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   "source": [
    "### <center>San Jose State University<br>Department of Applied Data Science<br><br>**DATA 200<br>Computational Programming for Data Analytics**<br><br>Spring 2024<br>Instructor: Ron Mak</center>"
   ]
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   "source": [
    "## 6.3.2 Mathematical Set Operations\n",
    "\n",
    "### Union "
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5} | {2, 3, 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5}.union([20, 20, 3, 40, 40])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Intersection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5} & {2, 3, 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5}.intersection([1, 2, 2, 3, 3, 4, 4])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The **difference** operator `-` computes the difference between two sets. It's a set containing the elements in the left set that are **not** in the right set. You can also use the `difference()` set method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5} - {2, 3, 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5, 7}.difference([2, 2, 3, 3, 4, 4])"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Symmetric Difference "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The **symmetric difference** operator `^` computes the set containing the elements from both sets that are not in common with one another. You can also use the `symmetric_difference()` set method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5} ^ {2, 3, 4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5, 7}.symmetric_difference([2, 2, 3, 3, 4, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set1 = {1, 3, 5}\n",
    "set2 = {2, 3, 4}\n",
    "\n",
    "set1 ^ set2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set1 - set2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "set2 - set1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "(set1 - set2) | (set2 - set1)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Disjoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The `isdisjoint()` set method returns `True` if two sets have no common elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5}.isdisjoint({2, 4, 6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "{1, 3, 5}.isdisjoint({4, 6, 1})"
   ]
  },
  {
   "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 2024 by Ronald Mak"
   ]
  }
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