Python之Numpy 常用函数总结

mport numpy as np
# # 产生一个数组
arr=np.arange(15)
arr
>>array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])
# 对里面的元素进行开根号处理
np.sqrt(arr)
>>array([0. , 1. , 1.41421356, 1.73205081, 2. ,
2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ,
3.16227766, 3.31662479, 3.46410162, 3.60555128, 3.74165739])
# 对里面的元素进行幂的运算
np.exp(arr)
>>array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,
5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,
2.98095799e+03, 8.10308393e+03, 2.20264658e+04, 5.98741417e+04,
1.62754791e+05, 4.42413392e+05, 1.20260428e+06])
# 比较那个数组里面的最大值，返回最大值
np.maximum(np.sqrt(arr),np.exp(arr))
>>array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,
5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,
2.98095799e+03, 8.10308393e+03, 2.20264658e+04, 5.98741417e+04,
1.62754791e+05, 4.42413392e+05, 1.20260428e+06])
# 可以返回多个数组，比如小数部分和整数部分
a,b=np.modf(np.exp(arr))
a
>>array([0. , 0.71828183, 0.3890561 , 0.08553692, 0.59815003,
0.4131591 , 0.42879349, 0.63315843, 0.95798704, 0.08392758,
0.46579481, 0.1417152 , 0.791419 , 0.39200892, 0.28416478])
b
>>array([1.000000e+00, 2.000000e+00, 7.000000e+00, 2.000000e+01,
5.400000e+01, 1.480000e+02, 4.030000e+02, 1.096000e+03,
2.980000e+03, 8.103000e+03, 2.202600e+04, 5.987400e+04,
1.627540e+05, 4.424130e+05, 1.202604e+06])