🔥Best Solution in Pandas || One-liner || With explanation ||🔥
Explanation\nreturn students.dropna(subset=[\'name\'])\nThis line removes rows with missing values (NaN) in the \'name\' column of the DataFrame students. The .
# Explanation\n`return students.dropna(subset=[\'name\'])`\nThis line removes rows with missing values (NaN) in the \'name\' column of the DataFrame students. The `.dropna()` method in pandas is used for this purpose. By specifying `subset=[\'name\']`, it ensures that only the \'name\' column is considered when checking for missing values. The method returns a new DataFrame with rows containing missing values in the specified subset removed.\n\nOverall, this function takes a DataFrame containing student data as input, removes rows with missing values in the \'name\' column, and returns the modified DataFrame with missing values removed.\n\n\n\n# Complexity\n- Time complexity: $$O(n)$$, where n is the number of rows in the input DataFrame.\n\n- Space complexity: The function returns a new DataFrame with rows containing missing values removed. The space required to store this new DataFrame depends on the number of rows in the original DataFrame that contain missing values. However, the space complexity for this operation is typically not explicitly considered in the analysis, as it is necessary to store the resulting data.\n\n# Code\n```\ndef dropMissingData(students: pd.DataFrame) -> pd.DataFrame:\n    return students.dropna(subset=[\'name\'])\n```\n![honest-work-meme-cb0f0fb2227fb84b77b3c9a851ac09b095ab74d8-s1100-c50.jpg](https://assets.leetcode.com/users/images/293ae63d-105b-4f40-ab81-ca511664b17f_1710695602.3147526.jpeg)\n
['Python', 'Python3', 'Pandas']