diff --git a/.DS_Store b/.DS_Store
index 8089655..e32069e 100644
Binary files a/.DS_Store and b/.DS_Store differ
diff --git a/AGENTS.md b/AGENTS.md
index 95b60ae..1ae95e7 100644
--- a/AGENTS.md
+++ b/AGENTS.md
@@ -10,15 +10,15 @@ Technical handoff for coding agents and contributors working on this repository.
 - No backend/runtime services required
 - Source dataset and generated card data are JSON files in repo
 
-## Current Snapshot (2026-03-21)
+## Current Snapshot (2026-03-22)
 
-- `topic_cards.json`: **174 total cards**
+- `topic_cards.json`: **27 total cards**
 - `deck_groups`: **6** (`week-1` through `week-6`)
 - Weeks covered in the dataset: **1-6**
-- Exam-topic cards (`exam_stats.total_hits > 0`): **136**
+- Exam-topic cards (`exam_stats.total_hits > 0`): **18**
 - Duplicate normalized topic labels: **0**
-- Total key points across all cards: **935**
-- Optional key-point detail blocks: **256**
+- Total key points across all cards: **106**
+- Optional key-point detail blocks: **96**
 
 Curation state:
 
diff --git a/README.md b/README.md
index cbf2405..4143571 100644
--- a/README.md
+++ b/README.md
@@ -66,6 +66,10 @@ python3 -m http.server 8000
 
 Then open [http://localhost:8000](http://localhost:8000).
 
+## Course Materials
+
+Official course files are still kept at their canonical paths under `materials/`, but there is now also a cleaner week-ordered view under [materials/by_week](materials/by_week) for easier browsing.
+
 ## Support This Project
 
 This project was made by just one student, and the AI coding tools used to build and improve it cost real money.
diff --git a/docs/curation/TOPIC_MERGING_GUIDELINES.md b/docs/curation/TOPIC_MERGING_GUIDELINES.md
index 8dde811..9b3710e 100644
--- a/docs/curation/TOPIC_MERGING_GUIDELINES.md
+++ b/docs/curation/TOPIC_MERGING_GUIDELINES.md
@@ -14,14 +14,16 @@ Use this guide when you need to:
 
 ## Current Baseline
 
-At the time this guide was written:
+As of 2026-03-22, the merged deck baseline was:
 
-- `topic_cards.json` has **174** cards.
+- `topic_cards.json` has **27** merged cards.
 - `deck_groups` covers **6** weeks.
-- exam-topic cards (`exam_stats.total_hits > 0`) total **136**.
+- exam-topic cards (`exam_stats.total_hits > 0`) total **18**.
 - exact duplicate normalized topic labels are **0**.
 - exam-topic key points have optional detail coverage (examples/tables/explanations) and should stay selectable in UI.
 
+Treat `AGENTS.md` as the authoritative current snapshot for repo-wide counts. These numbers can drift as manual curation continues.
+
 Important: even with exact duplicates removed, semantic overlap can still exist.
 Important: curation should be evidence-driven; do not force fixed per-topic item counts.
 
@@ -63,13 +65,13 @@ Start with highest-impact cards (highest exam hits), then low-hit cards.
 
 Suggested review bundles:
 
-1. `For Loops`, `Range()`
-2. `Conditions`, `truthy_falsy`, `Boolean operators`, `comparison_operators`, `walrus_operator`
-3. `Functions`, `Return / None`, `Default arguments`, `*args`, `*args / **kwargs`
-4. `mutable_immutable`, `mutable args`, `mutable_default`, `Objects`, `Names`
-5. `Dictionaries`, `Sets`, `dict / average`
-6. `Indexing`, `Slicing`, `Strings`
-7. `Lambda`, `Enumerate`, `Zip`
+1. `Python Basics`, `Objects and Names`, `Sequences and Access`
+2. `Operators and Truth`, `Conditions`, `Conversion and Truthiness`
+3. `Functions and Imports`, `Defining and Calling Functions`, `Return Behavior`, `Scope`, `Arguments`, `Higher-Order Patterns`
+4. `Dictionaries and Mappings`, `Lists and Sets`, `Comprehensions`, `Generators and Iterators`
+5. `String Fundamentals`, `String Operations and Methods`, `String Formatting`
+6. `OOP Fundamentals`, `Error Handling`
+7. `Pandas Core Structures`, `Inspecting and Selecting Data`, `Working With Values`, `Combining Data`, `Datetime`
 
 These are semantic-review bundles, not mandatory merges.
 
