🔥Best Solution in Pandas || One-liner || With explanation ||🔥
Explanation\nreturn pd.concat([df1, df2])\nThis line concatenates the two input DataFrames df1 and df2 along the rows using the pd.concat() function. The concat
# Explanation\n`return pd.concat([df1, df2])`\nThis line concatenates the two input DataFrames df1 and df2 along the rows using the pd.concat() function. The concatenated DataFrame is directly returned by the function.\n\n\n# Complexity\n- Time complexity: The time complexity of `pd.concat()` depends on the size of the input DataFrames `df1` and `df2` and the number of rows and columns they contain. The operation involves copying data from both DataFrames to create the concatenated DataFrame.\nTherefore, the time complexity is generally $$O(m + n)$$, where `m` and `n` are the numbers of rows in `df1` and `df2`, respectively.\n\n- Space complexity: The space complexity of the `pd.concat()` operation also depends on the size of the input DataFrames. It creates a new DataFrame containing the concatenated data, so the space complexity is $$O(m + n)$$, where `m` and `n` are the numbers of rows in `df1` and `df2`, respectively.\n\n# Code\n```\nimport pandas as pd\n\ndef concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:\n    return pd.concat([df1, df2])\n    \n```\n![honest-work-meme-cb0f0fb2227fb84b77b3c9a851ac09b095ab74d8-s1100-c50.jpg](https://assets.leetcode.com/users/images/1140cd2c-01e0-4980-9aeb-4f7c7885164d_1710721285.1588638.jpeg)\n
['Python', 'Python3', 'Pandas']