Python: If dataframe column matches dictionary key then return boolean for new column

I’m having difficulties trying to get this to work. So to summarize: I have a dataframe, I have a dictionary with keys of certain values in a certain dataframe value, and I want to create a new dataframe column of true/false if they match or not.

So here is an example of the data:

animal_list = ['dog', 'cat', 'dog', 'fish', 'bird', 'dog', 'bug', 'bird', 'snake']
animal_dict = {'dog': [3], 'bird': [2]}

animals = pd.DataFrame(animal_list, columns = ['Pets'])
animals

dataframe image

Basically I want a new column with boolean True if it matches the key, or False if not.

ALTERNATIVELY I suppose I could skip the dictionary altogether if I could create the new column with a query on if there are multiple instances of a value in existing column (‘Pets’). So it would be True for dogs and birds, but false for everything else.

UPDATE: Here is the solution I used, for others who may be searching for it. I originally had this function but was using apply method instead of map (as suggested by algebruh below)

def find_key(find_value):
    for k, v in animal_dict.items():
        if k == find_value:
            return True
    return False

animals['multiple'] = animals['Pets'].map(find_key)

>Solution :

You can simply check if animals['Pets'] isin animals_dict:

animals['multiple'] = animals['Pets'].isin(animal_dict)

Output:

    Pets  multiple
0    dog      True
1    cat     False
2    dog      True
3   fish     False
4   bird      True
5    dog      True
6    bug     False
7   bird      True
8  snake     False