Efficiently update a pandas DataFrame column by group using vectorized transform/map or merge (no apply)

Learn how to update a pandas DataFrame column by group with cross-row conditions using vectorized transform, map, and merge. Avoid slow groupby.apply. Efficient.

Updating a DataFrame column based on conditions that span multiple rows in the same group is a common task, but it can be deceptively tricky when those conditions must reference other rows. Here’s a clear, performant way to do it in pandas without falling back to slow, row-wise operations.

Problem setup

We have data grouped by group_id. For a given row, we want to set status to "resolved" only if all of the following are true: the row is currently pending, there exists at least one active row in the same group, and the related_id points to a row that belongs to the same group. The sample data looks like this.

import pandas as pd

records = {
    'id': [1, 2, 3, 4, 5, 6, 7],
    'group_id': ['A', 'A', 'A', 'B', 'B', 'B', 'B'],
    'status': ['pending', 'active', 'pending', 'pending', 'active', 'pending', 'pending'],
    'related_id': [None, None, 1, None, None, 4, 4]
}
frame = pd.DataFrame(records)
print(frame)

The target result is that rows 3, 6, and 7 have status switched to resolved.

Why group-wise logic is failing with naive approaches

Attempting this with groupby().apply() is tempting but inefficient. It is a slow operation and rarely necessary here. The condition itself also hides ambiguity: “there is at least one other row with status active and the related_id matches the id of the current row.” In the provided example, the rows referenced by related_id (ids 1 and 4) are pending, not active. That means the check is either about verifying that related_id exists within the same group while the group has some active row, or it is about directly looking up the row referenced by related_id and testing its status. Both interpretations can be implemented efficiently.

Solution 1: Vectorized checks with transform and map

The first reading is: resolve a pending row if its related_id belongs to the same group and there exists at least one active row in that group. This avoids apply and uses pure vectorized operations.

# Is the row currently pending?
cond_pending = frame['status'].eq('pending')

# Does related_id point to a row in the same group?
cond_same_group = frame['related_id'].map(frame.set_index('id')['group_id']).eq(frame['group_id'])

# Is there at least one active row in this group?
cond_active_exists = frame['status'].eq('active').groupby(frame['group_id']).transform('any')

# Update statuses where all conditions hold
frame.loc[cond_pending & cond_same_group & cond_active_exists, 'status'] = 'resolved'
print(frame)

Expected output:

   id group_id    status  related_id
0   1        A   pending         NaN
1   2        A    active         NaN
2   3        A  resolved         1.0
3   4        B   pending         NaN
4   5        B    active         NaN
5   6        B  resolved         4.0
6   7        B  resolved         4.0

This matches the intended result and runs efficiently by keeping the logic fully vectorized.

Solution 2: Explicit lookup via merge

If you prefer to explicitly fetch the row referenced by related_id and test its status, use a merge. In this variant, we mark a pending row as resolved when the row it references (by related_id within the same group) has status pending.

# Pending mask for current rows
mask_pending = frame['status'].eq('pending')

# For pending rows, look up the referenced row (same group) and check its status
mask_lookup = (
    frame.loc[mask_pending, ['related_id', 'group_id']].reset_index()
        .merge(
            frame[['id', 'group_id', 'status']],
            left_on=['related_id', 'group_id'],
            right_on=['id', 'group_id']
        )
        .set_index('index')['status']
        .eq('pending')
)

# Apply the update for rows that meet both conditions
frame.loc[mask_pending & mask_lookup, 'status'] = 'resolved'
print(frame)

Expected output:

   id group_id    status  related_id
0   1        A   pending         NaN
1   2        A    active         NaN
2   3        A  resolved         1.0
3   4        B   pending         NaN
4   5        B    active         NaN
5   6        B  resolved         4.0
6   7        B  resolved         4.0

Which approach to choose

Both approaches are vectorized and avoid groupby.apply. Choose the transform-based variant if the rule is “related_id stays within the same group and the group contains any active row.” Choose the merge-based variant if the rule is “resolve based on the status of the row explicitly referenced by related_id within the same group.”

Why this matters

Relying on groupby.apply for cross-row checks often leads to unnecessary slowdowns. Using transform, map, and merge keeps the operation within pandas’ optimized vectorized path. This scales better and is easier to reason about when conditions involve membership in a group and lookups against other rows.

Takeaways

Be explicit about the rule you are enforcing. If the condition is about group-wide existence (for example, any active in the group) plus a structural constraint (related_id belongs to the same group), then combine transform and map. If the condition requires inspecting the exact row pointed to by related_id, merge is a direct and efficient solution. In both cases, you avoid row-wise apply, keep the code concise, and get predictable performance.