diff --git a/docs/specs/RM-008-external-cheatsheet-gap-audit.md b/docs/specs/RM-008-external-cheatsheet-gap-audit.md
new file mode 100644
index 0000000..40fcf33
--- /dev/null
+++ b/docs/specs/RM-008-external-cheatsheet-gap-audit.md
@@ -0,0 +1,180 @@
+# RM-008: External Cheat-Sheet Gap Audit
+
+## Metadata
+
+- ID: `RM-008`
+- Status: `done`
+- Priority: `High`
+- Owner: `Codex`
+- Last Updated: `2026-03-22`
+
+## Problem
+
+The repo needed a manual completeness audit against public cheat sheets for the same course before wider student release. The live [topic_cards.json](../../topic_cards.json) is also materially different from the older `174`-card snapshot still described in some repo docs, so the audit had to compare against the current merged `27`-card deck rather than the stale documentation.
+
+## Goals
+
+- Manually review all 9 PDFs in [materials/example_cheat_sheets](../../materials/example_cheat_sheets) using rendered page images, not text-only extraction.
+- Normalize overlapping concepts across the public sheets into one actionable inventory.
+- Classify each concept as `covered`, `partial`, `missing-course-backed`, or `missing-extra-helpful`.
+- Suggest concrete manual additions without mutating [topic_cards.json](../../topic_cards.json) yet.
+
+## Non-Goals
+
+- Automatically importing public-sheet material into [topic_cards.json](../../topic_cards.json).
+- Treating generic Python helper content as mandatory course content.
+- Changing app behavior or data schema.
+
+## Proposed Solution
+
+Render every public PDF to PNG, review the rendered pages manually with the PDF skill, de-duplicate the extracted concept families, and compare them against the live merged deck by actual snippet/key-point/example presence rather than card titles. Record the results as a split report:
+
+- `Course-backed gaps`: missing or partial concepts that fit the current course-backed card model.
+- `Extra-helpful additions`: useful public-sheet content that is broader than the current evidence-backed scope.
+
+## Implementation Plan
+
+1. Render all 9 PDFs to `tmp/pdfs/render/` with `pdftoppm`.
+2. Have 3 subagents manually review the rendered pages in parallel using the PDF skill.
+3. Manually spot-check representative rendered pages in the main thread to verify the extracted concept families.
+4. Normalize duplicates across the 9 PDFs into one concept inventory.
+5. Compare each concept family against the live `27` merged cards in [topic_cards.json](../../topic_cards.json).
+6. Record the split report and full inventory in this spec.
+7. Align stale contributor docs with the live dataset snapshot.
+
+## Manual Review Notes
+
+- Rendered output: `43` PNG pages under `tmp/pdfs/render/`.
+- Review method: rendered-page reading with the PDF skill, not `pdftotext` alone.
+- Subagent clusters:
+  - `athena-python-endterm-study-guide-key-concepts-functions.pdf`
+  - `python-resit-cheat-sheet-2022-key-concepts-functions.pdf`
+  - `python-exam-content-summary-key-formulas-concepts.pdf`
+  - `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf`
+  - `python-cheatsheet-for-final-exam-preparation.pdf`
+  - `python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf`
+  - `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf`
+  - `python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf`
+  - `summary-introduction-to-python.pdf`
+- Main-thread visual spot checks covered rendered pages including:
+  - `athena...:2`
+  - `python-101...:2,5,9`
+  - `python-codes...:2`
+  - `python-cheatsheet-for-final-exam-preparation:2`
+  - `python-exam-content-summary...:2`
+  - `summary-introduction-to-python:3,6`
+
+## Summary Findings
+
+- The public sheets are mostly redundant with the current deck; they do not reveal large missing topic families across the core course.
+- The clearest true content gap is a compact `NumPy` reference block.
+- Most other differences are narrower `partial` gaps inside existing merged topics:
+  - list aliasing vs copying
+  - dict utility idioms
+  - typed `except` examples
+  - richer string-formatting styles
+  - datetime ISO/day-of-year helpers
+  - pandas mutation/missing-data/value-op reminders
+- The biggest extra-helpful but lower-evidence additions are:
+  - practical console workflow (`pwd`, `python3 file.py`, `pip3 install`, `help(...)`)
+  - timezone-aware datetime helpers
+  - one compact worked aggregation example
+
+Normalized concept-family counts:
+
+- `covered`: 16
+- `partial`: 9
+- `missing-course-backed`: 1
+- `missing-extra-helpful`: 3
+
+## Course-Backed Gaps
+
+| Concept | Source PDF + page | Current mapping | Status | Proposed manual addition |
+| --- | --- | --- | --- | --- |
+| List copy and alias semantics (`y = x`, `x[:]`, `list(x)`, shallow vs deep copy) | `summary-introduction-to-python.pdf:3`<br>`athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3` | `Objects and Names` / `Lists and Sets` | `partial` | Add a compact example showing aliasing vs real copies, and note that `copy.deepcopy(...)` is needed for nested structures. |
+| Dict utility patterns (`update`, `pop`, sorted accumulation, `zip`, `enumerate`) | `python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Dictionaries and Mappings` | `partial` | Add one mini reference block with `dict.update`, `dict.pop`, `dict(zip(...))`, and a small loop over `.items()`. |
+| Typed error handling vs broad `except` | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2` | `Error Handling` | `partial` | Add a minimal `try/except ValueError` example and a note that bare `except` is broad. |
+| String immutability and reassignment after methods | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3` | `String Operations and Methods` | `partial` | Add one reminder that `replace`, `capitalize`, `split`, and `join` return new strings and do not mutate the original. |
+| Formatting styles beyond f-strings (`.format`, conversion flags, compact specifiers) | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2` | `String Formatting` | `partial` | Extend the formatting card with `.format(...)`, `!r`, `!s`, and a `{:02d}` example. |
+| Constructor/default-argument and instance-vs-class-attribute pitfalls | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:3`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `OOP Fundamentals` | `partial` | Add one compact class example covering `__init__`, default arguments, and the difference between instance and class attributes. |
+| Datetime ISO/day-of-year helpers | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Datetime` | `partial` | Add quick refs for `isoweekday()`, `isocalendar()`, and `day_of_year = (dt - datetime(dt.year, 1, 1)).days + 1`. |
+| NumPy arrays, vectorized arithmetic, boolean masks, 2D indexing, and `shape` | `summary-introduction-to-python.pdf:6` | `none` | `missing-course-backed` | Add a small `NumPy` topic block or extend an existing week-1/values card with `import numpy as np`, `np.array(...)`, vectorized math, boolean subsetting, 2D indexing, and `.shape`. |
+| Pandas value/mutation/missing-data helpers (`T`, `sort_index`, `sort_values`, `drop`, `dropna`, `fillna(method=..., limit=...)`, `set_index`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`summary-introduction-to-python.pdf:3`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:3` | `Working With Values` / `Combining Data` | `partial` | Add a compact reminder block covering sort/drop/index/missing-data helpers, and flag `DataFrame.append(...)` as legacy with `pd.concat(...)` preferred. |
+| Pandas vectorized string helpers and `apply(..., axis=...)` patterns | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2,3` | `Working With Values` | `partial` | Add one short side-by-side example for `.str`, `.map(...)`, and `df.apply(..., axis=0/1)`. |
+
+## Extra-Helpful Additions
+
+| Concept | Source PDF + page | Current mapping | Status | Proposed manual addition |
+| --- | --- | --- | --- | --- |
+| Timezone-aware datetime variants (`today`, `utcnow`, `datetime.now(tz=...)`, `pytz.timezone(...)`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3` | `Datetime` | `missing-extra-helpful` | Add a small optional note for UTC vs local time and timezone-aware construction if you want broader real-world usefulness. |
+| Practical console workflow (`pwd`, `python3 file.py`, `pip3 install`, `help(...)`) | `summary-introduction-to-python.pdf:2` | `Python Basics` | `missing-extra-helpful` | Add a tiny “run Python locally” helper block near the intro or splash help text. |
+| Worked aggregation / weighted-average style practice snippet | `python-exam-content-summary-key-formulas-concepts.pdf:4` | `Loops` / `Working With Values` | `missing-extra-helpful` | Add one loop-based average or weighted-average example if you want one more exam-ready worked pattern. |
+
+## Full Normalized Inventory
+
+| Concept | Source PDF + page | Mapped current topic | Status | Proposed target topic | Proposed section type | Proposed manual addition |
+| --- | --- | --- | --- | --- | --- | --- |
+| Core primitives, comments, print, input, and basic control-flow framing | `athena-python-endterm-study-guide-key-concepts-functions.pdf:2`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:2`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2`<br>`summary-introduction-to-python.pdf:2` | `Python Basics` / `Functions and Imports` | `covered` | `Python Basics` | `key_points_to_remember` | No addition needed. |
+| Numeric operators and boolean logic | `athena-python-endterm-study-guide-key-concepts-functions.pdf:2`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:2`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2` | `Operators and Truth` | `covered` | `Operators and Truth` | `key_points_to_remember` | No addition needed. |
+| Sequence indexing, slicing, containment, and `range` stop-exclusion | `athena-python-endterm-study-guide-key-concepts-functions.pdf:2`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:2`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2`<br>`summary-introduction-to-python.pdf:3` | `Sequences and Access` | `covered` | `Sequences and Access` | `key_points_to_remember` | No addition needed. |
+| Basic list mutation (`append`, `remove`, slicing, deletion) | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2`<br>`summary-introduction-to-python.pdf:3` | `Lists and Sets` | `covered` | `Lists and Sets` | `key_points_to_remember` | No addition needed. |
+| List copy and alias semantics (`y = x`, `x[:]`, `list(x)`, shallow vs deep copy) | `summary-introduction-to-python.pdf:3`<br>`athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3` | `Objects and Names` / `Lists and Sets` | `partial` | `Objects and Names` | `key_points_to_remember` | Add alias-vs-copy example plus a `copy.deepcopy(...)` note for nested structures. |
+| Basic dict creation, lookup, membership, iteration, and `dict(zip(...))` | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`summary-introduction-to-python.pdf:3` | `Dictionaries and Mappings` | `covered` | `Dictionaries and Mappings` | `lecture_snippets` | No addition needed. |
+| Dict utility patterns (`update`, `pop`, sorted accumulation, `zip`, `enumerate`) | `python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Dictionaries and Mappings` | `partial` | `Dictionaries and Mappings` | `ai_examples` | Add a compact helper block with `update`, `pop`, `.items()`, and `dict(zip(...))`. |
+| Loops, `break`, `continue`, `enumerate()`, and dict iteration | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:2,3`<br>`summary-introduction-to-python.pdf:3` | `Loops` | `covered` | `Loops` | `ai_examples` | No addition needed. |
+| Function and import basics | `athena-python-endterm-study-guide-key-concepts-functions.pdf:2,3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:2,3`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3` | `Functions and Imports` | `covered` | `Functions and Imports` | `lecture_snippets` | No addition needed. |
+| Default args, `*args`, `**kwargs`, `lambda`, `map`, `filter`, `reduce`, `zip`, and closures | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`summary-introduction-to-python.pdf:4`<br>`athena-python-endterm-study-guide-key-concepts-functions.pdf:2,3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:2,3`<br>`python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2` | `Arguments` / `Higher-Order Patterns` | `covered` | `Higher-Order Patterns` | `ai_examples` | No addition needed. |
+| Typed error handling vs broad `except` | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2` | `Error Handling` | `partial` | `Error Handling` | `key_points_to_remember` | Add a small typed-`except` pattern and warn about bare `except`. |
+| String quoting, escaping, and method overview | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`summary-introduction-to-python.pdf:2,3` | `String Fundamentals` / `String Operations and Methods` | `covered` | `String Operations and Methods` | `lecture_snippets` | No addition needed. |
+| String immutability and reassignment after methods | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3` | `String Operations and Methods` | `partial` | `String Operations and Methods` | `key_points_to_remember` | Add a reminder that methods like `replace` and `capitalize` return new strings. |
+| Formatting styles beyond f-strings (`.format`, conversion flags, compact specifiers) | `python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `String Formatting` | `partial` | `String Formatting` | `lecture_snippets` | Extend with `.format(...)`, `!r`, `!s`, and `{:02d}`; mention `Template.safe_substitute(...)` as optional broader context. |
+| OOP basics (`class`, `self`, `__init__`, instantiation, attributes) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-exam-content-summary-key-formulas-concepts.pdf:2`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2` | `OOP Fundamentals` | `covered` | `OOP Fundamentals` | `lecture_snippets` | No addition needed. |
+| Constructor/default-argument and instance-vs-class-attribute pitfalls | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:3`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `OOP Fundamentals` | `partial` | `OOP Fundamentals` | `ai_examples` | Add a compact class example with a safe default pattern and class-vs-instance attribute note. |
+| Comprehensions | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2` | `Comprehensions` | `covered` | `Comprehensions` | `ai_examples` | No addition needed. |
+| Generators and iterators | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Generators and Iterators` | `covered` | `Generators and Iterators` | `ai_examples` | No addition needed. |
+| Truthiness, equality, and coercion worked examples | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2` | `Conversion and Truthiness` | `covered` | `Conversion and Truthiness` | `ai_examples` | No addition needed. |
+| Local scope vs returned values | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2` | `Scope` / `Return Behavior` | `covered` | `Return Behavior` | `key_points_to_remember` | No addition needed. |
+| Datetime basics (`now`, `strftime`, `strptime`, `timedelta`, `replace`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Datetime` | `covered` | `Datetime` | `lecture_snippets` | No addition needed. |
+| Datetime ISO/day-of-year helpers | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2` | `Datetime` | `partial` | `Datetime` | `ai_examples` | Add `isoweekday()`, `isocalendar()`, and reusable day-of-year helper snippets. |
+| Timezone-aware datetime variants (`today`, `utcnow`, `datetime.now(tz=...)`, `pytz.timezone(...)`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3` | `Datetime` | `missing-extra-helpful` | `Datetime` | `key_points_to_remember` | Optional timezone note for broader usefulness. |
+| NumPy arrays, vectorized arithmetic, boolean masks, 2D indexing, and `shape` | `summary-introduction-to-python.pdf:6` | `none` | `missing-course-backed` | `NumPy` or existing week-1/values card | `lecture_snippets` | Add a compact NumPy starter reference with arrays, masks, indexing, and `.shape`. |
+| Pandas core structures, indexing, and selection basics (`Series`, `DataFrame`, `.loc`, `.iloc`, `.head`, `.tail`, `.describe`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-exam-content-summary-key-formulas-concepts.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`summary-introduction-to-python.pdf:3`<br>`python-101-intro-to-python-basics-and-data-handling-in-week-1-8.pdf:9` | `Pandas Core Structures` / `Inspecting and Selecting Data` | `covered` | `Inspecting and Selecting Data` | `lecture_snippets` | No addition needed. |
+| Pandas value/mutation/missing-data helpers (`T`, `sort_index`, `sort_values`, `drop`, `dropna`, `fillna(method=..., limit=...)`, `set_index`) | `athena-python-endterm-study-guide-key-concepts-functions.pdf:3`<br>`python-resit-cheat-sheet-2022-key-concepts-functions.pdf:3`<br>`python-cheat-sheet-methods-on-immutable-lists-dictionaries-final.pdf:2`<br>`summary-introduction-to-python.pdf:3`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:3` | `Working With Values` / `Combining Data` | `partial` | `Working With Values` | `key_points_to_remember` | Add a compact DataFrame manipulation helper block; mention `append` only as a legacy note. |
+| Pandas vectorized string helpers and `apply(..., axis=...)` patterns | `python-endterm-cheat-sheet-key-concepts-code-snippets.pdf:2`<br>`python-cheatsheet-for-final-exam-preparation.pdf:2`<br>`python-codes-for-exam-dictionary-datetime-and-pandas-operations.pdf:2,3` | `Working With Values` | `partial` | `Working With Values` | `ai_examples` | Add one short `.str` example plus row-wise vs column-wise `apply`. |
+| Practical console workflow (`pwd`, `python3 file.py`, `pip3 install`, `help(...)`) | `summary-introduction-to-python.pdf:2` | `Python Basics` | `missing-extra-helpful` | `Python Basics` | `key_points_to_remember` | Optional quick-start helper block for running code locally. |
+| Worked aggregation / weighted-average style practice snippet | `python-exam-content-summary-key-formulas-concepts.pdf:4` | `Loops` / `Working With Values` | `missing-extra-helpful` | `Working With Values` | `ai_examples` | Optional loop-based average or weighted-average worked example. |
+
+## Risks and Mitigations
+
+- Risk: public sheets contain generic Python advice that is useful but not strongly course-backed.
+- Mitigation: keep `missing-extra-helpful` separate from course-backed gaps.
+- Risk: older repo docs still imply a `174`-card deck and can cause future audits to compare against the wrong baseline.
+- Mitigation: align the contributor docs with the live `27`-card snapshot as part of this task.
+- Risk: some public snippets use legacy pandas APIs.
+- Mitigation: flag legacy patterns explicitly instead of promoting them unqualified.
+
+## Test Plan
+
+- Manual checks:
+  - Confirm all 9 PDFs were rendered to `43` PNG pages in `tmp/pdfs/render/`.
+  - Confirm all 9 PDFs were reviewed from rendered pages, not text extraction alone.
+  - Confirm the inventory compares against the live [topic_cards.json](../../topic_cards.json), not stale docs.
+- Validation commands:
+  - Run the `topic_cards.json` integrity check from [AGENTS.md](../../AGENTS.md).
+  - Run `make leave-better`.
+
+## Rollout and Validation
+
+- Commands run:
+  - `pdftoppm -png -rx 180 -ry 180 materials/example_cheat_sheets/*.pdf tmp/pdfs/render/...`
+  - `python3 - <<'PY' ... integrity check from AGENTS.md ... PY`
+  - `make leave-better`
+- Success criteria:
+  - Audit artifact exists and lists every normalized concept family from the reviewed PDFs.
+  - Gaps are split into course-backed vs extra-helpful.
+  - Contributor docs no longer claim the live deck still has `174` cards.
+
+## Open Questions
+
+- If follow-up curation is implemented, should `NumPy` become its own merged topic, or should the starter snippets be folded into an existing week-1 or values-focused card?
+- If extra-helpful additions are accepted, should practical workflow notes live in the card dataset or in UI/help copy outside the printable cheat sheet?
diff --git a/materials/README.md b/materials/README.md
new file mode 100644
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--- /dev/null
+++ b/materials/README.md
@@ -0,0 +1,39 @@
+# Materials Layout
+
+This folder now has two layers:
+
+- the original canonical source paths used by the data pipeline and live dataset
+- a non-breaking organized index under `materials/by_week/`
+
+## Canonical Source Paths
+
+The canonical files stay where they were so existing references in `data/study_db.json`, `topic_cards.json`, scripts, and tests do not break.
+
+Examples:
+
+- `materials/lectures/`
+- `materials/notebooks/`
+- `materials/post_midterm/`
+- `materials/exams/`
+
+## Organized Week View
+
+Use `materials/by_week/` when you want a cleaner human-facing overview of the official course files.
+
+- `week-01` through `week-06` group lecture, notebook, exercise, solution, and helper files by week
+- `shared-assessments/` groups official exams and cross-week assessment material that does not belong to a single week
+
+These entries are symlinks that point back to the canonical originals.
+
+## How to Use This Folder
+
+- Clone the repo with Git when you want `materials/by_week/` to behave like a link index.
+- Avoid relying on ZIP downloads for this view. ZIP exports may flatten or drop symlinks, which can leave tiny plain-text files instead of working links.
+- On Windows, symlink support may require Developer Mode, an elevated shell, WSL, or a Git/shell setup with symlink support enabled.
+- If an entry under `materials/by_week/` opens as a short path string instead of the expected file, re-clone the repo with Git in an environment with symlink support.
+
+## Notes
+
+- Week 4-6 lecture decks also exist under `materials/lectures/`, but the organized view points to the `materials/post_midterm/` copies because those are the paths used by the live dataset.
+- `OOP.py` is grouped under `week-06` because the repo already classifies it as week 6 material.
+- External comparison PDFs stay in `materials/example_cheat_sheets/` and are not part of the official week index.
diff --git a/materials/by_week/shared-assessments/post-midterm/Sample Final plus answers.pdf b/materials/by_week/shared-assessments/post-midterm/Sample Final plus answers.pdf
new file mode 120000
index 0000000..52017be
--- /dev/null
+++ b/materials/by_week/shared-assessments/post-midterm/Sample Final plus answers.pdf	
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+../../../post_midterm/Sample Final plus answers.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/pre-midterm/2023.pdf b/materials/by_week/shared-assessments/pre-midterm/2023.pdf
new file mode 120000
index 0000000..4d43e45
--- /dev/null
+++ b/materials/by_week/shared-assessments/pre-midterm/2023.pdf
@@ -0,0 +1 @@
+../../../exams/2023.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/pre-midterm/2024.pdf b/materials/by_week/shared-assessments/pre-midterm/2024.pdf
new file mode 120000
index 0000000..25d2de0
--- /dev/null
+++ b/materials/by_week/shared-assessments/pre-midterm/2024.pdf
@@ -0,0 +1 @@
+../../../exams/2024.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/pre-midterm/Midterm Extra Practice.txt b/materials/by_week/shared-assessments/pre-midterm/Midterm Extra Practice.txt
new file mode 120000
index 0000000..703046b
--- /dev/null
+++ b/materials/by_week/shared-assessments/pre-midterm/Midterm Extra Practice.txt	
@@ -0,0 +1 @@
+../../../exams/Midterm Extra Practice.txt
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/pre-midterm/trial midterm.pdf b/materials/by_week/shared-assessments/pre-midterm/trial midterm.pdf
new file mode 120000
index 0000000..90d9356
--- /dev/null
+++ b/materials/by_week/shared-assessments/pre-midterm/trial midterm.pdf	
