Replacing blank values (white space) with NaN in pandas

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.

If you want to replace an empty string and records with only spaces, the correct answer is!:

df = df.replace(r'^\s*$', np.nan, regex=True)

The accepted answer

df.replace(r'\s+', np.nan, regex=True)

Does not replace an empty string!, you can try yourself with the given example slightly updated:

df = pd.DataFrame([
    [-0.532681, 'foo', 0],
    [1.490752, 'bar', 1],
    [-1.387326, 'fo o', 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'))

Note, also that 'fo o' is not replaced with Nan, though it contains a space. Further note, that a simple:

df.replace(r'', np.NaN)

Does not work either - try it out.

How about:

d = d.applymap(lambda x: np.nan if isinstance(x, basestring) and x.isspace() else x)

The applymap function applies a function to every cell of the dataframe.