Python & Spreadsheets: 2017 Edition
summary
Shows concrete code using OpenPyXL and Jupyter Notebooks to read .xlsx, treat rows as a generator of cell objects, coerce dates to datetime, aggregate ~1,000-row timesheets (examples mention 10–20k), build an Aggregate Time worksheet, save an output .xlsx and emit JSON. Demonstrates set/list comprehensions for grouping, how hidden sheets and cell styles affect parsing, and where merged cells or macros create trouble. Good if wanting a working spreadsheet→spreadsheet and aggregate timesheet → JSON recipe.
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chapters (10)
- 0:00Start
- 5:58Demo
- 7:13Simulated Timesheet Data
- 8:14Utilization
- 9:09Open Pi Excel
- 22:04Cell Attributes
- 23:58Aggregating Timesheet Info
- 26:40Python Indexes from Zero
- 30:55Output Data
- 35:53Pivot Tables
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