Remplacer Nan en pandas
df['DataFrame Column'] = df['DataFrame Column'].fillna(0)
Dark Duck
df['DataFrame Column'] = df['DataFrame Column'].fillna(0)
df = df.replace(r'^\s*$', np.NaN, regex=True)df.replace(np.nan,0)
data["Gender"].fillna("No Gender", inplace = True) df['workclass'].replace('?', np.NaN)
df = pd.DataFrame([
[-0.532681, 'foo', 0],
[1.490752, 'bar', 1],
[-1.387326, 'foo', 2],
[0.814772, 'baz', ' '],
[-0.222552, ' ', 4],
[-1.176781, 'qux', ' '],
], columns='A B C'.split(), index=pd.date_range('2000-01-01','2000-01-06'))
# replace field that's entirely space (or empty) with NaN
print(df.replace(r'^\s*$', np.nan, regex=True))
# output
# 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