pandas replace nan
data["Gender"].fillna("No Gender", inplace = True)pandas replace nan
data["Gender"].fillna("No Gender", inplace = True)fillna with mean pandas
sub2['income'].fillna((sub2['income'].mean()), inplace=True)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 0.000000df.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 -0.202838Copyright © 2021 Codeinu
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