Efficiently Filter a Pandas DataFrame: Find Null place and Positive stock Rows Without Loops

Learn a vectorized pandas DataFrame pattern: build boolean masks to filter rows where place is NaN and stock > 0, then count matches with shape[0] or len.

When you need to filter a pandas DataFrame by null checks and numeric thresholds, iterating row by row is the slowest and least concise option. A clean, vectorized approach with boolean masks is both clearer and significantly more aligned with how pandas is designed to work.

Problem

Given a dataset with columns ID, place, and stock, the task is to find rows where place is null and stock is greater than zero, then count how many such rows exist.

ID, place, stock
1, 1, 4
2, NaN, 2
3, NaN, NaN
4, 1, 1

An iterative attempt might look like this, scanning a nulls list and branching inside the loop:

for ridx, flag in enumerate(tbl['place'].isnull().tolist()):
    pass

What’s going on and why it’s not ideal

Calling isnull() and converting to a Python list breaks the vectorized pipeline and forces you into Python-level iteration. That defeats the strengths of pandas, which shines when you build boolean masks and filter the entire DataFrame in one go. Instead of enumerating and manually checking, you can compose conditions and apply them directly for selection and counting.

Solution

The core idea is to build a combined mask and use it to filter, then count the resulting rows. This is a compact vectorized pattern that avoids loops entirely.

match_count = tbl[tbl['place'].isnull() & (tbl['stock'] > 0)].shape[0]

If you prefer to make the logic explicit with intermediate masks, that’s equally valid and often more readable, especially when you scale to multiple conditions:

is_place_null = tbl['place'].isnull()
is_stock_positive = tbl['stock'] > 0
subset = tbl[is_place_null & is_stock_positive]
count_rows = len(subset)

Both count_rows and match_count give you the number of rows that satisfy “place is null and stock > 0.” The selection expression returns the filtered DataFrame; using shape[0] or len(...) gives the row count. You can combine additional criteria the same way by chaining more masks with the & operator. For reference, the pandas documentation on DataFrame.loc also covers multiple conditional selection.

Why this matters

This approach is concise, avoids control-flow noise, and keeps computation inside pandas’ vectorized engine. It is also straightforward to maintain and extend when you need to check many columns against different criteria. The result is code that is easier to reason about and fits naturally with pandas’ idioms.

Wrap-up

Skip the Python-level loops. Build boolean masks with expressions like tbl['place'].isnull() and (tbl['stock'] > 0), combine them with &, filter once, and take either .shape[0] or len(...) to count. This pattern scales cleanly to many columns and keeps your DataFrame operations efficient and readable.