Absolutely! You can achieve this requirement using drop_duplicates—you just need to combine it with conditional filtering, since drop_duplicates doesn’t support conditional deduplication directly in a single call. Let’s break this down with your sample DataFrame.

First, let’s recap your data and requirement:

Your DataFrame df:

Name	State
Down	NY
Down	NY
Down	NY
Next In	NJ
Next In	NJ
Next In	NJ

Requirement: Only deduplicate rows where the Name field’s length exceeds 5 characters (so keep all "Down" rows, but reduce "Next In" to one unique row).

Solution 1: Split, Deduplicate, and Combine #

The most straightforward approach is to split your DataFrame into two subsets, process each, then recombine:

1. Create a boolean mask to identify rows where Name length >5:

   mask = df['Name'].str.len() > 5
   

2. Deduplicate only the rows that match the mask (using drop_duplicates on the Name column):

   deduplicated_rows = df[mask].drop_duplicates(subset='Name', keep='first')
   

3. Keep all rows that don’t match the mask (no deduplication needed):

   keep_all_rows = df[~mask]
   

4. Combine the two subsets and restore the original order (optional):

   result_df = pd.concat([keep_all_rows, deduplicated_rows]).sort_index()
   

Solution 2: Use groupby with Conditional Logic #

If you prefer a more concise approach, you can use groupby to apply deduplication only to groups where Name meets the length condition:

result_df = df.groupby('Name', group_keys=False).apply(
    lambda group: group.drop_duplicates() if group['Name'].str.len().iloc[0] > 5 else group
).reset_index(drop=True)

What You’ll Get #

Both methods will produce this final DataFrame:

Which perfectly aligns with your requirement: all short-named rows are preserved, while long-named duplicates are removed.

内容的提问来源于stack exchange，提问作者Pav