Two weeks of Gmail and Calendar. Now picture your research data workflow — a shared Sheet you append to after every lab session, a Docs file where you keep running notes. How much time goes to keeping those files current?
After each lab session I open Sheets, scroll to the bottom, add a row with the date, what I did, and the status. Then update the Docs file with any new findings. Ten minutes minimum, every session.
This week those ten minutes become one function call. Google Sheets and Docs use the same toolset.execute_action pattern. A row in Sheets is just a list of strings — and you already know how to work with lists from Python Track Week 2. Day 17 reads a range from a Sheet. Day 18 appends a row. Day 19 searches for spreadsheets by name. Day 20 reads a Doc by ID. Day 21 creates a new Doc.
A row in Sheets is just a list of strings? That means I can process it with all the Python I already know — filter, sort, loop.
Everything connects. The Sheets values API returns a 2D list — rows as lists, cells as strings. The same list operations from Week 2 apply here. That's the systems thinking moment for Week 3.
read_range: read a Sheets A1 range with GOOGLESHEETS_BATCH_GETappend_row: append a progress row with GOOGLESHEETS_SPREADSHEETS_VALUES_APPENDsearch_sheets: find a spreadsheet by name with GOOGLESHEETS_SEARCH_SPREADSHEETSread_doc: read a Doc's content with GOOGLEDOCS_GET_DOCUMENT_BY_IDcreate_doc: create a new Doc with GOOGLEDOCS_CREATE_DOCUMENTGoal: your lab session log writes itself — one function call appends the row and your Docs notes stay current.
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Two weeks of Gmail and Calendar. Now picture your research data workflow — a shared Sheet you append to after every lab session, a Docs file where you keep running notes. How much time goes to keeping those files current?
After each lab session I open Sheets, scroll to the bottom, add a row with the date, what I did, and the status. Then update the Docs file with any new findings. Ten minutes minimum, every session.
This week those ten minutes become one function call. Google Sheets and Docs use the same toolset.execute_action pattern. A row in Sheets is just a list of strings — and you already know how to work with lists from Python Track Week 2. Day 17 reads a range from a Sheet. Day 18 appends a row. Day 19 searches for spreadsheets by name. Day 20 reads a Doc by ID. Day 21 creates a new Doc.
A row in Sheets is just a list of strings? That means I can process it with all the Python I already know — filter, sort, loop.
Everything connects. The Sheets values API returns a 2D list — rows as lists, cells as strings. The same list operations from Week 2 apply here. That's the systems thinking moment for Week 3.
read_range: read a Sheets A1 range with GOOGLESHEETS_BATCH_GETappend_row: append a progress row with GOOGLESHEETS_SPREADSHEETS_VALUES_APPENDsearch_sheets: find a spreadsheet by name with GOOGLESHEETS_SEARCH_SPREADSHEETSread_doc: read a Doc's content with GOOGLEDOCS_GET_DOCUMENT_BY_IDcreate_doc: create a new Doc with GOOGLEDOCS_CREATE_DOCUMENTGoal: your lab session log writes itself — one function call appends the row and your Docs notes stay current.