{"action":"opened","issue":{"url":"https://api.github.com/repos/rogeliozunigarubio/data_science_portfolio/issues/1","labels_url":"https://api.github.com/repos/rogeliozunigarubio/data_science_portfolio/issues/1/labels{/name}","comments_url":"https://api.github.com/repos/rogeliozunigarubio/data_science_portfolio/issues/1/comments","events_url":"https://api.github.com/repos/rogeliozunigarubio/data_science_portfolio/issues/1/events","html_url":"https://github.com/rogeliozunigarubio/data_science_portfolio/issues/1","id":126993763,"number":1,"title":"HW1 Feedback","user":{"login":"lemonsoup","id":4931103,"avatar_url":"https://avatars.githubusercontent.com/u/4931103?v=3","gravatar_id":"","url":"https://api.github.com/users/lemonsoup","html_url":"https://github.com/lemonsoup","followers_url":"https://api.github.com/users/lemonsoup/followers","following_url":"https://api.github.com/users/lemonsoup/following{/other_user}","gists_url":"https://api.github.com/users/lemonsoup/gists{/gist_id}","starred_url":"https://api.github.com/users/lemonsoup/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/lemonsoup/subscriptions","organizations_url":"https://api.github.com/users/lemonsoup/orgs","repos_url":"https://api.github.com/users/lemonsoup/repos","events_url":"https://api.github.com/users/lemonsoup/events{/privacy}","received_events_url":"https://api.github.com/users/lemonsoup/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2016-01-16T02:07:14Z","updated_at":"2016-01-16T02:07:14Z","closed_at":null,"body":"Status:  Pass (Homework is graded on \"Pass\" or \"Needs Improvement\")\r\nComments:\r\nWell done Ro!  And yes, NOT being on the Titanic was perhaps a good way to survivie the sinking ship..  Your analysis is clear and easy to follow, and you effectively explore the data. I encourage you to add titles to your charts in order to make your intent as obvious as possible to your reader. Great use of groupby and describe() as a quick way to see the impacts of the various categories on the survival rates. In order to get survival rates within each group, try using groupby() on the whole dataframe, not just for ['Survived']==1.  That way, when you apply describe(), the mean of the 'Survived' column will tell you the percentage of people who survived in each group.  Good work!\r\n\r\n"}}