Numpy - 合并&分割

合并

• numpy.concatenate((a1, a2, ...)  axis=1按列合并，axis=0按行合并
• numpy.hstack(tup)   按列合并
• numpy.vstack(tup)   按行合并

分割

• numpy.split(ary, indices_or_sections, axis=0) 将数组拆分为多个子数组

代码分析：

import numpy as np

# 创建一个符合正态分布的500个股票504天的涨跌幅数据
stock_day_rise = np.random.normal(0, 1, (500, 504))

a = stock_day_rise[:2, 0:4]
b = stock_day_rise[10:12, 0:4]

# axis=1时候，按照数组的列方向拼接在一起
# axis=0时候，按照数组的行方向拼接在一起
ab = np.concatenate([a, b], axis=0)
print(ab)
"""
[[ 1.31310416 -1.21522993 -0.62558622  1.32071579]
 [-0.01132551  1.51174213 -0.64821137 -0.78082249]
 [ 1.21860002  0.89126428 -0.60071647 -1.15030537]
 [ 1.37237328 -0.38175207  1.03866532  0.85553051]]
 """
# hstack, 按照数组的列方向拼接在一起
c = np.hstack([a, b])
print(c)
"""
[[ 1.31310416 -1.21522993 -0.62558622  1.32071579  1.21860002  0.89126428
  -0.60071647 -1.15030537]
 [-0.01132551  1.51174213 -0.64821137 -0.78082249  1.37237328 -0.38175207
   1.03866532  0.85553051]]
"""
# vstack, 按照数组的行方向拼接在一起
d = np.vstack([a, b])
print(d)
"""
[[ 1.31310416 -1.21522993 -0.62558622  1.32071579]
 [-0.01132551  1.51174213 -0.64821137 -0.78082249]
 [ 1.21860002  0.89126428 -0.60071647 -1.15030537]
 [ 1.37237328 -0.38175207  1.03866532  0.85553051]]
"""
# 分隔成2个数组
e = np.split(ab, 2, axis=0)
print(e)
"""
[array([[ 1.31310416, -1.21522993, -0.62558622,  1.32071579],
       [-0.01132551,  1.51174213, -0.64821137, -0.78082249]]), array([[ 1.21860002,  0.89126428, -0.60071647, -1.15030537],
       [ 1.37237328, -0.38175207,  1.03866532,  0.85553051]])]
"""

计算实践：实现sigmoid function, 利用np.exp()

import numpy as np

def sigmoid(x):
    """
    定义一个sigmoid函数
    x is input: An array
    This function is to compute the sigmoid function value
    """
    s = 1.0 / (1 + (1/np.exp(x)))
    return s

m = np.array([1,2,3])
print(sigmoid(m))