Question

我有一个数据框，其中有两列我要转换为数字类型。我使用以下代码：

df[["GP","G"]]=df[["GP","G"]].apply(pd.to_numeric)

Python返回以下错误消息：

File "C:\Users\Alexandros_7\Anaconda3\lib\site-packages\pandas\core\frame.py", line 4157, in _apply_standard
results[i] = func(v)

File "C:\Users\Alexandros_7\Anaconda3\lib\site-packages\pandas\tools\util.py", line 115, in to_numeric
coerce_numeric=coerce_numeric)

File "pandas\src\inference.pyx", line 612, in pandas.lib.maybe_convert_numeric (pandas\lib.c:53558)

File "pandas\src\inference.pyx", line 598, in      pandas.lib.maybe_convert_numeric (pandas\lib.c:53344)

ValueError: ('Unable to parse string', 'occurred at index GP')

如何解决此问题？如何使用命令一次转换多个列类型？谢谢！

Answer 1

只有将所有数据都解析为数字时，您的代码才有效。

如果没有，则数据框中至少有一个值不可转换为数字。在这种情况下，您可以根据自己的选择使用errors参数。这是一个例子。

>>> df = pd.DataFrame({'A' : list('aabbcd'), 'B' : list('ffghhe')})

>>> df
   A  B
0  a  f
1  a  f
2  b  g
3  b  h
4  c  h
5  d  e

>>> df.apply(pd.to_numeric, errors='ignore')
   A  B
0  a  f
1  a  f
2  b  g
3  b  h
4  c  h
5  d  e

>>> df.apply(pd.to_numeric, errors='coerce')
    A   B
0 NaN NaN
1 NaN NaN
2 NaN NaN
3 NaN NaN
4 NaN NaN
5 NaN NaN

>>> df.apply(pd.to_numeric, errors='raise')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 4042, in apply
    return self._apply_standard(f, axis, reduce=reduce)
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 4138, in _apply_standard
    results[i] = func(v)
  File "/usr/local/lib/python2.7/dist-packages/pandas/core/frame.py", line 4020, in f
    return func(x, *args, **kwds)
  File "/usr/local/lib/python2.7/dist-packages/pandas/tools/util.py", line 98, in to_numeric
    coerce_numeric=coerce_numeric)
  File "pandas/src/inference.pyx", line 612, in pandas.lib.maybe_convert_numeric (pandas/lib.c:53932)
  File "pandas/src/inference.pyx", line 598, in pandas.lib.maybe_convert_numeric (pandas/lib.c:53719)
ValueError: ('Unable to parse string', u'occurred at index A')
>>>

以下是errors

的文档

错误：{'忽略'，'加注'，'强制'}，默认'加注'

如果'raise'，则无效的解析将引发异常

如果'强制'，则无效解析将设置为NaN

如果'忽略'，则无效的解析将返回输入

.apply（pd.to_numeric）返回错误消息

1 个答案: