I have a column in pandas which has string and numbers mixed I want to strip numbers from the string.
A
11286011
11268163
C7DDA72897
C8ABC557
Abul
C80DAS577
C80DSS665
Want an output as
A
C7DDA72897
C8ABC557
Abul
C80DAS577
C80DSS665
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Abul
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2Possible duplicate of [remove non-numeric rows in one column with pandas](http://stackoverflow.com/questions/33961028/remove-non-numeric-rows-in-one-column-with-pandas) – Andy Apr 13 '17 at 13:26
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Possible duplicate of [Pandas select only numeric or integer field from dataframe](http://stackoverflow.com/questions/24883503/pandas-select-only-numeric-or-integer-field-from-dataframe) – dot.Py Apr 13 '17 at 13:27
2 Answers
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In [52]: df
Out[52]:
A
0 11286011
1 11268163
2 C7DDA72897
3 C8ABC557
4 C80DAS577
5 C80DSS665
In [53]: df = pd.to_numeric(df.A, errors='coerce').dropna()
In [54]: df
Out[54]:
0 11286011.0
1 11268163.0
Name: A, dtype: float64
or using RegEx:
In [59]: df.loc[~df.A.str.contains(r'\D+')]
Out[59]:
A
0 11286011
1 11268163
MaxU - stand with Ukraine
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You can use .str.isnumeric to use in boolean slicing.
df[df.A.astype(str).str.isnumeric()]
A
0 11286011
1 11268163
As pointed out by @MaxU, assuming every element is already a string, you can limit this to
df[df.A.str.isnumeric()]
A
0 11286011
1 11268163
piRSquared
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I think `A` is already of string (object) dtype, so we can omit `.astype(str)`... – MaxU - stand with Ukraine Apr 13 '17 at 13:31
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1@MaxU `dtype==object` doesn't imply that all elements are strings. You can have an actual float or int in the array. If you do, `str.isnumeric` will fail. – piRSquared Apr 13 '17 at 13:51
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this is a good point! I just have tested it - `.isnumeric()` will retyren `NaN` in this case – MaxU - stand with Ukraine Apr 13 '17 at 14:04