在熊猫中用NaN替换空白值（空白）

我认为能

df.replace()

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

产生：

        A    B   C2000-01-01 -0.532681  foo   02000-01-02  1.490752  bar   12000-01-03 -1.387326  foo   22000-01-04  0.814772  baz NaN2000-01-05 -0.222552  NaN   42000-01-06 -1.176781  qux NaN

正如Temak指出的那样，请

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