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#REQ_DEV
Prompt Category: Requirement Development Prompt: Suggest Python code, to do the following: - Calculate Mean Rating: - Average of (Context Awareness, Autonomy, Experience, Output Quality) - Calculate difference: - Difference = Mean Rating - Overall Rating - Determine Result status: - "Ok" (green) if -1 ≤ difference ≤ 1 - "Not ok" (red) if difference < -1 or > 1 extract_data.py User Observation: Observed that the required columns were extracted and the Mean Rating was calculated Response ID: c6053bc4-9ef2-475c-b2df-0ac54cbd104b
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‎.coverage

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‎.gitignore

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credentials.json
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app.log
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app.log
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.coverage

‎app.log

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2025-03-13 17:52:54,403 - WARNING - Column 'POD' not found in sheet
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2025-03-13 17:52:54,405 - INFO - Successfully filtered required columns
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2025-03-13 17:52:54,405 - INFO - Data retrieval successful
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2025-03-13 18:05:17,826 - INFO - Successfully loaded credentials
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2025-03-13 18:05:17,826 - INFO - Successfully created Google Sheets service
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2025-03-13 18:05:17,826 - ERROR - An error occurred: API Error
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Traceback (most recent call last):
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File "/Users/surya.sandeep.boda/Desktop/Marscode Zero to One 3/extract_data.py", line 44, in get_google_sheet_data
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result = sheet.values().get(
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1093, in __call__
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return self._mock_call(*args, **kwargs)
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1097, in _mock_call
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return self._execute_mock_call(*args, **kwargs)
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1152, in _execute_mock_call
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raise effect
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Exception: API Error
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2025-03-13 18:05:17,829 - INFO - Successfully loaded credentials
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2025-03-13 18:05:17,829 - INFO - Successfully created Google Sheets service
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2025-03-13 18:05:17,829 - ERROR - No data found in the sheet
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2025-03-13 18:05:17,830 - INFO - Successfully loaded credentials
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2025-03-13 18:05:17,830 - INFO - Successfully created Google Sheets service
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2025-03-13 18:05:17,831 - INFO - Retrieved 1 rows of data
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2025-03-13 18:05:17,834 - WARNING - Column 'Autonomy Rating' not found in sheet
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2025-03-13 18:05:17,834 - WARNING - Column 'Experience Rating' not found in sheet
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2025-03-13 18:05:17,834 - WARNING - Column 'Output Quality Rating' not found in sheet
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2025-03-13 18:05:17,834 - WARNING - Column 'Overall Satisfaction' not found in sheet
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2025-03-13 18:05:17,834 - WARNING - Column 'Unique ID' not found in sheet
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2025-03-13 18:05:17,834 - WARNING - Column 'POD' not found in sheet
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2025-03-13 18:05:17,835 - INFO - Successfully filtered required columns
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2025-03-13 18:10:37,427 - INFO - Successfully loaded credentials
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2025-03-13 18:10:37,427 - INFO - Successfully created Google Sheets service
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2025-03-13 18:10:37,428 - ERROR - An error occurred: API Error
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Traceback (most recent call last):
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File "/Users/surya.sandeep.boda/Desktop/Marscode Zero to One 3/extract_data.py", line 44, in get_google_sheet_data
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result = sheet.values().get(
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1093, in __call__
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return self._mock_call(*args, **kwargs)
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1097, in _mock_call
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return self._execute_mock_call(*args, **kwargs)
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File "/Library/Developer/CommandLineTools/Library/Frameworks/Python3.framework/Versions/3.9/lib/python3.9/unittest/mock.py", line 1152, in _execute_mock_call
