✅☑️ Best Pandas Solution with simple meaning of every line
\n\npython\nimport pandas as pd\n\n- import pandas as pd: Imports the Pandas library and assigns it the alias pd, allowing us to refer to Pandas functions and c
\n\n```python\nimport pandas as pd\n```\n- `import pandas as pd`: Imports the Pandas library and assigns it the alias `pd`, allowing us to refer to Pandas functions and classes using `pd`.\n\n```python\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n```\n- `def dropMissingData(students: pd.DataFrame) -> pd.DataFrame`: Defines a function named `dropMissingData` that takes one argument, `students`, which is expected to be a Pandas DataFrame. It specifies that the function returns a Pandas DataFrame. This function appears to remove rows containing missing values in the DataFrame based on the `\'name\'` column.\n\n```python\n    return students.dropna(subset=[\'name\'])\n```\n- `students.dropna(subset=[\'name\'])`: Calls the `dropna()` method on the DataFrame `students`. The `subset` parameter specifies that only the `\'name\'` column should be considered when dropping rows. This means any row with a missing value in the `\'name\'` column will be removed from the DataFrame.\n\n```python\n    return students\n```\n- `return students`: Returns the modified DataFrame. In this case, since `dropna()` creates a new DataFrame without modifying the original one, this line isn\'t necessary because the function already returns the result of `students.dropna(subset=[\'name\'])`. However, it doesn\'t affect the behavior of the function.\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']