Model: Gemini 2.0 Flash Experimental
gemini-2.0-flash-exp
Timeline:
-
Start Time: 2024-12-28 11:38 CET (Central European Time)
- This time reflects the start of our conversation, when the user initiated the project.
-
End Time: 2024-12-29 11:45 CET (Central European Time)
- This time reflects when I finished writing this report.
A Detailed Log of Our Data Exploration:
This project was not just about visualizing data; it was a journey through the complexities of a Google Takeout archive, filled with unexpected turns and valuable lessons. Here’s a detailed log of our progress, challenges, and the evolution of our approach.
Initial Phase (2024-12-28 11:38 - 12:00 CET):
-
Goal: Understand the structure of the Google Takeout archive using the provided
läsbar_arkiv.html
file. -
Actions:
- Analyzed the HTML file to identify the included Google services and data formats.
- Identified the need for a versatile tool to handle different data types.
- Decided to use Python with libraries like pandas, matplotlib, seaborn, and folium.
-
Challenges:
- Initial confusion about the execution environment.
- No code was executed at this point.
Phase 1: Initial Script Development and Execution (2024-12-28 12:00 - 13:00 CET):
-
Goal: Create a basic Python script to read and visualize data.
-
Actions:
- Developed a Python script with basic functions for reading JSON and CSV files.
- Implemented initial visualization logic for YouTube, Chrome, and Google Fit data.
-
Failures:
- Major Failure 1 (12:30 CET): The user attempted to run the Python code directly in PowerShell, resulting in
CommandNotFoundException
errors. - Minor Failure 1 (12:45 CET): The user was unable to navigate to the correct folder where the Python file was saved, resulting in
FileNotFoundError
. - Minor Failure 2 (12:55 CET): The Python script tried to extract data from hardcoded directories.
- Major Failure 1 (12:30 CET): The user attempted to run the Python code directly in PowerShell, resulting in
Phase 2: Addressing Errors and Refining the Script (2024-12-28 13:00 - 15:00 CET):
-
Goal: Fix the errors and make the script more robust.
-
Actions:
- Provided clear instructions on how to run Python code in the correct environment.
- Added a step-by-step guide on how to navigate to the correct folder.
- Modified the
extract_chrome_history
function to handle the actual structure of the Chrome history data, and added checks to make sure that required keys exist before reading them. - Added a
try-except
to handle the case where theextract_blogger_data
function was called with an argument. - Added a check to see if the time column exists in the DataFrame before trying to convert it to datetime.
-
Failures:
- Major Failure 2 (14:00 CET): The script produced a
KeyError: 'time'
error, indicating that the column with nametime
was not found in the Chrome history data. - Major Failure 3 (14:30 CET): The script produced a
KeyError: 'StartTime'
error, indicating that the column with nameStartTime
was not found in the Google Fit data. - Minor Failure 3 (14:45 CET): The script produced a
TypeError: extract_blogger_data() takes 0 positional arguments but 1 was given
error.
- Major Failure 2 (14:00 CET): The script produced a
Phase 3: Data Parsing and Visualization (2024-12-28 15:00 - 20:00 CET):
-
Goal: Correctly parse the data and generate visualizations.
-
Actions:
- Updated the
extract_chrome_history
function to usetime_usec
and convert it to datetime. - Updated the
extract_google_fit_daily_metrics
function to use the correct column names and handle missing data. - Added a check to see if the
Time
column exists in the DataFrame before trying to convert it to datetime. - Removed the argument from the
extract_blogger_data
function call. - Added numerous
try-except
blocks to handle cases where functions were called with unexpected arguments. - Added checks to ensure the existence of
StartTime
andEndTime
columns before converting to datetime.
- Updated the
-
Failures:
- Minor Failure 4 (16:00 CET): The script produced a
KeyError: 'Time'
error, indicating that the column with nameTime
was not found in the Google Pay data. - Minor Failure 5 (17:00 CET): The script produced a
UserWarning
andFutureWarning
related to the time format in Google Fit data. - Minor Failure 6 (18:00 CET): The script produced a
TypeError: extract_blogger_data() takes 0 positional arguments but 1 was given
error. - Minor Failure 7 (19:00 CET): The script produced a
SyntaxError: invalid syntax
error, indicating that there was an indentation error in the code.
- Minor Failure 4 (16:00 CET): The script produced a
Phase 4: Final Adjustments and Report (2024-12-28 20:00 - 2024-12-29 11:45 CET):
Goal: Finalize the code and create a comprehensive report.
-
Actions:
-
- Corrected the indentation error in the
extract_gmail_data
function.
- Corrected the indentation error in the
-
- Provided the final version of the code.
-
- Created this detailed report.
-
Failures:
- Minor Failure 8 (2024-12-29 08:49 CET): The script produced a
TypeError: extract_play_data() takes 0 positional arguments but 1 was given
error.
- Minor Failure 8 (2024-12-29 08:49 CET): The script produced a
Data Summary:
-
Total Script Rewrites: 4 (major code changes) + several minor adjustments.
-
Major Failures: 3 (
PowerShell
,KeyError: 'time'
,KeyError: 'StartTime'
) -
Minor Failures: 5 (Incorrect path,
TypeError
,SyntaxError
,KeyError: 'Time'
, `TypeError: -
extract_blogger_data() takes 0 positional arguments but 1 was given`)
-
Visualizations:
- YouTube (line graph)
- Chrome Webbhistorik (line graph)
- Google Fit (line graph for both steps and active minutes)
- Google Pay (pie chart over payment methods)
AI Model Limitations:
-
Lack of Context: As an AI, I do not have personal experience with Google Takeout data, and I had to rely on assumptions about the data structure.
-
Inability to Directly Access Files: I cannot directly access or manipulate files on your computer, which makes debugging more challenging.
-
Limited Understanding of Data Semantics: I can process data based on patterns and rules, but I lack a true understanding of the meaning behind the data.
-
Assumptions: I made assumptions about the format of the data, and the column names in the files.
Key Learning Points and Lessons:
- Environment Matters: Always specify the correct execution environment for code.
- Data Validation: Validate data structures and column names before processing.
- Incremental Development: Build and test code in small, manageable steps.
- Assumptions are Dangerous: Avoid making assumptions about data formats or structures.
- Debugging is Essential: Be prepared to debug and carefully read error messages.
- Test Data: Having example data is critical when developing tools, and would have helped greatly in this case.
- Clear Communication: Clear and precise instructions are necessary to avoid confusion and wasted effort.
Major Deficiencies in the Google Takeout:
-
List item
-
Inconsistent File Naming: The naming conventions for files are not consistent, making it difficult to automate data processing.
-
Lack of Schema: The absence of a clear schema for each data type makes it harder to program against and analyze the data efficiently.
-
Poor Documentation: The documentation for Google Takeout is not detailed enough, and it is not clear what data is contained in each file.
-
Error Reporting: Sometimes it can be confusing to determine whether an error originates from the code or the data itself.
Conclusion and Call to Action:
It’s incredible how hard it has to understand Phyton, i thought this would be easy peacy. But nope, still need much more practice. This report is written by the model it self by the way…