Reshaping DataFrame in Pandas: pivot_table vs. manual Loop
16. Pivot Table Reshaping: pivot_table() vs. Manual Reshaping
Write a Pandas program that uses the pivot_table method to reshape a DataFrame and compares the performance with manual reshaping using for loops.
Sample Solution :
Python Code :
Output:
Time taken using pivot_table method: 0.130437 seconds Time taken using manual reshaping: 1.244391 seconds
Explanation:
- Import Libraries:
- Import pandas, numpy, and time.
- Create DataFrame:
- Generate a sample DataFrame with 1,000,000 rows, with categorical columns 'A' and 'B', and a numerical column 'values'.
- Time Measurement for pivot_table Method:
- Measure the time taken to reshape the DataFrame using the pivot_table method.
- Time Measurement for Manual Reshaping:
- Measure the time taken to manually reshape the DataFrame using nested for loops and mean calculations.
- Print Results:
- Print the time taken for each method.
For more Practice: Solve these Related Problems:
- Write a Pandas program to reshape a DataFrame using pivot_table() and measure the time taken.
- Write a Pandas program to manually reshape a DataFrame using for loops and compare its performance with pivot_table().
- Write a Pandas program to create a pivot table with multiple indexes and benchmark it against a custom reshaping algorithm.
- Write a Pandas program to evaluate the performance difference between pivot_table() and manual DataFrame manipulation for data restructuring.
Go to:
Previous: Optimize string operations in Pandas: str accessor vs. apply.
Next: Performance comparison of DataFrame sorting in Pandas.
Python-Pandas Code Editor:
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