@@ -0,0 +1 @@
+../../../exams/trial midterm.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-62oop21.pdf b/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-62oop21.pdf
new file mode 120000
index 0000000..2be569f
--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-62oop21.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/final-exam-solutions-for-python-programming-62oop21.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-course-code-308088-308234.pdf b/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-course-code-308088-308234.pdf
new file mode 120000
index 0000000..e68fa32
--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/final-exam-solutions-for-python-programming-course-code-308088-308234.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/final-exam-solutions-for-python-programming-course-code-308088-308234.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/final-exam-study-guide-trial-python-basics-2023.pdf b/materials/by_week/shared-assessments/previous-years/final-exam-study-guide-trial-python-basics-2023.pdf
new file mode 120000
index 0000000..9a5329e
--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/final-exam-study-guide-trial-python-basics-2023.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/final-exam-study-guide-trial-python-basics-2023.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/introduction-to-python-trial-final-exam-solutions-py22.pdf b/materials/by_week/shared-assessments/previous-years/introduction-to-python-trial-final-exam-solutions-py22.pdf
new file mode 120000
index 0000000..0e97846
--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/introduction-to-python-trial-final-exam-solutions-py22.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/introduction-to-python-trial-final-exam-solutions-py22.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/resit-exam-guidelines-for-intro-to-python-6013b0470y-july-2023.pdf b/materials/by_week/shared-assessments/previous-years/resit-exam-guidelines-for-intro-to-python-6013b0470y-july-2023.pdf
new file mode 120000
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--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/resit-exam-guidelines-for-intro-to-python-6013b0470y-july-2023.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/resit-exam-guidelines-for-intro-to-python-6013b0470y-july-2023.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/resit-solutions-for-introduction-to-python-35761538.pdf b/materials/by_week/shared-assessments/previous-years/resit-solutions-for-introduction-to-python-35761538.pdf
new file mode 120000
index 0000000..07d6dd2
--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/resit-solutions-for-introduction-to-python-35761538.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/resit-solutions-for-introduction-to-python-35761538.pdf
\ No newline at end of file
diff --git a/materials/by_week/shared-assessments/previous-years/trial-final-exam-solutions-introduction-to-python-3077951.pdf b/materials/by_week/shared-assessments/previous-years/trial-final-exam-solutions-introduction-to-python-3077951.pdf
new file mode 120000
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--- /dev/null
+++ b/materials/by_week/shared-assessments/previous-years/trial-final-exam-solutions-introduction-to-python-3077951.pdf
@@ -0,0 +1 @@
+../../../post_midterm/practice_exams_previous_years/trial-final-exam-solutions-introduction-to-python-3077951.pdf
\ No newline at end of file
diff --git a/materials/by_week/week-01/lectures/Lecture Week 1.md b/materials/by_week/week-01/lectures/Lecture Week 1.md
new file mode 120000
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+++ b/materials/by_week/week-01/lectures/Lecture Week 1.md	
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+../../../lectures/Lecture Week 1.md
\ No newline at end of file
diff --git a/materials/by_week/week-01/lectures/Lecture Week 1.pptx b/materials/by_week/week-01/lectures/Lecture Week 1.pptx
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+++ b/materials/by_week/week-01/lectures/Lecture Week 1.pptx	
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+../../../lectures/Lecture Week 1.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-01/notebooks/Notebook Week 1.ipynb b/materials/by_week/week-01/notebooks/Notebook Week 1.ipynb
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+../../../notebooks/Notebook Week 1.ipynb
\ No newline at end of file
diff --git a/materials/by_week/week-02/lectures/Lecture Week 2.md b/materials/by_week/week-02/lectures/Lecture Week 2.md
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+++ b/materials/by_week/week-02/lectures/Lecture Week 2.md	
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+../../../lectures/Lecture Week 2.md
\ No newline at end of file
diff --git a/materials/by_week/week-02/lectures/Lecture Week 2.pptx b/materials/by_week/week-02/lectures/Lecture Week 2.pptx
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+../../../lectures/Lecture Week 2.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-02/notebooks/Notebook Week 2.ipynb b/materials/by_week/week-02/notebooks/Notebook Week 2.ipynb
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+../../../notebooks/Notebook Week 2.ipynb
\ No newline at end of file
diff --git a/materials/by_week/week-03/lectures/Lecture Week 3.md b/materials/by_week/week-03/lectures/Lecture Week 3.md
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+++ b/materials/by_week/week-03/lectures/Lecture Week 3.md	
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+../../../lectures/Lecture Week 3.md
\ No newline at end of file
diff --git a/materials/by_week/week-03/lectures/Lecture Week 3.pptx b/materials/by_week/week-03/lectures/Lecture Week 3.pptx
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+../../../lectures/Lecture Week 3.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-03/notebooks/Notebook Week 3.ipynb b/materials/by_week/week-03/notebooks/Notebook Week 3.ipynb
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+++ b/materials/by_week/week-03/notebooks/Notebook Week 3.ipynb	
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+../../../notebooks/Notebook Week 3.ipynb
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.1.py b/materials/by_week/week-04/exercises/Exercise_4.1.py
new file mode 120000
index 0000000..bf4d75a
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+++ b/materials/by_week/week-04/exercises/Exercise_4.1.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.1.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.2.py b/materials/by_week/week-04/exercises/Exercise_4.2.py
new file mode 120000
index 0000000..e62e7cf
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.2.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.2.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.3.py b/materials/by_week/week-04/exercises/Exercise_4.3.py
new file mode 120000
index 0000000..3fa4fda
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.3.py
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+../../../post_midterm/Exercise_4.3.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.4.py b/materials/by_week/week-04/exercises/Exercise_4.4.py
new file mode 120000
index 0000000..75a60fa
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+++ b/materials/by_week/week-04/exercises/Exercise_4.4.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.4.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.5.py b/materials/by_week/week-04/exercises/Exercise_4.5.py
new file mode 120000
index 0000000..3b38542
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.5.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.5.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.6.py b/materials/by_week/week-04/exercises/Exercise_4.6.py
new file mode 120000
index 0000000..f7ea1d5
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.6.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.6.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.7.py b/materials/by_week/week-04/exercises/Exercise_4.7.py
new file mode 120000
index 0000000..0121495
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.7.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.7.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.8.py b/materials/by_week/week-04/exercises/Exercise_4.8.py
new file mode 120000
index 0000000..b016355
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.8.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.8.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/exercises/Exercise_4.9.py b/materials/by_week/week-04/exercises/Exercise_4.9.py
new file mode 120000
index 0000000..65bd350
--- /dev/null
+++ b/materials/by_week/week-04/exercises/Exercise_4.9.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_4.9.py
\ No newline at end of file
diff --git a/materials/by_week/week-04/lectures/Lecture Week 4.pptx b/materials/by_week/week-04/lectures/Lecture Week 4.pptx
new file mode 120000
index 0000000..0b9963a
--- /dev/null
+++ b/materials/by_week/week-04/lectures/Lecture Week 4.pptx	
@@ -0,0 +1 @@
+../../../post_midterm/Lecture Week 4.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-04/notebooks/Notebook Week 4.ipynb b/materials/by_week/week-04/notebooks/Notebook Week 4.ipynb
new file mode 120000
index 0000000..1584c0c
--- /dev/null
+++ b/materials/by_week/week-04/notebooks/Notebook Week 4.ipynb	
@@ -0,0 +1 @@
+../../../post_midterm/Notebook Week 4.ipynb
\ No newline at end of file
diff --git a/materials/by_week/week-04/solutions/Solutions week 4.txt b/materials/by_week/week-04/solutions/Solutions week 4.txt
new file mode 120000
index 0000000..58407b3
--- /dev/null
+++ b/materials/by_week/week-04/solutions/Solutions week 4.txt	
@@ -0,0 +1 @@
+../../../post_midterm/Solutions week 4.txt
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.1.py b/materials/by_week/week-05/exercises/Exercise_5.1.py
new file mode 120000
index 0000000..376a5cc
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.1.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.1.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.2.py b/materials/by_week/week-05/exercises/Exercise_5.2.py
new file mode 120000
index 0000000..1b3a0a2
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.2.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.2.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.3.py b/materials/by_week/week-05/exercises/Exercise_5.3.py
new file mode 120000
index 0000000..0b09ea5
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.3.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.3.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.4.py b/materials/by_week/week-05/exercises/Exercise_5.4.py
new file mode 120000
index 0000000..8287093
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.4.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.4.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.5.py b/materials/by_week/week-05/exercises/Exercise_5.5.py
new file mode 120000
index 0000000..4a0a62e
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.5.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.5.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.6.py b/materials/by_week/week-05/exercises/Exercise_5.6.py
new file mode 120000
index 0000000..6385c81
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.6.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.6.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.7.py b/materials/by_week/week-05/exercises/Exercise_5.7.py
new file mode 120000
index 0000000..75d28dc
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.7.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.7.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.8.py b/materials/by_week/week-05/exercises/Exercise_5.8.py
new file mode 120000
index 0000000..a4ee483
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.8.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.8.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/exercises/Exercise_5.9.py b/materials/by_week/week-05/exercises/Exercise_5.9.py
new file mode 120000
index 0000000..0e4d7eb
--- /dev/null
+++ b/materials/by_week/week-05/exercises/Exercise_5.9.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_5.9.py
\ No newline at end of file
diff --git a/materials/by_week/week-05/lectures/Lecture Week 5.pptx b/materials/by_week/week-05/lectures/Lecture Week 5.pptx
new file mode 120000
index 0000000..650f567
--- /dev/null
+++ b/materials/by_week/week-05/lectures/Lecture Week 5.pptx	
@@ -0,0 +1 @@
+../../../post_midterm/Lecture Week 5.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-05/notebooks/Notebook Week 5.ipynb b/materials/by_week/week-05/notebooks/Notebook Week 5.ipynb
new file mode 120000
index 0000000..86d9351
--- /dev/null
+++ b/materials/by_week/week-05/notebooks/Notebook Week 5.ipynb	
@@ -0,0 +1 @@
+../../../post_midterm/Notebook Week 5.ipynb
\ No newline at end of file
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new file mode 120000
index 0000000..f9cfe28
--- /dev/null
+++ b/materials/by_week/week-05/solutions/Solutions week 5.txt	
@@ -0,0 +1 @@
+../../../post_midterm/Solutions week 5.txt
\ No newline at end of file
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new file mode 120000
index 0000000..5620ae3
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.1.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.1.py
\ No newline at end of file
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new file mode 120000
index 0000000..bc4a7c8
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.2.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.2.py
\ No newline at end of file
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new file mode 120000
index 0000000..fe1eb01
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.3.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.3.py
\ No newline at end of file
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new file mode 120000
index 0000000..5eea140
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.4.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.4.py
\ No newline at end of file
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new file mode 120000
index 0000000..ba675b1
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.5.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.5.py
\ No newline at end of file
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new file mode 120000
index 0000000..4040488
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.6.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.6.py
\ No newline at end of file
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new file mode 120000
index 0000000..397579c
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.7.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.7.py
\ No newline at end of file
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new file mode 120000
index 0000000..eda6ddd
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.8.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.8.py
\ No newline at end of file
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new file mode 120000
index 0000000..0c032bc
--- /dev/null
+++ b/materials/by_week/week-06/exercises/Exercise_6.9.py
@@ -0,0 +1 @@
+../../../post_midterm/Exercise_6.9.py
\ No newline at end of file
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new file mode 120000
index 0000000..3740377
--- /dev/null
+++ b/materials/by_week/week-06/helpers/OOP.py
@@ -0,0 +1 @@
+../../../post_midterm/OOP.py
\ No newline at end of file
diff --git a/materials/by_week/week-06/lectures/Lecture Week 6.pptx b/materials/by_week/week-06/lectures/Lecture Week 6.pptx
new file mode 120000
index 0000000..23b4149
--- /dev/null
+++ b/materials/by_week/week-06/lectures/Lecture Week 6.pptx	
@@ -0,0 +1 @@
+../../../post_midterm/Lecture Week 6.pptx
\ No newline at end of file
diff --git a/materials/by_week/week-06/notebooks/Notebook Week 6.ipynb b/materials/by_week/week-06/notebooks/Notebook Week 6.ipynb
new file mode 120000
index 0000000..0bc8caf
--- /dev/null
+++ b/materials/by_week/week-06/notebooks/Notebook Week 6.ipynb	
@@ -0,0 +1 @@
+../../../post_midterm/Notebook Week 6.ipynb
\ No newline at end of file
diff --git a/tests/test_materials_by_week_symlinks.py b/tests/test_materials_by_week_symlinks.py
new file mode 100644
index 0000000..a507089
--- /dev/null
+++ b/tests/test_materials_by_week_symlinks.py
@@ -0,0 +1,30 @@
+from pathlib import Path
+import os
+import unittest
+
+
+class MaterialsByWeekSymlinkTests(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls) -> None:
+        cls.root = Path(__file__).resolve().parents[1]
+        cls.by_week = cls.root / "materials" / "by_week"
+
+    def test_by_week_leaf_entries_are_symlinks_with_existing_targets(self) -> None:
+        entries = []
+        for path in self.by_week.rglob("*"):
+            if path.name.startswith("."):
+                continue
+            if path.is_dir() and not path.is_symlink():
+                continue
+            entries.append(path)
+
+        self.assertGreater(len(entries), 0, "Expected materials/by_week to contain linked material entries")
+
+        for entry in entries:
+            self.assertTrue(entry.is_symlink(), f"{entry}: expected by_week entry to be a symlink")
+            target = (entry.parent / os.readlink(entry)).resolve()
+            self.assertTrue(target.exists(), f"{entry}: symlink target does not exist: {target}")
+
+
+if __name__ == "__main__":
+    unittest.main()
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\ No newline at end of file
diff --git a/tmp/pdfs/text/week1.txt b/tmp/pdfs/text/week1.txt
new file mode 100644
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+++ b/tmp/pdfs/text/week1.txt
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+Introduction to Python
+Week 1
+
+1
+
+What is Python?
+• It is a programming language
+• A Python script is readable for human beings
+• A Python script is not readable for computers: a python script must be translated,
+before a computer can read and execute it
+
+• Python is translated in two steps:
+1. The Python script is translated line by line into so-called byte code
+2. This byte-code is translated into machine-code
+
+• Writing code must be precise. Ambiguities, that would be correctly interpreted by
+humans, will lead to errors in Python
+• We use a so-called monospaced font for code on these slides.
+This means that all characters take an equal amount of space.
+Using a monospaced font for code is a common practice
+2
+
+Why Python?
+• Python forces you to work in a structured way, and this is one of the
+reasons it is used in places, like universities, where programming is taught
+• Python is hot as it is used a lot in (packages for) artificial intelligence/machine
+learning
+
+• Some trade-offs:
+• Python is relatively slow
+• Python is very expandable
+• It is easy to let Python cooperate with programs written in faster (e.g. compiled) languages like C, C+
++, FORTRAN and RUST
+• There are a lot of packages (extensions) available for Python, and some have faster programming
+languages under the hood
+
+3
+
+Program for Today
+1. Organization of the Course
+2. Python
+
+4
+
+Organization of the course
+• Instructors
+• Topics
+• Tools
+• Communication
+• Study advice
+• Pythonanywhere
+• Assesment
+
+5
+
+Instructors
+• Mathijs Janssen (course coordinator, lecturer, instructor for
+tutorial groups 5 and 10)
+• Reza Armakan (instructor for tutorial groups 11, 12, and 13)
+• Alessandro Capra (instructor for tutorial groups 6, 7, 8 and 9)
+• Oliver Feltham (instructor for tutorial groups 1, 2, 3 and 4)
+• Ieva Rudzite and Catalin Zaharia (Discussions page and
+email: intropython-eb@uva.nl)
+
+6
+
+Communication
+• Central to our communication is the discussion board on Canvas
+• If you have questions about Python, you can ask them via the discussion
+board
+• They can be answered and discussed by your fellow students, but your
+teachers will regularly check the discussion board and solve any loose
+ends
+
+• Questions that are more personal can be asked via the mail
+function of canvas
+
+7
+
+Learning goals
+• Understanding code (Tutorials, lectures), this is exam material
+• Writing code (Tutorials, lectures), this is homework and can lead
+to a higher grade
+• Getting an idea about Python in the real world, possible guest
+lectures
+
+8
+
+Tools
+• Lectures (beginning of the week)
+• presentation of material about the python language that you need to know for the exam and
+that you can use for the programming exercises
+• Lectures are supported by lecture slides and Jupyter notebooks
+• Every week, you will get a set of personalized questions and a Python program that you can
+use to send your answers to our server and that server will tell you immediately whether your
+answer produce the right results, these questions can be quite complicated
+• You can send in solutions as often as you want, till the deadline Sunday night 23:59
+• Your answers will be tested automatically. Your code will be applied to several testsets. If a
+program produces the right result, you get a point, if it doesn't you won't get a point for that
+question
+• At the beginning of the next week, you will get feedback that tells you whether your solution
+worked correctly and also will give an example how to solve these exercises with the material,
+you had to study so far
+• These feedback solutions are also material to learn for the exam
+
+• DataCamp: a website with videos about the course material. The website will
+present you with questions, and instant feedback to your answers
+9
+
+Tools
+• Tutorials: here you can work on your homework and discuss problems with
+your fellow students and the tutors
+• Homework exercises can be more difficult than exam questions and exam
+questions will be more difficult/complex than DataCamp questions
+• Q&A sessions (at the end of the week):
+• Tying loose ends
+• Material we could not deal with in the first lecture of the week
+• Questions you asked, during lectures, during tutorials and on the discussion board
+
+• The internet, has tons of well-done material on Python, some examples:
+• https://www.w3schools.com/python/ (the level is comparable to DataCamp, but a bit
+more extensive)
+• https://www.StackOverflow.com (the holy grail if you need help writing programs)
+• https://realpython.com/ (good tutorials, but not all are free. If you want to become very
+good, maybe spend some money here)
+10
+
+Running Python
+• There are a lot of options, but we will only mention a few
+• Notebooks can be run in the browser on Colab
+(https://colab.research.google.com/), If you want to be fancier have
+a look at the Anaconda project
+(https://anaconda.org/anaconda/anaconda-project)
+• We advise you to use Pythonanywhere for writing and testing your
+Python programs for the homework
+• Pythonanywhere runs in your browser
+• Pythonanywhere is very easy to set up and use and your teachers can help
+you with it
+
+• You are free to use other systems to run Python, but we
+cannot always offer help. There are just too many options
+11
+
+Study advice
+• Before the lecture, study the DataCamp material
+• After the lecture have a good look again at the notebook and
+the slides for that week
+• Play with the notebooks. Run the code, change the code and
+run it again
+• Come to the tutorials and do your homework exercises
+• If you don't understand something, google for a solution, and
+ask a question on the discussion board
+
+12
+
+Assessment
+• Midterm and Final exam
+• Multiple choice
+
+• Questions will test your ability to understand code
+• Examples:
+•
+•
+•
+•
+•
+
+Which code fragment will print the following output
+What will be printed by the following code fragment
+Which code fragment prints the same output as the following code fragment
