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 object.

I'd select the record where A value are integer or numeric to have:

```
     A    B
0    1    green
1    2    red
3    3    yellow

```

Thanks

Answer: 
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](http://pandas.pydata.org/pandas-docs/stable/whatsnew.html#whatsnew-0160-enhancements-string) 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`](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.str.isdigit.html)

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

```