pandas.to_numeric - 找出它无法解析的字符串

Question

应用于pandas.to_numeric包含表示数字(以及可能的其他不可解析字符串)的字符串的数据帧列会导致出现如下错误消息:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-66-07383316d7b6> in <module>()
      1 for column in shouldBeNumericColumns:
----> 2     trainData[column] = pandas.to_numeric(trainData[column])

/usr/local/lib/python3.5/site-packages/pandas/tools/util.py in to_numeric(arg, errors)
    113         try:
    114             values = lib.maybe_convert_numeric(values, set(),
--> 115                                                coerce_numeric=coerce_numeric)
    116         except:
    117             if errors == 'raise':

pandas/src/inference.pyx in pandas.lib.maybe_convert_numeric (pandas/lib.c:53558)()

pandas/src/inference.pyx in pandas.lib.maybe_convert_numeric (pandas/lib.c:53344)()

ValueError: Unable to parse string

看看哪个值无法解析会不会有帮助？

Answer 1

我想你可以添加参数errors='coerce'来转换坏的非数值NaN,然后检查这个值isnull并使用boolean indexing:

print (df[pd.to_numeric(df.col, errors='coerce').isnull()])

样品:

df = pd.DataFrame({'B':['a','7','8'],
                   'C':[7,8,9]})

print (df)
   B  C
0  a  7
1  7  8
2  8  9

print (df[pd.to_numeric(df.B, errors='coerce').isnull()])
   B  C
0  a  7

或者如果需要在混合列中找到所有字符串 - 使用字符串值的numerice检查type值是否为string:

df = pd.DataFrame({'B':['a',7, 8],
                   'C':[7,8,9]})

print (df)
   B  C
0  a  7
1  7  8
2  8  9

print (df[df.B.apply(lambda x: isinstance(x, str))])
   B  C
0  a  7