{
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   "source": [
    "### <center>San Jose State University<br>Dep|artment of Applied Data Science<br><br>**DATA 200<br>Computational Programming for Data Analytics**<br><br>Spring 2024<br>Instructor: Ron Mak</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.11 Arbitrary Argument Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can define a function with an **arbitrary argument list**, meaning we can call it with any number of arguments. In the function definition, we use a parameter of the form `*args`. That tells Python to pack the arguments into a tuple by that name. By convention, the name`args` is used, but you can use any name."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Defining a Function with an Arbitrary Argument List"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def average(*args):\n",
    "    return sum(args) / len(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average(5, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average(5, 10, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average(5, 10, 15, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### If the function has multiple parameters, the `*args` parameter must be the rightmost one."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Passing an Iterable’s Individual Elements as Function Arguments "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### You can use the `*` operator to unpack a list, tuple, or any other iterable object to pass its individual items to a function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "grades = [88, 75, 96, 55, 83]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average(*grades)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This is equivalent to:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "average(88, 75, 96, 55, 83)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### This function `print_minimum()` must be called with at **least two arguments**, but there can be more."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_minimum(parm1, parm2, *args):\n",
    "    m = min(parm1, parm2, *args)\n",
    "    print(f'The minimum of {len(args) + 2} values is {m}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_minimum(34, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_minimum(34, 12, 7, 46, 91)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_minimum(34, 12, *range(5, 11))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_minimum(34)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### **EXAMPLE:** Create a function named `calculate_product` that receives an arbitrary argument list and returns the product of all the arguments. Call the function with the arguments `10`, `20` and `30` then with the sequence of integers produced by `range(1, 6, 2)`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_product(*args):\n",
    "    product = 1\n",
    "    \n",
    "    for value in args:\n",
    "        product *= value\n",
    "        \n",
    "    return product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_product(10, 20, 30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "calculate_product(*range(1, 6, 2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Keyworded Variable Argument Lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can define a function with a **keyworded variable argument list**. By convention, the name `kwargs` is used as the function parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def employee_info(**kwargs):\n",
    "    for key, value in kwargs.items():\n",
    "        print(f'{key = }, {value = }')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "employee_info(first='Bob', last='Smith', id=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def misc_items(label, **kwargs):\n",
    "    print(f'{label}:')\n",
    "\n",
    "    for key, value in kwargs.items():\n",
    "        print(f'  {key} is {value}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "misc_items('Colors', background='white', border='black', margins='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "misc_items('Companies', computer='Apple', food=\"MacDonald's\", university='SJSU', city='San Jose')"
   ]
  },
  {
   "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": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Additional material (C) Copyright 2023 by Ronald Mak"
   ]
  }
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