easy simple solution||beginner friendly
here is my solution with explanantion\n\n\nRemoving Rows with Any Missing Data:\n\nstudents.dropna() removes all rows that contain missing values in any column.
here is my solution with explanantion\n\n\nRemoving Rows with Any Missing Data:\n\nstudents.dropna() removes all rows that contain missing values in any column.\nRemoving Rows with Missing Data in Specific Columns:\n\nstudents.dropna(subset=[\'name\']) removes rows where the \'name\' column has missing values but retains rows where other columns might have missing data.\n```pythondata []\nimport pandas as pd\n\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    return students.dropna(subset=[\'name\'])\n\n```
['Pandas']