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The code provided defines a function that creates a pivot table from a DataFrame. Here\u2019s a breakdown of what each part does and the overall effect of the c
The code provided defines a function that creates a pivot table from a DataFrame. Here\u2019s a breakdown of what each part does and the overall effect of the code:\n\n### Function Definition\n\n```python\nimport pandas as pd\n\ndef pivotTable(df: pd.DataFrame) -> pd.DataFrame:\n    return df.pivot(index=\'month\', columns=\'city\', values=\'temperature\')\n```\n\n### Explanation\n\n1. **`df.pivot(...)`**:\n   - This is a method from pandas that reshapes data in a DataFrame to produce a new DataFrame with a different structure.\n   \n2. **Parameters of `pivot` Method**:\n   - **`index=\'month\'`**: This specifies that the unique values of the \'month\' column will be used as the index (rows) of the pivot table.\n   - **`columns=\'city\'`**: This specifies that the unique values of the \'city\' column will be used as the columns of the pivot table.\n   - **`values=\'temperature\'`**: This specifies that the values from the \'temperature\' column will fill the cells of the pivot table.\n\n### Result\n\nThe function will return a DataFrame where:\n* Each row represents a unique month.\n* Each column represents a unique city.\n* The cell values represent the temperature for that month and city.\n\n### Example\n\nConsider the following sample DataFrame:\n\n```python\ndata = {\n    \'month\': [\'Jan\', \'Jan\', \'Feb\', \'Feb\', \'Mar\', \'Mar\'],\n    \'city\': [\'New York\', \'Los Angeles\', \'New York\', \'Los Angeles\', \'New York\', \'Los Angeles\'],\n    \'temperature\': [30, 65, 28, 70, 50, 75]\n}\n\ndf = pd.DataFrame(data)\n```\n\nUsing the `pivotTable` function:\n\n```python\npivot_df = pivotTable(df)\nprint(pivot_df)\n```\n\nThe resulting pivot table DataFrame would look like:\n\n```\ncity     Los Angeles  New York\nmonth                            \nFeb              70        28\nJan              65        30\nMar              75        50\n```\n\n### Key Points\n\n* The `pivot` method is useful for transforming long-format data (where each row represents a single observation) into wide-format data (where each unique value of a specified column becomes a column in the output DataFrame).\n* This transformation is often used for summarizing and analyzing data, especially when preparing data for visualization.\n\nThis function is ideal for situations where you want to summarize temperature data across different months and cities, making it easier to compare and analyze the temperatures in a structured format.
['Pandas']