Conditionally offseting values by group with Pandas

Question

I am looking for a more efficient and maintainable way to offset values conditionally by group. Easiest to show an example.

Value is always non-negative for Offset == False and always negative for Offset == True. What I'm looking to do is "collapse" positive Values (flooring at 0) against negative ones by Label.

Note Label + Offset combined are always unique. Since Offset is Boolean, you can only have a maximum of 2 rows per Label.

Example 1

df = pd.DataFrame({'Label': ['L1', 'L2', 'L3', 'L3'],
                   'Offset': [False, False, False, True],
                   'Value': [100, 100, 50, -100]})

# input
#   Label Offset  Value
# 0    L1  False    100
# 1    L2  False    100
# 2    L3  False     50
# 3    L3   True   -100

Desired output:

  Label Offset  Value
0    L1  False    100
1    L2  False    100
2    L3  False      0
3    L3   True    -50

Example 2

df = pd.DataFrame({'Label': ['L1', 'L2', 'L3', 'L3'],
                   'Offset': [False, False, False, True],
                   'Value': [100, 100, 100, -50]})

# input
#   Label Offset  Value
# 0    L1  False    100
# 1    L2  False    100
# 2    L3  False    100
# 3    L3   True    -50

Desired output:

  Label Offset  Value
0    L1  False    100
1    L2  False    100
2    L3  False     50
3    L3   True      0

Current inefficient solution

My current solution is a manual loop which is slow and difficult to maintain:

for label in df['Label'].unique():
    mask = df['Label'] == label
    if len(df.loc[mask]) == 2:
        val_false = df.loc[~df['Offset'] & mask, 'Value'].iloc[0]
        val_true = df.loc[df['Offset'] & mask, 'Value'].iloc[0]
        if val_false > abs(val_true):
            df.loc[~df['Offset'] & mask, 'Value'] += val_true
            df.loc[df['Offset'] & mask, 'Value'] = 0
        else:
            df.loc[~df['Offset'] & mask, 'Value'] = 0
            df.loc[df['Offset'] & mask, 'Value'] += val_false

I'm looking for a vectorised, or at least partially vectorised, solution to improve performance and get rid of this mess.

Answer 1

Maybe:

label_sums = df.Value.groupby(df.Label).transform(sum)
df["new_sum"] = label_sums.where(np.sign(label_sums) == np.sign(df.Value), 0)

which gives me

In [42]: df
Out[42]: 
  Label  Offset  Value  new_sum
0    L1   False    100      100
1    L2   False    100      100
2    L3   False     50        0
3    L3    True   -100      -50
4    L4   False    100      100
5    L5   False    100      100
6    L6   False    100       50
7    L6    True    -50        0