Removing a nan from a list

What you can do is simply get a cleaned list where you don't put the values that, once converted to strings, are 'nan'.

The code would be :

incoms = [incom for incom in incoms if str(incom) != 'nan']

I think you need dropna for remove NaNs:

incoms=data['int_income'].dropna().unique().tolist()
print (incoms)
[75000.0, 50000.0, 0.0, 200000.0, 100000.0, 25000.0, 10000.0, 175000.0, 150000.0, 125000.0]

And if all values are integers only:

incoms=data['int_income'].dropna().astype(int).unique().tolist()
print (incoms)
[75000, 50000, 0, 200000, 100000, 25000, 10000, 175000, 150000, 125000]

Or remove NaNs by selecting all non NaN values by numpy.isnan:

a = data['int_income'].unique()
incoms= a[~np.isnan(a)].tolist()
print (incoms)
[75000.0, 50000.0, 0.0, 200000.0, 100000.0, 25000.0, 10000.0, 175000.0, 150000.0, 125000.0]

---

a = data['int_income'].unique()
incoms= a[~np.isnan(a)].astype(int).tolist()
print (incoms)
[75000, 50000, 0, 200000, 100000, 25000, 10000, 175000, 150000, 125000]

Pure python solution - slowier if big DataFrame:

incoms=[x for x in  list(set(data['int_income'])) if pd.notnull(x)]
print (incoms)
[0.0, 100000.0, 200000.0, 25000.0, 125000.0, 50000.0, 10000.0, 150000.0, 175000.0, 75000.0]

---

incoms=[int(x) for x in  list(set(data['int_income'])) if pd.notnull(x)]
print (incoms)
[0, 100000, 200000, 25000, 125000, 50000, 10000, 150000, 175000, 75000]

A possibility in that particular case is to remove nans earlier to avoid to do it in the list:

incoms=data['int_income'].dropna().unique().tolist()