个人认为比较有用的地方

1. 数组属性

# Array properties

a = np.array([[11, 12, 13, 14, 15],

              [16, 17, 18, 19, 20],

              [21, 22, 23, 24, 25],

              [26, 27, 28 ,29, 30],

              [31, 32, 33, 34, 35]])

print(type(a)) # >>><class 'numpy.ndarray'>

print(a.dtype) # >>>int64

print(a.size) # >>>25

print(a.shape) # >>>(5, 5)

print(a.itemsize) # >>>8

print(a.ndim) # >>>2

print(a.nbytes) # >>>200

2. 基本操作符

# Basic Operators

a = np.arange(25)

a = a.reshape((5, 5))

b = np.array([10, 62, 1, 14, 2, 56, 79, 2, 1, 45,

              4, 92, 5, 55, 63, 43, 35, 6, 53, 24,

              56, 3, 56, 44, 78])

b = b.reshape((5,5))

print(a + b)

print(a - b)

print(a * b)

print(a / b)

print(a ** 2)

print(a < b)

print(a > b)

print(a.dot(b))

3. 数组特殊运算符

# dot, sum, min, max, cumsum

a = np.arange(10)

print(a.sum()) # >>>45

print(a.min()) # >>>0

print(a.max()) # >>>9

print(a.cumsum()) # >>>[ 0  1  3  6 10 15 21 28 36 45]

4. 花式索引

# Fancy indexing

a = np.arange(0, 100, 10)

indices = [1, 5, -1]

b = a[indices]

print(a) # >>>[ 0 10 20 30 40 50 60 70 80 90]

print(b) # >>>[10 50 90]

5. 缺省索引

# Incomplete Indexing

a = np.arange(0, 100, 10)

b = a[:5]

c = a[a >= 50]

print(b) # >>>[ 0 10 20 30 40]

print(c) # >>>[50 60 70 80 90]

6. Where 函数

# Where

a = np.arange(0, 100, 10)

b = np.where(a < 50)

c = np.where(a >= 50)[0]

print(b) # >>>(array([0, 1, 2, 3, 4]),)

print(c) # >>>[5 6 7 8 9]