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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": [
    "# 3.17 Intro to Data Science: Measures of Central Tendency—Mean, Median and Mode "
   ]
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    "#### **Measures of central tendency** in descriptive statistics:\n",
    "- **mean**: the *average* of a set of values\n",
    "- **median**: the *middle value* of a set of **sorted** values\n",
    "- **mode**: the *most frequently occuring* value in a set of values\n",
    "\n",
    "#### Each is a single value that represents a \"central\" value of the set of values, one that is most \"typical\" of the values."
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    "grades = [85, 93, 45, 89, 85]"
   ]
  },
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   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'mean = {sum(grades)/len(grades)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recall the `min()` and `max()` functions:"
   ]
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   "cell_type": "code",
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   "outputs": [],
   "source": [
    "print(f'min = {min(grades)}')\n",
    "print(f'max = {max(grades)}')"
   ]
  },
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   "metadata": {},
   "source": [
    "#### The statistics module of the Standard Python Library has functions to calculate the mean, median, and mode of a list of values."
   ]
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   "source": [
    "import statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'mean = {statistics.mean(grades)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'sorted grades = {sorted(grades)}')\n",
    "print(f'median = {statistics.median(grades)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What if there are an even number of values in the list? Then the median is the average of the two middle values."
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "even_list = [86, 14, 55, 92, 75, 80]\n",
    "\n",
    "print(f'sorted grades = {sorted(even_list)}')\n",
    "print(f'median = {statistics.median(even_list)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(f'mode = {statistics.mode(grades)}')"
   ]
  },
  {
   "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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