{"action":"created","comment":{"url":"https://api.github.com/repos/Featuretools/featuretools/pulls/comments/239144453","pull_request_review_id":181856089,"id":239144453,"node_id":"MDI0OlB1bGxSZXF1ZXN0UmV2aWV3Q29tbWVudDIzOTE0NDQ1Mw==","diff_hunk":"@@ -1142,40 +1141,6 @@ def _import_from_dataframe(self,\n                 Defaults to False.\n         \"\"\"\n         variable_types = variable_types or {}\n-\n-        # DFS TODO: confirm we want this if else block\n-        if index is None:\n-            assert not make_index, \"Must specify an index name if make_index is True\"\n-            logger.warning((\"Using first column as index. \",\n-                            \"To change this, specify the index parameter\"))\n-            index = dataframe.columns[0]\n-        else:\n-            if index not in variable_types:\n-                variable_types[index] = vtypes.Index\n-\n-        created_index = None\n-\n-        if make_index and index in dataframe.columns:\n-            raise RuntimeError(\"Cannot make index: index variable already present\")\n-\n-        if make_index or index not in dataframe.columns:\n-            if not make_index:\n-                logger.warning(\"index %s not found in dataframe, creating new integer column\",\n-                               index)\n-            dataframe.insert(0, index, range(0, len(dataframe)))\n-            created_index = index\n-\n-        if time_index is not None and time_index not in dataframe.columns:\n-            raise LookupError('Time index not found in dataframe')\n-\n-        for c in dataframe.columns:\n-            if not is_string(c):\n-                raise ValueError(\"All column names must be strings (Column {} is not a string)\".format(c))\n-\n-            if dataframe[c].dtype.name.find('category') > -1:","path":"featuretools/entityset/entityset.py","position":51,"original_position":51,"commit_id":"dd5ec81dde11e30a6d1b6b97f73c43d1a2976123","original_commit_id":"dd5ec81dde11e30a6d1b6b97f73c43d1a2976123","user":{"login":"rwedge","id":5392142,"node_id":"MDQ6VXNlcjUzOTIxNDI=","avatar_url":"https://avatars2.githubusercontent.com/u/5392142?v=4","gravatar_id":"","url":"https://api.github.com/users/rwedge","html_url":"https://github.com/rwedge","followers_url":"https://api.github.com/users/rwedge/followers","following_url":"https://api.github.com/users/rwedge/following{/other_user}","gists_url":"https://api.github.com/users/rwedge/gists{/gist_id}","starred_url":"https://api.github.com/users/rwedge/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rwedge/subscriptions","organizations_url":"https://api.github.com/users/rwedge/orgs","repos_url":"https://api.github.com/users/rwedge/repos","events_url":"https://api.github.com/users/rwedge/events{/privacy}","received_events_url":"https://api.github.com/users/rwedge/received_events","type":"User","site_admin":false},"body":"I'm not sure.  It looks like from [this documentation](https://pandas.pydata.org/pandas-docs/stable/categorical.html#equality-semantics) that if we check for `df[variable].dtype == 'category'` instead we'll catch all CategoricalDtype.\r\n","created_at":"2018-12-05T16:40:46Z","updated_at":"2018-12-05T16:40:46Z","html_url":"https://github.com/Featuretools/featuretools/pull/336#discussion_r239144453","pull_request_url":"https://api.github.com/repos/Featuretools/featuretools/pulls/336","author_association":"CONTRIBUTOR","_links":{"self":{"href":"https://api.github.com/repos/Featuretools/featuretools/pulls/comments/239144453"},"html":{"href":"https://github.com/Featuretools/featuretools/pull/336#discussion_r239144453"},"pull_request":{"href":"https://api.github.com/repos/Featuretools/featuretools/pulls/336"}},"in_reply_to_id":238908015},"pull_request":{"url":"https://api.github.com/repos/Featuretools/featuretools/pulls/336","id":235607579,"node_id":"MDExOlB1bGxSZXF1ZXN0MjM1NjA3NTc5","html_url":"https://github.com/Featuretools/featuretools/pull/336","diff_url":"https://github.com/Featuretools/featuretools/pull/336.diff","patch_url":"https://github.com/Featuretools/featuretools/pull/336.patch","issue_url":"https://api.github.com/repos/Featuretools/featuretools/issues/336","number":336,"state":"open","locked":false,"title":"[WIP] Clean up entity creation logic","user":{"login":"rwedge","id":5392142,"node_id":"MDQ6VXNlcjUzOTIxNDI=","avatar_url":"https://avatars2.githubusercontent.com/u/5392142?v=4","gravatar_id":"","url":"https://api.github.com/users/rwedge","html_url":"https://github.com/rwedge","followers_url":"https://api.github.com/users/rwedge/followers","following_url":"https://api.github.com/users/rwedge/following{/other_user}","gists_url":"https://api.github.com/users/rwedge/gists{/gist_id}","starred_url":"https://api.github.com/users/rwedge/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/rwedge/subscriptions","organizations_url":"https://api.github.com/users/rwedge/orgs","repos_url":"https://api.github.com/users/rwedge/repos","events_url":"https://api.github.com/users/rwedge/events{/privacy}","received_events_url":"https://api.github.com/users/rwedge/received_events","type":"User","site_admin":false},"body":"The idea behind this PR is to make it easier to find where in the code we handle data type and variable type conversion when creating a new entity.  Currently it's handled in two places: `EntitySet._import_from_dataframe` and `Entity.__init__`.  \r\n\r\nThis PR moves the relevant logic in `EntitySet._import_from_dataframe` into `Entity.__init__` and removes a couple statements that try to infer variable types that can be handled by `Entity.infer_variable_types` 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