I have a data frame with many columns, say:
df:
name salary age title
John 100 35 eng
Bill 200 NaN adm
Lena NaN 28 NaN
Jane 120 45 eng
I want to replace the null values in salary and age, but no in the other columns. I know I can do something like this:
u = df[['salary', 'age']]
df[['salary', 'age']] = u.fillna(-1)
But this seems terse as it involves copying. Is there a more efficient way to do this?
According to Pandas documentation in 23.3
values = {'salary': -1, 'age': -1}
df.fillna(value=values, inplace=True)
Try this:
subset = ['salary', 'age']
df.loc[:, subset] = df.loc[:, subset].fillna(-1)
df.loc[:, ['salary', 'age']] = df.loc[:, ['salary', 'age']].fillna(-1) does.
It is not so beautiful, but it works:
df.salary.fillna(-1, inplace=True)
df.age.fillna(-1, inplace=True)
df
>>> name salary age title
0 John 101.0 35.0 eng
1 Bill 200.0 -1.0 adm
2 Lena -1.0 28.0 NaN
3 Jane 120.0 45.0 eng
I was hoping fillna() had subset parameter like drop(), maybe should post request to pandas however this is the cleanest version in my opinion.
df[["salary", "age"]] = df[["salary", "age"]].fillna(-1)
You can do:
df = df.assign(
salary=df.salary.fillna(-1),
age=df.age.fillna(-1),
)
if you want to chain it with other operations.
