Table: Activity
\n+--------------+---------+\n| Column Name | Type |\n+--------------+---------+\n| player_id | int |\n| device_id | int |\n| event_date | date |\n| games_played | int |\n+--------------+---------+\n(player_id, event_date) is the primary key (combination of columns with unique values) of this table.\nThis table shows the activity of players of some games.\nEach row is a record of a player who logged in and played a number of games (possibly 0) before logging out on someday using some device.\n\n\n
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Write a solution to report the fraction of players that logged in again on the day after the day they first logged in, rounded to 2 decimal places. In other words, you need to count the number of players that logged in for at least two consecutive days starting from their first login date, then divide that number by the total number of players.
\n\nThe result format is in the following example.
\n\n\n
Example 1:
\n\n\nInput: \nActivity table:\n+-----------+-----------+------------+--------------+\n| player_id | device_id | event_date | games_played |\n+-----------+-----------+------------+--------------+\n| 1 | 2 | 2016-03-01 | 5 |\n| 1 | 2 | 2016-03-02 | 6 |\n| 2 | 3 | 2017-06-25 | 1 |\n| 3 | 1 | 2016-03-02 | 0 |\n| 3 | 4 | 2018-07-03 | 5 |\n+-----------+-----------+------------+--------------+\nOutput: \n+-----------+\n| fraction |\n+-----------+\n| 0.33 |\n+-----------+\nExplanation: \nOnly the player with id 1 logged back in after the first day he had logged in so the answer is 1/3 = 0.33\n\n", "translatedTitle": null, "translatedContent": null, "isPaidOnly": false, "difficulty": "Medium", "likes": 709, "dislikes": 148, "isLiked": null, "similarQuestions": "[{\"title\": \"Game Play Analysis III\", \"titleSlug\": \"game-play-analysis-iii\", \"difficulty\": \"Medium\", \"translatedTitle\": null}, {\"title\": \"Game Play Analysis V\", \"titleSlug\": \"game-play-analysis-v\", \"difficulty\": \"Hard\", \"translatedTitle\": null}]", "exampleTestcases": "{\"headers\":{\"Activity\":[\"player_id\",\"device_id\",\"event_date\",\"games_played\"]},\"rows\":{\"Activity\":[[1,2,\"2016-03-01\",5],[1,2,\"2016-03-02\",6],[2,3,\"2017-06-25\",1],[3,1,\"2016-03-02\",0],[3,4,\"2018-07-03\",5]]}}", "categoryTitle": "Database", "contributors": [], "topicTags": [ { "name": "Database", "slug": "database", "translatedName": null, "__typename": "TopicTagNode" } ], "companyTagStats": null, "codeSnippets": [ { "lang": "MySQL", "langSlug": "mysql", "code": "# Write your MySQL query statement below\n", "__typename": "CodeSnippetNode" }, { "lang": "MS SQL Server", "langSlug": "mssql", "code": "/* Write your T-SQL query statement below */\n", "__typename": "CodeSnippetNode" }, { "lang": "Oracle", "langSlug": "oraclesql", "code": "/* Write your PL/SQL query statement below */\n", "__typename": "CodeSnippetNode" }, { "lang": "Pandas", "langSlug": "pythondata", "code": "import pandas as pd\n\ndef gameplay_analysis(activity: pd.DataFrame) -> pd.DataFrame:\n ", "__typename": "CodeSnippetNode" }, { "lang": "PostgreSQL", "langSlug": "postgresql", "code": "-- Write your PostgreSQL query statement below\n", "__typename": "CodeSnippetNode" } ], "stats": "{\"totalAccepted\": \"104.8K\", \"totalSubmission\": \"294.1K\", \"totalAcceptedRaw\": 104844, \"totalSubmissionRaw\": 294050, \"acRate\": \"35.7%\"}", "hints": [], "solution": { "id": "1602", "canSeeDetail": true, "paidOnly": false, "hasVideoSolution": false, "paidOnlyVideo": true, "__typename": "ArticleNode" }, "status": null, "sampleTestCase": "{\"headers\":{\"Activity\":[\"player_id\",\"device_id\",\"event_date\",\"games_played\"]},\"rows\":{\"Activity\":[[1,2,\"2016-03-01\",5],[1,2,\"2016-03-02\",6],[2,3,\"2017-06-25\",1],[3,1,\"2016-03-02\",0],[3,4,\"2018-07-03\",5]]}}", "metaData": "{\"mysql\": [\"Create table If Not Exists Activity (player_id int, device_id int, event_date date, games_played int)\"], \"mssql\": [\"Create table Activity (player_id int, device_id int, event_date date, games_played int)\"], \"oraclesql\": [\"Create table Activity (player_id int, device_id int, event_date date, games_played int)\", \"ALTER SESSION SET nls_date_format='YYYY-MM-DD'\"], \"database\": true, \"name\": \"gameplay_analysis\", \"pythondata\": [\"Activity = pd.DataFrame([], columns=['player_id', 'device_id', 'event_date', 'games_played']).astype({'player_id':'Int64', 'device_id':'Int64', 'event_date':'datetime64[ns]', 'games_played':'Int64'})\"], \"postgresql\": [\"\\nCreate table If Not Exists Activity (player_id int, device_id int, event_date date, games_played int)\"], \"database_schema\": {\"Activity\": {\"player_id\": \"INT\", \"device_id\": \"INT\", \"event_date\": \"DATE\", \"games_played\": \"INT\"}}}", "judgerAvailable": true, "judgeType": "large", "mysqlSchemas": [ "Create table If Not Exists Activity (player_id int, device_id int, event_date date, games_played int)", "Truncate table Activity", "insert into Activity (player_id, device_id, event_date, games_played) values ('1', '2', '2016-03-01', '5')", "insert into Activity (player_id, device_id, event_date, games_played) values ('1', '2', '2016-03-02', '6')", "insert into Activity (player_id, device_id, event_date, games_played) values ('2', '3', '2017-06-25', '1')", "insert into Activity (player_id, device_id, event_date, games_played) values ('3', '1', '2016-03-02', '0')", "insert into Activity (player_id, device_id, event_date, games_played) values ('3', '4', '2018-07-03', '5')" ], "enableRunCode": true, "enableTestMode": false, "enableDebugger": false, "envInfo": "{\"mysql\": [\"MySQL\", \"
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