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Feat/jupyter (#17)
* jupyter notebooks * fixup * fixup * fixup * fixup * fixup * fixup * fixup * fixup
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-419
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‎.github/workflows/quality.yml‎

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@@ -22,23 +22,8 @@ jobs:
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- name: Install dependencies
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run: poetry install
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- name: Run Linter (Ruff)
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run: poetry run ruff check .
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- name: Run Formatter Check (Ruff)
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run: poetry run ruff format --check .
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- name: Run Static Type Checking (BasedPyright)
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run: poetry run basedpyright src/
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- name: Run Static Type Checking (MyPy)
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run: poetry run mypy src/
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- name: Run Import Cycle Check (Pylint)
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run: poetry run pylint src
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- name: Run Tests with Coverage
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run: poetry run pytest
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- name: Run CI
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run: poetry run poe ci
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- name: Upload Coverage Reports
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uses: actions/upload-artifact@v4

‎docs/01_core_concepts.ipynb‎

Lines changed: 54 additions & 57 deletions
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@@ -39,18 +39,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ Pydantic model created successfully from valid data.\n",
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"✅ Pydantic correctly raised a ValidationError for invalid data.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from typing import Annotated\n",
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"\n",
@@ -72,6 +63,15 @@
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"# 1. Test the successful case\n",
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"valid_data = {\"id\": 1, \"name\": \"Alice\", \"age\": 30}\n",
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"user = User.model_validate(valid_data)\n",
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"user\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"assert user.id == 1\n",
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"assert user.name == \"Alice\"\n",
@@ -120,19 +120,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ `parse_user_id` returned Success for valid input.\n",
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"✅ `parse_user_id` returned Failure for invalid format.\n",
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"✅ `parse_user_id` returned Failure for invalid value.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from returns.result import Failure, Result, Success\n",
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"\n",
@@ -153,18 +143,45 @@
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"\n",
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"# 1. Test the Success case\n",
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"success_result = parse_user_id(\"123\")\n",
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"success_result\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"assert isinstance(success_result, Success)\n",
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"assert success_result.unwrap() == 123\n",
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"print(\"✅ `parse_user_id` returned Success for valid input.\")\n",
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"\n",
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"# 2. Test the Failure case (format)\n",
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"failure_result_format = parse_user_id(\"abc\")\n",
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"failure_result_format"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"assert isinstance(failure_result_format, Failure)\n",
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"assert \"Invalid format\" in failure_result_format.failure()\n",
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"print(\"✅ `parse_user_id` returned Failure for invalid format.\")\n",
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"\n",
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"# 3. Test the Failure case (value)\n",
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"failure_result_value = parse_user_id(\"0\")\n",
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"failure_result_value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"assert isinstance(failure_result_value, Failure)\n",
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"assert \"must be positive\" in failure_result_value.failure()\n",
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"print(\"✅ `parse_user_id` returned Failure for invalid value.\")"
@@ -188,18 +205,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pipeline: ' This is a Long String of Text ' to '\"this is a !\"'\n",
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"✅ Pipeline correctly working\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from returns.pipeline import pipe\n",
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"\n",
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"raw_input = \" This is a Long String of Text \"\n",
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"\n",
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"# Use pipe to compose the functions into a pipeline\n",
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"processed_text = pipe(clean_text, truncate_text, emphasize_text)(raw_input) # pyright: ignore\n",
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"processed_text = pipe(clean_text, truncate_text, emphasize_text)(raw_input)\n",
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"processed_text\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"# --- Verification ---\n",
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"print(f\"Pipeline: '{raw_input}' to '{processed_text}'\")\n",
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"\u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36m__main__:<module>:19\u001b[0m - \u001b[34m\u001b[1mThis is a debug message. Useful for developers.\u001b[0m\n",
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"\u001b[1mINFO \u001b[0m | \u001b[36m__main__:<module>:20\u001b[0m - \u001b[1mApplication is starting up...\u001b[0m\n",
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"\u001b[32m\u001b[1mSUCCESS \u001b[0m | \u001b[36m__main__:<module>:21\u001b[0m - \u001b[32m\u001b[1mA task was completed successfully.\u001b[0m\n",
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"\u001b[33m\u001b[1mWARNING \u001b[0m | \u001b[36m__main__:<module>:22\u001b[0m - \u001b[33m\u001b[1mSomething looks a bit strange, but it's not an error.\u001b[0m\n",
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"\u001b[31m\u001b[1mERROR \u001b[0m | \u001b[36m__main__:<module>:23\u001b[0m - \u001b[31m\u001b[1mAn error occurred! This needs attention.\u001b[0m\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"✅ Loguru demonstrated various log levels.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",

‎docs/02_functional_patterns.ipynb‎

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Original file line numberDiff line numberDiff line change
@@ -4,16 +4,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Functional Patterns: A Python \"Ramda\" Cookbook"
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"# Functional Patterns"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This notebook explores functional programming patterns for common data manipulation tasks, particularly for those coming from a JavaScript/TypeScript background with experience using libraries like Ramda or Lodash.\n",
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"\n",
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"Python's functional capabilities are often built directly into the language or provided by libraries like `returns`. We'll explore how to achieve the same declarative style in a way that is idiomatic to Python."
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"This notebook explores functional programming patterns for common data manipulation tasks."
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]
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},
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{
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"source": [
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"A core tenet of functional programming is to express *what* you want to do, not *how* you want to do it. Instead of writing imperative `for` loops, we use declarative approaches like `map` and `filter`.\n",
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"\n",
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"### Style 1: Pipeline-based (Ramda-like)\n",
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"### Style 1: Pipeline-based\n",
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"\n",
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"Using `returns.pipe` with `map` and `filter` feels very similar to Ramda's `R.pipe`. This style is excellent for long chains of transformations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ Pipeline correctly transformed the list to [4, 16, 36]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from returns.pipeline import pipe\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ List comprehension correctly transformed the list to [4, 16, 36]\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"numbers = [1, 2, 3, 4, 5, 6]\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ Currying works as expected, creating specialized functions.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from returns.curry import curry\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"✅ `all_pass` helper correctly combines predicates.\n"
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]
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}
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],
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"outputs": [],
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"source": [
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"from collections.abc import Callable\n",
186152
"from typing import TypeVar\n",

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