I think df.replace()
does the job, since pandas 0.13:
df = pd.DataFrame([
[-0.532681, 'foo', 0],
[1.490752, 'bar', 1],
[-1.387326, 'foo', 2],
[0.814772, 'baz', ' '],
[-0.222552, ' ', 4],
[-1.176781, 'qux', ' '],
], columns="A B C".split(), index=pd.date_range('2000-01-01','2000-01-06'))
# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^\s*$', np.nan, regex=True))
Produces:
A B C
2000-01-01 -0.532681 foo 0
2000-01-02 1.490752 bar 1
2000-01-03 -1.387326 foo 2
2000-01-04 0.814772 baz NaN
2000-01-05 -0.222552 NaN 4
2000-01-06 -1.176781 qux NaN
As Temak pointed it out, use df.replace(r'^\s+$', np.nan, regex=True)
in case your valid data contains white spaces.