+Of the following code fragments, one prints an output that differs from the other fragments
+Which of the following code fragments solves the following problem correctly
+
+•
+•
+•
+•
+
+None of the above
+Both
+All
+An error
+
+• Options can include:
+
+• You're allowed to use one (double-sided) A4-cheatsheet during the exam
+
+• Making a good cheat sheet will help you to learn the material for the exam, and maybe you
+won't need it during the exam anymore
+
+• Final exam grade must be at least 5.0
+• Total grade must be at least 5.5
+13
+
+Python
+• Logical and physical lines of code
+• Comments
+• Objects
+• Operators
+
+14
+
+Logical lines of codes
+• A Python program consists of
+• Logical lines of code
+
+• Simple statements consist of one logical line of code
+a = 1
+
+• Compound statements consist of several logical lines of code
+if a > 0:
+print("a is positive")
+else:
+print("a zero or negative")
+
+15
+
+Physical lines of code
+• In the previous slide every logical line was one physical line
+• Two or more physical lines can be joined into one logical line with the help of
+backward slashes, this is called explicit line joining:
+a = 'Two or more physical lines can be joined' +\
+' into one logical line with the help of backward slashes'
+
+• Sometimes one can use parts of a logical line inside a pair of parentheses, or
+square brackets, or curly brackets, to join physical lines, this is called implicit
+joining
+a = ['Sometimes one can use parts of a logical line inside a',
+'pair of parentheses, or square brackets, or curly brackets, ',
+'to join physical lines , this is called implicite joining ']
+
+• These examples of joining are easy to follow, but the precise rules of the
+explicit and implicit joining are a bit more complicated. However, in the exam
+material you can always assume the rules for joining are applied correctly
+16
+
+Comments
+# This whole physical line is a comment
+a = 1 # Everything after the hashtag (#) is a comment
+# This is a
+# multiline comment
+
+17
+
+Objects
+• Every object has exactly one unique id that cannot be changed
+• Every object has exactly one type that cannot be changed
+• Every objects has exactly one value
+• The type of an object defines which type of value can be stored in the object
+• The type of an object defines whether a value is mutable or is immutable
+• The value of a mutable objects can be changed, the value of an immutable
+object cannot be changed
+• An Object can have zero, one or more names bound/referring to it
+• A name can only refer to one object, at any time
+• Names can be unbound from an object and bound to another object any time
+18
+
+Objects
+• An object can be deleted
+• By using a del statement: del(name_of_object)
+• When an object is not referred to, not by a name or anything else, it can no
+longer be used in a program and Python's garbage collection will
+automatically remove the object
+• Objects created in a program [function], will only exist during the lifetime of
+the program [function], and will be automatically deleted when the program
+[function] ends
+
+• Everything in Python is an object
+
+19
+
+Assignment statements (I)
+• A common way to create an object is using an assignment statement, e.g.
+name_1 = 300
+
+• To follow exactly what Python will do, it is best to read these assignment
+statements from right to left:
+• Python creates a new object with value 300, type int, and a unique id
+• Python infers the object type from the value on the right side of the assignment
+statement. In this case the type will be int (integer)
+• Python binds the name on the left side of the assignment statement name_1 to this
+newly created object
+
+• We have created an object with the name name_1 and we can now display
+the value, the type and the id of the object:
+print (name_1)
+print (type(name_1))
+print (id(name_1))
+20
+
+Using assignment statements to
+create objects(II)
+• Let's now create a second object with value 300 and type int
+name_2 = 300
+
+• We can show, that name_1, and name_2 refer to objects that
+have the same value, and the same type, but have different id's
+print(name_1 == name_2)
+print(type(name_1) == type(name_2))
+print(id(name_1) != id(name_2))
+or shorter:
+print(name_1 not is name_2)
+
+21
+
+Using assignment statements to
+create objects(III)
+• You can also create an object and give it two (or more) names:
+name_3 = 300
+name_4 = name_3
+name_5 = name_3
+name_6 = name_4
+or shorter:
+name_3 = name_4 = name_5 = name_6 = 300
+
+• We can show, that name_3, that name_4, that name_5, and name_6
+all refer to the same object, and have the same value and type:
+print(name_3 == name_4 == name_5 == name_6)
+print(type(name_3) == type(name_4) == type(name_5) == type(name_6))
+print(name_3 is name_4 is name_5 is name_6)
+22
+
+Types of objects
+• Integer
+var_1 = 123
+print(type(var_1) == int)
+• Floating point number
+var_1 = 123.3
+print(type(var_1) == float)
+• String
+var_1 = '123'
+print(type(var_1) == str)
+• Boolean
+var_1 = True
+print(type(var_1) == bool)
+23
+
+Types of objects
+• Tuple
+var_1 = ('123', 123)
+print(type(var_1) == tuple)
+
+• List
+var_1 = [123, '123']
+print(type(var_1) == list)
+
+• Dictionary
+var_1 = {'key_1': 'elem_1', 'key_2': 'elem_2'}
+print(type(var_1) == dict)
+
+• Set
+var_1 = {123, '123'}
+print(type(var_1) == set)
+24
+
+Question
+• Which of the following statements is correct:
+I. Two objects can have different values, while having the same type
+II. Two objects can have the same value, while having different types
+
+a. I is correct
+b. II is correct
+c. I and II are both correct
+d. Neither is correct
+
+25
+
+Answer
+• Which of the following statements is correct:
+I. Two objects can have different values, while having the same type
+II. Two objects can have the same value, while having different types
+
+a. I is correct
+b. II is correct
+c. I and II are both correct
+d. Neither is correct
+I is correct for example:
+a = 1
+b = 2
+
+II is not correct, it is not possible to create 2 objects, with names a and b
+for which:
+print (a == b) gives True
+print (type(a) == type(b) gives False this
+
+26
+
+Names of objects
+• Names of objects can consist of (capitalized) letters, digits, and
+underscores (_)
+• Names of objects can start with (capitalized) letters, or an
+underscore
+• Names of objects are case sensitive, e.g. name_1 and Name_1 are
+different names
+• These are the minimal technical rules you have to abide too, if you
+don't follow them, Python throws an error
+• Organizations where you work can have extra rules
+• A common followed list of rules, can be found in the PEP 8 – Style
+Guide for Python Code (https://peps.python.org/pep-0008/ )
+27
+• In this course we only follow the minimal technical rules
+
+Arithmetic Operators
+• a + b, a – b, a * b
+• a / b, a // b
+• a / b results in a float
+• a // b results in the highest integer that is smaller or equal to the result of a
+division
+
+• a % b gives the remainder of the integer division
+• a ** b exponentiation
+• NB ^ (what is used in excel for exponentiation) is also a Python-operator but
+does something entirely different
+
+• Question: What is the result of (a//b)*b + a%b
+28
+
+Arithmetic Operators
+• a + b, a – b, a * b
+• a / b, a // b
+
+• a / b results in a float
+• a // b results in the highest integer that is smaller or equal to the result of a division
+
+• a % b gives the remainder of the integer division
+• a ** b exponentiation
+• NB ^ (what is used in excel for exponentiation) is also a Python-operator but does
+something entirely different
+
+• Question: What is the result of (a//b)*b + a%b
+• Answer: a
+29
+
+Comparison operators
+• a == b, a != b, a > b, a < b, a >= b, a <= b
+• Always lead to True or False
+• Comparing floats can be tricky: 1.1 + 2.2 according to Python is not equal to
+3.3. This is because floating point numbers are not precise. Computers use a
+binary system and that means that floats are not stored as precise as you maybe
+expect. For homeworkexercises we have built-in a tolerance so that when your
+answer is close enough you get a full point
+• If you are interested in how to solve this problem with floating points, have a look
+at the isclose function or the Decimal type, this is not part of the exam
+• Comparing strings, tuples and lists all work the same. Elements are compared
+from left to right, and the first elements that differs decides the result. If no
+element differs and the structures are of equal length, the structures are seen as
+equal. If no element differs and the structures are of different length, the longest
+structure is seen as larger
+30
+
+Boolean operators
+• x and y: True if both are True, otherwise False
+• x or y: False if both are False, otherwise True
+• not x: True if x is False, False if x is True
+• NB && and || (which are used in some programming languages for and
+and or) are also a Python-operators but do something entirely different
+
+31
+
+Mutable vs immutable
+• List (mutable):
+l1 = [1,2,3]
+print(l1)
+# Onscreen: [1, 2, 3]
+l1[2] = 4
+print(l1)
+# Onscreen: [1, 2, 4]
+
+• Tuple (immutable):
+t1 = (1,2,3)
+print(t1)
+# Onscreen: (1, 2, 3)
+t1[2] = 4
+# Onscreen: TypeError Traceback (most recent call last)
+Input In [2], in <cell line: 1>()
+----> 1 t1[2] = 4
+TypeError: 'tuple' object does not support item assignment
+32
+
+Question
+• We know objects with a string value are immutable
+• Why then does the following code work
+box_1 = 'content'
+print(box_1)
+box_1 = 'new content'
+print(box_1)
+
+33
+
+Answer
+• We know objects with a string value are immutable
+• Why then does the following code work
+box_1 = 'content'
+print(box_1)
+box_1 = 'new content'
+print(box_1)
+
+• Answer: we didn't change the object, we created a new object and gave it
+an old name
+box_1 = 'content'
+print(box_1)
+old_id = id(box_1)
+box_1 = 'new content'
+print(box_1)
+new_id = id(box_1)
+print(old_id == new_id)
+34
+
+An example with a string (immutable)
+name_1 = 's1'
+
+Python creates an object with the value 's1', and binds the name name_1 to that object.
+We have now one object with the string value 's1' and with the name name_1
+
+name_2 = name_1
+
+To the object with value 's1' and with the name name_1, Python binds a second
+name name_2.
+We have now one object with the value 's1', and with two names name_1 and
+name_2
+
+name_1 = 's2'
+
+From the object with value 's1' the name name_1 is removed.
+Python creates a new object with the value 's2' and binds the name name_1 to
+this new object.
+We have now one object with the value 's2' and with the name name_1,
+and we have one object with the value 's1' and with the name name_2
+
+name_2 = 's3'
+
+From the object with the value ‘s1' the name name_2 is removed.
+If as is the case here, this is the only reference left to that object, Python's garbage
+collection will remove this object.
+35
+We will now have one object with value 's2' and with the name name_1
+
+An example with a list (mutable)
+l1 = ['s1', 's2']
+
+Python creates two objects, one with value 's1' and another one with value 's2'.
+Next, Python creates an object of type list that refers to these two objects and binds the
+name l1 to that list.
+(The objects containing 's1' and 's2' have no names, but have id's and can be
+referred to.)
+
+l2 = l1
+
+To the list referring to the two string objects, Python binds a second name l2.
+
+l1[0] = 's3'
+
+Python creates a new object with the value 's3'.
+Next, Python mutates the list, with the names l1 and l2. The first object referred
+to by that list (with the names l1 and l2) is no longer the object with value 's1',
+but the object with value 's3', while the second object referred to by that list (with
+the names l1 and l2) is still the object with the value 's2'.
+
+l2 = ['s4', 's5']
+
+From the first list the name l2 is removed.
+As that list is still referenced by the name l1, the list will not be removed by Python's
+garbage collection.
+Next, Python creates two objects, one object with the value 's4' and another object
+with the value ‘s5'.
+36
+Now Python creates a list referring to these two objects and binds the name l2 to it.
+
+Indexing
+• l1 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
+• The first element, seen from the left, is 0, the second is 1,
+and the last is len(l1)-1
+• The first element, seen from the right, is -1, the second is -2,
+and the last is -len(l1)
+• print(l1[0] == l1[-len(l1)] )
+
+37
+
+Slicing (I)
+• Slicing creates a new object made consisting of elements of another object
+• Slicing can be used with objects of type list, string and tuple and the result will be
+an object of the same type
+• Syntax: new_list = existing_list[start:end:step_size]
+• Start, end, and step_size are parameters of the slicing operation
+
+• Start is inclusive, and tells Python with which index to start the slicing
+• End is not inclusive, and tells Python with which index to stop the slicing (this
+could be beyond the highest or lowest index value)
+• The first element is 0, the second is 1, and the last is len(existing_list)
+• You can also use -1 for the last element and -len(existing_list)
+for the first
+• Step_size
+• len is a built_in function that gives you the number of elements when applied on a list,
+string or tuple
+
+38
+
+Slicing (I): examples when stepsize >= 1
+• Recall: new_list = existing_list[start:end:step_size]
+
+• l1 = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H']
+• print(l1[0:6:2])
+• print(l1[1:6:1] == l1[1:6])
+• print(l1[1:6:1] == l1[1:6:])
+• print(l1[0:6:2] == l1[:6:2])
+• print(l1[2:len(l1):2] == l1[2::2])
+• print(l1[0:len(l1):1] == l1[::])
+39
+
+Question: what is the result of the
+following program?
+• l1 = [1, 2, 3, 4]
+l2 = l1
+l3 = l1[::]
+l1[-1] = 5
+print(l1 == l2 , l2 == l3)
+• And why?
+
+40
+
+Question: what is the result of the
+following program?
+• l1 = [1, 2, 3, 4]
+l2 = l1
+l3 = l1[::]
+l1[-1] = 5
+print(l1 == l2 , l2 == l3)
+• Answer: True False
+• Look at the definition of slicing: Slicing creates a new object
+
+41
+
+Slicing (II): examples when stepsize >=1
+• Recall: new_list = existing_list[start:end:step_size]
+• l1 = [1,2,3,4,5,6,7,8]
+• print(l1[7]) # You ask for the 8th element
+# Onscreen: 8
+• print(l1[-1]) # You ask for the last element
+# Onscreen: 8
+• print(l1[-5:5])
+# Onscreen: [4, 5]
+• print(l1[:-5])
+# Onscreen: [1, 2, 3]
+
+42
+
+Slicing (III): examples with negative
+steps
+
+• While with positive steps you go from the start to the right,
+with a negative step you go from the end to the left
+
+• l1 = [1,2,3,4,5,6,7,8]
+• print(l1[5:1:-2]) # Onscreen: [6, 4]
+• print(l1[5::-1]) # Onscreen: [6, 5, 4, 3, 2, 1]
+• print(l1[:1:-1]) # Onscreen: [8, 7, 6, 5, 4, 3]
+• print(l1[1:5:-2]) # Onscreen: []
+• Python will check whether you are already past the end index, it works
+similar as print(l1[5:1:2]) # Onscreen: []
+
+43
+
+Question: What will be printed?
+• l1 = [1, 2, 3, 4]
+• print(l1[::-1])
+
+44
+
+Question: What will be printed?
+• l1 = [1, 2, 3, 4]
+• print(l1[::-1])
+• Answer: [4, 3, 2, 1]
+
+45
+
+Slicing with strings and tuples
+• Slicing can also be done with strings and tuples
+• t1 = (1, 2, 3)
+print(t1[1:]) # Onscreen: (2, 3)
+• s1 = 'uva Amsterdam'
+print(s1[5:-3:2]) # Onscreen: mtr
+
+46
+
+Changing a slice of a list
+• Changing a list:
+l1 = [1, 2, 3, 4, 5, 6]
+l1[1:4] = l1[1:4][::-1]
+print(l1) # Onscreen: [1, 4, 3, 2, 5, 6]
+• Tricky:
+l1 = [1, 2, 3, 4, 5, 6]
+l1[1:2] = [1, 1]
+print(l1) # Onscreen: [1, 1, 1, 3, 4, 5, 6]
+vs
+l1 = [1, 2, 3, 4, 5, 6]
+l1[1] = [1, 1]
+print(l1) # Onscreen: [1, [1, 1], 3, 4, 5, 6]
+47
+
+'Changing' a slice of a string or a
+tupple
+• 'changing' a string:
+
+• s1 = 'uva Amxterdam'
+s1[6] = 's'
+# Gives an error
+• s1 = 'uva Amxterdam'
+s1 = s1[:6] + 's' + s1[7:]
+print(s1)
+# works
+
+• 'changing' a tuple:
+• t1 = (1, 4, 3)
+t1[1] = 2
+# Gives an error
+• t1 = (1, 4, 3)
+t1 = t1[:1] + (2,) + t1[2:]
+print(t1)
+# works
+48
+
+Range
+• range(start, stop, step)
+
+• Range function is used to generate a series of numbers. The
+start, stop and step parameters work similar as in list slicing, but
+as you will see the defaults work a bit different.
+x = list(range(6))
+print(x)
+# Onscreen: [0, 1, 2, 3, 4, 5]
+x = list(range(3,6))
+print(x)
+# Onscreen: [3, 4, 5]
+x = list(range(3,6,2))
+print(x)
+# Onscreen: [3, 5]
+49
+
+Functions
+• We often want to (re)use pieces of code
+• Pieces of code can be wrapped into a function, given a name, and then re-used
+by referencing that name
+• We can pass input to a function, these values are called arguments
+• Functions can create output, we say functions can return a value
+def size(length, width):
+return length * width
+print(size (2,3)) # Onscreen: 6
+
+• Python comes with a lot of built-in functions, we already saw:
+print
+id
+len
+type
+del
+
+• We don't much care how these built-in functions are written, we just want to know
+what it does ( = how it turns inputs into outputs)
+• We will write our own functions from Week 3 onwards
+50
+
+Objects
+• Organizing your code in functions is a good idea, but organizing your code
+in objects may be even better
+• Objects can encapsulate (≈ contain) functions and data
+• Objects are always defined on the basis of a blueprint, and that blueprint is
+called a class, so you first have to define the class:
+class Rectangle:
+def __init__(self, length, width):
+self.length = length
+self.width = width
+def size(self):
+return self.length * self.width
+
+51
+
+Objects
+• class Rectangle:
+def __init__(self, length, width):
+self.length = length
+self.width = width
+def size(self):
+return self.length * self.width
+• And now you can define your objects:
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3,3)
+print(rectangle_1.size(), rectangle_2.size())
+# Onscreen: 6 9
+
+52
+
+In Python, everything is an object
+• Recall that every object has a type, a value, and zero or more names
+• Functions that are encapsulated in objects are called methods
+• Depending on the type, an object has several methods
+• Methods can take arguments, and they also have access to the data
+inside the object
+• For every datatype there is already a built-in class, that defines what you
+can do with an object of that type
+
+53
+
+In Python, everything is an object
+• Sometimes, methods return a value
+• Sometimes, methods change the object itself, but this is only possible if the object
+is mutable
+• If an object is immutable, the method applied on it must return a value as it cannot change
+that object itself
+
+• Example of a method on an immutable object:
+• s1='UVA Amsterdam'
+s1=s1.upper() or s1.upper()
+• Which code is correct?
+• Answer: s1=s1.upper(), since the method upper() returns a new string. If you just do
+s1.upper() Python is ok with that, but the string itself cannot be changed. This error can be
+very difficult to find.
+
+• For methods working on mutable objects, you have to know whether it changes
+the object or returns a value.
+• Example (l1 is a list): l1. sort() sorts the list itself
+• Example (l1 is a list): l1. index('a') returns the index of the first element in the list with value
+'a'
+54
+
+Importing additional functionality from
+packages
+• Python ecosystem = core language + standard library + third-party
+packages
+• The ecosystem is enormous, there is no way to make it available by
+default
+• That's why we need to install third-party packages separately, for example
+• Even the standard library is large; it's functionality must be imported to be
+used
+• E.g. import math
+
+• Importing is about making the contents of a module (= Python file)
+available in the namespace of our program
+
+55
+
+Example: Importing additional
+functionality from packages
+• Example: how to access the randint function in the random module of the
+numpy package
+• Via import, you make an object available to your program
+• print(numpy.random.randint(1, 10)) # error
+• import numpy
+print(numpy.random.randint(1, 10)) # works
+• import numpy as np
+print(np.random.randint(1, 10))
+# works
+print(numpy.random.randint(1, 10)) # error
+• import numpy.random as rnd
+print(rnd.randint(1, 10))
+# works
+print(numpy.random.randint(1, 10)) # error
+56
+
+Example: Importing additional
+functionality from packages
+• from numpy.random import randint
+print(randint(1, 10))
+# works
+print(numpy.random.randint(1, 10)) # error
+• from numpy.random import randint as
+give_me_a_random_integer
+print(give_me_a_random_integer(1, 10)) # works
+print(numpy.random.randint(1, 10))
+# error
+• NB!! Notice you can only give one name to an imported object, so if you
+use as to change the name, you no longer can use the original name
+
+57
+
+
\ No newline at end of file
diff --git a/tmp/pdfs/text/week4.txt b/tmp/pdfs/text/week4.txt
new file mode 100644
index 0000000..cc0a038
--- /dev/null
+++ b/tmp/pdfs/text/week4.txt
@@ -0,0 +1,794 @@
+Introduction to Python
+Week 4
+
+1
+
+Program for This Week
+1.
+2.
+3.
+4.
+5.
+6.
+7.
+
+Defining strings
+Basic string manipulation
+String methods
+F-strings
+Object-oriented programming (OOP) concepts
+Error handling
+Midterm and homework questions (next week)
+
+3
+
+Defining strings with quotes
+• When using single quotes, Python searches for a matching quote, and stops
+searching at the End of the Line (EOL)
+• The following give an error (SyntaxError: EOL while scanning string):
+• print("it')
+• print('it")
+
+• When using triple quotes, Python searches for a matching triple, and stops
+searching at the End of the File (EOF)
+• The following give an error (EOF while scanning string literal):
+• print('''it's
+a new line)
+• print("""it's
+another new line
+"")
+4
+
+Quotes inside a string
+• The following works:
+• print("it's") # Onscreen: it's
+• print('it"s') # Onscreen: it"s
+
+• The following gives an error (SyntaxError: invalid syntax):
+• print("it"s")
+• print('it's')
+
+• Alternatively, working with escape characters also works:
+• print("it\"s\'s") # Onscreen: it"s's
+• print('it\"s\'s') # Onscreen: it"s's
+
+• Triple quotes work as well:
+• print("""it"s's""") # Onscreen: it"s's
+• print('''it"s's''') # Onscreen: it"s's
+
+5
+
+Escape characters
+• \\ = \
+• \’ = '
+• \" = "
+• \n = go to the next line
+• \t = insert a tab
+• print('The info, it\'s on the next\\following line in two columns:\n1\t2')
+# Onscreen: The info, it's on the next\following line in two columns:
+# 1 2
+• This is great, but if you must type the string yourself, using triple single or double quotes may be easier:
+• print("""The info, it's on the next\following line in two columns:
+1 2""")
+# Onscreen: The info, it's on the next line in two columns:
+# 1 2
+
+6
+
+Basic string manipulation: built-in functions
+• There are also some built-in functions that can be used with strings
+• Built-in means that they are part of the Python system, you don't have to program them yourself
+• The function len() gives you the length of a string:
+• a = 'abcd'
+print(len(a)) # Onscreen: 4
+• Concatenation, which is used to concatenate strings, uses the + operator:
+• a = 'abcd'
+b = 'ef'
+a = a + b
+print(a) # Onscreen: abcdef
+• That you can use an operator for several different operations is called operator overloading, in this case
+the + operator to add numbers, to concatenate strings, to append to lists
+• The function sorted() can also be used to a string:
+• print(sorted('acbd')) # Onscreen: ['a', 'b', 'c', 'd']
+• The result of sorting a string is a list. Later this lecture we will use the join method to transform
+this list back into a string
+7
+
+String methods
+• Methods are functions
+• Methods are, just like functions, called by adding round brackets after the method
+• Within these round brackets you can add arguments that you want to pass to the method
+
+• The difference with a function is that you always must pass the object on which
