Pandas Strip numbers from a string column in python

Question

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

Possible duplicate of remove non-numeric rows in one column with pandas — Andy
– Andy ♦, Commented Apr 13, 2017 at 13:26
Possible duplicate of Pandas select only numeric or integer field from dataframe — dot.Py
– dot.Py, Commented Apr 13, 2017 at 13:27

MaxU - stand with Ukraine · Accepted Answer · 2017-04-13 13:27:19Z

4

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

answered Apr 13, 2017 at 13:27

MaxU - stand with Ukraine

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Comments

piRSquared · Accepted Answer · 2017-04-13 13:33:46Z

4

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

edited Apr 13, 2017 at 13:33

answered Apr 13, 2017 at 13:28

piRSquared

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3 Comments

MaxU - stand with Ukraine Over a year ago

I think A is already of string (object) dtype, so we can omit .astype(str)...

piRSquared Over a year ago

@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.

MaxU - stand with Ukraine Over a year ago

this is a good point! I just have tested it - .isnumeric() will retyren NaN in this case

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