decimal

python_fundamentals/exercises/day_24_decimal/product_invoice.py

@@ -0,0 +1,46 @@
+from decimal import Decimal
+
+from pydantic import BaseModel, Field, computed_field
+
+
+class Product(BaseModel):
+    name: str = Field(min_length=3, max_length=20)
+    price: Decimal
+    quantity: int
+
+    @computed_field
+    def subtotal(self) -> Decimal:
+        return self.price * self.quantity
+
+
+class Invoice(BaseModel):
+    invoice_id: str
+    customer_name: str = Field(min_length=3, max_length=20)
+    products: list[Product]
+    gst_percentage: Decimal
+
+    @computed_field
+    def subtotal_total(self) -> Decimal:
+        total: Decimal = Decimal("0")
+        for product in self.products:
+            total += product.subtotal
+        return total
+
+    @computed_field
+    def grand_total(self) -> Decimal:
+        subtotal: Decimal = self.subtotal_total
+        gst_amount = subtotal * (self.gst_percentage / 100)
+        return Decimal(subtotal + gst_amount)
+
+
+p1: Product = Product(name="Sugar", price=Decimal("20.00"), quantity=1)
+p2: Product = Product(name="Oil", price=Decimal("120.00"), quantity=2)
+
+invoice: Invoice = Invoice(
+    invoice_id="123",
+    customer_name="Tejas Dixit",
+    products=[p1, p2],
+    gst_percentage=Decimal("18"),
+)
+
+print(invoice.model_dump())

python_fundamentals/exercises/day_25_date_datetime/README.md

@@ -0,0 +1,74 @@
+# Day 25 — Date & DateTime
+
+## Objective
+
+Understand when to use `date` and `datetime` in Pydantic models and how Pydantic parses ISO formatted strings into Python objects.
+
+---
+
+## Concepts Covered
+
+- `date`
+- `datetime`
+- Pydantic parsing
+- `ValidationError`
+- `model_dump()`
+- `model_dump_json()`
+
+---
+
+## Business Use Cases
+
+### `date`
+
+- Employee joining date
+- Date of birth
+- Manufacturing date
+
+### `datetime`
+
+- User login
+- Payment timestamp
+- Audit logs
+- Record creation time
+
+---
+
+## Key Learning
+
+- Use `date` when only the calendar date is required.
+- Use `datetime` when both date and time are required.
+- Python validates `date()` and `datetime()` constructors.
+- Pydantic parses strings into `date` and `datetime` objects.
+- Invalid parsed values raise `ValidationError`.
+
+---
+
+## Run
+
+```bash
+uv run python employee.py
+```
+
+---
+
+## Expected Output
+
+- Successful creation of a valid `Employee`.
+- `model_dump()` returns Python objects.
+- `model_dump_json()` returns JSON.
+- Invalid input raises `ValidationError`.
+
+---
+
+## Files
+
+```
+employee.py
+```
+
+---
+
+## Status
+
+✅ Completed

python_fundamentals/exercises/day_25_date_datetime/employee.py

@@ -0,0 +1,35 @@
+#!/usr/bin/env python3.12
+from datetime import date, datetime
+
+from pydantic import BaseModel, Field, ValidationError
+
+
+class Employee(BaseModel):
+    emp_name: str = Field(min_length=3, max_length=20)
+    joining_date: date
+    created_at: datetime
+
+
+try:
+    tejas_dixit: Employee = Employee(
+        emp_name="Tejas Dixit",
+        joining_date=date(year=2024, month=1, day=29),
+        created_at=datetime(year=2024, month=1, day=29, hour=13, minute=00, second=20),
+    )
+
+    print(f"Python Representaion of Employee Object: {tejas_dixit.model_dump()}")
+    print(f"Pydantic Representaion of Employee Object: {tejas_dixit.model_dump_json()}")
+
+    vignesh_gawali: Employee = Employee(
+        emp_name="Vignesh Gawali",
+        joining_date="2024-12-12",  # Intentional: testing Pydantic parsing
+        created_at="0000-12-12 12:12",  # Intentional: testing Pydantic parsing
+    )
+
+    print(f"Python Representaion of Employee Object: {vignesh_gawali.model_dump()}")
+    print(
+        f"Pydantic Representaion of Employee Object: {vignesh_gawali.model_dump_json()}"
+    )
+
+except (ValueError, ValidationError) as exc:
+    print(exc)