+the method is applied as first argument to the method
+• This can be done in several ways, but most easily by using the dot operator (dot = .)
+
+• Compare a function call:
+• print(len('abcd')) # Onscreen: 4
+
+• With a method call:
+• 'abcd'.index('b') # Onscreen: 1
+
+• Though it seems like only one argument is passed to the method, two arguments
+are passed to this index method: the string object with value 'abcd', and an object
+with value 'b'
+8
+
+String methods
+• Methods can either:
+• Change the object that is passed to them with the dot operator
+• Return a changed version of the object as a new object
+
+• If you program methods yourself, you can even let them do both
+• You can assume that the built-in methods of the standard library take only one option
+• For a built-in method, you must know which option it will be
+• With methods used in the Pandas library you can often change the default option via a parameter
+
+• Question: What will the option be in the case of a string method?
+• As strings are immutable, string methods can only return a new object
+
+• Technically, even a method that changes an object returns a value: just like a function
+without a return statement, a method without a return statement will return None
+• However, that returned value is not what you will be interested in
+
+9
+
+String methods: Question
+• For example, if you want to capitalize (make the first character uppercase) a
+string, which of the following code fragments will print: Abcd
+1. s1 = 'abcd'
+s1 = s1.capitalize()
+print(s1)
+2. s1 = 'abcd'
+s1.capitalize()
+print(s1)
+a) Both are correct
+b) Both are incorrect
+c) Only 1 is correct
+d) Only 2 is correct
+
+10
+
+String methods: Question
+• For example, if you want to capitalize (make the first character uppercase) a
+string, which of the following code fragments will print: Abcd
+1. s1 = 'abcd'
+s1 = s1.capitalize()
+print(s1)
+2. s1 = 'abcd'
+s1.capitalize()
+print(s1)
+a) Both are correct
+b) Both are incorrect
+c) Only 1 is correct
+d) Only 2 is correct
+•
+
+Correct answer: c. s1.capitalize doesn't change string s1, but returns a new string. You
+must give that new string a name else you cannot use it in the print statement. The second
+code fragment, actually prints abcd
+11
+
+String methods: change and test case
+• Change the case:
+• print('ABcd'.upper()) # Onscreen: ABCD
+• print('ABcd'.lower()) # Onscreen: abcd
+
+• Change only the first character into uppercase:
+• print('abcd'.capitalize()) # Onscreen: Abcd
+
+• NB! I use the word change a lot, but don't forget the string cannot be changed,
+we actually create a new string on the basis of the old string
+• Test the case of the letters in the string:
+• print('ABCD'.isupper()) # Onscreen: True
+• print('ABcd'.isupper()) # Onscreen: False
+• print('ABcd'.islower()) # Onscreen: False
+• print('abcd'.islower()) # Onscreen: True
+• print('ABcd'[:2].isupper()) # Onscreen: True
+
+12
+
+String methods: split
+• Split a string: for example, consider s1 = 'axbcxdefxg h\nij\tk'
+• print(s1.split('x')) # Onscreen: ['a', 'bc', 'def', 'g h\nij\tk']
+• print(s1.split('x', 2)) # Onscreen: ['a', 'bc', 'defxg h\nij\tk']
+• print(s1.rsplit('x', 2)) # Onscreen: ['axbc', 'def', 'g h\nij\tk']
+• print(s1.split()) # Onscreen: ['axbcxdefxg', 'h', 'ij', 'k']
+• print(s1.split('q')) # Onscreen: ['axbcxdefxg h\nij\tk']
+• The argument you pass to split, is used to split the string and is also removed from the string
+• If the argument is not in the string, Python will return an element with only one element, which is a copy
+of the string
+• If you also add a number, then that will be the maximum number of times Python will split the string
+• The default is splitting around spaces, new lines (\n), and tabs (\t)
+• To split a string in physical lines:
+• s1 = 'axbcx\rdefxg \nhij '
+• print(s1.split('\n'))
+# Onscreen: ['axbcx\rdefxg ', 'hij']
+• print(s1.splitlines())
+# Onscreen: ['axbcx', 'defxg ', 'hij']
+• I prefer the first
+
+13
+
+String methods: join
+• Join a string: for example: l1 = ['abc', 'de', 'fg', 'h']
+• print('xy'.join(l1)) # Onscreen: abcxydexyfgxyh
+• print(''.join(l1)) # Onscreen: abcdefgh
+
+• How to sort a string:
+• a = sorted('0123456789', key=lambda x: str(int(x) % 2 == 1) + x))
+• print(''.join(a)) # Onscreen: '0246813579'
+
+14
+
+String methods: strip
+• Strip a string: for example, consider s1 = ' fgedee '
+• print(s1.strip(' e')) # Onscreen: fged
+• print(s1.strip()) # Onscreen: fgedee
+• print(s1.lstrip(' e')) # Onscreen: fgedee..
+• print(s1.lstrip()) # Onscreen: fgedee..
+• print(s1.rstrip(' e')) # Onscreen: ..fged
+• print(s1.rstrip()) # Onscreen: ..fgedee
+• Python removes characters from the object if they are in the passed string and stops as soon as it
+encounters a character that is not in the list
+• strip works from both sides of the string, lstrip starts from the left side, and rstrip starts
+from the right side
+• s1.strip(' e') and s1.lstrip(' e').rstrip(' e') give the same result
+• The default is removing spaces, new lines (\n), and tabs (\t)
+• More types of so-called white spaces will be removed, but only these 3 you must know
+• NB! The spaces in the strip examples are represented by dots in the Onscreen
+15
+
+String methods: replace
+• Replace a substring: consider for example s1 = 'abcdefghabcdefgh':
+• print(s1.replace('a', 'aa')) # Onscreen: aabcdefghaabcdefgh
+• print(s1.replace('a', 'aa', 1)) # Onscreen: aabcdefghabcdefgh
+• print(s1.replace('a', '')) # Onscreen: bcdefghbcdefgh
+• print(s1.replace('q', 'qq')) # Onscreen: abcdefghabcdefgh
+
+• The first passed string is the old substring and it will be replaced by the second passed
+string, that is the new substring
+• The new and the old substring can have any length
+• Only the new string can be an empty string: you can use this to remove substrings from the string
+
+• You can add an integer, and that will tell Python how many changes to make
+• If you don't add a number Python will replace all old substrings
+
+16
+
+String methods: find, index
+• Find a substring: consider for example
+• s1 = 'abcdefghabcdefgh'
+• print(s1.find('e'))
+# On screen: 4
+• print(s1.index('e', 5))
+# On screen: 12
+• print(s1.find('e', 5, 10))
+# On screen: -1
+• print(s1.index('e', 5, 13))
+# On screen: 12
+• print(s1.find('ef'))
+# On screen: 4
+• print(s1.index('q'))
+# On screen: ValueError: substring not found
+• With these methods, you can find the index of a substring in a string
+• If you add one integer as an extra parameter, it will tell Python from where to start searching in the string
+• If you add another integer as an extra parameter, it will tell Python before which index to stop searching
+• The only difference between find and index is that if find cannot find anything, find will return –
+1 and if index cannot find anything index will throw an error. In all other cases find and index will
+return a non-negative integer.
+17
+
+String methods: count
+• Find a substring: consider for example
+• s1 = 'abcdefghabcdefgh'
+• print(s1.count('e'))
+• print(s1.count('e', 5))
+• print(s1.count('e', 5, 10))
+• print(s1.count('e', 5, 13))
+• print(s1.count('ef'))
+• print(s1.count('q'))
+
+# Onscreen: 2
+# Onscreen: 1
+# Onscreen: 0
+# Onscreen: 1
+# Onscreen: 2
+# Onscreen: 0
+
+• With these methods, you can find how often a substring occurs in a string
+• If you add one integer as an extra parameter, it will tell Python from where to start
+searching
+• If you add another integer as an extra parameter, it will tell Python before which index
+to stop searching
+19
+
+String methods: Question
+• You have a string called 's1', with only lowercase letters. You want to create a new
+string where only the last letter is capitalized and give it the name 's1'. Which of the
+following codes, gives the correct result?
+1. s1 = s1[::-1].capitalize()[::-1]
+2. s1 = s1[:-1]+ s1[-1].upper()
+3. s1 = ''.join(list(s1)[:-1]+ [list(s1)[-1].upper()])
+
+a) Only 1 is correct
+b) Only 2 is correct
+c) They are all correct
+d) They are all incorrect
+
+20
+
+String methods: Question
+• You have a string called 's1', with only lowercase letters. You want to create a new
+string where only the last letter is capitalized and give it the name 's1'. Which of the
+following codes, gives the correct result?
+1. s1 = s1[::-1].capitalize()[::-1]
+2. s1 = s1[:-1]+ s1[-1].upper()
+3. s1 = ''.join(list(s1)[:-1]+ [list(s1)[-1].upper()])
+
+a) Only 1 is correct
+b) Only 2 is correct
+c) They are all correct
+d) They are all incorrect
+e) Correct answer: c. This is quite a difficult question best to run it and see what
+happens, and try to understand
+21
+
+String methods: Question
+• How to sort a string?
+1. s1 = 'acbd'
+l1 = sorted(s1)
+s1 = ''.join(l1)
+2. s1 = 'acbd'
+l1 = list(s1)
+l1 = l1.sort()
+s1 = ''.join(l1)
+
+a) Both are correct
+b) Both are incorrect
+c) Only 1 is correct
+d) Only 2 is correct
+
+22
+
+String methods: Question
+• How to sort a string?
+1. s1 = 'acbd'
+l1 = sorted(s1)
+s1 = ''.join(l1)
+2. s1 = 'acbd'
+l1 = list(s1)
+l1 = l1.sort()
+s1 = ''.join(l1)
+
+a) Both are correct
+b) Both are incorrect
+c) Only 1 is correct
+d) Only 2 is correct
+• Correct answer: c. In the third line of code in option 2, l1.sort(), changes l1 and
+returns None, and so in line 4 l1 is now None and Python will throw an error
+23
+
+String module
+• import string
+print(string.ascii_lowercase) # Onscreen: abcdefghijklmnopqrstuvwxyz
+print(string.ascii_uppercase) # Onscreen: ABCDEFGHIJKLMNOPQRSTUVWXYZ
+print(string.ascii_letters)
+# Onscreen: abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
+print(string.digits) # Onscreen: 0123456789
+print(string.punctuation) # Onscreen: "#$%&'()*+,-./:;<=>?@[\]^_`{|}
+~
+print(string.whitespace) # Onscreen:
+print ('\t' in string.whitespace, '\n' in string.whitespace, ' '
+in string.whitespace) # Onscreen: True True True
+
+• These are all strings that you can use after importing string
+• In general, laziness is a good thing for a programmer. Although it is hardly more
+work, using these constants instead of typing all those characters yourself, avoids
+making errors.
+24
+
+F-strings
+• F-strings can be used to format output
+• They can be very convenient
+• Say you want a function that multiplies two integers and e.g., when you multiply 7 and 13, returns a string that
+looks as follows: "0007 times 0013 gives 0091"
+• This can be done by writing a function with what we learned in the first 3 weeks:
+def main(a, b):
+def format1(x):
+return ('0000' + str(x))[-4:]
+return format1(a) + ' times ' + format1(b) + ' gives ' + format1(a * b)
+
+25
+
+F-strings
+• With an F-string, the task gets more convenient and more readable:
+• def main(a, b):
+return f'{a:04d} times {b:04d} gives {a * b:04d}'
+
+• Inside the curly braces, before the colon you have the value and after the colon
+the formatting of that value
+• You don't have to know the part after the colon for the exam, just the value part
+
+• In this case, the f means you deal with an integer, the 4 gives you the length,
+and the 0 tells Python to add extra zeroes in front if the number is shorter than 4
+digits
+
+26
+
+F-strings
+• If you leave out the formatting part, Python will just apply the str function to the
+value
+• print(f'{a} {b}' == str(a) + ' ' + str(b)) # Onscreen: True
+
+• The values can be just simple values, or the returns of functions/methods, or slices
+or values from sequences or dictionaries: in short, all expressions that Python will
+evaluate to a value
+• For example:
+• s1 = 'tekst'
+t1 = (1, 2, 3)
+l1 = [4, 5, 6]
+capitals = {'Netherlands' : 'Amsterdam'}
+print(f'{s1} {s1.upper()} {t1[2]} {l1} {capitals["Netherlands"]}')
+# Onscreen: tekst TEKST 3 [4, 5, 6] Amsterdam
+
+27
+
+F-strings
+• Just like with strings in general, if you use one type of quotes on the outside,
+you should use the other type of quotes on the inside
+• If you cannot solve this problem, you can always use escape characters (\' and \")
+
+• If you need to escape a { or } you cannot achieve that with a backward slash, but
+you can by doubling the braces {{ and }}
+• Although f-strings are very convenient, I find the documentation on the internet a
+bit difficult to comprehend, so I always end with trial and error, and success in the
+end
+• A nice trick for debugging purposes:
+a = 10
+b = 'Amsterdam'
+print (f'{a=}{b=}')
+
+# Onscreen: a=10 b='Amsterdam'
+
+28
+
+Object-Oriented Programming
+• Programming philosophy/approach
+• A way of organising code
+
+• Well-organised code: easier to understand, fix bugs and
+maintain/extend
+• Trade-off with performance
+• Usually only relevant in large projects
+• OOP is helpful for organisation, but not a guarantee for good
+organisation
+• (Alan Kay, “inventor” of OOP: better named “process-oriented
+programming”)
+29
+
+Object-Oriented Programming (Background)
+• OOP is hard-wired into Python, but we can do more of it.
+• Everything is an object/process
+• Encapsulation: objects have data/attributes (name, age, colour) and
+methods/stuff we want to do with the data (index, sort, draw)
+• Abstraction: focus on what object does, hide how it does it.
+• Inheritance: objects take properties (attributes and methods) from
+superclasses (circle is a type of shape, consumer is a type of agent)
+• Polymorphism: conceptually similar methods have same name, even
+if implementation is different (draw circle vs draw rectangle)
+30
+
+Object-Oriented Programming (Background)
+• Advantage: if well chosen abstractions, focus on problem at hand
+• Easier to read, fix and extend
+• Compare list1.len()and
+length = 0
+for x in list1:
+length +=1
+
+return length
+
+• If not well-chosen, classes can be a confusing mess
+• Choosing well is beyond the scope of this course
+
+31
+
+Class definition
+class Rectangle:
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+def size(self, data_type=int):
+return f"The size = {data_type(self.length * self.width)}."
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+rectangle_3 = Rectangle(2,2)
+for object in [rectangle_1, rectangle_2, rectangle_3, Rectangle(4,4)]:
+print(object.size(float))
+# On screen: 6.0
+3.0
+4.0
+16.0
+
+32
+
+Class definition
+• A class definition starts with the class keyword
+• The first line of the class definition ends with a colon (:), so the next
+line(s) should be indented
+• Everything indented more than the class keyword is seen as part of
+the class definition
+• The class definition stops when the level of indentation is the same as that of
+the class keyword
+
+33
+
+Object creation
+class Rectangle:
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+def size(self, data_type=int):
+return f"The size = {data_type(self.length * self.width)}."
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+rectangle_3 = Rectangle(2,2)
+for object in [rectangle_1, rectangle_2, rectangle_3, Rectangle(4,4)]:
+print(object.size())
+
+34
+
+Object creation
+• rectangle_1 = Rectangle(2,3)
+• Python will:
+1. Create a new object (empty at this point)
+2. Pass that object and the arguments between the brackets, in this case 2 and 3,
+to the __init__ method that is defined inside the class definition
+• Python calls the __init__ method in the following
+way: __init__(rectangle_1, 2, 3)
+3. Assign the name rectangle_1 to the new object
+
+• The creation of an object this way, is called the instantiation of the
+object
+
+35
+
+Class name
+class Rectangle:
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+def size(self, data_type=int):
+return f"The size = {data_type(self.length * self.width)}."
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+rectangle_3 = Rectangle(2,2)
+for object in [rectangle_1, rectangle_2, rectangle_3, Rectangle(4,4)]:
+print(object.size())
+
+• It is a convention to start class names with a capital letter
+• Convention means: Python doesn't care, but people do
+
+36
+
+Methods
+class Rectangle:
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+def size(self):
+return f"The size = {self.length * self.width}."
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+rectangle_3 = Rectangle(2,2)
+for object in [rectangle_1, rectangle_2, rectangle_3, Rectangle(4,4)]:
+print(object.size())
+
+37
+
+Methods
+• Definitions of functions and methods look the same
+• The difference is that a method always expects an object as the first argument
+• The object itself is passed to the method via the dot operator:
+• The method call rectangle_1.size(float) passes the object with the
+name rectangle_1 to the method as the first argument, float is the second argument
+
+• By convention, the corresponding parameter is called self
+• You call these methods with the help of round brackets, pass the object via the dot
+operator, and pass other arguments between the brackets
+• print(rectangle_1.size()) # Onscreen: 6
+• print(rectangle_2.size()) # Onscreen: This rectangle is 3 by 1
+
+• You have two type of methods:
+• Methods you call directly like size()
+• Methods that you call indirectly like __init__
+38
+
+__init__ method
+class Rectangle:
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+def size(self, data_type=int):
+return f"The size = {data_type(self.length * self.width)}."
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+rectangle_3 = Rectangle(2,2)
+for object in [rectangle_1, rectangle_2, rectangle_3,
+Rectangle(4,4)]:
+print(object.size())
+
+39
+
+__init__ method (a dunder method)
+• The __init__ method is called automatically by Python after Python creates an object
+• The object itself and the arguments are passed to the __init__ method
+• Python calls the __init__ method for you, so the name should be exactly __init__
+• The first thing you should do is storing the passed arguments into the objects itself, if the
+information is to be used outside the __init__ method. Otherwise, the information is only
+available during the execution of the __init__ method
+• self.length = length
+• self.width = width
+• The __init__ method can have a return statement, but that is only allowed to return None
+• The __init__ method is a so-called dunder method. Dunder means double underscore. Dunder
+methods are automatically called by Python. For example, __add__ is automatically called when
+you use the plus operator. As the __add__ dunder method can be defined for several classes, it
+can have a different working depending on the context. This is the so-called operator overloading
+• Dunder methods are in general not directly called by you but are automatically called by Python.
+However, it is often possible to call them directly. The dunder method __add__ is the method
+automatically called when Python processes the + operator, can also be called directly
+print([1,2,3].__add__([4,5]))
+# On screen: [1, 2, 3, 4, 5]
+
+40
+
+Class attributes
+class Rectangle:
+total_size = 0
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+Rectangle.total_size += self.length * self.width
+print(f'{self.length} by {self.width} created, {Rectangle.total_size = }')
+def size(self, data_type=int):
+return f"The size = {data_type(self.length * self.width)}."
+def __del__(self):
+Rectangle.total_size -= self.length * self.width
+print(f'{self.length} by {self.width} deleted, {Rectangle.total_size = }')
+print(Rectangle.total_size)
+rectangle_1 = Rectangle(2,3)
+
+# On screen: 0
+# On screen: 2 by 3 created, Rectangle.total_size = 6
+
+rectangle_2 = Rectangle(2)
+del rectangle_1
+
+# On screen: 2 by 1 created, Rectangle.total_size
+= 8
+41
+# On screen: 2 by 3 deleted, Rectangle.total_size = 2
+
+Class attributes
+• Class attributes are objects where you store information that you want to use in
+several objects created on the basis of the same class
+• You can reference class attributes outside the class:
+• print(Rectangle.objects_in_existence) # Onscreen: 2
+
+42
+
+__del__ method
+class Rectangle:
+objects_in_existence = 0
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+Rectangle.objects_in_existence += 1
+def size(self):
+return f"size = {self.length * self.width}"
+def info(self):
+return f'This rectangle is {self.length} by {self.width}'
+def __del__(self):
+Rectangle.objects_in_existence -= 1
+rectangle_1 = Rectangle(2,3)
+rectangle_2 = Rectangle(3)
+43
+
+__del__ method
+• The __del__ method is called automatically just before an object is deleted
+• While a class definition without an __init__ method makes no sense, a class
+without a __del__ method is very common
+• You can delete an object with a del statement, or have it automatically deleted
+when there is no longer any reference to this object (this is
+called garbage collection), as explained in week 1
+• In both cases Python will check whether you defined a __del__ method and call it for you
+
+44
+
+Referencing and defining attributes outside class and object
+• You can reference and define class and object attributes from the 'outside'
+class Rectangle:
+objects_in_existence = 0
+def __init__(self, length, width=1):
+self.length = length
+self.width = width
+Rectangle.objects_in_existence += 1
+def info_about_attribute_a(self):
+return f'{Rectangle.a=}, {self.a=}'
+• rectangle_1 = Rectangle(2,3)
+• We can define attributes, and refer to attributes from outside a class/object
+rectangle_1 = Rectangle(2,3)
+Rectangle.a = 5
+rectangle_1.a = 10
+print(Rectangle.a, rectangle_1.a)
+#On screen: 1 5 10
+• Now we can define, and refer to class/object attributes from outside the class/object
+print(rectangle_1.info_about_attribute_a()
+# On screen: Rectangle.a=5, self.a=10
+• Though insightful, not an example to follow
+
+45
+
+Error handling
+• We saw:
+
+s1 = 'abcdefghabcdefgh'
+print(s1.find('i')) # Onscreen: -1
+print(s1.index('i')) # Onscreen: ValueError: substring not found
+
+• Both ways to tell the caller of the method that the substring is not
+found are fine
+• However, you could prefer the way the find method deals with not finding the
+substring over the way the index method deals with it (by throwing an error),
+or the other way around
+
+• Python has a nice way to deal with errors, which is used a lot in the real
+world
+• It is not part of the exam, but it will be used in one future homework question
+
+46
+
+Error handling
+• If you want to have an index function that when not finding returns -1 instead of
+throwing an error, you can do the following:
+• def my_index(s,needle):
+try:
+result = s.index(needle)
+•
+except:
+result = -1
+•
+return result
+• s1 ='abcdefghabcdefgh'
+print(my_index(s1, 'e')) # Onscreen: 4
+print(my_index(s1, 'i')) # Onscreen: -1
+
+• Although you can make it more complicated, this is the basis: you tell Python in the
+try part to run some code and tell Python in the except part, what Python should
+do in case of an exception (error)
+• Instead of Python throwing an error and killing your program, which means that
+the rest of your code won't be executed, your program stays in control
+
+47
+
+Error handling
+• If you want to have a find function that when not finding throws an error, instead of
+returning -1, you can do the following:
+• def my_find(s,needle):2
+if (result := s.find(needle)) == -1:
+•
+raise ValueError("substring not found")
+•
+return result
+• s1 ='abcdefghabcdefgh'
+print(my_find(s1, 'e')) # Onscreen: 4
+print(my_find(s1, 'i')) # Onscreen: ValueError: substring not
+found
+
+• Not throwing an error can be tricky:
+• s1 = 'abcdefghabcdefgh'
+• Question: What is the result of print(s1[s1.find('i')])
+48
+
+
\ No newline at end of file
diff --git a/tmp/pdfs/text/week5.txt b/tmp/pdfs/text/week5.txt
new file mode 100644
index 0000000..7f9b415
--- /dev/null
+++ b/tmp/pdfs/text/week5.txt
@@ -0,0 +1,2943 @@
+Introduction to Python
+Week 5
+
+1
+
+Program for This Week: Data analysis with Pandas
+1. Dataframe
+2. Series
+3. Selection
+4. Missing data
+5. Operations on data
