Pandas 将 NULL 读取为 NaN 浮点数而不是 str [重复]

给定文件：

$ cat test.csv 
a,b,c,NULL,d
e,f,g,h,i
j,k,l,m,n

其中第三列被视为str.

当我对列执行字符串函数时，pandas已阅读NULLstr 作为一个NaN float:

>>> import pandas as pd
>>> df = pd.read_csv('test.csv', names=[0,1,2,3,4], dtype={0:str, 1:str, 2:str, 3:str, 4:str})

>>> df[3].apply(str.strip)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.5/site-packages/pandas/core/series.py", line 2355, in apply
    mapped = lib.map_infer(values, f, convert=convert_dtype)
  File "pandas/_libs/src/inference.pyx", line 1569, in pandas._libs.lib.map_infer (pandas/_libs/lib.c:66440)
TypeError: descriptor 'strip' requires a 'str' object but received a 'float'

核实：

>>> for i in df[3]:
...    print (type(i), i)
... 
<class 'float'> nan
<class 'str'> h
<class 'str'> m

我已经指定了dtype在初始化时但不知何故它被覆盖了。

如何强制固定特定列的类型？

有没有办法自动发现这些异常NaN浮动并改变然后返回'NULL' string?

对我来说有效astype http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.astype.html:

df[3] = df[3].astype(str)

for i in df[3]:
    print (type(i), i)

<class 'str'> nan
<class 'str'> h
<class 'str'> m

另一种解决方案是使用keep_default_na=False in read_csv http://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html:

import pandas as pd
from pandas.compat import StringIO

temp=u"""a,b,c,NULL,d
e,f,g,h,i
j,k,l,m,n"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),  names=[0,1,2,3,4], keep_default_na=False)
print (df)
   0  1  2     3  4
0  a  b  c  NULL  d
1  e  f  g     h  i
2  j  k  l     m  n

for i in df[3]:
    print (type(i), i)
<class 'str'> NULL
<class 'str'> h
<class 'str'> m

那么就可以使用na_values如果需要解析参数NaN在数字列中，但它必须不同，例如NA:

import pandas as pd
from pandas.compat import StringIO

temp=u"""a,b,c,NULL,1
e,f,g,h,2
j,k,l,m,NA"""
#after testing replace 'StringIO(temp)' to 'filename.csv'
df = pd.read_csv(StringIO(temp),  names=[0,1,2,3,4], keep_default_na=False, na_values=['NA'])
print (df)
   0  1  2     3    4
0  a  b  c  NULL  1.0
1  e  f  g     h  2.0
2  j  k  l     m  NaN

for i in df[3]:
    print (type(i), i)
<class 'str'> NULL
<class 'str'> h
<class 'str'> m

for i in df[4]:
    print (type(i), i)
<class 'numpy.float64'> 1.0
<class 'numpy.float64'> 2.0
<class 'numpy.float64'> nan