python_fundamentals/exercises/day_26_model_dump_serialization/README.md

@@ -0,0 +1,66 @@
+# Day 26 — model_dump()
+
+## Objective
+
+Understand how `model_dump()` serializes a validated Pydantic model into a Python dictionary and how to control serialized output using `include` and `exclude`.
+
+---
+
+## Concepts Covered
+
+- `model_dump()`
+- Serialization
+- `include`
+- `exclude`
+- Business Views
+
+---
+
+## Business Use Cases
+
+- HR View
+- Admin View
+- Public Profile
+- API Response
+- Logging
+
+---
+
+## Key Learning
+
+- `model_dump()` serializes a validated Pydantic model.
+- It returns a standard Python dictionary.
+- Use `include` to serialize only required fields.
+- Use `exclude` to omit sensitive or unnecessary fields.
+- One model can produce multiple business-specific representations.
+
+---
+
+## Run
+
+```bash
+uv run python employee.py
+```
+
+---
+
+## Expected Output
+
+- Python dictionary representation of the model.
+- HR View containing selected fields.
+- Admin View excluding selected fields.
+- Automatic parsing of ISO formatted strings into Python objects.
+
+---
+
+## Files
+
+```
+employee.py
+```
+
+---
+
+## Status
+
+✅ Completed

python_fundamentals/exercises/day_26_model_dump_serialization/employee.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3.12
+
+# model_dump() serializes a validated Pydantic model into a Python dictionary.
+# Prefer model_dump() over __dict__ because it supports Pydantic features
+# such as include, exclude, aliases and serializers.
+
+
+from datetime import date, datetime
+
+from pydantic import BaseModel, Field, ValidationError
+
+
+class Employee(BaseModel):
+    """This Model Represents and Employee of an Companay"""
+
+    emp_name: str = Field(min_length=3, max_length=20)
+    joining_date: date
+    created_at: datetime
+
+
+def format_emp() -> None:
+    print(
+        "-" * 120,
+        end="\n",
+    )
+
+
+try:
+    emp: Employee = Employee(
+        emp_name="Tejas Dixit",
+        joining_date=date(year=2024, month=1, day=29),
+        created_at=datetime(year=2024, month=1, day=25, hour=9, minute=30),
+    )
+
+    print(
+        f"HR View: {emp.model_dump(include={'emp_name', 'joining_date'})}"
+    )  # exclude `created_at` field.
+
+    print(
+        f"ADMIN View: {emp.model_dump(exclude={'joining_date'})}"
+    )  # excludes `joining_date` and include each field.
+
+    format_emp()
+
+    another_emp: Employee = Employee(
+        emp_name="Tejas Dixit",
+        joining_date="2024-12-29",
+        # Intentional: Pydantic will conert it into date format
+        created_at="2024-12-28 12:12",
+        # Intentional: Pydantic model will convert it into datetime format
+    )
+
+    print(
+        f"HR View: {another_emp.model_dump(include={'emp_name', 'joining_date'})}"
+    )  # exclude `created_at` field.
+
+    print(f"ADMIN View: {another_emp.model_dump(exclude={'joining_date'})}")
+
+
+except (ValueError, ValidationError) as exc:
+    print(exc)

python_fundamentals/exercises/employee_field_serializer.py

@@ -0,0 +1,27 @@
+from datetime import date, datetime
+from decimal import Decimal
+
+from pydantic import BaseModel, field_serializer
+
+
+class Employee(BaseModel):
+    name: str
+    salary: Decimal
+    joining_date: date
+
+    @field_serializer("salary")
+    def salary_in_dollars(self, salary: Decimal) -> str:
+        return f"${salary}"
+
+    @field_serializer("joining_date")
+    def format_date(self, date: datetime) -> str:
+        return date.strftime("%d %b %Y")
+
+
+emp1: Employee = Employee(
+    name="Tejas Dixit",
+    salary=Decimal(25000),
+    joining_date=date(year=2024, month=1, day=29),
+)
+
+print(emp1.model_dump())