I have 2 columns from 2 different dataframes. I want to check if column 1 is a subset of column 2.

I was using the following code:

set(col1).issubset(set(col2))

The issue with this is that if col1 has only integers and col2 has both integers and strings, then this returns false. This happens because elements of col2 are coerced into strings. For example,

set([376, 264, 365, 302]) & 
set(['302', 'water', 'nist1950', '264', '365', '376'])

I tried using isin from pandas. But if col1 and col2 are series then this gives a series of Boolean values. I want True or False.

How do I solve this? Is there a simpler function that I have missed?

Edit 1

Adding an example.

col1
0    365
1    376
2    302
3    264
Name: subject, dtype: int64col2
0     nist1950
1     nist1950
2        water
3        water
4          376
5          376
6          302
7          302
8          365
9          365
10         264
11         264
12         376
13         376
Name: subject, dtype: object

Edit 2

col1 and col2 can have integers, strings, floats etc. I would like to not make any prejudgement about what is in these columns.

You could use isin with all to check whether all of your col1 elements contains in col2. For converting to numeric you could use pd.to_numeric:

s1 = pd.Series([376, 264, 365, 302])
s2 = pd.Series(['302', 'water', 'nist1950', '264', '365', '376'])res = s1.isin(pd.to_numeric(s2, errors='coerce')).all()In [213]: res
Out[213]: True

More detailed:

In [214]: pd.to_numeric(s2, errors='coerce')
Out[214]:
0    302
1    NaN
2    NaN
3    264
4    365
5    376
dtype: float64In [215]: s1.isin(pd.to_numeric(s2, errors='coerce'))
Out[215]:
0    True
1    True
2    True
3    True
dtype: bool

Note pd.to_numeric works with pandas version >=0.17.0 for previous you cound use convert_objects with convert_numeric=True

EDIT

If you prefer solution with set you could convert your first set to str as well and then compare them with your code:

s3 = set(map(str, s1))In [234]: s3
Out[234]: {'264', '302', '365', '376'}

Then you could use issubset for s2:

In [235]: s3.issubset(s2)
Out[235]: True

or for set(s2):

In [236]: s3.issubset(set(s2))
Out[236]: True

EDIT2

s1 = pd.Series(['376', '264', '365', '302'])
s4 = pd.Series(['nist1950', 'nist1950', 'water', 'water', '376', '376', '302', '302', '365', '365', '264', '264', '376', '376'])In [263]: s1.astype(float).isin(pd.to_numeric(s4, errors='coerce')).all()
Out[263]: True