Typorithm learning path
pandas: reliable tabular analytics
Build reliable table pipelines with explicit schemas, labelled alignment, nullable values, pandas 3 Copy-on-Write and string semantics. Validate canonical identities and join cardinality, retain the right observations, report honest grouped populations, reshape unique cells, handle timezones and daily windows, iterate CSV chunks and reconcile pandas with owned SQLite. 18 topics with six substantial sections, worked examples, controlled mistakes, small staged tasks and two hints. Type focused code and recall it on a computer; read examples and answer explained questions in the app. The downloadable local lab executes complete fixtures verified with Python 3.13, pandas 3.1.1 and NumPy 2.5.4. Synthetic owned data only.
Study with a practical destination
Use the linked lessons in order, complete the lab and explain the decisions without the reference. Select the path and depth that match your role.
pandas: reliable tables before statistical analysis
Before starting: Python foundations and the NumPy shape, dtype and missing-value lessons. The local lab executes real pandas; the website trains typing and the app supports reasoning.
Build and explain: Build identity-preserving table pipelines, reject ambiguous joins, retain the right observations and reconcile measured populations with explicit coverage.
- 1. Series, DataFrame and a declared table schema
- 2. Indexes and label alignment instead of positional guessing
- 3. loc, iloc and shape-preserving selection
- 4. Missing values, nullable dtypes and three-valued logic
- 5. Copy-on-Write and updating the owning DataFrame
- 6. Nullable text and explicit string normalization
- 7. Canonical keys without merging distinct external IDs
- 8. Deterministic deduplication of repeated observations
- 9. Merging with cardinality, null and provenance checks
- 10. Groupby populations, observed counts and missing keys
- 11. Transform and row-level group context
- 12. Long and wide layouts with unique cells
- 13. Timezone-aware instants and declared calendar days
- 14. Daily grids and rolling-window meaning
- 15. Concatenating batches with schema and identity guarantees
- 16. Chunked CSV ingestion and bounded aggregate state
- 17. Reconciling pandas and SQL aggregates
- 18. Catalog analytics with identity, chronology and honest coverage
Review evidence: Type focused decisions and recall them without the reference. Change one policy in a working local baseline, run exact DataFrame, CSV parser and independent SQL checks, then explain the failing counterexample. Saved progress is learning evidence, not certification.
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Module 1 · 1 lessons
Series, DataFrame and a declared table schema
Build a table whose columns, nullable dtypes and row count are explicit.
Module 2 · 1 lessons
Indexes and label alignment instead of positional guessing
Align measurements by identity and preserve missing matches.
Module 3 · 1 lessons
loc, iloc and shape-preserving selection
Use labels for named data and positions for explicit ordered slices.
Module 4 · 1 lessons
Missing values, nullable dtypes and three-valued logic
Separate unknown measurements from zeros and make eligibility explicit.
Module 5 · 1 lessons
Copy-on-Write and updating the owning DataFrame
Use one loc assignment and distinguish a detached result from a parent update.
Module 6 · 1 lessons
Nullable text and explicit string normalization
Normalize country text without inventing strings for missing input.
Module 7 · 1 lessons
Canonical keys without merging distinct external IDs
Normalize a documented company key while preserving opaque job identity.
Module 8 · 1 lessons
Deterministic deduplication of repeated observations
Choose one role observation with explicit chronology and a tie-breaker.
Module 9 · 1 lessons
Merging with cardinality, null and provenance checks
Enrich roles without multiplying them or silently matching missing keys.
Module 10 · 1 lessons
Groupby populations, observed counts and missing keys
Report role counts separately from measurement coverage and means.
Module 11 · 1 lessons
Transform and row-level group context
Attach a group mean without collapsing rows or leaking across groups.
Module 12 · 1 lessons
Long and wide layouts with unique cells
Reshape measurements while rejecting ambiguous role-metric pairs.
Module 13 · 1 lessons
Timezone-aware instants and declared calendar days
Require timestamp offsets and choose the reporting timezone before extracting dates.
Module 14 · 1 lessons
Daily grids and rolling-window meaning
Complete an observed calendar range before computing a daily rolling mean.
Module 15 · 1 lessons
Concatenating batches with schema and identity guarantees
Combine rows once and distinguish index resetting from deduplication.
Module 16 · 1 lessons
Chunked CSV ingestion and bounded aggregate state
Read owned CSV chunks with explicit text identity and declared measurement coverage.
Module 17 · 1 lessons
Reconciling pandas and SQL aggregates
Compare independent implementations using the same grouping population and NULL semantics.
Module 18 · 1 lessons
Catalog analytics with identity, chronology and honest coverage
Build a synthetic technical-role report without stale duplicates or invented measurements.