INF110-Projects/Midterm/Midterm_Coding_S22.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Midterm Exam Part B\n",
    "\n",
    "Your work must be independent but you may refer to your notes, book, or previous assignments.\n",
    "\n",
    "In these questions, you will be using NAU's spreadsheet of liberal study class data.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the notebook in this cell (import statements)!\n",
    "from datascience import *\n",
    "import numpy\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plotter\n",
    "plotter.style.use('fivethirtyeight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 1.** Load the file class_list.csv into a table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use load csv function to create a table of the data\n",
    "table_file = Table.read_table('https://raw.githubusercontent.com/marctollis/INF110-Discovering-Informatics/main/class_list.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 2.** Create Python code that calculates the number of BIO classes listed in the class list table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "number_of_bio_classes = table_file.where('COURSE', are.containing('BIO')).num_rows\n",
    "number_of_bio_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 3.** Using Python code, answer the question, \"how many classes have a minimum of four units and satisfy the \"Quantitative Reasoning\" liberal study essential skill?\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find out how many classes have both four units, and are considered quantitative reasoning\n",
    "num_quant_reason_four_unit_classes = table_file.where('LS_ESSENTIAL_SKILL', are.containing('Quantitative Reasoning')).where('UNITS_MINIMUM', are.above_or_equal_to(4)).num_rows\n",
    "num_quant_reason_four_unit_classes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Question 4.** Create a bar chart that summarizes the number of classes that satisfy each of the different liberal study essential skills (column 7). Order the chart by most number of classes to least."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create bar chart to visualize how many classes of each essential skill there are\n",
    "# section off the table that has the skills\n",
    "table_section = table_file.select('LS_ESSENTIAL_SKILL')\n",
    "charted_table = table_section.group(\"LS_ESSENTIAL_SKILL\")\n",
    "# remove the nan values\n",
    "charted_table.remove(charted_table.num_rows - 1)\n",
    "charted_table.barh('LS_ESSENTIAL_SKILL')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}