Replacing values with groupby means

Question

I have a DataFrame with a column that has some bad data with various negative values. I would like to replace values < 0 with the mean of the group that they are in.

For missing values as NAs, I would do:

data = df.groupby(['GroupID']).column
data.transform(lambda x: x.fillna(x.mean()))

But how to do this operation on a condition like x < 0?

Thanks!

Answer 1

Using @AndyHayden's example, you could use groupby/transform with replace:

df = pd.DataFrame([[1,1],[1,-1],[2,1],[2,2]], columns=list('ab'))
print(df)
#    a  b
# 0  1  1
# 1  1 -1
# 2  2  1
# 3  2  2

data = df.groupby(['a'])
def replace(group):
    mask = group<0
    # Select those values where it is < 0, and replace
    # them with the mean of the values which are not < 0.
    group[mask] = group[~mask].mean()
    return group
print(data.transform(replace))
#    b
# 0  1
# 1  1
# 2  1
# 3  2