Answers for "df.fillna"

pandas replace nan

data["Gender"].fillna("No Gender", inplace = True)

python pandas convert nan to 0

pandas.DataFrame.fillna(0)

dataframe fillna with 0

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

python pandas fillna

# importing pandas module
import pandas as pd  
# making data frame from csv file
nba = pd.read_csv("nba.csv")
  
# replacing na values in college with No college
nba["College"].fillna("No College", inplace = True)
# OR
nba.College.fillna("No College", inplace = True)
  
print(nba)

python dataframe replace nan with 0

In [7]: df
Out[7]: 
          0         1
0       NaN       NaN
1 -0.494375  0.570994
2       NaN       NaN
3  1.876360 -0.229738
4       NaN       NaN

In [8]: df.fillna(0)
Out[8]: 
          0         1
0  0.000000  0.000000
1 -0.494375  0.570994
2  0.000000  0.000000
3  1.876360 -0.229738
4  0.000000  0.000000

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