存量题库数据更新

leetcode-cn/originData/drop-missing-data.json

@@ -2,14 +2,14 @@
     "data": {
         "question": {
             "questionId": "3075",
-            "questionFrontendId": "100016",
-            "categoryTitle": "Algorithms",
+            "questionFrontendId": "2883",
+            "categoryTitle": "pandas",
             "boundTopicId": 2467491,
             "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",
-            "translatedTitle": null,
-            "translatedContent": null,
+            "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": "删去丢失的数据",
+            "translatedContent": "<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>在 <code>name</code> 列里有一些具有缺失值的行。</p>\n\n<p>编写一个解决方案，删除具有缺失值的行。</p>\n\n<p>返回结果格式如下示例所示。</p>\n\n<p>&nbsp;</p>\n\n<p><b>示例 1:</b></p>\n\n<pre>\n<strong>输入：\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>输出：\n</strong>+------------+---------+-----+\n| student_id | name    | age |\n+------------+---------+-----+\n| 32         | Piper   | 5   |\n| 779        | Georgia | 20  | \n| 849        | Willow  | 14  | \n+------------+---------+-----+\n<b>解释：\n</b>学号为 217 的学生所在行在 name 列中有空值，因此这一行将被删除。</pre>\n",
             "isPaidOnly": false,
             "difficulty": "Easy",
             "likes": 0,
@@ -28,13 +28,13 @@
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                 }
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-            "stats": "{\"totalAccepted\": \"14\", \"totalSubmission\": \"14\", \"totalAcceptedRaw\": 14, \"totalSubmissionRaw\": 14, \"acRate\": \"100.0%\"}",
+            "stats": "{\"totalAccepted\": \"1.4K\", \"totalSubmission\": \"2.4K\", \"totalAcceptedRaw\": 1407, \"totalSubmissionRaw\": 2375, \"acRate\": \"59.2%\"}",
             "hints": [
                 "Consider using a build-in function in pandas library to remove the rows with missing values based on specified data."
             ],
             "solution": null,
             "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",
@@ -48,7 +48,7 @@
             "editorType": "CKEDITOR",
             "ugcQuestionId": null,
             "style": "LEETCODE",
-            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,\"Grace\",19],[779,null,20],[849,null,14]]}}",
+            "exampleTestcases": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,null,19],[779,\"Georgia\",20],[849,\"Willow\",14]]}}",
             "__typename": "QuestionNode"
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