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            "title": "Drop Missing Data",
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            "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",
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                    "code": "import pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    ",
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                "Consider using a build-in function in pandas library to remove the rows with missing values based on specified data."
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            "sampleTestCase": "{\"headers\":{\"students\":[\"student_id\",\"name\",\"age\"]},\"rows\":{\"students\":[[32,\"Piper\",5],[217,\"Grace\",19],[779,null,20],[849,null,14]]}}",
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