{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### San Jose State University
Department of Applied Data Science
**DATA 200
Computational Programming for Data Analytics**
Spring 2023
Instructor: Ron Mak"
]
},
{
"attachments": {
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"
}
},
"cell_type": "markdown",
"metadata": {},
"source": [
"# Polymorphism\n",
"#### **Polymorphism** is an important feature of subclassing and inheritance. Look again at the `Shape` inheritance tree\n",
"\n",
"#### If superclass `Shape` defines a method `draw()`, it would be inherited by all the subclasses. But a circle is drawn differently than a square, and a sphere is drawn differently than a cube. Therefore, each subclasses must override the `draw()` method accordingly."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Now assume variable `sh` is assigned one of the shape objects, and our code includes the call `sh.draw()`. What will be drawn at run time? Polymorphism gives Python the ability to look at the type of shape object that was assigned to `sh` -- was it a `Circle`, a `Square`, a `Cube`, etc. and call the appropriate `draw()` method. Thus, if a `Triangle` object was assigned to `sh`, then Python will call the `draw()` method of the `Triangle` subclass."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 10.8.3 Processing `CommissionEmployee`s and `SalariedCommissionEmployee`s Polymorphically"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### We can demonstrate polymorphism with the `CommissionEmployee` and `SalariedCommissionEmployee` classes. In the following `for` loop, variable `employee` is first assigned a `CommissionEmployee` object (`c`) and then a `SalariedCommissionEmployee` object (`s`). When we call `employee.earnings()` to print it, polymorphism enables Python to first call the `earnings()` method of class `CommissionEmployee` and then call the `earnings()` method of class `SalariedCommissionEmployee`."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from decimal import Decimal\n",
"from commissionemployee import CommissionEmployee\n",
"from salariedcommissionemployee import SalariedCommissionEmployee"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "InvalidOperation",
"evalue": "[]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mInvalidOperation\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m c \u001b[38;5;241m=\u001b[39m CommissionEmployee(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mSue\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mJones\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m333-33-3333\u001b[39m\u001b[38;5;124m'\u001b[39m, \n\u001b[0;32m----> 2\u001b[0m Decimal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m1s0_000.00\u001b[39m\u001b[38;5;124m'\u001b[39m), Decimal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.06\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m 4\u001b[0m s \u001b[38;5;241m=\u001b[39m SalariedCommissionEmployee(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBob\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLewis\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m444-44-4444\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 5\u001b[0m Decimal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m5000.00\u001b[39m\u001b[38;5;124m'\u001b[39m), Decimal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m0.04\u001b[39m\u001b[38;5;124m'\u001b[39m), Decimal(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m300.00\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
"\u001b[0;31mInvalidOperation\u001b[0m: []"
]
}
],
"source": [
"c = CommissionEmployee('Sue', 'Jones', '333-33-3333', \n",
" Decimal('1_0_000.00'), Decimal('0.06'))\n",
"\n",
"s = SalariedCommissionEmployee('Bob', 'Lewis', '444-44-4444',\n",
" Decimal('5000.00'), Decimal('0.04'), Decimal('300.00'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for employee in [c, s]:\n",
" print(employee)\n",
" print(f'{employee.earnings() = :,.2f}\\n')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"##########################################################################\n",
"# (C) Copyright 2019 by Deitel & Associates, Inc. and #\n",
"# Pearson Education, Inc. All Rights Reserved. #\n",
"# #\n",
"# DISCLAIMER: The authors and publisher of this book have used their #\n",
"# best efforts in preparing the book. These efforts include the #\n",
"# development, research, and testing of the theories and programs #\n",
"# to determine their effectiveness. The authors and publisher make #\n",
"# no warranty of any kind, expressed or implied, with regard to these #\n",
"# programs or to the documentation contained in these books. The authors #\n",
"# and publisher shall not be liable in any event for incidental or #\n",
"# consequential damages in connection with, or arising out of, the #\n",
"# furnishing, performance, or use of these programs. #\n",
"##########################################################################\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Additional material (C) Copyright 2023 by Ronald Mak"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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