+6. Merging (concatenate and join)
+7. Grouping
+Merging and grouping are part of the weekly exercises but are not exam material.
+
+2
+
+Pandas DataFrame
+• Pandas is one of the standard libraries in data science
+• The most important datatype used in Pandas is the DataFrame
+• A DataFrame contains tabular data just like a spreadsheet in excel
+• A Dataframe with only one column is called a Series
+
+• Pandas is built on top of NumPy, so there is overhead because of the conversion to and from NumPy structures
+• NumPy lacks the flexibility of Python, and therefore is fast when crunching numbers
+• Pandas works fast with uniform numerical data, but can be slow when working with string data
+• Pandas is very flexible, this comes with a performance price
+• Like NumPy, Pandas offers vectorization: you can apply a function to all elements of a column/row/frame.
+Vectorization will probably be faster than loops or the map function in Python, but that is not a given
+• Vectorization can make your code more readable
+• An interesting library for data science is Polar, written in Rust, avoiding Pandas/Numpy conversion overhead.
+https://www.datacamp.com/blog/an-introduction-to-polars-python-s-tool-for-large-scale-data-analysis.
+• Interesting is also Microsoft's project 'Python in excel' where your interface is not a Pandas DataFrame but an
+excel spreadsheet. https://support.microsoft.com/en-us/office/introduction-to-python-in-excel-55643c2e-ff563
+4168-b1ce-9428c8308545
+
+DataFrame, rownames and colnames
+Name
+
+Length
+
+Weight
+
+First
+
+A
+
+153
+
+55
+
+Second
+
+B
+
+160
+
+70
+
+Third
+
+C
+
+150
+
+60
+
+• Name, Length and Weight are the names of the columns and First, Second, and Third are the names of the rows (also
+called indices)
+• In these slides we will call these colnames and rownames, respectively, and you can get these the following way:
+# Onscreen: Index(['First', 'Second', 'Third'],
+• print(df.index)
+dtype='object')
+# Onscreen: Index(['Name', 'Length', 'Weight'],
+• print(df.columns)
+dtype='object')
+• df.index and df.columns are special attributes, over which you can iterate, and apply slicing and indexing to
+• These objects also have a map method, which you can use to apply a function to their elements
+• Elements of these objects cannot be changed, but they can be overwritten in full:
+# Onscreen: Error
+• df.index[1] = 'fourth'
+# Works
+• df.index = ['First', 'Fourth', 'Third']
+4
+
+Creating a DataFrame using a dictionary
+• Say you want to create the following dataframe:
+Name
+
+Length
+
+Weight
+
+First
+
+A
+
+153
+
+55
+
+Second
+
+B
+
+160
+
+70
+
+Third
+
+C
+
+150
+
+60
+
+• One of the easiest ways to create this dataframe is by using a dictionary:
+import pandas as pd
+d1 = {'Name':['A', 'B', 'C'],'Length':[153, 160, 150],'Weight':[55, 70, 60]}
+df1 = pd.DataFrame (d1, index=['First', 'Second', 'Third'])
+print(df1)
+
+5
+
+Creating a DataFrame using a dictionary
+• Let's do some closer inspection of the code, line by line:
+• import pandas as pd
+• You need to import the Pandas package just like the math package as it is not standard imported in
+Python
+• We rename the package to pd, this is simply a convention
+• d1 = {'Name':['A','B','C'],'Length':[153,160,150],'Weight':[55,70,60]}
+• We create a dictionary, where each key:value pair represents a column: the keys are the colnames
+and the values of the columns are in the sublists
+• df1 = pd.DataFrame (d1, index=['First','Second','Third'])
+• We use the DataFrame class from the Pandas package to create a dataframe object and give it the
+name df1
+• The index parameter defines the rownames
+• print(df1)
+• Print the created dataframe object to see whether it turned out as we planned
+6
+
+Creating a DataFrame using a list of lists
+• Alternatively, you can create a dataframe using a list of lists, where every sublist represents a
+row
+• import pandas as pd
+l1 = [['A', 153, 55], ['B', 160, 70], ['C', 150, 60]]
+df1 = pd.DataFrame (l1, index=['First', 'Second',
+'Third'], columns=['Name', 'Length', 'Weight'])
+print(df1)
+
+• Since there are no keys, you must pass the names of the columns another way
+to the DataFrame class
+• Instead of a list of lists, you can use a tuple of lists, or a list of tuples, or tuples of tuples
+
+7
+
+Creating a DataFrame using the transpose operator
+• import pandas as pd
+d1 = {'First':['A', 153, 55],'Second':['B', 160, 70],'Third':['C', 150, 60]}
+df1 = pd.DataFrame (d1, index=['Name', 'Length', 'Weight'] )
+• print(df1)
+First
+
+Second
+
+Third
+
+Name
+
+A
+
+B
+
+C
+
+Length
+
+153
+
+160
+
+150
+
+Weight
+
+55C
+
+70
+
+60
+
+Name
+
+Length
+
+Weight
+
+First
+
+A
+
+153
+
+55
+
+Second
+
+B
+
+160
+
+70
+
+Third
+
+C
+
+150
+
+60
+
+• df1 = df1.T
+• print(df1)
+
+8
+
+Creating a DataFrame using default col and row names
+• import pandas as pd
+l1 = [['A', 153, 55], ['B', 160, 70], ['C', 150, 60]]
+df1 = pd.DataFrame (l1)
+
+• The result of this code will be:
+​
+
+0​
+
+1​
+
+2​
+
+0​
+
+A​
+
+153​
+
+55​
+
+1​
+
+B​
+
+160​
+
+70​
+
+2​
+
+C​
+
+150​
+
+60​
+
+• You might think they look a lot like indices from a list, and it does, but looks can
+be deceiving since they are colnames and rownames, and are more like keys in
+dictionaries
+
+9
+
+Creating a series using a dictionary or a list
+• Say you want to create the following series
+First
+
+153​
+
+Second
+
+160​
+
+Third
+
+150​
+
+Name: Length
+
+• import pandas as pd
+d1 = {'First': 153, 'Second': 160, 'Third': 150}
+s1 = pd.Series(d1, name = 'Length')
+print(s1)
+• import pandas as pd
+l1 = [153, 160, 150]
+s1 = pd.Series(l1, name = 'Length', index=['First', 'Second', 'Third'])
+print(s1)
+10
+
+Series using defaults
+• import pandas as pd
+l1 = [153, 160, 150]
+s1 = pd.Series(l1)
+print(s1)
+• The result of this code will be:
+0​
+
+153​
+
+1​
+
+160​
+
+2​
+
+150​
+
+11
+
+Dataframe and Series indices are like keys in dictionaries
+• With a list you can do the following:
+• l1 = [153, 160, 150]
+del l1[1]
+del l1[1]
+print (l1)
+# Onscreen: [153]
+• You cannot do that with a dictionary:
+• d1 = {0:153, 1:160, 2:150}
+del d1[1]
+del d1[1]
+# Onscreen: KeyError: 1
+• And, not with a series (or dataframe):
+• s1 = pd.Series([153, 160, 150])
+s1 = s1.drop(1)
+s1 = s1.drop(1)
+# Onscreen: KeyError: '[1] not found in axis'
+• The reason is that de indices of a list give relative positions and are recalculated, while indices in a series
+(and in a dataframe), and keys in a dictionary are not recalculated
+12
+
+Viewing data
+• Assume you created a dataframe like this:
+• import pandas as pd
+l1 = ['A', 'B', 'C', 'D'] * 250
+l2 = [160, 170, 180, 190, 200] * 200
+l3 = [55, 66] * 500
+d1 = {'Name':l1, 'Length': l2, 'Weight': l3}
+df1 = pd.DataFrame (d1)
+
+• If you do: print(df1), Python will only show you the first 5 and the last 5 rows
+• To have a bit more control, you could use:
+• print(df1.head(3)) to print the first 3 rows
+• print(df1.tail(7)) to print the last 7 rows
+• You can use 'any' number as an argument in head() and tail(), but the default is 5
+
+13
+
+Viewing data
+• If you are more interested in some quick statistics, you can use describe()
+• We can apply this to our dataframe with 1000 rows: print(df1.describe())
+• This results in:
+•
+
+Length
+Weight
+count 1000.000000 1000.000000
+mean
+180.000000
+60.500000
+std
+14.149212
+5.502752
+min
+160.000000
+55.000000
+25%
+170.000000
+55.000000
+50%
+180.000000
+60.500000
+75%
+190.000000
+66.000000
+max
+200.000000
+66.000000
+
+14
+
+Sorting dataframes
+1.
+2.
+3.
+4.
+
+Sorting a dataframe by rownames
+Sorting a dataframe by colnames
+Sorting a dataframe by the values in a row
+Sorting a dataframe by the values in a column
+
+15
+
+Sorting a dataframe by rownames
+• You have the following dateframe:
+​
+
+Name​
+
+Length​
+
+Weight​
+
+First​
+
+A​
+
+153​
+
+55​
+
+Second​
+
+B​
+
+160​
+
+70​
+
+Third​
+
+C​
+
+150​
+
+60​
+
+• df1 = df1.sort_index(axis=0, ascending=False))
+​
+
+Name​
+
+Length​
+
+Weight​
+
+Third​
+
+C​
+
+150​
+
+60​
+
+Second​
+
+B​
+
+160​
+
+70​
+
+First​• ascending A​
+153​
+= True is the default
+
+55​
+
+• axis = 0, and ascending = True are the defaults
+16
+
+Sorting a dataframe by colnames
+• You have the following dateframe:
+​
+
+Name​
+
+Length​
+
+Weight​
+
+First​
+
+A​
+
+153​
+
+55​
+
+Second​
+
+B​
+
+160​
+
+70​
+
+Third​
+
+C​
+
+150​
+
+60​
+
+• df1=df1.sort_index(axis=1, ascending=False)
+Weight
+
+Name
+
+Length
+
+First
+
+55
+
+A
+
+153
+
+Second
+
+70
+
+B
+
+160
+
+Third
+
+60
+
+C
+
+150
+
+• ascending = True is the default
+17
+
+Sorting a dataframe by the values in a column
+• You have the following dateframe:
+​
+
+Name​
+
+Length​
+
+Weight​
+
+First​
+
+A​
+
+153​
+
+55​
+
+Second​
+
+B​
+
+160​
+
+70​
+
+Third​
+
+C​
+
+150​
+
+60​
+
+• df1=df1.sort_values(by='Length', axis=0, ascending=False)
+​
+
+Name​
+
+Length​
+
+Weight​
+
+Second​
+
+B​
+
+160​
+
+70​
+
+First​
+
+A​
+
+153​
+
+55​
+
+Third​
+C​
+150​
+60​
+• This only works if all elements in the column have datatypes for which the > operator works
+• You can pass a list with several colnames to the by parameter, and several booleans to the ascending
+parameter
+18
+• axis = 0, and ascending = True are the defaults
+
+Sorting a dataframe by the values in a row
+• You have the following dateframe:
+​
+
+Name​
+
+Length​
+
+Weight​
+
+First​
+
+100​
+
+153​
+
+55​
+
+Second​
+
+200​
+
+160​
+
+70​
+
+Third​
+
+300​
+
+150​
+
+60​
+
+• df1=df1.sort_values(by='First', ascending=False, axis=1)
+​
+
+Length​
+
+Name​
+
+Weight​
+
+First​
+
+153​
+
+100​
+
+55​
+
+Second​
+
+160​
+
+200​
+
+70​
+
+Third​
+
+150​
+
+300​
+
+60​
+
+• This only works if all elements in a row have datatypes for which the > operator works
+• You can pass a list with several rownames to the by parameter, and several booleans to the
+ascending parameter
+• axis = 0, and ascending = True are the defaults
+
+19
+
+Selecting from a dataframe using columns
+• You can use the column names to select the corresponding column,
+similar to using keys in a dictionary:
+• df1 = df1[‘Length’]
+• df1 = df1[[‘Length’,’Width’]]
+
+• Notice that several columns are passed as a list!
+
+20
+
+Selecting from a dataframe, using loc
+• loc works on the rownames and the colnames of the dataframe
+• df1.loc[row_selection, col_selection]
+• Between the square brackets there are two parts:
+• Before the comma, you give your selection of rownames
+• After the comma, you give your selection of colnames
+
+• row_selection can be filled in in different ways:
+• Slicing: first_name : last_name
+• You select from the rows from the top to the bottom
+• Your selection is a list of rownames: the first one first_name, the last one last_name (inclusive!) and
+all the rows in between
+• If you leave out first_name the default is the first row and if you leave out the last_name the default
+is the last row
+• So, if you leave out first_name and last_name, you select all the rows
+• A list with 1 or more rownames: e.g. ['First', Third']
+
+• One rowname: e.g. 'First'
+21
+
+Selecting from a dataframe: loc
+• col_selection can be filled in in exactly the same ways:
+• Slicing: first_name : last_name
+• You make a selection from the columns from left to right
+• Your selection is a list of colnames: the first one first_name, the last one last_name (inclusive!) and
+all the columns in between
+• If you leave out first_name the default is the first column and if you leave out the last_name the
+default is the last column
+• So, if you leave out first_name and last_name, you select all the columns
+• A list with 1 or more colnames: e.g. ['Second', 'Third']
+
+• One colname: e.g. 'Third'
+
+• The only difference between row_selection and col_selection is that you can
+leave out the col_selection
+• The default is all colnames
+
+22
+
+Selecting from a dataframe, using iloc
+• iloc works almost the same as loc, but there are important differences:
+• iloc doesn't work with colnames and rownames, but with their relative position, like an index in a
+list
+• When slicing with iloc the end is not inclusive, like an index in a list
+• Just like with lists you can use -1 etc. to count from the opposite corner
+• You can use negative numbers when slicing, but also when you select rows/columns with a list of index
+numbers
+
+23
+
+Aside on slicing and indexing (Dijkstra)
+• Slices could be [inclusive:inclusive], [inclusive:exclusive],
+[exclusive:inclusive] or [exclusive:exclusive].
+• [in:ex]and [ex:in]: length is difference (len([5:18])=18-5), and consecutive
+slices have overlapping point ([5:12] + [12:18] == 5:18])
+• [ex:in]and [ex:ex]: starting at zero means [-1:…](ugly, and already used)
+• [in:in]and [ex:in]: empty list is [n:n-1]. Very ugly.
+• So [in:ex]it is.
+• Given that, index of the last element of [:n]is n if we start indexing at 0, but it is
+n if we start indexing at 1.
+• So we start indexing at 0.
+• (Argument doesn’t apply whenslicing by names instead of numbers. But still
+annoying…)
+
+24
+
+Selecting from a dataframe
+• The result of a selection is quite intuitive
+• All the cells that are at the same time part of one of the selected columns and one of the selected
+rows are selected
+
+• What may be less intuitive is the resulting datatype:
+Row_selection​
+
+Col_selection​
+
+Result​
+
+A list/slice of rows​
+
+A list/slice of cols​ A dataframe​
+
+A list/slice of rows​
+
+One col​
+
+One row​
+
+A list/slice of cols​ A series, where the values are from the row, the rownames
+are from the column selection and the name attribute of
+the series now contains the rowname​
+
+One row​
+
+One col​
+
+A series, where the values are from the column, the
+rownames are from the row selection. The name attribute
+of the series now contains the colname ​
+
+A scalar, like an integer or a string​
+
+• NB! A list/slice containing only one name is still a list/slice
+25
+
+df1.loc[:,:] or df1.iloc[:, :]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+26
+
+df1.loc[:] or df1.iloc[:]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+27
+
+df1.loc[:,10:12] or df1.iloc[:,:-1]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+28
+
+df1.loc[[3,0]] or df1.iloc[[0,3]]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+29
+
+df1.loc[[2]] or df1.iloc[[1]]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+30
+
+df1.loc[2] or df1.iloc[1]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​10
+
+B​
+
+13​
+
+160​
+
+12​
+
+70​
+
+17​
+
+11​
+
+Name: 2​
+
+​
+
+• <class 'pandas.core.series.Series'>
+
+31
+
+df1.loc[[2],[13]] or df1.iloc[[1],[1]]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• <class 'pandas.core.frame.DataFrame'>
+
+32
+
+df1.loc[[2],13] or df1.iloc[[-3],-3]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+2
+
+160
+
+Name: 13
+
+​
+
+• <class 'pandas.core.series.Series'>
+
+33
+
+df1.loc[2,13] or df1.iloc[-3,1]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+160
+
+• <class 'numpy.int64'>
+
+34
+
+Name attribute
+​
+
+10​
+
+13​
+
+12​
+
+17​
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• s1 = df1.loc[2]
+print(s1.name) # On screen: 2
+• s1 = df1.iloc[1]
+print(s1.name) # On screen: 2
+
+• s1 = df1.loc[:,13]
+print(s1.name)
+# On screen: 13
+• s1 = df1.iloc[:,1]
+print(s1.name)
+# On screen: 13
+
+35
+
+Question, what is the result of
+​
+
+0​
+
+2​
+
+3​
+
+A​
+
+153​
+
+2​
+
+B​
+
+160​
+
+• print(df2.iloc[2, 2])
+
+36
+
+Question, what is the result of
+​
+
+0​
+
+2​
+
+3​
+
+A​
+
+153​
+
+2​
+
+B​
+
+160​
+
+• print(df1.iloc[2, 2])
+• IndexError: index 2 is out of bounds for axis 0 with size 2
+
+37
+
+Update a selection
+• You can use the selection mechanism also to change parts of a DataFrame
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• df1.loc[[3,0]] = [['E', 100, 40, 20], ['F', 101, 41, 21]]
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+E​
+
+100
+
+40
+
+20
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+F
+
+101
+
+41
+
+21
+38
+
+Selecting from a dataframe: boolean indexing
+• Previously, we said that the selection of the columns and the rows in
+df1.loc[row_selection, col_selection]
+
+can be done via a slice, a list with names or one name.
+• There is a fourth way: instead of giving a list of names, you can give a list or series with
+Boolean values (True, False)
+• This list with Boolean values has to have the same length as the number of rows
+• Then every row that corresponds with a True in the list is selected
+
+• Instead of a list with Booleans, you can also use a series with Boolean values
+
+39
+
+Boolean indexing
+• We want to select the green parts
+​
+
+10​
+
+13​
+
+12​
+
+17​
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• With loc: df1.loc[[3,2],[13,17]]
+• With iloc: df1.iloc[:2,[1,-1]]
+• With Boolean indexing: df1.loc[[True, True, False, False],[False, True,
+False, True]]
+
+• You could also mix and match:
+• df1.loc[[True, True, False, False],[13,17]]
+• df1.loc[[3,2],[False, True, False, True]]
+40
+
+Boolean indexing
+• We want to select all rows, where the values in column 13 < 155
+​
+
+10​
+
+13​
+
+12​
+
+17​
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• With loc: df1.loc[[3,1]]
+• With iloc: df1.iloc[::2]
+• With Boolean indexing: df1.loc[df1.13 < 155]
+• You could also mix and match:
+• df1.loc[[True, True, False, False],[13,17]]
+• df1.loc[[3,2],[False, True, False, True]]
+
+• You can also apply Boolean indexing to series
+41
+
+Selecting from a dataframe: shortcuts
+• df1.Name
+• df1.loc[:,'Name']
+• df1['Average Weight']
+• df1.loc[:,'Average Weight']
+• df1[['Name']]
+• df1.loc[:,['Name']]
+• df1[['Name', 'Weight']]
+• df1.loc[:,['Name', 'Weight']]
+• df1.Name['First']
+• df1.loc['First','Name']
+• df1.Name[['First', 'Second']]
+• df1.loc[['First', 'Second'],'Name']
+
+42
+
+Question: What will be printed by
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• print(df1[[10,13]][[1,2]])
+
+43
+
+Question: What will be printed by: print(df1[[10,13]][[1,2]])?
+​
+
+10​
+
+13
+
+12
+
+17
+
+3​
+
+A​
+
+153​
+
+55​
+
+10​
+
+2​
+
+B​
+
+160​
+
+70​
+
+11​
+
+1​
+
+C​
+
+150​
+
+60​
+
+12​
+
+0​
+
+D​
+
+190​
+
+80​
+
+13​
+
+• df1 = df1[[10,13]]
+​
+
+10​
+
+13
+
+3​
+
+A​
+
+153​
+
+2​
+
+B​
+
+160​
+
+1​
+
+C​
+
+150​
+
+0​
+
+D​
+
+190​
+
+• print(df1[[1,2]])
+• An error
+
+44
+
+Selecting from a Series: shortcuts
+• s1[1]
+• df1.iloc[1]
+• s1[:2]
+• s1.iloc[:2]
+
+45
+
+Vectorized operations
+• Vectorized operations: you apply the same operation to all elements of one or
+several columns, to a row or to a series
+• This is a very powerful part of Pandas
+
+• The topics we will discuss are:
+• Broadcasting
+• Basic statistical operations are available as Series methods
+• Mapping a function to Series elements
+• Vectorized string methods
+• Applying a function to each row or to each column
+
+46
+
+Basic operations: broadcasting
+• Broadcasting can be done for the whole dataframe or for one column or row
+• This works with scalars (integers, floats)
+
+• Consider the following dataframe, called df:
+​
+
+A​
+
+B​
+
+0​
+
+2​
+
+3​
+
+1​
+
+5​
+
+4​
+
+2​
+
+6​
+
+7​
+
+• The following give the same result:
+• df += 1
+• df.A += 1; df.B +=1
+​
+
+A​
+
+B​
+
+0​
+
+3​
+
+4​
+
+1​
+
+6​
+
+5​
+
+2​
+
+7​
+
+8​
+47
+
+Basic operations: broadcasting
+• Instead of using broadcasting, you can also work with a more complex
+structure, as long as it has the same shape
+• For example: df *= df:
+​
+
+A​
+
+B​
+
+0​
+
+4
+
+9
+
+1​
+
+25
+
+16​
+
+2​
+
+36
+
+49
+
+df:
+​
+
+A​
+
+B​
+
+0​
+
+2​
+
+3​
+
+1​
+
+5​
+
+4​
+
+2​
+
+6​
+
+7​
+
+• The following also works: df.A /= [1, 2, 3]
+​
+
+A​
+
+B​
+
+0​
+
+2.0
+
+3
+
+1​
+
+2.5
+
+4
+
+2​
+
+2.0
+
+7
+
+• But the following gives an error: df.A //= [1,2]
+# ValueError: operands could not be broadcast together with shapes (3,) (2,)
+48
+
+Basic statistical operations as Series methods
+​
+
+A​
+
+B​
+
+0​
+
+2​
+
+3​
+
+1​
+
+5​
+
+4​
+
+2​
+
+6​
+
+7​
+
+• mean, max, min, median, mode, sum, value_counts, std, ... all work as
+
+methods of series
+
+• For example, df.A.mean() returns 4.333333333333333
+
+• These methods can be used in a vectorized way too
+• For example, df.mean() returns the series:
+A 4.333333
+B 4.666667
+
+49
+
+Mapping a function to all elements
+
+df:
+
+• For example:
+• df.A = df.A.map(lambda x: 3 * x)
+• def triple(x):
+return x*3
+df.A = df.A.map(triple)
+
+​
+
+A​
+
+B​
+
+​
+
+A​
+
+B​
+
+0​
+
+6
+
+3​
+
+0​
+
+2​
+
+3​
+
+1​
+
+15
+
+4​
+
+1​
+
+5​
+
+4​
+
+2​
+
+18
+
+7​
+
+2​
+
+6​
+
+7​
+
+• You can only apply this map method to a series
+• If you want to apply a function to all elements (all cells) in the dataframe, you must
+use applymap:
+• For example: df = df.applymap(lambda x: 3 * x)
+​
+
+A​
+
+B​
+
+0​
+
+6
+
+9
+
+1​
+
+15
+
+12
+
+2​
+
+18
+
+21
+
+50
+
+Vectorized string methods
+• Consider a Series called s that contains only strings
+• You may want to apply the same string method, indexing or slicing to every element of
+this series
+• We already saw that we can do this with the help of s.map
+• But, remember that last week we learnt that an object is always build based on a class
+• The basis of a string object is the class str
+• By using this class name str we can directly call the methods in the class
+• With this trick we can apply all methods that we know work for strings, slicing and
+indexing to the series
+• And because this is pandas, this will be broadcast to all elements of the series
+
+51
+
+Vectorized string methods
+• The following pairs of code work exactly the same way:
+• s.map(lambda x: x[::-1])
+s.str[::-1]
+• s.map(lambda x: x.lower())
+s.str.lower()
+• s.map(lambda x: x.count('a'))
+s.str.count('a')
+• s.map(lambda x: x.isupper())
+s.str.isupper()
+
+• And just like any other method, you can chain them with dot operators in between, if
+the result of the operation is a new series with only strings
+
+52
+
+Question
+• What will the following program print:
+
+import pandas as pd
+s = pd.Series(['abc', 'xAef', 'aAgh', 'xyz'])
+print(s.str.upper().str.count('A').mean())
+
+a. 0.0
+b. 0.5
+c. 1.0
+d. 1.5
+
+53
+
+Answer
+• What will the following program print:
+
+import pandas as pd
+s = pd.Series(['abc', 'xAef', 'aAgh', 'xyz'])
+print(s.str.upper().str.count('A').mean())
+
+a. 0.0
+b. 0.5
+c. 1.0
+d. 1.5
+
+Answer: option c is correct.
+
+54
+
+The vectorized in operator
+• The in operator is used to test whether a string is a substring of another string or a
+string is an element of a list (or tuple etc.)
+• The vectorized version is called isin
+• isin and in are not defined as methods in the str class
+• The following work the same:
+• s.map(lambda x: x in 'abcd')
+s.isin(list('abcd'))
+• s.map(lambda x: x not in 'abcd')
+~s.isin(list('abcd'))
+
+• The ~ operator works like the not operator but is vectorized
+• If you have a series with only Boolean values, and you apply the ~ operator all Trues will become
+False and all Falses will become True
+
+55
+
+Applying a function to each row/column
+• Say you want a new row with the averages of each column:
+df.loc['Av'] = df.apply(lambda x: x.mean(), axis = 0)
+
+df:
+​
+
+A​
+
+B​
+
+0​
+
+2​
+
+3​
+
+​
+
+A​
+
+B​
+
+1​
+
+5​
+
+4​
+
+0​
+
+2.000000​
+
+3.000000​
+
+2​
+
+6​
+
+7​
+
+1​
+
+5.000000​
+
+4.000000​
+
+2​
+
+6.000000​
+
+7.000000​
+
+Av​
+
+4.333333​
+
+4.666667​
+
+• Say you want a new column with the averages of each row:
+df.loc[:,'Av'] = df.apply(lambda x: x.mean(), axis = 1)
+​
+
+A​
+
+B​
+
+Av​
+
+0​
+
+2​
+
+3​
+
+2.5​
+
+1​
+
+5​
+
+4​
+
+4.5​
+
+2​
+
+6​
+
+7​
+
+6.5​
+
+56
+
+Adding data to a dataframe
+• In the previous slide we saw an example of adding a column to a dataframe
+• When adding data to a column, the following applies:
+• A scalar value is broadcast to all rows
+• Collections of values (arrays, lists) must have a length and structure equal to the number of rows