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raise effect
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Exception: API Error
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2025-03-13 18:10:37,430 - INFO - Successfully loaded credentials
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2025-03-13 18:10:37,430 - INFO - Successfully created Google Sheets service
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2025-03-13 18:10:37,430 - ERROR - No data found in the sheet
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2025-03-13 18:10:37,431 - INFO - Successfully loaded credentials
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2025-03-13 18:10:37,431 - INFO - Successfully created Google Sheets service
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2025-03-13 18:10:37,434 - INFO - Retrieved 1 rows of data
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2025-03-13 18:10:37,436 - WARNING - Column 'Autonomy Rating' not found in sheet
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2025-03-13 18:10:37,436 - WARNING - Column 'Experience Rating' not found in sheet
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2025-03-13 18:10:37,436 - WARNING - Column 'Output Quality Rating' not found in sheet
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2025-03-13 18:10:37,436 - WARNING - Column 'Overall Satisfaction' not found in sheet
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2025-03-13 18:10:37,436 - WARNING - Column 'Unique ID' not found in sheet
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2025-03-13 18:10:37,436 - WARNING - Column 'POD' not found in sheet
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2025-03-13 18:10:37,437 - INFO - Successfully filtered required columns
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2025-03-13 18:15:32,290 - INFO - Successfully loaded credentials
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2025-03-13 18:15:32,291 - INFO - file_cache is only supported with oauth2client<4.0.0
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2025-03-13 18:15:32,292 - INFO - Successfully created Google Sheets service
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2025-03-13 18:15:35,144 - INFO - Retrieved 144 rows of data
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2025-03-13 18:15:35,147 - WARNING - Column 'Tool Used' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Feature' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Context Awareness Rating' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Autonomy Rating' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Experience Rating' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Output Quality Rating' not found in sheet
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2025-03-13 18:15:35,147 - WARNING - Column 'Overall Satisfaction' not found in sheet
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2025-03-13 18:15:35,148 - WARNING - Column 'POD' not found in sheet
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2025-03-13 18:15:35,150 - INFO - Successfully filtered required columns
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2025-03-13 18:15:35,151 - INFO - Data retrieval successful
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2025-03-13 18:17:11,477 - INFO - Successfully loaded credentials
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2025-03-13 18:17:11,478 - INFO - file_cache is only supported with oauth2client<4.0.0
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2025-03-13 18:17:11,479 - INFO - Successfully created Google Sheets service
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2025-03-13 18:17:13,625 - INFO - Retrieved 144 rows of data
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2025-03-13 18:17:13,625 - INFO - Available columns in sheet: ['Timestamp', 'Email Address', 'Tool being used', 'Feature used', 'Task Objective', 'Repository Link', 'Repository: Public/Private', 'Repository: Lines of Code', '[Turn 1]: Prompt Category', '[Turn 1]: Prompt', '[Turn 1]: Model Response', '[Turn 1]: Model Response - File [Use only when response is too long]', '[Turn 1]: Observations', '[Turn 1]: Screenshots', '[Turn 2]: Prompt Category', '[Turn 2]: Prompt', '[Turn 2]: Model Response', '[Turn 2]: Model Response - File [Use only when response is too long]', '[Turn 2]: Observations', '[Turn 2]: Screenshot', '[Turn 3]: Prompt Category', '[Turn 3]: Prompt', '[Turn 3]: Model Response', '[Turn 3]: Model Response - File [Use only when response is too long]', '[Turn 3]: Observations', '[Turn 3]: Screenshot', '[Turn 4]: Prompt Category', '[Turn 4]: Prompt', '[Turn 4]: Model Response', '[Turn 4]: Model Response - File [Use only when response is too long]', '[Turn 4]: Observations', '[Turn 4]: Screenshot', '[Turn 5]: Prompt Category', '[Turn 5]: Prompt', '[Turn 5]: Model Response', '[Turn 5]: Model Response - File [Use only when response is too long]', '[Turn 5]: Observations', '[Turn 5]: Screenshot', '[Turn 6]: Prompt Category', '[Turn 6]: Prompt', '[Turn 6]: Model Response', '[Turn 6]: Model Response - File [Use only when response is too long]', '[Turn 6]: Observations', '[Turn 6]: Screenshot', '[Turn 7]: Prompt Category', '[Turn 7]: Prompt', '[Turn 7]: Model Response', '[Turn 7]: Model Response - File [Use only when response is too long]', '[Turn 7]: