Learn data coding through small, checkable tables

Your starting point

You can open a text file and run a small program. Use synthetic records with totals you can check yourself.

• Basic variables, conditions, and loops
• A local environment that can read CSV and write files

Your finish line

You can explain where a reported number came from, reject invalid rows, and rerun a small import predictably.

Stack up four small wins

1. STEP 1 OF 4

   Read a tiny table by hand

   Create six sample order rows and calculate one grouped total on paper before writing code.

   Bro tip: Label the expected row count and one grouped total before running code. If the output surprises you, trace just that group first.

   What you’ll learn

   • What one row represents
   • Column types, units, and missing values
   You’re ready to move on when

   You can state the expected row count and total without relying on the program.

   I can do this step

2. STEP 2 OF 4

   Build a repeatable report

   Build the sample sales report and compare every total with your small fixture.

   Bro tip: Keep the original fixture beside the report and compare each group, not just the grand total. Rerun before changing the data.

   What you’ll learn

   • CSV parsing
   • Grouped aggregation
   • Sorting and output formatting
   You’re ready to move on when

   The same input produces the same report and no source rows change.

   Open project: Pocket sales report

   I can do this step

3. STEP 3 OF 4

   Check quality before calculating

   Add a data-quality gate with explicit missing-value and duplicate-key policies.

   Bro tip: Choose one rejected row and follow its reason through the output. Then reconcile the accepted and rejected counts with the input.

   What you’ll learn

   • Validation rules
   • Rejected-row reports
   • Source row identifiers
   You’re ready to move on when

   Every input row is accounted for as accepted or rejected with a reason.

   Open project: CSV quality gate

   I can do this step

4. STEP 4 OF 4

   Import into durable records

   Import a tiny catalog into a local database and prove that a second run does not create extra records.

   Bro tip: Save the expected final records before the first import. Compare them after a rerun and after the planned interruption.

   What you’ll learn

   • Stable keys
   • Upsert behavior
   • Transactions and reruns
   You’re ready to move on when

   A repeated import and a controlled interrupted run reach the documented final state.

   Open project: Rerunnable catalog importer

   I can do this step

Your build lineup

1. Pocket sales report

   Read a tiny synthetic CSV and produce grouped totals that you can verify by hand.

2. CSV quality gate

   Validate sample rows before using them, with an audit trail that accounts for every record.

3. Rerunnable catalog importer

   Import a tiny catalog into a local database without duplicating records when you rerun it.

Watch it. Then build it.

Start with a worked example, then connect the recording to your own small, checkable build. Videos open on their creator’s or publisher’s website.

Read Vibe Bro’s written lesson briefing ↗

1. Beginner · Video course · About 10 hours

   Data Analysis with Python Course - Numpy, Pandas, Data Visualization

   freeCodeCamp.org · Aakash N S

   Introduces Python and Jupyter, then NumPy arrays, pandas tables, grouping, merging, and visualization.

   For step 2: Build a repeatable report

   Before watching: Basic arithmetic. The opening modules introduce Python; familiarity with variables, loops, and functions helps when moving into pandas.

   Watch, then try

   Use the pandas section to reproduce the six-row sales report. Check row counts and hand-calculated category totals before adding charts.

   The course teaches Python, Jupyter, NumPy, and pandas. Our starter can use your project's established stack. Follow your current environment's setup instructions when older notebook or hosting screens differ.

   Watch course ↗ · Practice: Pocket sales report

   Publisher’s course details · checked October 28, 2026

2. Next step · Video course · About 5 hours

   Pandas & Python for Data Analysis by Example – Full Course for Beginners

   freeCodeCamp.org · Santiago Basulto

   Practices DataFrames, filtering, and sorting before moving into data cleaning, joins, and grouped analysis.

   For step 3: Check quality before calculating

   Before watching: Basic Python and an introductory understanding of pandas DataFrames. Complete the earlier data lesson first if tables and filtering are unfamiliar.

   Watch, then try

   Begin with the introductory DataFrame exercises, then adapt the missing-value and duplicate checks into the quality gate. Account for each source row as accepted or rejected.

   Later projects reach intermediate and advanced topics. Start with the beginner and cleaning sections. Add the starter's explicit rejection reasons and row audit as your own checks.

   Watch course ↗ · Practice: CSV quality gate

   Publisher’s course details · checked October 28, 2026

Video links selected October 28, 2026. Selection checks cover publisher metadata and topic fit.

The docs behind it

These references explain the underlying concepts and APIs. The prompt templates and project plans are original examples to adapt to your own setup.

• Python — CSV dialects and parsing
• pandas — Grouped aggregation
• SQLite — UPSERT conflict handling

Documentation links checked October 27, 2026.

Keep the momentum going

• Analyze a table with an audit trailCopyable prompt
• Data frameCoding term
• Data join cardinalityCoding term