Dropping rows based on unique ID's in a single column in Pandas

Asked Jun 21 '18 at 22:24

Active Jun 21 '18 at 22:24

Viewed 27 times

I imported a csv into a dataframe and I am looking to drop rows based on a list of unique ID's.

Name     Song_ID     Status
song 1   12345       Pending
song 1   12345       Validating
song 2   18284       Validating
song 3   17385       Pending
song 3   17385       Pending
song 4   13467       Pending
song 5   19999       Pending
song 5   19999       Pending

For this example, I would like to drop the rows with Song_ID's: 12345, 17385, 19999

Any tips/suggestions would be much appreciated!

asked Jun 21 '18 at 22:24

KirklandShawty

Use `isin`, get a mask, and then index into `df`. – cs95 Jun 21 '18 at 22:28
@coldspeed, with all my respect, how is the "dup" related to the question? I bet you did not read it carefully. – DYZ Jun 21 '18 at 22:29
@DyZ he's not dropping duplicates, he dropping songs with specific ids. – user3483203 Jun 21 '18 at 22:37
@user3483203 Oops. – DYZ Jun 21 '18 at 22:39
1

@DyZ though it's possible they specifically mentioned those 3 unique IDs because there exist more than one of those. Whose to say. – cs95 Jun 21 '18 at 22:42

Dropping rows based on unique ID's in a single column in Pandas

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