Count elements in defined groups in pandas dataframe

Say I have a dataframe and I want to count how many times we have element e.g [1,5,2] in a/each column.

I could do something like

elem_list = [1,5,2]

for e in elemt_list:
 (df["col1"]==e).sum()

but isn’t there a better way like

elem_list = [1,5,2]
df["col1"].count_elements(elem_list)

#1 5    # 1 occurs 5 times
#5 3    # 5 occurs 3 times
#2 0    # 2 occurs 0 times

Note it should count all the elements in the list, and return "0" if an element in the list is not in the column.

>Solution :

Pass to the Categorical which will return 0 for missing item

pd.Categorical(df['col1'],elem_list).value_counts()
Out[62]: 
1    3
5    0
2    1
dtype: int64