Pandas select only numeric or integer field from dataframe

Question

I have this Pandas dataframe (df):

     A    B
0    1    green
1    2    red
2    s    blue
3    3    yellow
4    b    black

A type is obje

Answer 1

Call apply on the dataframe (note the double square brackets df[['A']] rather than df['A']) and call the string method isdigit(), we then set param axis=1 to apply the lambda function row-wise. What happens here is that the index is used to create a boolean mask.

In [66]:
df[df[['A']].apply(lambda x: x[0].isdigit(), axis=1)]
Out[66]:
       A       B
Index           
0      1   green
1      2     red
3      3  yellow

Update

If you're using a version 0.16.0 or newer then the following will also work:

In [6]:
df[df['A'].astype(str).str.isdigit()]

Out[6]:
   A       B
0  1   green
1  2     red
3  3  yellow

Here we cast the Series to str using astype and then call the vectorised str.isdigit

Also note that convert_objects is deprecated and one should use to_numeric for the latest versions 0.17.0 or newer

Answer 2

Note that convert_objects is deprecated

>>> df[['A']].convert_objects(convert_numeric=True)
__main__:1: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.

From 0.17.0: use pd.to_numeric, set errors='coerce' so that incorrect parsing returns NaN. Use notnull to return a boolean mask to use on the original dataframe:

>>> df[pd.to_numeric(df.A, errors='coerce').notnull()]
   A       B
0  1   green
1  2     red
3  3  yellow

Answer 3

Personally, I think it will be more succinct to just use the built-in map compared with .apply()

In [13]: df[map(pred, df['B'])]

Answer 4

You can use convert_objects, which when convert_numeric=True will forcefully set all non-numeric to nan. Dropping them and indexing gets your result.

This will be considerably faster that using apply on a larger frame as this is all implemented in cython.

In [30]: df[['A']].convert_objects(convert_numeric=True)
Out[30]: 
    A
0   1
1   2
2 NaN
3   3
4 NaN

In [31]: df[['A']].convert_objects(convert_numeric=True).dropna()
Out[31]: 
   A
0  1
1  2
3  3

In [32]: df[['A']].convert_objects(convert_numeric=True).dropna().index
Out[32]: Int64Index([0, 1, 3], dtype='int64')

In [33]: df.iloc[df[['A']].convert_objects(convert_numeric=True).dropna().index]
Out[33]: 
   A       B
0  1   green
1  2     red
3  3  yellow