+• A series of values added to a column will be fit into the dataframe according to the rownames of the
+dataframe and the indexnames of the series
+• Missing values may result if the two indices are not fully overlapping
+
+57
+
+Adding data to a dataframe
+• When adding data to a row, the following applies:
+• A scalar value is broadcasted to all columns
+• Collections of values (arrays, lists) must have a length equal to the number of columns
+• A Series of values added to a row will be fit into the DataFrame according to the columnames of the
+dataframe and the indexnames in the series
+• Missing values may result if the two indices are not fully overlapping
+
+58
+
+Missing data
+• A special value from Numpy is used to denote missing data: np.nan
+• Looks like 'NaN' when printed, but it is not a string: it's something like True, False, and None
+
+• Indicate missing values: df.isna(), creates the same shape as df, but with Trues and Falses
+• Replace missing data with a value: df.fillna(value)
+• Drop rows / columns with missing data: df.dropna()
+• Missing data is sometimes automatically generated
+• e.g. by merging datasets with (partly) incompatible names/indices
+
+59
+
+Concatenating DataFrames
+• Dataframes can be concatenated either vertically or horizontally:
+• Vertically: pd.concat([df1, df2])
+• Horizontally: pd.concat([df1, df2], axis=1)
+
+• The resulting DataFrame has the union of the index / column labels in the direction of
+concatenation
+• Missing values are created when the 'unionized' labels are not fully overlapping
+• axis=0, is the default
+
+60
+
+Joining / merging DataFrames
+• Data we want to use is often dispersed across several tables
+• Example:
+• We have millions of tax records, including municipality of residence, in one table
+• We have each municipality and the province it is part of in another table
+• We want to calculate taxes paid per province
+• We'd have to match the province from the second table to each tax record in the first table, using
+the municipality name as a key for the matching, before we can calculate province-level statistics
+• We'd have to join / merge the DataFrames
+
+61
+
+Merging DataFrames
+dftax
+​
+
+Name​
+
+Income​
+
+Tax​
+
+City​
+
+0​
+
+A​
+
+100​
+
+10​
+
+Amsterdam​
+
+1​
+
+B​
+
+200​
+
+60​
+
+Rotterdam​
+
+2​
+
+C​
+
+150​
+
+30​
+
+London​
+
+dfprov
+​
+
+City​
+
+Province​
+
+0​
+
+Amsterdam​
+
+Noord Holland​
+
+1​
+
+Rotterdam​
+
+Zuid Holland​
+
+2​
+
+Utrecht​
+
+Utrecht​
+
+62
+
+Merging DataFrames: on parameter
+•
+•
+•
+•
+
+print(dftax.merge(dfprov, how='left'))
+print(pd.merge(dftax, dfprov, how='left'))
+print(dftax.merge(dfprov, on= 'City', how='left'))
+print(dftax.merge(dfprov, left_on='City', right_on = 'City', how='left'))
+
+​
+
+Name​
+
+Income​
+
+Tax​
+
+City​
+
+Province​
+
+0​
+
+A​
+
+100​
+
+10​
+
+Amsterdam​
+
+Noord Holland​
+
+1​
+
+B​
+
+200​
+
+60​
+
+Rotterdam​
+
+Zuid Holland​
+
+2​
+
+C​
+
+150​
+
+30​
+
+London​
+
+NaN​
+
+• If both dataframes share some colnames, Pandas will use those to connect the data
+
+• If you want only some of the columns with the same name as the connection, you should use the on
+parameter
+• If the columns you want to use for the connection have different names, you have
+to use right_on and left_on
+• For example, if the city column in dfprov was called Cities you would need:
+right_on =‘Cities',
+
+left_on = ‘City'
+
+• The first dataframe is called left, the second one right
+
+63
+
+Merging DataFrames: how parameter
+• print(dftax.merge(dfprov,
+
+how='left'))
+
+​
+
+Name​
+
+Income​
+
+Tax​
+
+City​
+
+Province​
+
+0​
+
+A​
+
+100​
+
+10​
+
+Amsterdam​
+
+Noord Holland​
+
+1​
+
+B​
+
+200​
+
+60​
+
+Rotterdam​
+
+Zuid Holland​
+
+2​
+
+C​
+
+150​
+
+30​
+
+London​
+
+NaN​
+
+• print(dftax.merge(dfprov,
+
+how='right'))
+
+​
+
+Name​
+
+Income​
+
+Tax​
+
+City​
+
+Province​
+
+0​
+
+A​
+
+100​
+
+10​
+
+Amsterdam​
+
+Noord Holland​
+
+1​
+
+B​
+
+200​
+
+60​
+
+Rotterdam​
+
+Zuid Holland​
+
+2​
+
+NaN​
+
+NaN​
+
+NaN​
+
+Utrecht​
+
+Utrecht​
+
+64
+
+Grouping DataFrames (Split-Apply-Combine)
+1. Splitting the data into groups based on some criteria
+2. Applying a function to each group independently
+3. Combining the results into a data structure
+• Example:
+• In the tax records example earlier, we might split the data by province names, calculate total taxes
+paid for each province, then combine the resulting numbers into a Series indexed by province
+names
+
+65
+
+Grouping DataFrames
+• dfcombined = dftax.merge(dfprov, how='left')
+dfcombined.loc[:,'Province'] = dfcombined.loc[:,'Province'].fillna('Rest')
+# We replace the missing values with a string
+# As the groupby method ignores NaN values
+result = dfcombined.groupby('Province')['Tax'].sum()
+result = result.sort_values(ascending = False)
+# We want the highest taxed Province first
+
+•The result will be:
+Zuid Holland​
+
+60​
+
+Rest​
+
+35​
+
+Noord Holland​
+
+10​
+
+• You get the total tax with:
+print(result.sum())
+
+• And the tax for Noord Holland with:
+
+print(result['Noord Holland'])
+
+# Onscreen 105
+# Onscreen: 10
+66
+
+
\ No newline at end of file
diff --git a/tmp/pdfs/text/week6.txt b/tmp/pdfs/text/week6.txt
new file mode 100644
index 0000000..7c6803f
--- /dev/null
+++ b/tmp/pdfs/text/week6.txt
@@ -0,0 +1,694 @@
+Introduction to Python
+Week 6
+
+1
+
+Program for This Week
+1. Comprehensions
+2. Generators
+3. Handling dates and times with the datetime module
+4. On Friday we finish the slides if needed and if we have time left, we can do some
+questions of the trial exam
+
+3
+
+Comprehensions
+• Comprehensions are just a very concise way to write a for loop
+• You don't learn new logic here, but you learn a new way to write your loops
+• Everything you can do with a comprehension you can do with a loop
+• I also will show two examples of a loop and a comprehension doing the same,
+and use colors, so you can see you are passing Python the same information
+
+4
+
+List comprehensions
+• Example: you have a list called l1 with integers and you must write code that
+creates a new list where all integers are ignored when they are divisible by 3, are
+doubled when they are odd and not divisible by 3, and halved when they are
+even and not divisible by 3.
+The new list should only contain integers and be called l2.
+• For example, l1 = [1, 3, 2, 4] should result in l2 = [2, 1, 2]
+
+• Based on what you have learned so far, your solution could be:
+l2 = []
+for x in l1:
+if x % 3 == 0:
+continue
+addition = x * 2 if x%2 == 1 else x // 2
+l2.append(addition)
+
+• With a comprehension, the solution could be:
+
+l2 = [x * 2 if x%2 == 1 else x // 2 for x in l1 if x % 3 != 0]
+5
+
+List comprehensions
+• These options look very different, but both code fragments contain the same information for
+Python
+• l2 = []
+for x in l1:
+if x % 3 == 0:
+continue
+addition = x * 2 if x%2==1 else x // 2
+l2.append(addition)
+• l2 = [x * 2 if x%2==1 else x // 2 for x in l1 if x % 3 != 0]
+• This tells Python, what the name of the new list should be
+• This tells Python that we want to create a list, and append the results of the processed elements to
+that list
+• This tells Python what the source is of which we want to process the elements
+• This tells Python, which elements to include for processing (be careful in this set-up of the for loop
+you tell what to exclude but in a comprehension what to include)
+• This tells Python, what to do with the elements that should be processed
+
+6
+
+Dictionary comprehensions
+• Example: you have two lists called l1, l2 with integers and you must write code that creates
+a dictionary where the keys come from l1 and the values are triple those from l2, except when
+the key and/or the value can be divided by 2, then these values should be ignored.
+The new dictionary should be called d1.
+• For example, l1 = [1, 3, 2, 5], l2 = [1, 2, 6, 3] should result in d1 =
+{1:3, 5:9}
+• Based on what you learned so far, the solution could be:
+d1 = {}
+for k,v in zip(l1,l2):
+if k%2 == 0 or v%2 == 0:
+continue
+d1[k] = v * 3
+• With a comprehension, the solution could be:
+d1 = {k: v*3 for k,v in zip(l1,l2) if not (k%2 == 0 or v%2 == 0)}
+
+7
+
+Dictionary comprehensions
+• d1 = {}
+for k,v in zip(l1,l2):
+if k%2 == 0 or v%2 == 0:
+continue
+d1[k] = v * 3
+• d1 = {k: v*3 for k,v in zip(l1,l2) if not (k%2 == 0 or v%2 == 0)}
+• This tells Python, what the name of the new list should be
+• This tells Python that we want to create a dictionary, and append the results of the processed
+elements to that dictionary
+• This tells Python what the source is of which we want to process the elements
+• This tells Python, which elements to include for processing (be careful in this set-up of the for
+loop you tell what to exclude but in a comprehension what to include)
+• This tells Python, what to do with the elements that should be processed
+
+8
+
+Set comprehensions
+• Example: you have a list called l1 with integers and you have to write code that
+creates a set, with all the values from the list doubled, while ignoring values from
+the list that are even.
+The new set should be called s1.
+• For example, l1 = [1, 3, 2, 5] should result in s1 = {2, 6, 10}
+• Based on what you learned so far, the solution could be:
+s1 = set()
+for x in l1:
+if x%2 == 0:
+continue
+s1.add(x * 2)
+
+• With a comprehension, the solution could be:
+
+s1 = {x * 2 for x in l1 if not (x%2 == 0)}
+
+• Question: Why does Python understand you are building a set and not a
+dictionary?
+9
+
+Set comprehensions
+• Example: you have a list called l1 with integers and you have to write code that
+creates a set, with all the values from the list doubled, while ignoring values from
+the list that are even. The new set should be called s1.
+• For example, l1 = [1, 3, 2, 5] should result in s1 = {2, 6, 10}
+• Based on what you learned so far, the solution could be:
+s1 = set()
+for x in l1:
+if x%2 == 0:
+continue
+s1.add(x * 2)
+
+• With a comprehension, the solution could be:
+
+s1 = {x * 2 for x in l1 if not (x%2 == 0)}
+
+• Question: why does Python understand you are building a set and not a
+dictionary?
+• Answer: because you don't create key: value pairs, but single values
+10
+
+'string comprehensions'
+• Example: you have a string called s1 with only letters and digits and you have to
+write code that creates a string where all vowels doubled, all consonants tripled,
+and all digits ignored. The new string should be called s2
+• For example, s1 = 'a1BcDE2FgH3i' should result in
+s2 = 'aaBBBcccDDDEEFFFgggHHHii'
+
+• Based on what you learned so far, the solution could be:
+
+import string
+s2 = ''
+for x in s1:
+if x in string.digits:
+continue
+s2 += x*2 if x.lower() in 'aeiou' else x*3
+
+• Question: string comprehensions don't exist; so how would you solve this
+problem with a comprehension?
+Hint: Remember how we sorted a string.
+11
+
+'string comprehensions'
+• With a comprehension, the solution could be:
+
+import string
+l1 = [x * 2 if x.lower() in 'aeiou' else x * 3 for x in s1 if x in
+string.ascii_letters]
+
+• s2 = ''.join(l1)
+
+• So, you use a list comprehension to solve the question
+• There is actually no such thing as a string comprehension, and therefore I put quotes
+around it
+
+12
+
+Nested comprehensions
+• Example: you want to create a dictionary result, with keys being the integers between 1 and 4, and values are
+sub-dictionaries. The sub-dictionaries have keys that start with the same integer as the key in result till 4 and the
+values are sum of these keys.
+• For example:
+print(result) should give : {1: {1: 2, 2: 3, 3: 4}, 2: {2: 4, 3: 5}, 3: {3: 6}}
+print(result[2][3]) should give 5
+• func = lambda x,y: x+y
+• result = {}
+for x in range(1, 4):
+result1 = {}
+for y in range(x, 4):
+result1[y] = func(x,y)
+result[x] = result1
+• With a comprehension, the solution could be:
+• result = {x:{y: func(x,y) for y in range(x, 4)} for x in range(1, 4)}
+• Read the comprehension right to left
+13
+
+Nested comprehensions
+• Example: you want to create a dictionary result, with keys being tuples of two integers (second one greater
+equal) between 1 and 4, and values that are the sum of these numbers.
+• For example:
+print(result) should give :
+{(1, 1): 2, (1, 2): 3, (1, 3): 4, (2, 2): 4, (2, 3): 5, (3, 3): 6}
+print(result[(2, 3)] should give 5
+• func = lambda x,y: x+y
+• result = {}
+for x in range(1, 4):
+for y in range(x, 4):
+result[x,y] = func(x,y)
+• With a comprehension, the solution could be:
+• result = {(x, y): func(x,y) for x in range(1, 4) for y in range(x, 4)}
+• Read the comprehension from right to left
+
+14
+
+List comprehensions (old school)
+• In week 3 Hugo explained, in one of the Datacamp video's, lambda functions in
+combinations with map and filter. This combination also can make your code very concise
+• I will show you how that works, and I hope you will agree, that comprehensions are the
+better option
+• Say you have a list called l1 with integers and you have to write code that creates a new list
+where all integers are ignored when they are divisible by 3, are doubled when they are odd
+and not divisible by 3, and halved when they are even and not divisible by 3.
+The new list should only contain integers and be called l2
+• For example, l1 = [1, 3, 2, 4] should result in l2 = [2, 1, 2]
+• With a comprehension, the solution could be:
+l2 = [x * 2 if x%2 == 1 else x // 2 for x in l1 if x % 3 != 0]
+• With map, filter and lamba, the solution could be:
+l2 = list(map(lambda x: x * 2 if x%2 == 1 else x//2, filter(lambda
+x: x % 3 != 0, l1)))
+• There is a reason map and filter are used less since the introduction of comprehensions in
+Python
+15
+
+How to read this
+• l2 = list(map(lambda x: x * 2 if x%2 == 1 else x//2, filter(lambda x: x % 3 !
+= 0, l1)))
+• Start at the deepest level wrt the brackets
+• filtered_values = filter(lambda x: x % 3 != 0, l1)
+• processed_values = map(lambda x: x * 2 if x%2 == 1 else x//2,
+filtered_values)
+• list_of_processed_values = list(processed_values)
+• print(list_of_processed_values)
+
+16
+
+Question
+We already have the following code:
+s1 = '01212321'
+set1 = {int(x) * 2 for x in s1 if x in '02468'}
+Which of the following lines of code will print True?
+a. print(set1 == {'0', '4', '4', '4'})
+b. print(set1 == {4, 0})
+c.
+
+Both will print True
+
+d. Neither one will print True
+
+17
+
+Question
+We already have the following code:
+s1 = '01212321'
+set1 = {int(x) * 2 for x in s1 if x in '02468'}
+Which of the following lines of code will print True?
+a. print(set1 == {'0', '4', '4', '4'})
+b. print(set1 == {4, 0})
+c.
+
+Both will print True
+
+d. Neither one will print True
+e. Cannot be correct as the created elements are clearly integers
+f.
+
+Is correct. Sets only keep unique values, and there is no order
+Actually print(set1 == {4, 4, 4, 4, 0}) would also lead to True, as the created set object will only keep
+two unique values: 0 and 4
+18
+
+Keeping state between function calls
+• Assume, you must write a function that returns a certain start value, and next times you
+call the function returns the previous value plus a certain step value, until the value would
+be greater than a certain stop value, in which case the function throws an error
+• For example, if you have a start value = 1, a stop value of 5, and a step value of 2,
+• The first time that function is called, it should return 1, the second time 3, the third time 5,
+and the fourth time it should throw an error
+• This looks like a very simple problem. Adding 2 to a number is not that complicated,
+checking whether a number is greater than the stop value also looks very doable
+• The problem is that in between function calls you need to keep track of the value of the
+last number. (This is called keeping track of the state.)
+• After execution, a function and its local names will all have disappeared
+• How to solve this?
+1. Using a Standard function
+2. Using a Standard function and the global keyword
+3. Using Object oriented programming
+4. Using an iterator
+19
+5. Using a generator function
+
+Keeping state with a standard function
+• def myfunc(start, stop, step):
+num = start
+start += step
+if num > stop:
+raise StopIteration('End of numbers')
+else:
+return num, start, stop, step
+• sta = 1
+sto = 5
+ste = 2
+• num, sta, sto, ste = myfunc(sta, sto, ste)
+print(num)
+# Onscreen: 1
+• num, sta, sto, ste = myfunc(sta, sto, ste)
+print(num)
+# Onscreen: 3
+num, sta, sto, ste = myfunc(sta, sto, ste)
+print(num)
+# Onscreen: 5
+• num, sta, sto, ste = myfunc(sta, sto, ste)
+# Onscreen: StopIteration: End of numbers
+• This solution is a bit inconvenient as you must to store all values between calls yourself
+• If we want to store behavior and data together, using a class looks like a good solution
+
+20
+
+Keeping state with a standard function and the global keyword
+• def myfunc():
+global start, stop, step
+num = start
+start += step
+if num > stop:
+raise Exception('End of numbers')
+else:
+return num
+• start = 1
+stop = 5
+step = 2
+• print(myfunc()) # Onscreen: 1
+print(myfunc()) # Onscreen: 3
+print(myfunc()) # Onscreen: 5
+print(myfunc()) # Onscreen: Exception: End of numbers
+• This solution is a bit inconvenient as you must store the data outside the function, and
+use the global keyword
+• If we want to store behavior and data together, using a class looks like a good solution
+21
+
+Keeping state with a standard class
+• class Myfunc:
+def __init__(self, start, stop, step):
+self.start = start
+self.stop = stop
+self.step = step
+•
+
+def following(self):
+self.num = self.start
+self.start += self.step
+if self.num > self.stop:
+raise Exception('End of numbers')
+else:
+return self.num
+
+• myfunc = Myfunc(1, 5, 2)
+• print(myfunc.following())
+print(myfunc.following())
+print(myfunc.following())
+print(myfunc.following())
+
+# Onscreen: 1
+# Onscreen: 3
+# Onscreen: 5
+# Onscreen: Exception: End of numbers
+
+• This works, but Python offers a more dedicated solution: iterators
+
+22
+
+Keeping state with a dedicated class: iterators
+• class Myiterator:
+def __init__(self, start, stop, step):
+self.start = start
+self.stop = stop
+self.step = step
+•
+
+def __next__(self):
+self.num = self.start
+self.start += self.step
+if self.num > self.stop:
+raise StopIteration('End of numbers')
+else:
+return self.num
+
+• myiterator = Myiterator(1, 5, 2)
+• print(next(myiterator))
+print(next(myiterator))
+print(next(myiterator))
+print(next(myiterator))
+
+# Onscreen: 1
+# Onscreen: 3
+# Onscreen: 5
+# Onscreen: Exception: End of numbers
+
+• This works well, but sometimes Python offers another nice solution to create iterators: generator23
+
+Keeping state with a generator function
+• def generatorfunction(start, stop, step):
+•
+
+while (num:=start) <= stop:
+yield num
+start += step
+return 'End of numbers'
+
+• mygenerator = generatorfunction(1, 5, 2) # Creating the generator
+• print(next(mygenerator))
+print(next(mygenerator))
+print(next(mygenerator))
+print(next(mygenerator))
+
+# Onscreen: 1
+# Onscreen: 3
+# Onscreen: 5
+# Onscreen: StopIteration: End of numbers
+
+24
+
+Generator functions
+• A generatorfunction is a function that contains a yield statement and that you use to construct a generator
+• A yield statement returns a value, stops the function, but Python keeps the function and all its objects in
+memory (the data of standard functions are all lost after the function execution
+• You can compare it with a computer that goes in sleep (or hibernate) mode, if you restart the computer, you will
+resume where you were, on the moment, when the computer went in sleep mode
+• If the generator is called again your program runs the statement that should be executed after the yield
+statement
+• def generatorfunction():
+num = 1
+•
+yield num
+num += 2
+yield num
+num += 2
+yield num
+# Creating the generator
+• mygenerator = generatorfunction()
+# Onscreen: 1
+• print(next(mygenerator))
+print(next(mygenerator))
+# Onscreen: 3
+print(next(mygenerator))
+# Onscreen: 5
+25
+print(next(mygenerator))
+# Onscreen: StopIteration: End of numbers
+
+Using a Generator function inside a loop
+• def generatorfunction(start, stop, step):
+•
+
+while (num:=start) <= stop:
+yield num
+start += step
+
+•
+
+return 'End of numbers'
+
+• for x in generatorfunction(1, 5, 2):
+print(x)
+# Onscreen: 1
+3
+5
+• No error here, as the for loop catches that error for you
+
+26
+
+Turning a generatorfunction into a list
+• def generatorfunction(start, stop, step):
+•
+
+while (num:=start) <= stop:
+yield num
+start += step
+
+•
+
+return 'End of numbers'
+
+• print(list(generatorfunction(1, 5, 2))) # Onscreen: [1, 3, 5]
+• So, you can do this, but in a way, defies the idea of a generator
+• A generator is very efficient on memory usage, only delivering a next value if asked for it
+• Question: As always there is a trade-off, could you guess what?
+• Answer: for small lists, the overhead of the generator starts to count
+
+27
+
+Turning a list into an iterator
+• myiterator = iter([1,3,5])
+• print(next(myiterator))
+print(next(myiterator))
+print(next(myiterator))
+print(next(myiterator))
+
+# Onscreen: 1
+# Onscreen: 3
+# Onscreen: 5
+# Onscreen: StopIteration
+
+• Works because the list class has an __iter__ and a __next__ method
+
+28
+
+Generator comprehension
+• mygenerator = (x for x in range(100000000) if x%3 == 0 or x%4 ==
+0)
+• print(next(mygenerator))
+print(next(mygenerator))
+print(next(mygenerator))
+print(next(mygenerator))
+
+# Onscreen: 0
+# Onscreen: 3
+# Onscreen: 4
+# Onscreen: 6
+
+• vs
+• l1 = [x for x in range(100000000) if x%3 == 0 or x%4 == 0]
+• print(l1[0])
+print(l1[1])
+print(l1[2])
+print(l1[3])
+
+29
+
+Handling date and times
+• Dates and times in Python are measured as seconds since
+1970-01-01 00:00:00 UTC Coordinated Universal Time, also
+known as Greenwich Mean Time (GMT)
+• UTC doesn't have daylight saving time, so every day has 24
+hours
+• To get the time in seconds since that moment:
+• from datetime import datetime
+print(datetime.now().timestamp())
+# On screen: 1716179932.022811
+
+• This is great for computers, but not so much for humans
+30
+
+Classes in the datetime module
+There are several classes defined in the datetime module, we start with the following
+three:
+1. datetime.date
+This object stores a date and has the year, month, and day as attributes. It is based
+on the Gregorian calendar, and assumes the world always used the Gregorian
+calender
+2. datetime.time
+This object stores a time and has the hour, minute, second, microsecond, and tzinfo
+(time zone information) as attributes. It assumes there are 86,400 seconds in every
+day, so no leap seconds
+3. datetime.datetime
+This object is a combination of a datetime.date and a datetime.time. It has all the
+attributes of both classes
+(Here you see, that while the name of user-built classes starts with a capital, the names of
+built-in classes do not (always))
+We will only use the datetime class in these slides. But the idea for datetime.date and
+datetime.time is the same
+31
+
+Creating a datetime object
+• Creating a datetime object by passing integers
+from datetime import datetime
+• d = datetime (year=2023, month=5, day=22, hour=17, minute = 10, second=30)
+• d = datetime (hour=17, month=5, day=22, second=30, minute = 10, year=2023)
+• d = datetime (2023, 5, 22, 17, 10, 30 )
+• d = datetime (2023, 5, 22, minute = 10, day = 22, hour = 17, second=30 )
+• print(d)
+• The order of the positional parameters: year>month>day>hour>minute>second
+
+32
+
+attributes of a datatime object
+• from datetime import datetime
+• d = datetime.now()
+• print(f'{d.year = }, {d.month = }, {d.day = }, {d.hour = }, {d.minute
+= }, {d.second = }, {d.microsecond = }')
+• print(f'''
+Datum en tijd in ISO format: {d.isoformat()}
+Day of the week (1=Monday): {d.isoweekday()}
