浅谈python 中的 type(), dtype(), astype()的区别

如下所示：

测试代码：

import numpy as np
class Myclass():
pass

a = [[1,2,3],[4,5,6]]
b = {'a':1,'b':2,'c':3}
c = np.array([1,2,3])
d = Myclass()
e = np.linspace(1,5,10)
c_ = c.astype(np.float)
f = 10

print("type(a)=",type(a))
print("type(b)=",type(b))
print("type(c)=",type(c))
print("type(d)=",type(d))
print("type(e)=",type(e))
print("type(f)=",type(f))
print("type(c_)=",type(c_))

# print(a.dtype) ## AttributeError: 'list' object has no attribute 'dtype'
# print(b.dtype) ## AttributeError: 'dict' object has no attribute 'dtype'
print(c.dtype)
# print(d.dtype) ## AttributeError: 'Myclass' object has no attribute 'dtype'
print(e.dtype)
print(c_.dtype)
# print(f.dtype) ## AttributeError: 'int' object has no attribute 'dtype'

# print(a.astype(np.int)) ## AttributeError: 'list' object has no attribute 'astype'
# print(b.astype(np.int)) ## AttributeError: 'dict' object has no attribute 'astype'
print(c.astype(np.int))
# print(d.astype(np.int)) ## AttributeError: 'Myclass' object has no attribute 'astype'
print(e.astype(np.int))
# print(f.astype(np.int)) ## AttributeError: 'int' object has no attribute 'astype'

补充知识：pandas astype()错误

由于数据出现错误

DataError: No numeric types to aggregate

改正以后才认识到astype的重要性。

Top15['populations'] = Top15['Energy Supply'].div(Top15['Energy Supply per Capita']).astype(float)
df_mean = ((df.set_index('Continent').groupby(level=0)['populations'].agg({'mean' : np.mean})))
#加了astype(float)后无错误

来源：https://blog.csdn.net/Strive_For_Future/article/details/92811293