Observations', '[Turn 7]: Screenshot', 'Context Awareness', 'Context Awareness: Errors', 'Rationale for Context Awareness', 'Output Quality', 'Output Quality: Errors', 'Rationale for Output Quality', 'Autonomy', 'Autonomy: Errors', 'Rationale for Autonomy', 'Iteration Size and Speed', 'Iteration Size and Speed: Errors', 'Rationale for Iteration Size and Speed', 'Experience', 'Experience - Flexibility, Ease of Use and Reliability: Errors', 'Rationale for Experience', 'Loom Recording Link', 'Overall Rating', 'Rationale behind the overall rating', '[Turn 1]: Final Turn?', '[Turn 2]: Final Turn?', '[Turn 3]: Final Turn?', '[Turn 4]: Final Turn?', '[Turn 5]: Final Turn?', '[Turn 6]: Final Turn?', '[Turn 7]: Final Turn?', '[Turn 1]: Turn ID', '[Turn 2]: Turn ID', '[Turn 3]: Turn ID', '[Turn 4]: Turn ID', '[Turn 5]: Turn ID', '[Turn 6]: Turn ID', '[Turn 7]: Turn ID', 'Unique ID']
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2025-03-13 18:17:13,627 - WARNING - Column 'Tool Used' not found in sheet
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2025-03-13 18:17:13,627 - WARNING - Column 'Feature' not found in sheet
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2025-03-13 18:17:13,627 - WARNING - Column 'Context Awareness Rating' not found in sheet
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2025-03-13 18:17:13,628 - WARNING - Column 'Autonomy Rating' not found in sheet
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2025-03-13 18:17:13,628 - WARNING - Column 'Experience Rating' not found in sheet
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2025-03-13 18:17:13,628 - WARNING - Column 'Output Quality Rating' not found in sheet
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2025-03-13 18:17:13,628 - WARNING - Column 'Overall Satisfaction' not found in sheet
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2025-03-13 18:17:13,628 - WARNING - Column 'POD' not found in sheet
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2025-03-13 18:17:13,630 - INFO - Successfully filtered required columns
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2025-03-13 18:17:13,631 - INFO - Data retrieval successful
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2025-03-13 18:19:11,025 - INFO - Successfully loaded credentials
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2025-03-13 18:19:11,027 - INFO - file_cache is only supported with oauth2client<4.0.0
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2025-03-13 18:19:11,029 - INFO - Successfully created Google Sheets service
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2025-03-13 18:19:13,585 - INFO - Retrieved 144 rows of data
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2025-03-13 18:19:13,585 - INFO - Available columns in sheet: ['Timestamp', 'Email Address', 'Tool being used', 'Feature used', 'Task Objective', 'Repository Link', 'Repository: Public/Private', 'Repository: Lines of Code', '[Turn 1]: Prompt Category', '[Turn 1]: Prompt', '[Turn 1]: Model Response', '[Turn 1]: Model Response - File [Use only when response is too long]', '[Turn 1]: Observations', '[Turn 1]: Screenshots', '[Turn 2]: Prompt Category', '[Turn 2]: Prompt', '[Turn 2]: Model Response', '[Turn 2]: Model Response - File [Use only when response is too long]', '[Turn 2]: Observations', '[Turn 2]: Screenshot', '[Turn 3]: Prompt Category', '[Turn 3]: Prompt', '[Turn 3]: Model Response', '[Turn 3]: Model Response - File [Use only when response is too long]', '[Turn 3]: Observations', '[Turn 3]: Screenshot', '[Turn 4]: Prompt Category', '[Turn 4]: Prompt', '[Turn 4]: Model Response', '[Turn 4]: Model Response - File [Use only when response is too long]', '[Turn 4]: Observations', '[Turn 4]: Screenshot', '[Turn 5]: Prompt Category', '[Turn 5]: Prompt', '[Turn 5]: Model Response', '[Turn 5]: Model Response - File [Use only when response is too long]', '[Turn 5]: Observations', '[Turn 5]: Screenshot', '[Turn 6]: Prompt Category', '[Turn 6]: Prompt', '[Turn 6]: Model Response', '[Turn 6]: Model Response - File [Use only when response is too long]', '[Turn 6]: Observations', '[Turn 6]: Screenshot', '[Turn 7]: Prompt Category', '[Turn 7]: Prompt', '[Turn 7]: Model Response', '[Turn 7]: Model Response - File [Use only when response is too long]', '[Turn 7]: Observations', '[Turn 7]: Screenshot', 'Context Awareness', 'Context Awareness: Errors', 'Rationale for Context Awareness', 'Output Quality', 'Output Quality: Errors', 'Rationale for Output Quality', 'Autonomy', 'Autonomy: Errors', 'Rationale for Autonomy', 'Iteration Size and Speed', 'Iteration Size and Speed: Errors', 'Rationale for Iteration Size and Speed', 'Experience', 'Experience - Flexibility, Ease of Use and Reliability: Errors', 'Rationale for Experience', 'Loom Recording Link', 'Overall Rating', 'Rationale behind the overall rating', '[Turn 1]: Final Turn?', '[Turn 2]: Final Turn?', '[Turn 3]: Final Turn?', '[Turn 4]: Final Turn?', '[Turn 5]: Final Turn?', '[Turn 6]: Final Turn?', '[Turn 7]: Final Turn?', '[Turn 1]: Turn ID', '[Turn 2]: Turn ID', '[Turn 3]: Turn ID', '[Turn 4]: Turn ID', '[Turn 5]: Turn ID', '[Turn 6]: Turn ID', '[Turn 7]: Turn ID', 'Unique ID']
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2025-03-13 18:19:13,590 - INFO - Successfully filtered required columns
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2025-03-13 18:19:13,591 - INFO - Data retrieval successful

