Easy Solution Explained
\n# Approach\n Describe your approach to solving the problem. \nThe approach to solving this problem is to use the dropna function provided by the Pandas librar
\n# Approach\n<!-- Describe your approach to solving the problem. -->\nThe approach to solving this problem is to use the dropna function provided by the Pandas library to remove rows with missing values. Specifically, we target the \'name\' column by setting the subset parameter to \'name\'.\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\nO(n), where n is the number of rows in the \'students\' DataFrame.\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nO(n), as the function creates a new DataFrame with missing values dropped. \n# Code\n```\nimport pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    return students.dropna(subset=\'name\')\n```
['Pandas']