Simple and easy to understand
Intuition\n Describe your first thoughts on how to solve this problem. \nThe goal is to pivot the weather DataFrame such that each month becomes a row, and each
# Intuition\n<!-- Describe your first thoughts on how to solve this problem. -->\nThe goal is to pivot the weather DataFrame such that each month becomes a row, and each city becomes a column, with temperatures as the values. This is a common reshaping task in pandas, useful when we want to compare data points (temperature) across multiple categories (cities) over a specific dimension (months).\n# Approach\n<!-- Describe your approach to solving the problem. -->\n1. Input Verification: First, confirm that the input weather is a DataFrame. If it\'s already a DataFrame, the explicit conversion df = pd.DataFrame(weather) is not necessary, but it ensures uniformity.\n\n2. Pivoting:\n Use the pivot() method to reshape the DataFrame.\n- index=\'month\': Specifies that each row represents a unique month.\n- columns=\'city\': Each city gets its own column in the new DataFrame.\n- values=\'temperature\': The cell values come from the temperature column.\n\n3. Returning the Pivoted Table: After pivoting, the function returns the new DataFrame.\n\n\n# Complexity\n- Time complexity:\n<!-- Add your time complexity here, e.g. $$O(n)$$ -->\nThe time complexity is O(n), where n is the number of rows in the original DataFrame. This is because each row needs to be processed to place it correctly in the pivoted DataFrame.\n\n\n\n- Space complexity:\n<!-- Add your space complexity here, e.g. $$O(n)$$ -->\nThe space complexity is O(n), where n is the number of rows in the DataFrame. The function creates a new DataFrame to store the pivoted values, so the space required is proportional to the number of rows in the input data.\n# Code\n```pythondata []\nimport pandas as pd\n\ndef pivotTable(weather: pd.DataFrame) -> pd.DataFrame:\n    df=pd.DataFrame(weather)\n    pivot_table=df.pivot(index=\'month\',columns=\'city\',values=\'temperature\')\n    return pivot_table\n    \n```
['Pandas']