‎extract_data.py

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from googleapiclient.discovery import build
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import pandas as pd
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import logging
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import numpy as np
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# Configure logging
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logging.basicConfig(
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LOGGER.info("Retrieved %d rows of data", len(df_data))
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# Log available columns for debugging
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LOGGER.debug("Available columns in sheet: %s", list(df_data.columns))
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LOGGER.info("Available columns in sheet: %s", list(df_data.columns))
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# Column mapping configuration
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column_mapping = {
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'Email Address': 'Email address',
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'Tool Used': 'Tool being used',
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'Feature': 'Feature used',
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'Context Awareness Rating': 'Context Awareness',
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'Autonomy Rating': 'Autonomy',
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'Experience Rating': 'Experience',
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'Output Quality Rating': 'Output Quality',
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'Overall Satisfaction': 'Overall Rating',
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'Unique ID': 'Unique ID',
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'POD': 'Pod'
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'Email Address': 'Email Address',
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'Tool being used': 'Tool being used',
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'Feature used': 'Feature used',
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'Context Awareness': 'Context Awareness',
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'Autonomy': 'Autonomy',
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'Experience': 'Experience',
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'Output Quality': 'Output Quality',
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'Overall Rating': 'Overall Rating',
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'Unique ID': 'Unique ID'
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}
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# Process columns
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filtered_df = df_data[required_columns]
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LOGGER.info("Successfully filtered required columns")
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# After getting filtered_df, add calculations
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rating_columns = ['Context Awareness', 'Autonomy', 'Experience', 'Output Quality']
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# Convert rating columns to numeric
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for col in rating_columns + ['Overall Rating']:
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filtered_df[col] = pd.to_numeric(filtered_df[col], errors='coerce')
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# Calculate Mean Rating
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filtered_df['Mean Rating'] = filtered_df[rating_columns].mean(axis=1)
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LOGGER.info("Calculated mean ratings")
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# Calculate difference
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filtered_df['Difference'] = filtered_df['Mean Rating'] - filtered_df['Overall Rating']
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# Determine Result status
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def get_result_status(difference):
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if -1 <= difference <= 1:
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return 'Ok'
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return 'Not ok'
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filtered_df['Result'] = filtered_df['Difference'].apply(get_result_status)
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# Create color mapping for results
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filtered_df['Result_Color'] = filtered_df['Result'].map({
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'Ok': 'green',
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'Not ok': 'red'
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})
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LOGGER.info("Calculated results and color mapping")
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# Reorder columns
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final_columns = [
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'Email Address', 'Tool being used', 'Feature used',
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'Context Awareness', 'Autonomy', 'Experience', 'Output Quality',
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'Overall Rating', 'Mean Rating', 'Difference', 'Result',
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'Unique ID'
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]
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filtered_df = filtered_df[final_columns]
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return filtered_df
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except Exception as error:
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if result_data is not None:
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LOGGER.info("Data retrieval successful")
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print("\nFirst 5 rows of retrieved data:")
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pd.set_option('display.max_columns', None) # Show all columns
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print(result_data.head())
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# Print summary statistics
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print("\nSummary Statistics:")
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print(f"Total Records: {len(result_data)}")
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print(f"Ok Results: {len(result_data[result_data['Result'] == 'Ok'])}")
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print(f"Not Ok Results: {len(result_data[result_data['Result'] == 'Not ok'])}")
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else:
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LOGGER.error("Failed to retrieve data")

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