How to replace scalar NaN from a Pandas left merge with NumPy NaN arrays of fixed length

Learn how to handle Pandas left merges with NumPy arrays by replacing scalar NaN with NaN arrays of the correct length using map and a pad for consistency.

When you merge two Pandas DataFrames and one of the columns contains numpy arrays, missing matches in a left join turn into scalar NaN. If downstream logic expects arrays of a fixed length, those scalar NaNs break everything. The goal is to replace those scalar NaNs with a numpy array of NaN values of the same length as the arrays already present in the column.

Repro setup and the problem in code

The scenario below creates a base table, a lookup with numpy arrays, and then performs a left merge. The merged column contains either arrays of equal length or a single NaN when there was no match.

import pandas as pd
import numpy as np
base_tbl = pd.DataFrame({'c1': ['A', 'B', 'C', 'D', 'E'], 'c2': [1, 2, 3, 4, 5]})
ref_tbl = pd.DataFrame({
    'c1': ['A', 'B', 'C'],
    'c2': [1, 2, 3],
    'c3': [
        np.array((1, 2, 3, 4, 5, 6)),
        np.array((6, 7, 8, 9, 10, 11)),
        np.full((6,), np.nan)
    ]
})
joined = base_tbl.merge(ref_tbl, how='left', on=['c1', 'c2'])

After the merge, rows with matches carry arrays, while missing rows have a single NaN in c3. The next transformation requires arrays of consistent length everywhere, so scalar NaNs must be replaced by arrays filled with NaN.

Why the straightforward attempts fail

Directly assigning an array to multiple selected rows seems natural, but Pandas interprets the right-hand side as an iterable to be aligned with the index, not as a single object to be broadcast per row. That produces an error about length mismatch.

joined.loc[pd.isnull(joined.c3), 'c3'] = np.full((6,), np.nan)
# ValueError: Must have equal len keys and value when setting with an iterable

Iterating row by row and assigning based on a check across the whole column does not help. The expression all(pd.isnull(joined.c3)) evaluates the entire column, not the current row, and leads to an unexpected recursion error when the object being assigned is misinterpreted in the assignment pipeline.

for ridx in joined.index:
    joined.at[ridx, 'c3'] = np.full((6,), np.nan) if all(pd.isnull(joined.c3)) else joined.c3
# RecursionError: maximum recursion depth exceeded

Trying to prefill another column with arrays runs into the classic sequence-assignment issue in a Pandas column, since Pandas expects scalar-like values or a sequence matching the index length, not nested arrays stuffed into a vectorized assignment.

for ridx in base_tbl.index:
    base_tbl.at[ridx, 'c4'] = np.full((6,), np.nan)
# ValueError: setting an array element with a sequence 
base_tbl['c4'] = np.full((6,), np.nan)
# ValueError: Length of values (6) does not match length of index (5)

If you do need to address a single row, you should select a single-cell location, not the whole column, because joined.c3 is the entire Series and not the current row. Using something like joined.at[row_index, 'c3'] gives you the single value to work with.

The essence of the issue

The merged column mixes two types of entries: numpy arrays of uniform size and scalar NaN for non-matches. Pandas does not automatically broadcast an array object across multiple rows on assignment. You need a single array object of the intended length and a row-wise transformation that replaces only the entries that aren’t arrays. The length should be derived from what already exists in the column rather than hardcoded, ensuring you align with the data you actually have.

Solution: map with a precomputed NaN array

The robust approach is to inspect c3 to determine the array length, create a single NaN-filled array of that exact size, and then map over the column to replace non-array entries with that array.

# derive the target length from existing arrays and build the padding array
pad = np.full(
    joined['c3'].map(lambda v: np.size(v) if isinstance(v, np.ndarray) else 0).max(),
    np.nan
)
fixed = joined.assign(
    c3=joined['c3'].map(lambda v: pad if not isinstance(v, np.ndarray) else v)
)

This determines the length from the arrays already present and swaps any non-array value in c3 with the NaN array. The result contains arrays of a consistent length across all rows, including the previously missing matches.

Output:

  c1  c2                              c3
0  A   1              [1, 2, 3, 4, 5, 6]
1  B   2            [6, 7, 8, 9, 10, 11]
2  C   3  [nan, nan, nan, nan, nan, nan]
3  D   4  [nan, nan, nan, nan, nan, nan]
4  E   5  [nan, nan, nan, nan, nan, nan]

Why this matters

Consistency of shapes inside a DataFrame column is essential when the next steps expect element-wise operations on arrays. A single scalar NaN introduces edge cases and breaks vectorized logic. By normalizing the missing entries to arrays of the same length, you keep the data model coherent and make subsequent processing deterministic.

Takeaways

When a left merge mixes arrays with scalar NaN, extract the expected array length from the data you have, construct one NaN-filled array of that size, and use a map that replaces only non-array entries. Avoid assigning arrays directly to a slice expecting element alignment; use a row-wise transformation that preserves the original arrays and fills the gaps in a single pass.