Pandas填充NaN并转int：获取填充值类型与优化方案咨询

1. Retrieving the Data Type of ffill Fill Values #

When using fillna(method='ffill'), the fill values are just the most recent valid values from the same column. To check their data type, you have a couple straightforward options:

• Before filling: Grab the first non-NaN value in the column and check its type. Since forward fill reuses existing values, this will match the type of the fill values:

  print(type(X_train['pixel1'].dropna().iloc[0]))
  

• After filling: Pick a value that was previously NaN and inspect its type. For example, if you know row index 4 had a NaN before filling:

  print(type(X_train.loc[4, 'pixel1']))
  

Since your original data was integer, these fill values should still be integers (even if the column was float at the time of filling—once cast back to int, they'll revert to their original type).

2. Avoiding the astype(int) Step #

The root issue here is that standard integer dtypes in pandas don't support NaN values, so adding NaNs forces the column to float. The fix is to use pandas' nullable integer dtype (Int64, note the capital I), which natively supports NaNs while retaining integer type.

Here are a few ways to implement this:

Option 1: Convert Existing Float Column to Nullable Integer #

If your column is already float due to NaNs, convert it directly to Int64—this works even with NaNs present:

X_train['pixel1'] = X_train['pixel1'].astype('Int64')

Option 2: Fill NaNs First, Then Use Nullable Integer #

If you still want to forward-fill missing values, you can do so and then switch to the nullable dtype (though converting first also works):

X_train.fillna(method='ffill', inplace=True)
X_train['pixel1'] = X_train['pixel1'].astype('Int64')

Pro Tip: Use pd.NA When Adding NaNs Manually #

If you're the one inserting missing values into the original integer column, use pd.NA instead of np.nan. This keeps the column in the Int64 dtype from the start, so you never have to deal with unwanted float conversion:

# Example of adding missing values without forcing float
X_train.loc[some_index, 'pixel1'] = pd.NA

This approach keeps your data type consistent with the original integer intent, eliminating the need for the post-fill astype(int) step entirely.

内容的提问来源于stack exchange，提问作者CIsForCookies