PYTHON

dataframe fillna with 0

df['column'] = df['column'].fillna(0)

pandas fillna

# based on another column value
df['column_1'].fillna(df['column_2'], inplace=True)

pandas fillna with none

df.fillna(np.nan).replace([np.nan], [None])

df.fillna(-999,inplace=True)

df2.replace(-999, np.nan, inplace=True)
df2.fillna(df2.mean())

    EventId A       B        C
0   100000  0.91    124.711  2.666000
1   100001  0.91    124.711 -0.202838
2   100002  0.91    124.711 -0.202838
3   100003  0.91    124.711 -0.202838

pandas fillna with none

df.fillna(np.nan).replace([np.nan], [None])

pandas fillna

# selecting your desired columns
df[['a', 'b']] = df[['a', 'b']].fillna(df['c'], inplace=True)

pandas.DataFrame.fillna

values = {"A": 0, "B": 1, "C": 2, "D": 3}
>>> df.fillna(value=values)
     A    B    C    D
0  0.0  2.0  2.0  0.0
1  3.0  4.0  2.0  1.0
2  0.0  1.0  2.0  3.0
3  0.0  3.0  2.0  4.0

fillna pandas inplace

When inplace = True , the data is modified in place, which means it will return nothing and the dataframe is now updated. When inplace = False , which is the default, then the operation is performed and it returns a copy of the object. You then need to save it to something.

DataFrame.fillna

>>> df.fillna(0)
    A   B   C   D
0   0.0 2.0 0.0 0
1   3.0 4.0 0.0 1
2   0.0 0.0 0.0 5
3   0.0 3.0 0.0 4

pandas df.fillna selected columns

df[['a', 'b']] = df[['a','b']].fillna(value=0)