#ENV_SETUP

Prompt Category:
Environment and Framework Building

Prompt:
Problem Statement:I have a Google Sheet named “Marscode V2 responses”. Would like to extract specific columns like Context Awareness, Autonomy, Experience, Output Quality along with the ratings.
Requirements/Goals:
Access the Google Sheet “https://docs.google.com/spreadsheets/d/15FMeidgU2Dg7Q4JKPkLAdJmQ3IxWCWJXjhCo9UterCE/edit?gid=2052232470#gid=2052232470” using Google APIs with the Credentials.json
Extract specific columns like  Context Awareness, Autonomy, Experience, Output Quality along with the ratings.
Calculate the Mean Rating for all these metrics and introduce a new column in the output sheet named “Mean Rating”
Find the difference between Mean Rating and Overall Satisfaction column.
Introduce a new column called “Result” and mark this column “Ok” and color the cell green if the difference is in between -1 to 1.
Mark the “Result” column “Not ok” and color the cell “Red” if the difference is less than -1 and greater than +1.
The output columns should be “Email address”, “Tool used”, “Feature Used”, “Context Awareness”, “Autonomy”, “Experience”, “Output Quality”.”Overall Rating”, “Mean Rating”, “Difference”, “Result”, “Unique ID”, “Pod” and should be created in a Target Google sheet https://docs.google.com/spreadsheets/d/1FEqiDqqPfb9YHAWBiqVepmmXj22zNqXNNI7NLGCDVak/edit?gid=0#gid=0

Context/Tools:
Check that the Source Google sheet and Target Google sheet is shared with the Google Service Account for Read and Write access.
All the Python Google API dependencies are installed in the project
Task:
Please generate a concise, step-by-step plan or outline for addressing the above problem. The outline should include:
Overall Approach:
Summarize how you will structure the solution from start to finish.
Data/Resource Access:
Explain any required setup or authentication (if applicable).
Solution Steps:
List each major step to implement (e.g., retrieving data, processing/calculating values, handling edge cases, etc.).
Testing & Validation Strategy:
Briefly describe how you would test or verify each part of the solution.
Potential Edge Cases & Error Handling:
Indicate any likely pitfalls and how you’d handle them.
Format your response as a bullet-point or numbered outline. Avoid writing any actual code at this stage—focus on the plan itself.

User Observation:
I'll help you create a detailed plan for your Google Sheets data processing task. Here's a comprehensive outline:

### Overall Approach
- Create a Python script that will:
  1. Authenticate with Google Sheets API
  2. Read data from source spreadsheet
  3. Process the data (calculations and formatting)
  4. Write processed data to target spreadsheet

### Data/Resource Access
1. Authentication Setup:
   - Use credentials.json for Google Service Account authentication
   - Verify access permissions for both source and target sheets
   - Set up proper scopes for Google Sheets API

2. Required Dependencies:
   - google-auth
   - google-auth-oauthlib
   - google-auth-httplib2
   - google-api-python-client
   - pandas (for data processing)

### Solution Steps
1. Data Retrieval:
   - Connect to source spreadsheet using spreadsheet ID
   - Read specified columns:
     - Email address
     - Tool used
     - Feature Used
     - Context Awareness
     - Autonomy
     - Experience
     - Output Quality
     - Overall Rating
     - Unique ID
     - Pod

2. Data Processing:
   - Calculate Mean Rating:
     - Average of Context Awareness, Autonomy, Experience, and Output Quality
   - Calculate Difference:
     - Mean Rating minus Overall Rating
   - Determine Result:
     - "Ok" if difference is between -1 and 1
     - "Not ok" otherwise

3. Output Formatting:
   - Prepare data structure with all required columns
   - Set up color formatting:
     - Green for "Ok" results
     - Red for "Not ok" results

4. Data Writing:
   - Connect to target spreadsheet
   - Write processed data
   - Apply conditional formatting

### Testing & Validation Strategy
1. Authentication Testing:
   - Verify successful connection to both spreadsheets
   - Confirm read/write permissions

2. Data Validation:
   - Check for missing or null values
   - Verify numerical values are within expected ranges
   - Validate calculations with sample data
   - Confirm correct column mapping

3. Output Verification:
   - Verify all required columns are present
   - Check formatting is applied correctly
   - Validate calculation results
   - Compare source and target data counts

### Edge Cases & Error Handling
1. Data Issues:
   - Handle missing values in rating columns
   - Account for non-numeric ratings
   - Handle empty rows or partial data

2. Authentication Issues:
   - Handle expired credentials
   - Manage permission denied scenarios
   - Implement retry logic for API calls

3. Processing Safeguards:
   - Validate input data types
   - Implement bounds checking for calculations
   - Handle division by zero scenarios
   - Implement logging for debugging

4. API Limitations:
   - Handle API quota limits
   - Manage batch processing for large datasets
   - Implement rate limiting

This plan provides a structured approach to implementing your requirements while ensuring robust error handling and validation.

Response ID:
ed29f9b4-ed44-41d6-a879-1ccdbf466c97

Author: suryasandeepboda

.gitignore

@@ -0,0 +1 @@
+credentials.json

README.md

@@ -1,2 +1,28 @@
-# marscode_zero_to_one_3
-Marscode zero to one POC
+Problem Statement:
+
+I have a Google Sheet named “Marscode V2 responses”. Would like to extract specific columns like Context Awareness, Autonomy, Experience, Output Quality along with the ratings.
+Requirements/Goals:
+Access the Google Sheet “https://docs.google.com/spreadsheets/d/15FMeidgU2Dg7Q4JKPkLAdJmQ3IxWCWJXjhCo9UterCE/edit?gid=2052232470#gid=2052232470” using Google APIs with the Credentials.json
+Extract specific columns like  Context Awareness, Autonomy, Experience, Output Quality along with the ratings.
+Calculate the Mean Rating for all these metrics and introduce a new column in the output sheet named “Mean Rating”
+Find the difference between Mean Rating and Overall Satisfaction column.
+Introduce a new column called “Result” and mark this column “Ok” and color the cell green if the difference is in between -1 to 1.
+Mark the “Result” column “Not ok” and color the cell “Red” if the difference is less than -1 and greater than +1.
+The output columns should be “Email address”, “Tool used”, “Feature Used”, “Context Awareness”, “Autonomy”, “Experience”, “Output Quality”.”Overall Rating”, “Mean Rating”, “Difference”, “Result”, “Unique ID”, “Pod” and should be created in a Target Google sheet https://docs.google.com/spreadsheets/d/1FEqiDqqPfb9YHAWBiqVepmmXj22zNqXNNI7NLGCDVak/edit?gid=0#gid=0
+
+Context/Tools:
+Check that the Source Google sheet and Target Google sheet is shared with the Google Service Account for Read and Write access.
+All the Python Google API dependencies are installed in the project
+Task:
+Please generate a concise, step-by-step plan or outline for addressing the above problem. The outline should include:
+Overall Approach:
+Summarize how you will structure the solution from start to finish.
+Data/Resource Access:
+Explain any required setup or authentication (if applicable).
+Solution Steps:
+List each major step to implement (e.g., retrieving data, processing/calculating values, handling edge cases, etc.).
+Testing & Validation Strategy:
+Briefly describe how you would test or verify each part of the solution.
+Potential Edge Cases & Error Handling:
+Indicate any likely pitfalls and how you’d handle them.
+Format your response as a bullet-point or numbered outline. Avoid writing any actual code at this stage—focus on the plan itself.