How to fix pandas 2 FutureWarning about incompatible dtype when assigning rows: merge with combine_first

Learn why pandas 2 raises a FutureWarning on incompatible dtype during row-wise assignment to NaN columns, and how to fix it by merging with combine_first.

Working code that was harmless in pandas 1.x can start firing warnings in pandas 2 when you mix unknown dtypes and row-wise assignments. A common case is pre-creating a column with NaN and then trying to overwrite selected rows from another DataFrame whose column dtype you don’t know in advance. The result is a FutureWarning about incompatible dtype that will become an error in a future release.

Reproducing the issue

The following snippet mirrors the situation: one DataFrame is augmented with a new column filled with NaN, and then rows are assigned from another DataFrame. In pandas 2 this triggers a FutureWarning about incompatible dtype.

import pandas as pd
import numpy as np
base_df = pd.DataFrame({"i": [1, 2, 3, 4, 5], "a": [2, 4, 6, 8, 10]})
patch_df = pd.DataFrame({"i": [2, 4], "a": [3, 6], "b": [4, 8]})
base_df["b"] = np.nan
base_df.loc[patch_df.index, :] = patch_df

This raises the warning:

FutureWarning: Setting an item of incompatible dtype is deprecated and will raise an error in a future version of pandas.

What’s going on

The assignment attempts to place values from one DataFrame into another where the target column was initialized with NaN. If you don’t know the dtype of the incoming column beforehand, there is no guarantee that the pre-created column’s dtype can represent those values without upcasting or losing information. Pandas 2 surfaces this with a FutureWarning, signaling that silent coercion won’t be tolerated later on. Casting the column to match the incoming dtype would usually help, but in this scenario the dtype is unknown and may not support NaN, so you don’t have a reliable pre-cast.

The fix: merge with combine_first

Instead of creating a column and assigning rows, merge the two DataFrames so that values from the patch take precedence where present. This avoids brittle dtype juggling and works cleanly with missing values.

import pandas as pd
# sample
base_df = pd.DataFrame({"i": [1, 2, 3, 4, 5], "a": [2, 4, 6, 8, 10]})
patch_df = pd.DataFrame({"i": [2, 4], "a": [3, 6], "b": [4, 8]})
# solution
merged_df = patch_df.combine_first(base_df)[patch_df.columns]

The resulting DataFrame matches the desired behavior, with the patch applied and missing values preserved:

   i   a    b
0  2   3  4.0
1  4   6  8.0
2  3   6  NaN
3  4   8  NaN
4  5  10  NaN

Why this matters

Code that relies on implicit coercion will break as pandas tightens dtype rules. Replacing row-wise assignment onto NaN-initialized columns with a combine-first merge pattern removes the dependency on unknown dtypes and avoids future errors. It also makes intent explicit: take values from the patch where available, otherwise keep what was already there.

Takeaways

If you don’t control or can’t predict the incoming dtype, avoid pre-allocating columns with NaN and then assigning rows. Merge with combine_first so pandas handles the alignment and missing values for you, and keep an eye on full warning messages—they point to exactly the kind of changes that will hard-error in future versions.