Python pandas.to_numeric - 找出它无法解析的字符串
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pandas.to_numeric - find out which string it was unable to parse
提问by clstaudt
Applying pandas.to_numericto a dataframe column which contains strings that represent numbers (and possibly other unparsable strings) results in an error message like this:
应用于pandas.to_numeric包含表示数字的字符串（可能还有其他无法解析的字符串）的数据框列会导致如下错误消息：
---------------------------------------------------------------------------
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
Wouldn't it be helpful to see which value failed to parse?
查看哪个值解析失败会不会有帮助？
回答by jezrael
I think you can add parameter errors='coerce'for convert bad non numeric values to NaN, then check this values by isnulland use boolean indexing:
我认为您可以添加errors='coerce'用于将错误的非数值转换为 的参数NaN，然后通过以下方式检查该值isnull并使用boolean indexing：
print (df[pd.to_numeric(df.col, errors='coerce').isnull()])
Sample:
样本：
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
Or if need find all string in mixed column - numerice with string values check typeof values if is string:
或者，如果需要在混合列中查找所有字符串 - 带有字符串值的数值检查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
回答by 3novak
I have thought the very same thing, and I don't know if there's a better way, but my current workaround is to search for characters which aren't numbers or periods. This usually turns up the problem. There are cases where multiple periods can cause a problem, but I've found those are rare.
我也想过同样的事情，我不知道是否有更好的方法，但我目前的解决方法是搜索不是数字或句点的字符。这通常会出现问题。在某些情况下，多个时期可能会导致问题，但我发现这种情况很少见。
import pandas as pd
import re
non_numeric = re.compile(r'[^\d.]+')
df = pd.DataFrame({'a': [3,2,'NA']})
df.loc[df['a'].str.contains(non_numeric)]
