Filter pandas dataframe rows if any value on a list inside the dataframe is in another list

You can convert each list to sets, get intersection and convert to bool:

L = [480, 9, 104]
mask = np.array([bool(set(map(int, x)) & set(L))  for x in df['split_categories']])

Or convert list column to DataFrame, cast to float and compare with isin:

df1 = pd.DataFrame(df['split_categories'].values.tolist(), index=df.index)
mask = df1.astype(float).isin(L).any(axis=1)

---

df = df[mask]
print (df)
  album_id categories split_categories
0    66562    480.494       [480, 494]
3     1709          9              [9]
4    59239    105.104       [105, 104]

You can expand the inner list, and check if any items in the inner lists are contained in [480, 9, 104]:

l = [480, 9, 104]
df[df.categories.str.split('.', expand=True).isin(map(str,l)).any(axis=1)]

   album_id  categories split_categories
0     66562     480.494        [480,494]
3      1709       9.000              [9]
4     59239     105.104        [105,104]