replace nan in pandas
df['DataFrame Column'] = df['DataFrame Column'].fillna(0)
replace nan in pandas
df['DataFrame Column'] = df['DataFrame Column'].fillna(0)
pandas replace nan
data["Gender"].fillna("No Gender", inplace = True)
python fillna with mode
data['Native Country'] = data['Native Country'].fillna(data['Native Country'].mode()[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)
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
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.
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