浅谈Python numpy创建空数组的问题

>>> a = np.ones((3,2))
>>> b = 0.2
>>> _b = np.empty((3,1))
>>> _b[0, 0] = b
>>> c = np.c_[a, _b]
>>> print(c)
[[1.00000000e+000 1.00000000e+000 2.00000000e-001]
 [1.00000000e+000 1.00000000e+000 2.12199579e-313]
 [1.00000000e+000 1.00000000e+000 2.54639495e-313]]
>>>

y_true = np.ones((3, 1), dtype=np.int)
y_pred = np.ones((3, 1), dtype=np.int)
y = np.c_[y_true, y_pred]

accuracy = np.zeros(shape=(y_true.shape[0], 1), dtype=np.str)

# 此时若设置accuracy[0, 0] = '0.89'，最终accuracy[0, 0]存的是'0'，具体原因还不清楚

res = np.c_[y, accuracy]  # 先拼接起来
res[0, 2] = '0.89'  # 然后再设置就可以了

res = pd.DataFrame(res, columns=['y_true', 'y_pred', 'accuracy'])
res.to_csv('1.csv')  # 保存到文件中

a = pd.read_csv('1.csv', usecols=(1, 2, 3))
a = a.values
print(a, type(a), a.dtype)

import numpy as np
 
np.nan == np.nan
Out[3]: False
 
aa = np.array([1,2,3,np.nan,np.nan,4,5,np.nan])
aa
Out[5]: array([  1.,   2.,   3.,  nan,  nan,   4.,   5.,  nan])
 
aa[aa==np.nan] = 100  #错误方式
aa
Out[7]: array([  1.,   2.,   3.,  nan,  nan,   4.,   5.,  nan])
 
aa[np.isnan(aa)] = 100  #对nan操作的正确方式
aa
Out[9]: array([   1.,    2.,    3.,  100.,  100.,    4.,    5.,  100.])
 
type(np.nan)
Out[10]: float