✅Easy And Simple Solution With EXPLANATION✅
\n\n### Explanation\n\nThis code defines a function named dropMissingData that takes a pandas DataFrame as input and returns a new DataFrame with rows containin
\n\n### Explanation\n\nThis code defines a function named `dropMissingData` that takes a pandas DataFrame as input and returns a new DataFrame with rows containing missing values in the `name` column removed.\n\n#### Step-by-step Explanation:\n\n1. **Import pandas**:\n    ```python\n    import pandas as pd\n    ```\n    - This line imports the pandas library, which is used for data manipulation and analysis in Python.\n\n2. **Define the function**:\n    ```python\n    def dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    ```\n    - The function `dropMissingData` is defined with one parameter `students`. The type hint suggests that `students` should be a pandas DataFrame.\n    - The function returns a pandas DataFrame, as indicated by the type hint `-> pd.DataFrame`.\n\n3. **Drop Rows with Missing `name` Values**:\n    ```python\n    return students.dropna(subset=["name"])\n    ```\n    - The `dropna` method is called on the `students` DataFrame.\n    - The `subset` parameter is set to `["name"]`, indicating that rows with missing values in the `name` column should be dropped.\n    - The method returns a new DataFrame with rows containing missing `name` values removed.\n\n\n\n#### Summary:\n\n- The function takes a pandas DataFrame and removes rows with missing values in the `name` column.\n- The `dropna` method is used with the `subset` parameter set to `["name"]` to identify and remove such rows.\n- The function returns a new DataFrame with the rows containing missing `name` values removed.\n\n---\n\n# Code\n```\nimport pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    return students.dropna(subset="name")\n```
['Pandas']