

print(np.minimum.reduce([a, b, c]))


    

    




[0 0 2]

In [7]:

    

print(np.fmin.reduce([a, b, c]))


    

    




[0 0 2]

In [8]:

    

print(np.maximum.reduce((a, b, c)))


    

    




[3 2 6]

In [9]:

    

# print(np.maximum.reduce(a, b, c))
# TypeError: data type not understood


    

In [10]:

    

a_2d = np.arange(6).reshape(2, 3)
print(a_2d)


    

    




[[0 1 2]
 [3 4 5]]

In [11]:

    

# print(np.maximum.reduce([a_2d, b, c]))
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [12]:

    

print(np.maximum(np.maximum(a_2d, b), c))


    

    




[[3 2 6]
 [3 4 6]]

In [13]:

    

# print(np.maximum.reduce([4, b, c]))
# ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()


    

In [14]:

    

print(np.maximum(np.maximum(4, b), c))


    

    




[4 4 6]

In [15]:

    

print(a)


    

    




[0 1 2]

In [16]:

    

print(np.maximum.reduce(a))


    

    




2

In [17]:

    

print(a.max())


    

    




2

In [18]:

    

print(max(a))


    

    




2

In [19]:

    

a_2d = np.array([[0, 1, 2], [3, 0, 6], [1, 2, 3]])
print(a_2d)


    

    




[[0 1 2]
 [3 0 6]
 [1 2 3]]

In [20]:

    

print(np.maximum.reduce(a_2d))


    

    




[3 2 6]

In [21]:

    

print(np.maximum(np.maximum(a_2d[0], a_2d[1]), a_2d[2]))


    

    




[3 2 6]

In [22]:

    

print(a_2d.max(axis=0))


    

    




[3 2 6]