Very easy approach, single line statement with easy approach
Intuition\n Describe your first thoughts on how to solve this problem. \n\n# Approach\n Describe your approach to solving the problem. \nStep 1 : Select \'conte
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\n\n# Approach\n<!-- Describe your approach to solving the problem. -->\nStep 1 : Select \'content\' column from tweets dataframe, for this use:\ntweets[\'content\']\n\nStep 2: Find length of content which is greater than 15,for this use: tweets[\'content\'].apply(len)>15\nThe apply() function is particularly useful for performing element-wise operations on Series or DataFrame columns, especially when you want to transform or manipulate data using a custom function. It\'s often used to avoid explicit loops, which can be slower and less concise.\n\nStep 3: Filter data and find only \'tweet_id\' column. But the thing you remember here is that if you will return this like :\n\ntweets[tweets[\'content\'].apply(len)>15][\'tweet_id\'],\nthen the above command will return series, like \n<class \'pandas.core.series.Series\'> \nnot dataframe, but in this question we need to find dataframe,so convert the result into dataframe using :\npd.DataFrame(tweets[tweets[\'content\'].apply(len)>15][\'tweet_id\'])\n\n# Complexity\n- Time complexity: O(n+k)\nThe total time complexity of the code is O(n + k), where n is the total number of rows in the DataFrame and k is the number of rows that satisfy the condition.\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\n\n- Space complexity: O(k)\nThe new DataFrame df is created to store the selected \'tweet_id\' values, which takes O(k) space, where k is the number of rows that satisfy the condition.\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\n\n# Code\n```\nimport pandas as pd\n\ndef invalid_tweets(tweets: pd.DataFrame) -> pd.DataFrame:\n \n    df=pd.DataFrame(tweets[tweets[\'content\'].apply(len)>15][\'tweet_id\'])\n\n    #result=df[[\'tweet_id\']]\n\n    return df\n```
['Pandas']