Simple solution using dropna()
Intuition\n Describe your first thoughts on how to solve this problem. \nThe intuition for this problem is to identify and remove rows where the \'name\' column
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\nThe intuition for this problem is to identify and remove rows where the \'name\' column has missing values. In pandas, missing values are typically represented as NaN (Not a Number) or None.\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\nWe can use pandas\' dropna() method to remove rows with missing values. Here\'s the step-by-step approach:\n\nUse the dropna() method on the DataFrame.\nSpecify the \'name\' column as the subset to check for missing values.\nReturn the resulting DataFrame with the rows removed.\n\n# Complexity\nTime complexity: O(n)\n\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\nThe time complexity is O(n), where n is the number of rows in the DataFrame. The dropna() method needs to check each row in the specified column for missing values.\n\nSpace complexity: O(n)\n\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nThe space complexity is O(n) in the worst case, where there are no missing values and we need to keep all rows. In practice, it might be less if there are many rows with missing values, but we consider the worst-case scenario.\n\n# Code\n```pythondata []\nimport pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    return students.dropna(subset=["name"])\n```
['Pandas']