我有一个Pandas Dataframe如下：
User_ID Title Activity
9fbb7702-5209-46c8-b7c8-2c3d03550b56 On the Gold VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 The Amazing Spider-Man 2 VIEWED_TVSHOW
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Pearl Harbor VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Big Top Pee-wee VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 The Virginian VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Lies My Mother Told Me VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 My Fellow Americans VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 My Fellow Americans VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Yogi Bear MARKED_CONTENT_AS_FAVOURITE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 The Quick and the Dead VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 My Girl VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 My Girl 2 VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Taxi VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Monte Walsh VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 The Love Letter VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Rio Diablo VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Magic in the Moonlight VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 The Right Kite VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Worlds Collide VIEWED_TVSHOW
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Big Driver VIEWED_TVSHOW
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Big Driver VIEWED_MOVIE
cb1fc554-8566-4c9f-a3ca-f64be302d65e null MARKED_CONTENT_AS_FAVOURITE
cb1fc554-8566-4c9f-a3ca-f64be302d65e NULL VIEWED_CELEBRITY
6916484f-b7bd-431a-818d-d1a63ff7c717 NULL SEARCH
6916484f-b7bd-431a-818d-d1a63ff7c717 Spy Game SEARCH
6916484f-b7bd-431a-818d-d1a63ff7c717 Act of Valor VIEWED_MOVIE
6916484f-b7bd-431a-818d-d1a63ff7c717 One Direction: This Is Us VIEWED_MOVIE
6916484f-b7bd-431a-818d-d1a63ff7c717 The Amazing Spider-Man 2 VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 NULL VIEWED_MOVIE
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Spy Game SEARCH
9fbb7702-5209-46c8-b7c8-2c3d03550b56 Little Man Tate VIEWED_MOVIE
我想了解每位用户的每项活动，如下所示。
User_Id MARKED_CONTENT_AS_FAVOURITE RATE_CONTENT SEARCH VIEWED_CELEBRITY VIEWED_MOVIE VIEWED_TVSHOW
1 6916484f-b7bd-431a-818d-d1a63ff7c717 0 0 1 0 4 0
2 9fbb7702-5209-46c8-b7c8-2c3d03550b56 2 2 1 1 20 3
3 cb1fc554-8566-4c9f-a3ca-f64be302d65e 0 0 1 1 0 0
我已经使用过：
distinct_activity.groupby(['User_Id','Activity']).agg({"Activity": "count"})
但我没有按照需要获得输出。