TL, DR
Sometimes you need to drop columns in a Pandas DataFrame, but you may not be sure they actually exist. Here a few snippets to do it without raising errors.
Safely Dropping Columns That Might Not Exist
Sometimes, your dataset might include placeholder or optional columns such as "Unknown", "Other", or "None". You may want to remove them before analysis, but you don’t want your code to crash if they’re not actually present.
Pandas has a neat trick for this: the errors='ignore' parameter in .drop().
columns_to_drop = ["Unknown", "Other", "None"]
df_cleaned = df.drop(columns=columns_to_drop, errors='ignore')This line will drop any of the listed columns only if they exist . If not, it simply ignores them — no error, no drama.
Related links
- Pandas DataFrame.drop() documentation link
Do you like our content? Check more of our posts in our blog!