+Week of the year: {d.isocalendar()[1]}
+Year: {d.isocalendar()[0]}
+''')
+• In the next slide you will see that you can get all this info with the help of the strftime method
+• The difference is that these attributes give the information as integers, strftime gives it in string
+format. We can easily use str and int
+33
+
+Creating a string representation a datetime object
+• d = datetime.now()
+• print(d)
+• print(f'''
+{d.strftime('Year with century: %Y') = }
+{d.strftime('Year without century: %y') = }
+{d.strftime('Full month name: %B') = }
+{d.strftime('Month number: %m') = }
+{d.strftime('Day of the month: %d') = }
+• {d.strftime('Full weekday name: %A') = }
+{d.strftime('Weekday number: %w') = }
+• {d.strftime('Week number of the year: %U') = }
+{d.strftime('Day number of the year: %j') = }
+• {d.strftime('Hour number: %H') = }
+{d.strftime('Minute number: %M') = }
+{d.strftime('Second number: %S') = }
+• ''')
+
+34
+
+Creating a datetime object by passing a string
+• Say you want to create the following datetime object with date and time: 2024-05-20 00:00:00,
+the following codes all manage to do this:
+• from datetime import datetime
+• d = datetime.strptime('2024$05%20','%Y$%m%%%d')
+• d = datetime.strptime('24$05%20','%y$%m%%%d')
+• d = datetime.strptime('24$05%20','%y$%m%%%d')
+• d = datetime.strptime('2024*141','%Y*%j')
+• d = datetime.strptime('2024*20xyz1','%Y*%Uxyz%w')
+• d = datetime.strptime('202420Monday','%Y%U%A')
+• d = datetime.strptime('00:00:00 20-05-2024', '%H:%M:%S %d-%m-%Y')
+
+35
+
+Changing a datetime object with the replace method
+• You can change all (or some) attributes of a datetime object with the replace method:
+• from datetime import datetime
+d = datetime.now()
+• d = d.replace(year=2026, month=10, day=13, hour=13, minute=10,second=13,
+microsecond=123456)
+• You can do more advanced replacements:
+• from datetime import datetime
+• d = d.replace(year=d.year - 2)
+
+36
+
+Arithmetic with datetime and timedelta
+objects
+• If you subtract datetimes a timedelta object is automatically created:
+• from datetime import datetime
+birthday = datetime(1923, 10, 26)
+today = datetime.now()
+• age = today - birthday
+• print(f'{age.days}{age.seconds}') # On screen: 36732 14249
+• print(type(age))
+
+# On screen: Timedelta
+
+37
+
+Arithmetic with datetime and timedelta
+objects
+• Timedelta has only 3 attributes that you can access: days, seconds and microseconds. So, if you
+want to calculate the age in years, that is not that simple.
+• age = today.year - birthday.year - 1
+• age += today.month > birthday.month or today.month == birthday.month and
+today.day >= birthday.day
+
+• But if you don't subtract two datetimes, but want to add or subtract for example some days to or
+from a datetimet, you need to import the timedelta class explicitly, and create a timedelta object:
+• from datetime import timedelta, datetime
+• d = datetime(2022, 5, 24)
+• diff = timedelta(days=10)
+• print(d + diff) # Onscreen: 2022-06-03 00:00:00
+• print(type(d + diff)) # Onscreen: <class 'datetime.datetime'>
+
+38
+
+Arithmetic with datetime and timedelta
+objects
+• If you want to add or subtract days or seconds to a datetime object, the timedelta object can be very
+convenient.
+• While the timedelta object has three attributes accessible, there are more parameters one can use, to
+define a timedelta object
+• The following codes will all give the same result:
+• from datetime import datetime, timedelta
+• today = datetime.now()
+• future_day = datetime.now() + timedelta(days=100)
+• future_day = datetime.now() + timedelta(weeks=14, days=2)
+• future_day = datetime.now() + timedelta(hours=100*24)
+• future_day = datetime.now() + timedelta(minutes=100*24*60)
+• future_day = datetime.now() + timedelta(seconds=100*24*60*60)
+39
+
+
\ No newline at end of file
diff --git a/topic_cards.json b/topic_cards.json
index 640f5ec..bee76c3 100644
--- a/topic_cards.json
+++ b/topic_cards.json
@@ -485,7 +485,8 @@
               "ai-example-5",
               "ai-example-6",
               "ai-example-7",
-              "ai-example-8"
+              "ai-example-8",
+              "manual-objects-aliasing"
             ],
             "key_point_ids": [
               "kp-1",
@@ -494,6 +495,7 @@
               "kp-4",
               "kp-5",
               "kp-6",
+              "kp-manual-aliasing-copy",
               "kp-7",
               "kp-8",
               "kp-9"
@@ -1493,6 +1495,23 @@
               }
             ]
           },
+          {
+            "id": "kp-manual-aliasing-copy",
+            "text": "For mutable objects, `l2 = l1` aliases the same object; use slicing like `l1[:]` when you need an independent copy.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w1-objects-and-names-core",
+            "subtopic_title": "Objects, Types, Mutability, Assignment, and Names",
+            "details": [
+              {
+                "id": "kp-manual-aliasing-copy-d1",
+                "kind": "example",
+                "title": "Aliasing vs slicing copy",
+                "code": "l1 = [1, 2, 3]\nl2 = l1\nl3 = l1[:]\nl1[0] = 99\nprint(l2)  # [99, 2, 3]\nprint(l3)  # [1, 2, 3]"
+              }
+            ]
+          },
           {
             "id": "kp-7",
             "text": "Functions are objects. Defined with def. Called with (). Built-in functions: print, id, len, type, del. Functions can return values. Methods are functions attached to objects.",
@@ -1609,6 +1628,17 @@
             "subtopic_id": "w1-objects-and-names-core",
             "subtopic_title": "Objects, Types, Mutability, Assignment, and Names"
           },
+          {
+            "id": "manual-objects-aliasing",
+            "kind": "correct",
+            "title": "Aliasing vs slicing copy",
+            "code": "l1 = [1, 2, 3]\nl2 = l1\nl3 = l1[:]\nl1[0] = 99\nprint(l2)  # [99, 2, 3]\nprint(l3)  # [1, 2, 3]",
+            "why": "For mutable objects, `l2 = l1` aliases the same object; use slicing like `l1[:]` when you need an independent copy.",
+            "output": "",
+            "status": "curated",
+            "subtopic_id": "w1-objects-and-names-core",
+            "subtopic_title": "Objects, Types, Mutability, Assignment, and Names"
+          },
           {
             "id": "ai-example-8",
             "kind": "correct",
@@ -1683,8 +1713,7 @@
             ],
             "key_point_ids": [
               "kp-1",
-              "kp-2",
-              "kp-3"
+              "kp-2"
             ]
           }
         }
@@ -2113,8 +2142,7 @@
             ],
             "key_point_ids": [
               "kp-1",
-              "kp-2",
-              "kp-3"
+              "kp-2"
             ]
           }
         }
@@ -10069,7 +10097,8 @@
               "kp-4",
               "kp-5",
               "kp-6",
-              "kp-7"
+              "kp-7",
+              "kp-manual-string-method-results"
             ]
           }
         }
@@ -10357,7 +10386,7 @@
         ],
         "ai_summary": {
           "status": "curated",
-          "content": "Trace string slicing, search, replacement, splitting, joining, and stripping. Compare `.find` versus `.index`, and remember `join` is called on the separator.",
+          "content": "Trace string slicing, search, replacement, splitting, joining, and stripping. Compare `.find` versus `.index`, and remember string methods return new values unless you reassign the result.",
           "generator": "lecture-first-build",
           "model": null
         },
@@ -10552,6 +10581,23 @@
             "subtopic_id": "w4-string-operations-and-methods-core",
             "subtopic_title": "Slicing with Step, Search, replace, join, strip, and the string Module",
             "details": []
+          },
+          {
+            "id": "kp-manual-string-method-results",
+            "text": "String methods like `replace`, `capitalize`, `split`, and `join` return new values; the original string stays unchanged unless you assign the result.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w4-string-operations-and-methods-core",
+            "subtopic_title": "Slicing with Step, Search, replace, join, strip, and the string Module",
+            "details": [
+              {
+                "id": "kp-manual-string-method-results-d1",
+                "kind": "example",
+                "title": "replace() returns a new string",
+                "code": "number = '020-525-1400'\ncleaned = number.replace('-', '')\nprint(number)   # '020-525-1400'\nprint(cleaned)  # '0205251400'"
+              }
+            ]
           }
         ],
         "ai_examples": [
@@ -10677,7 +10723,7 @@
           "id": "w4-string-formatting-core",
           "title": "f-strings Basics, Formatting, and Debug Form",
           "order": 1,
-          "summary": "Use f-strings to embed expressions directly in text. Add format specs after `:` for width, alignment, or precision such as `{n:03d}` and `{x:.2f}`.",
+          "summary": "Use f-strings or `.format(...)` to embed values in text. Add format specs after `:` for width, alignment, or precision such as `{n:03d}` and `{x:.2f}`.",
           "item_ids": {
             "lecture_snippet_ids": [
               "ks-72dda5450c",
@@ -10689,12 +10735,14 @@
             "example_ids": [
               "ai-example-1",
               "ai-example-2",
-              "ai-example-3"
+              "ai-example-3",
+              "ai-example-4"
             ],
             "key_point_ids": [
               "kp-1",
               "kp-2",
-              "kp-3"
+              "kp-3",
+              "kp-4"
             ]
           }
         }
@@ -10766,7 +10814,7 @@
         "notebook_snippets": [],
         "ai_summary": {
           "status": "curated",
-          "content": "Use f-strings to embed expressions directly in text. Add format specs after `:` for width, alignment, or precision such as `{n:03d}` and `{x:.2f}`.",
+          "content": "Use f-strings or `.format(...)` to embed values in text. Add format specs after `:` for width, alignment, or precision such as `{n:03d}` and `{x:.2f}`.",
           "generator": "lecture-first-build",
           "model": null
         },
@@ -10837,6 +10885,23 @@
                 "code": "num = 7\nprint(f'{num:03d}') # '007'"
               }
             ]
+          },
+          {
+            "id": "kp-4",
+            "text": "Official assessments also use `.format(...)` placeholders. The same width and zero-padding ideas still apply there, for example `'{:04d}'.format(n)`.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w4-string-formatting-core",
+            "subtopic_title": "f-strings Basics, Formatting, and Debug Form",
+            "details": [
+              {
+                "id": "kp-4-d1",
+                "kind": "example",
+                "title": "Padding with `.format(...)`",
+                "code": "score = 91\nprint('Score: {:04d}'.format(score))"
+              }
+            ]
           }
         ],
         "ai_examples": [
@@ -10872,6 +10937,17 @@
             "status": "curated",
             "subtopic_id": "w4-string-formatting-core",
             "subtopic_title": "f-strings Basics, Formatting, and Debug Form"
+          },
+          {
+            "id": "ai-example-4",
+            "kind": "correct",
+            "title": "Using `.format(...)` with placeholders",
+            "code": "name = 'Jan'\nscore = 7\nprint('Student {} scored {:03d}'.format(name, score))",
+            "why": "The official resit material still uses `.format(...)`, so it is worth recognizing positional placeholders and width specs alongside f-strings.",
+            "output": "Student Jan scored 007",
+            "status": "curated",
+            "subtopic_id": "w4-string-formatting-core",
+            "subtopic_title": "f-strings Basics, Formatting, and Debug Form"
           }
         ],
         "recommended_ids": [
@@ -10928,13 +11004,16 @@
               "ai-example-1",
               "ai-example-2",
               "ai-example-3",
-              "ai-example-6"
+              "ai-example-6",
+              "manual-oop-init-defaults",
+              "manual-oop-attrs"
             ],
             "key_point_ids": [
               "kp-1",
               "kp-2",
               "kp-3",
-              "kp-4"
+              "kp-4",
+              "kp-manual-init-defaults"
             ]
           }
         }
@@ -11211,7 +11290,7 @@
         ],
         "ai_summary": {
           "status": "curated",
-          "content": "A class defines attributes and methods; `__init__` runs when you create an object and stores instance data on `self`.",
+          "content": "A class defines attributes and methods; `__init__` runs when you create an object, defaults can make constructor arguments optional, and class attributes are shared across instances.",
           "generator": "lecture-first-build",
           "model": null
         },
@@ -11315,6 +11394,29 @@
             "subtopic_id": "w4-oop-fundamentals-core",
             "subtopic_title": "Class Definition, __init__, self, and Attribute Basics",
             "details": []
+          },
+          {
+            "id": "kp-manual-init-defaults",
+            "text": "Constructor defaults can make arguments optional, but mutable per-instance state should start from `None` and be created inside `__init__`.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w4-oop-fundamentals-core",
+            "subtopic_title": "Class Definition, __init__, self, and Attribute Basics",
+            "details": [
+              {
+                "id": "kp-manual-init-defaults-d1",
+                "kind": "example",
+                "title": "Optional constructor argument",
+                "code": "class Vehicle:\n    def __init__(self, name, mode='land'):\n        self.name = name\n        self.mode = mode"
+              },
+              {
+                "id": "kp-manual-init-defaults-d2",
+                "kind": "example",
+                "title": "Fresh list per object",
+                "code": "class Book:\n    def __init__(self, title, review_scores=None):\n        self.title = title\n        self.review_scores = [] if review_scores is None else review_scores"
+              }
+            ]
           }
         ],
         "ai_examples": [
@@ -11372,6 +11474,17 @@
             "status": "curated",
             "subtopic_id": "w4-oop-fundamentals-core",
             "subtopic_title": "Class Definition, __init__, self, and Attribute Basics"
+          },
+          {
+            "id": "manual-oop-init-defaults",
+            "kind": "correct",
+            "title": "Safe default state in `__init__`",
+            "code": "class Book:\n    def __init__(self, title, review_scores=None):\n        self.title = title\n        self.review_scores = [] if review_scores is None else review_scores",
+            "output": "",
+            "why": "Constructor defaults can make arguments optional, but mutable per-instance state should start from `None` and be created inside `__init__`.",
+            "status": "curated",
+            "subtopic_id": "w4-oop-fundamentals-core",
+            "subtopic_title": "Class Definition, __init__, self, and Attribute Basics"
           }
         ],
         "recommended_ids": [
@@ -11575,6 +11688,23 @@
                 "code": "def check_positive(n):\n    if n < 0:\n        raise ValueError('Number must be positive')\n    return n"
               }
             ]
+          },
+          {
+            "id": "kp-3",
+            "text": "Catch the specific exception you expect when you know the failure mode. `except ValueError` documents the substring-missing case more clearly than a bare `except:`.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w4-error-handling-core",
+            "subtopic_title": "try/except and raise",
+            "details": [
+              {
+                "id": "kp-3-d1",
+                "kind": "example",
+                "title": "Specific exception handler",
+                "code": "s1 = 'abcdefghabcdefgh'\nneedle = 'i'\ntry:\n    position = s1.index(needle)\nexcept ValueError:\n    position = -1\nprint(position)"
+              }
+            ]
           }
         ],
         "ai_examples": [
@@ -12810,7 +12940,9 @@
               "ai-example-5",
               "ai-example-6",
               "ai-example-7",
-              "ai-example-8"
+              "ai-example-8",
+              "manual-working-values-string-vs-map",
+              "manual-working-values-axis-apply"
             ],
             "key_point_ids": [
               "kp-1",
@@ -12819,7 +12951,8 @@
               "kp-4",
               "kp-5",
               "kp-6",
-              "kp-7"
+              "kp-7",
+              "kp-manual-drop-missing"
             ]
           }
         }
@@ -13174,6 +13307,29 @@
                 "code": "df.fillna('No value') # Replaces all NaNs"
               }
             ]
+          },
+          {
+            "id": "kp-manual-drop-missing",
+            "text": "Use `.drop(...)` to remove labels and `.dropna()` to remove incomplete rows or columns; use `.fillna(...)` when you want to keep the shape and replace the missing values instead.",
+            "status": "curated",
+            "generator": "manual-gap-merge-rm008",
+            "model": null,
+            "subtopic_id": "w5-working-with-values-core",
+            "subtopic_title": "Sorting, Missing Data, Broadcasting, Vectorized Strings, isin, map, and apply",
+            "details": [
+              {
+                "id": "kp-manual-drop-missing-d1",
+                "kind": "example",
+                "title": "Dropping a column by label",
+                "code": "df = df.drop(1, axis=1)"
+              },
+              {
+                "id": "kp-manual-drop-missing-d2",
+                "kind": "example",
+                "title": "Removing rows with missing values",
+                "code": "df.dropna()"
+              }
+            ]
           }
         ],
         "ai_examples": [
@@ -13243,6 +13399,28 @@
             "subtopic_id": "w5-working-with-values-core",
             "subtopic_title": "Sorting, Missing Data, Broadcasting, Vectorized Strings, isin, map, and apply"
           },
+          {
+            "id": "manual-working-values-string-vs-map",
+            "kind": "correct",
+            "title": "`.str` methods vs `map(...)`",
+            "code": "s = pd.Series(['Aap', 'Nota', 'MIES'])\nprint(s.map(lambda x: x.lower()))\nprint(s.str.lower())",
+            "why": "Use `.str` for vectorized string operations; `map(...)` is useful when you need a custom per-value transformation.",
+            "output": "",
+            "status": "curated",
+            "subtopic_id": "w5-working-with-values-core",
+            "subtopic_title": "Sorting, Missing Data, Broadcasting, Vectorized Strings, isin, map, and apply"
+          },
+          {
+            "id": "manual-working-values-axis-apply",
+            "kind": "correct",
+            "title": "`apply(axis=0)` vs `apply(axis=1)`",
+            "code": "df.loc['Av'] = df.apply(lambda x: x.mean(), axis=0)\ndf['Avg'] = df.apply(lambda x: x.mean(), axis=1)",
+            "why": "`axis=0` works column by column; `axis=1` works row by row.",
+            "output": "",
+            "status": "curated",
+            "subtopic_id": "w5-working-with-values-core",
+            "subtopic_title": "Sorting, Missing Data, Broadcasting, Vectorized Strings, isin, map, and apply"
+          },
           {
             "id": "ai-example-7",
             "kind": "correct",
@@ -14706,6 +14884,7 @@
           "title": "now, Timestamps, strftime, strptime, timedelta, and Date Arithmetic",
           "order": 1,
           "summary": "Use `datetime.now()` for current time, `strftime` to format, `strptime` to parse, and `timedelta` for date arithmetic.",
+          "summary": "Use `datetime.now()` for current time, `strftime`/`strptime` for formatting and parsing, ISO/week helpers for calendar fields, and `timedelta` for date arithmetic.",
           "item_ids": {
             "lecture_snippet_ids": [
               "ks-e643ee5e77",
@@ -14728,8 +14907,22 @@
               "cs-b31e0142c3",
               "cs-1887901826"
             ],
-            "example_ids": [],
-            "key_point_ids": []
+            "example_ids": [
+              "manual-datetime-format",
+              "manual-datetime-parse",
+              "manual-datetime-delta",
+              "manual-datetime-replace",
+              "manual-datetime-iso",
+              "manual-datetime-day-of-year"
+            ],
+            "key_point_ids": [
+              "kp-manual-datetime-now",
+              "kp-manual-datetime-format",
+              "kp-manual-datetime-delta",
+              "kp-manual-datetime-replace",
+              "kp-manual-datetime-iso",
+              "kp-manual-datetime-day-of-year"
+            ]
           }
         }
       ],
@@ -14986,7 +15179,7 @@
         ],
         "ai_summary": {
           "status": "curated",
-          "content": "Use `datetime.now()` for current time, `strftime` to format, `strptime` to parse, and `timedelta` for date arithmetic.",
+          "content": "Use `datetime.now()` for current time, `strftime`/`strptime` for formatting and parsing, ISO/week helpers for calendar fields, and `timedelta` for date arithmetic.",
           "generator": "lecture-first-build",
           "model": null
         },
@@ -15041,6 +15234,14 @@
           {
             "id": "kp-manual-datetime-replace",
             "text": "Datetime objects are immutable, so `.replace(...)` returns a new datetime instead of modifying the original one."
+          },
+          {
+            "id": "kp-manual-datetime-iso",
+            "text": "Use `isoformat()` for an ISO-style timestamp, `isoweekday()` for Monday=1..Sunday=7, and `isocalendar()` when you need ISO year/week information."
+          },
+          {
+            "id": "kp-manual-datetime-day-of-year",
+            "text": "The day number within the year can be formatted with `%j` or computed with `(dt - datetime(dt.year, 1, 1)).days + 1`."
           }
         ],
         "ai_examples": [
@@ -15087,6 +15288,28 @@
             "status": "curated",
             "subtopic_id": "w6-datetime-core",
             "subtopic_title": "now, Timestamps, strftime, strptime, timedelta, and Date Arithmetic"
+          },
+          {
+            "id": "manual-datetime-iso",
+            "kind": "correct",
+            "title": "ISO and week-based helpers",
+            "code": "from datetime import datetime\n\nd = datetime(2024, 5, 20)\nprint(d.isoformat())\nprint(d.isoweekday())\nprint(d.isocalendar()[1])",
+            "output": "",
+            "why": "Use ISO helpers when the exam asks for an ISO string, the weekday with Monday as 1, or the ISO week number.",
+            "status": "curated",
+            "subtopic_id": "w6-datetime-core",
+            "subtopic_title": "now, Timestamps, strftime, strptime, timedelta, and Date Arithmetic"
+          },
+          {
+            "id": "manual-datetime-day-of-year",
+            "kind": "correct",
+            "title": "Compute the day number within the year",
+            "code": "from datetime import datetime\n\ndef day_of_year(dt):\n    return (dt - datetime(dt.year, 1, 1)).days + 1\n\nprint(day_of_year(datetime(2024, 5, 20)))",
+            "output": "",
+            "why": "Subtract January 1st of the same year, then add 1 so January 1st becomes day 1.",
+            "status": "curated",
+            "subtopic_id": "w6-datetime-core",
+            "subtopic_title": "now, Timestamps, strftime, strptime, timedelta, and Date Arithmetic"
           }
         ],
         "recommended_ids": [
@@ -15410,4 +15633,4 @@
       ]
     }
   ]
-}
\ No newline at end of file
+}