02 / selected work

selected work.

A collection of independently developed tools and experiments exploring automation, data processing, and practical software.

browser previewsynthetic data · not a hosted app

Invented neighborhood grocer. Weekly totals for three aisles and two counters.

  • rows84
  • orders4,340
  • revenue$34,487
075150Jan 5Jan 26Feb 16Mar 9Mar 30Produce, Jan 5: 57 ordersProduce, Jan 12: 104 ordersProduce, Jan 19: 74 ordersProduce, Jan 26: 114 ordersProduce, Feb 2: 65 ordersProduce, Feb 9: 75 ordersProduce, Feb 16: 106 ordersProduce, Feb 23: 95 ordersProduce, Mar 2: 85 ordersProduce, Mar 9: 64 ordersProduce, Mar 16: 85 ordersProduce, Mar 23: 56 ordersProduce, Mar 30: 116 ordersProduce, Apr 6: 86 ordersBakery, Jan 5: 97 ordersBakery, Jan 12: 112 ordersBakery, Jan 19: 115 ordersBakery, Jan 26: 119 ordersBakery, Feb 2: 80 ordersBakery, Feb 9: 112 ordersBakery, Feb 16: 110 ordersBakery, Feb 23: 124 ordersBakery, Mar 2: 101 ordersBakery, Mar 9: 74 ordersBakery, Mar 16: 148 ordersBakery, Mar 23: 137 ordersBakery, Mar 30: 84 ordersBakery, Apr 6: 98 ordersPantry, Jan 5: 134 ordersPantry, Jan 12: 71 ordersPantry, Jan 19: 97 ordersPantry, Jan 26: 125 ordersPantry, Feb 2: 149 ordersPantry, Feb 9: 150 ordersPantry, Feb 16: 80 ordersPantry, Feb 23: 134 ordersPantry, Mar 2: 140 ordersPantry, Mar 9: 107 ordersPantry, Mar 16: 149 ordersPantry, Mar 23: 108 ordersPantry, Mar 30: 131 ordersPantry, Apr 6: 72 orders
ProduceBakeryPantry

Hover a point to read its value. Open the demo to filter and export.

Independent demonstration · Synthetic data

data explorer

  • Python
  • Streamlit
  • Pandas
  • Data visualization

An interactive data processing and visualization concept designed to turn raw datasets into meaningful insights. Explore, filter, and analyze information through a streamlined interface.

open the demo

sample workflowfictional files

  1. 01

    excel files

    Two fictional workbooks arrive with the same kind of columns, named differently.

    • april_orders.xlsx
    • sku_list.xlsx
  2. 02

    processing

    Read each sheet, align the column names, and keep one row per order line.

    • normalize columns
    • match on sku
  3. 03

    validation

    Check that the expected columns exist, keys are unique, and quantities are numbers.

    • required columns
    • duplicate keys
    • numeric qty
  4. 04

    output

    Write a checked workbook. Nothing here is a client deliverable.

    • april_orders_checked.xlsx

Independent demonstration

workbook automation

  • Python
  • Excel
  • Workflow automation

Exploring how repetitive spreadsheet operations can be transformed into structured, repeatable workflows that reduce manual effort.

view the project