numpy判断数值类型、过滤出数值型数据的方法

> import math 
> math.isnan(1) 
False 
> math.isnan('a') 
Traceback (most recent call last): 
 File "<stdin>", line 1, in <module> 
TypeError: a float is required 
> math.isnan(float("nan")) 
True 
> 

> import numpy as np 
> test1=np.array([1,2,'aa',3]) 
> np.isnan(test1) 
Traceback (most recent call last): 
 File "<stdin>", line 1, in <module> 
TypeError: ufunc 'isnan' not supported for the input types, and the inputs could 
 not be safely coerced to any supported types according to the casting rule ''sa 
fe'' 
> test2=np.array([1,2,np.nan,3]) 
> np.isnan(test2) 
array([False, False, True, False], dtype=bool) 
> 

> import numpy as np 
> test1=np.array([1,2,'aa',3]) 
> list1=list(test1) 
> def filter_fun(x): 
... try: 
...  return isinstance(float(x),(float)) 
... except: 
...  return False 
... 
> list(filter(filter_fun,list1)) 
['1', '2', '3'] 
> np.array(filter(filter_fun,list1)) 
array(<filter object at 0x0339CA30>, dtype=object) 
> np.array(list(filter(filter_fun,list1))) 
array(['1', '2', '3'], 
 dtype='<U1') 
> np.array([float(x) for x in filter(filter_fun,list1)]) 
array([ 1., 2., 3.]) 
> 

> import numpy as np
> test1=np.array([1,2,'aa',3])
> list1=list(test1)
> def filter_fun(x):
... try:
...  return isinstance(float(x),(float))
... except:
...  return False
...
> import pandas as pd
> test=pd.DataFrame(test1,index=[1,2,3,4])
> test
 0
1 1
2 2
3 aa
4 3
> index=test.index
> index
Int64Index([1, 2, 3, 4], dtype='int64')
> bool_index=map(filter_fun,list1)
> bool_index=list(bool_index) #bool_index这样的迭代结果只能list一次，一次再list时会是空，所以保存一下list的结果
> bool_index
[True, True, False, True]
> new_data=test1[np.array(bool_index)]
> new_data
array(['1', '2', '3'],
 dtype='<U11')
> new_index=index[np.array(bool_index)]
> new_index
Int64Index([1, 2, 4], dtype='int64')
> test2=pd.DataFrame(new_data,index=new_index)
> test2
 0
1 1
2 2
4 3
>