Python beginner freindly solution || python3 with deep explainaiton.
Intuition\nWhen dealing with multiple datasets, it\'s often necessary to combine them into a single dataset for comprehensive analysis. If the datasets share th
# Intuition\nWhen dealing with multiple datasets, it\'s often necessary to combine them into a single dataset for comprehensive analysis. If the datasets share the same structure (i.e., columns), concatenation is a straightforward method to stack them together vertically.\n\n# Approach\n1. Use the `concat` function from the pandas library to concatenate two DataFrames.\n2. The `concat` function combines the DataFrames along a particular axis (by default, axis=0, which means vertical stacking).\n3. Return the combined DataFrame.\n\n# Complexity\n- Time complexity:\n  The time complexity of this operation is \\(O(n + m)\\), where \\(n\\) and \\(m\\) are the number of rows in `df1` and `df2`, respectively. This is because each row in both DataFrames needs to be processed once to form the combined DataFrame.\n\n- Space complexity:\n  The space complexity is \\(O(n + m)\\) because a new DataFrame is created that holds all the rows from both input DataFrames.\n\n# Code\n```python\nimport pandas as pd\n\ndef concatenateTables(df1: pd.DataFrame, df2: pd.DataFrame) -> pd.DataFrame:\n    return pd.concat([df1, df2])\n```\n\nThis function efficiently concatenates two DataFrames, ensuring that the resulting DataFrame includes all rows from both input DataFrames. The default behavior of `pd.concat` is to concatenate along the vertical axis (i.e., adding rows), which is suitable for combining datasets with the same structure.
['Pandas']