Python Fundamentals and Data Structures

Choose types that preserve meaning

Choose types that preserve meaning

Python values have types and operations with specific meanings. Strings preserve identifiers such as O001; integers preserve cent arithmetic; floats represent approximate numeric values. A boolean is a logical value, though Python treats bool as an int subclass, which matters for validation. A list preserves a sequence, a dictionary maps keys to values, and a set represents unique membership. Choose from the business meaning instead of converting everything that looks numeric. The included source records arrive as text and need explicit parsing before multiplication.

Follow a concrete example

Inspect the raw string values before parsing. The identifier O001 must retain its prefix and leading zeros; quantity"2" needs an integer conversion before multiplication. Keep the conversion close to a domain check so parseable values such as-1 do not automatically enter accepted data. A dictionary maps an order key to one validated parent record, not an arbitrary last duplicate.

Further reading: Python 3.12 tutorial, pandas 2.2 merge, NumPy 2.2 reference. The case and exercises are authored for DataCareerHub.

Your task

Inspect one order and line dictionary. Classify each field and predict the result of multiplying text "2" by 3 versus integer 2 by 3.

Use learner_worksheet.txt and the provided source/code files. Save your own artifact and compare the separate solution after attempting the task.

Iterate with a clear population rule

Iterate with a clear population rule

A loop applies a defined operation to records; a condition determines which records belong in the result. Build an order lookup, retain completed status and accumulate line value by month. Keep the running accumulator separate from source dictionaries so the original evidence remains unchanged. A dictionary key supplies the grouping dimension, while a set can count unique orders. Avoid deleting items from a list while iterating because positions shift. Reuse the earlier analysis module for pandas syntax; this lesson deliberately implements the logic with core Python first.

Follow a concrete example

Trace a three-line accumulator on paper. For each accepted record, derive integer cents, select the ISO month key, read the previous subtotal and add exactly once. Compare the hand calculation with a loop and then with the complete reference. When the same parent has two lines, add both line values but count the parent once for order measures.

Inspect the reference implementation

Language: Python 3.12. The complete downloadable file includes the named helpers and the fresh-output command-line runner. Trace the source, transformation and result against this lesson’s contract before running it locally.

def py_basics(source,out):
 values=sales(source);month={}
 for r in values:
  key=r['order_date'][:7];month[key]=month.get(key,0)+r['value_cents']
 return {'raw_orders':len(rows(source,'orders.csv')),'accepted_lines':len(values),'monthly_cents':month,'grand_cents':sum(month.values()),'average_completed_order_eur':sum(month.values())/21/100}

Further reading: Python 3.12 tutorial, pandas 2.2 merge, NumPy 2.2 reference. The case and exercises are authored for DataCareerHub.

Your task

Trace the first three completed lines through the month accumulator, then run worked_lab.py. Explain the role of get(key,0).

Use learner_worksheet.txt and the provided source/code files. Save your own artifact and compare the separate solution after attempting the task.

Test boundaries and explain the result

Test boundaries and explain the result

A script executing without an exception is not proof of analytical correctness. Check the selected population, key uniqueness, units and reconciliation. Calculate the grand total independently from monthly values. Distinguish line count from order count and keep cancelled records visible in source coverage. Use assertions to express invariants you can explain, not arbitrary constants copied from a solution. Baseline constants belong to the unchanged fixture; a controlled quantity change should alter the total while structural invariants remain valid.

Follow a concrete example

Define a baseline assertion for the unchanged119000-cent fixture and a separate invariant that monthly sums equal the grand total. Change one accepted quantity in a copy. The baseline total should change; conservation and key checks should still hold. Distinguishing these two kinds of tests prevents an expected-results file from becoming a rule that forces bad corrections.

Further reading: Python 3.12 tutorial, pandas 2.2 merge, NumPy 2.2 reference. The case and exercises are authored for DataCareerHub.

Your task

Write three invariants and one changed-input test. Explain the reported grand average order value.

Use learner_worksheet.txt and the provided source/code files. Save your own artifact and compare the separate solution after attempting the task.