Best solution for beginners with explaination.
Intuition\nThe goal is to clean the data by removing rows where the \'name\' column has missing values. This is a common preprocessing step in data analysis to 
# Intuition\nThe goal is to clean the data by removing rows where the \'name\' column has missing values. This is a common preprocessing step in data analysis to ensure that the dataset is complete and accurate.\n\n# Approach\n1. Use the `dropna` method provided by pandas to remove rows with missing values in the \'name\' column.\n2. The `subset` parameter of `dropna` is used to specify that we only care about missing values in the \'name\' column.\n3. The `inplace=True` parameter is used to modify the original DataFrame directly.\n\n# Complexity\n- Time complexity: \n  The time complexity of this operation is \\(O(n)\\), where \\(n\\) is the number of rows in the DataFrame. This is because each row needs to be checked for a missing value in the \'name\' column.\n\n- Space complexity:\n  The space complexity is \\(O(1)\\) if `inplace=True` is used, as it modifies the DataFrame in place without requiring additional memory for a new DataFrame. If `inplace=False`, the space complexity would be \\(O(n)\\) because it would create a new DataFrame.\n\n# Code\n```python\nimport pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    students.dropna(subset=[\'name\'], inplace=True)\n    return students\n```\n\nThis code efficiently removes rows with missing \'name\' values from the DataFrame, ensuring that the dataset is clean for further analysis.
['Pandas']