How can i add a column to a dataframe based on a conditional of another dataframe that has a different length, but shared column data

Question

I have two dataframes of different lengths and different columns, but a shared column with the same identifying data. They look like this

observations DF:

index	scientific_name	park_name	observations
0	name1	park1	10
1	name2	park2	12

species DF:

index	scientific_name	common_names	category
0	name1	name1,name2	Mammal
1	name2	name1,name2	Vascular plant

I am trying to create a new column in the observatiosn DF called 'category' that is filled with data based on the shared scientific_names between both tables. I've tried using pd.merge but it doesn't fill the category column the way I want. Concat does not either. When i tried using a list comprehension it gave me a value error too. Any thoughts?

I tried using a list comprehension like so:

observations['category'] = [el for el in species['category'] if observations['scientific_name'] == species['scientific_name]]

This results in an error.

Answer 1

If you only wanted to add the "category" column from species to observations based on the shared column "scientific_name", this should work.

observations = pd.merge(observations, species[['scientific_name', 'category']])