存量题库数据更新

leetcode/originData/drop-missing-data.json

@@ -6,17 +6,17 @@
             "boundTopicId": null,
             "title": "Drop Missing Data",
             "titleSlug": "drop-missing-data",
-            "content": "<pre>\nDataFrame students\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| student_id  | int    |\n| name        | object |\n| age         | int    |\n+-------------+--------+\n</pre>\n\n<p>There are some rows having missing values in the <code>name</code> column.</p>\n\n<p>Write a solution to remove the rows with missing values.</p>\n\n<p>The result format is in the following example.</p>\n\n<p>&nbsp;</p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:\n</strong>+------------+-------+-----+\n| student_id | name  | age |\n+------------+-------+-----+\n| 32         | Piper | 5   |\n| 217        | Grace | 19  |\n| 779        | None  | 20  |\n| 849        | None  | 14  |\n+------------+-------+-----+\n<strong>Output:\n</strong>+------------+-------+-----+\n| student_id | name  | age |\n+------------+-------+-----+\n| 32         | Piper | 5   |\n| 217        | Grace | 19  |\n+------------+-------+-----+\n<strong>Explanation:</strong> \nStudents with ids 779 and 849 have empty values in the name column, so they will be removed.</pre>\n",
+            "content": "<pre>\nDataFrame students\n+-------------+--------+\n| Column Name | Type   |\n+-------------+--------+\n| student_id  | int    |\n| name        | object |\n| age         | int    |\n+-------------+--------+\n</pre>\n\n<p>There are some rows having missing values in the <code>name</code> column.</p>\n\n<p>Write a solution to remove the rows with missing values.</p>\n\n<p>The result format is in the following example.</p>\n\n<p>&nbsp;</p>\n<p><strong class=\"example\">Example 1:</strong></p>\n\n<pre>\n<strong>Input:\n</strong>+------------+---------+-----+\n| student_id | name    | age |\n+------------+---------+-----+\n| 32         | Piper   | 5   |\n| 217        | None    | 19  |\n| 779        | Georgia | 20  |\n| 849        | Willow  | 14  |\n+------------+---------+-----+\n<strong>Output:\n</strong>+------------+---------+-----+\n| student_id | name    | age |\n+------------+---------+-----+\n| 32         | Piper   | 5   |\n| 779        | Georgia | 20  | \n| 849        | Willow  | 14  | \n+------------+---------+-----+\n<strong>Explanation:</strong> \nStudent with id 217 havs empty value in the name column, so it will be removed.</pre>\n",
             "translatedTitle": null,
             "translatedContent": null,
             "isPaidOnly": false,
             "difficulty": "Easy",
-            "likes": 7,
+            "likes": 25,
             "dislikes": 1,
             "isLiked": null,
             "similarQuestions": "[]",
-            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,\"Grace\",19],[779,null,20],[849,null,14]]}}",
-            "categoryTitle": "Algorithms",
+            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,null,19],[779,\"Georgia\",20],[849,\"Willow\",14]]}}",
+            "categoryTitle": "pandas",
             "contributors": [],
             "topicTags": [],
             "companyTagStats": null,
@@ -28,7 +28,7 @@
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-            "stats": "{\"totalAccepted\": \"270\", \"totalSubmission\": \"295\", \"totalAcceptedRaw\": 270, \"totalSubmissionRaw\": 295, \"acRate\": \"91.5%\"}",
+            "stats": "{\"totalAccepted\": \"10K\", \"totalSubmission\": \"15.4K\", \"totalAcceptedRaw\": 10032, \"totalSubmissionRaw\": 15398, \"acRate\": \"65.2%\"}",
             "hints": [
                 "Consider using a build-in function in pandas library to remove the rows with missing values based on specified data."
             ],
@@ -41,14 +41,14 @@
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             "status": null,
-            "sampleTestCase": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,\"Grace\",19],[779,null,20],[849,null,14]]}}",
+            "sampleTestCase": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,null,19],[779,\"Georgia\",20],[849,\"Willow\",14]]}}",
             "metaData": "{\n  \"pythondata\": [\n    \"students = pd.DataFrame([], columns=['student_id', 'name', 'age']).astype({'student_id':'Int64', 'name':'object', 'age':'Int64'})\"\n  ],\n  \"database\": true,\n  \"name\": \"dropMissingData\",\n  \"languages\": [\n    \"pythondata\"\n  ]\n}",
             "judgerAvailable": true,
             "judgeType": "large",
             "mysqlSchemas": [],
             "enableRunCode": true,
             "enableTestMode": false,
-            "enableDebugger": true,
+            "enableDebugger": false,
             "envInfo": "{\"pythondata\": [\"Pandas\", \"<p>Python 3.10 with Pandas 2.0.2 and NumPy 1.25.0</p>\"]}",
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