Conditionally Forward-Fill in Pandas: Propagate colC from the Latest colA==3 to colA==4, Keep colB Intact

Learn a robust Pandas pattern: mask colC by colA==3, ffill anchors, then assign to colA==4. Keep colB untouched for predictable, auditable, clear results.

Conditionally filling values across rows is a common data-cleaning task. Here the goal is simple: for every row where colA equals 4, replace colC with the most recent colC observed at a row where colA equals 3. The colB column must remain untouched.

Minimal example that shows the problem

The data comes as a Pandas DataFrame with three columns. Rows with colA equal to 3 provide the value we want to propagate; rows with colA equal to 4 should receive that propagated value in colC.

from pandas import DataFrame as Frame

source_df = Frame({
    'colA': [3, 4, 4, 4, 3, 4, 4, 3, 4],
    'colB': ['air', 'ground', 'ground', 'ground', 'air', 'ground', 'air', 'ground', 'ground'],
    'colC': ['00JTHYU1', '00JTHYU0', '00JTHYU0', '00JTHYU0', '00JTHYU4', '00JTHYU0', '00JTHYU0', '00JTHYU7', '00JTHYU0']
})

print(source_df)

Desired result: rows with colA equal to 4 should have colC set to the last colC where colA was 3, while colB stays exactly as is.

What’s actually going on

A plain ffill on colC won’t produce the right outcome because it will also forward-fill from rows that shouldn’t act as anchors. The intent is to forward-fill only from rows where colA equals 3, and ignore other rows as sources. That means you need to mask colC so that only the values aligned with colA equal to 3 act as the “anchor” values; everything else should be treated as missing during the fill.

Solution

The approach is to create a masked Series that keeps colC only where colA equals 3 and is NaN otherwise, forward-fill that masked Series, and then write those propagated values back strictly to rows where colA equals 4.

from pandas import DataFrame as Frame

data_tbl = Frame({
    'colA': [3, 4, 4, 4, 3, 4, 4, 3, 4],
    'colB': ['air', 'ground', 'ground', 'ground', 'air', 'ground', 'air', 'ground', 'ground'],
    'colC': ['00JTHYU1', '00JTHYU0', '00JTHYU0', '00JTHYU0', '00JTHYU4', '00JTHYU0', '00JTHYU0', '00JTHYU7', '00JTHYU0']
})

anchor_vals = data_tbl['colC'].where(data_tbl['colA'] == 3)
spread_vals = anchor_vals.ffill()

data_tbl.loc[data_tbl['colA'] == 4, 'colC'] = spread_vals

print(data_tbl)

This produces the intended result, where each block of 4s inherits the colC from the most recent 3, and colB is preserved.

   colA    colB      colC
0     3     air  00JTHYU1
1     4  ground  00JTHYU1
2     4  ground  00JTHYU1
3     4  ground  00JTHYU1
4     3     air  00JTHYU4
5     4  ground  00JTHYU4
6     4     air  00JTHYU4
7     3  ground  00JTHYU7
8     4  ground  00JTHYU7

Why this detail matters

Forward-filling without a mask seems convenient, but it quietly changes the semantics of your data by allowing non-anchor rows to influence the result. Masking makes the intent explicit: only rows where colA equals 3 define what should be propagated. That’s the difference between a quick fix and a robust, auditable transformation.

Takeaways

When you need to carry values forward based on a condition, build a conditional source series first, forward-fill it, and assign back only where needed. It’s simple to read, easy to reason about, and keeps columns like colB untouched while guaranteeing that colC for colA equal to 4 always reflects the latest value established at colA equal to 3.