Assignment 6.ipynb

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    "# Title: Assignment 6\n",
    "### Name: Sharmin Akhter\n",
    "### ID: 201891112\n",
    "### Date 31-03-2023\n",
    "### Master of Data Science"
   ]
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.graph_objs as go\n",
    "import plotly.express as px"
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    "# Question 1\n",
    "\n",
    "The radar plot below shows the average fuel efficiency (in units of L / 100 km) for Toyota Corolla vehicles for each month of the year. There is greater fuel efficiency in Summer ( 8 L / 100 km) and worse fuel efficiency in Winter ( 10 L / 100 km), representing roughly a 25% decrease in fuel efficiency.\n",
    "\n",
    "Re-create the above radar plot using Plotly and the fuel-efficiency.csv data set. The radar plot uses a smooth curve to join the points, called a spline (hint: line_shape). Make sure the ordering of the days of the week match the figure above."
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       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>efficiency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>10.420545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>10.691178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10.593444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>9.467789</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8.367721</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>6.760700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>7.709470</td>\n",
       "    </tr>\n",
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       "      <th>8</th>\n",
       "      <td>9</td>\n",
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       "      <th>9</th>\n",
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       "      <th>10</th>\n",
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       "      <td>9.093049</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>9.729146</td>\n",
       "    </tr>\n",
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       "    month  efficiency\n",
       "0       1   10.420545\n",
       "1       2   10.691178\n",
       "2       3   10.593444\n",
       "3       4    9.467789\n",
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       "7       8    7.709470\n",
       "8       9    8.724014\n",
       "9      10    8.892976\n",
       "10     11    9.093049\n",
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    "df = pd.read_csv(\"fuel-efficiency.csv\")\n",
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   ],
   "source": [
    "# Create month names\n",
    "month_names = [\"January\", \"February\", \"March\", \"April\",\n",
    "               \"May\", \"June\", \"July\", \"August\", \"September\", \"October\", \"November\", \"December\"]\n",
    "\n",
    "# Append January data to close the trace line\n",
    "month_names.append(month_names[0])\n",
    "efficiencies = df[\"efficiency\"].tolist()\n",
    "efficiencies.append(efficiencies[0])\n",
    "\n",
    "# Create the Radar Plot\n",
    "fig = go.Figure()\n",
    "\n",
    "fig.add_trace(go.Scatterpolar(\n",
    "    r = efficiencies,\n",
    "    theta = month_names,\n",
    "    mode = \"lines\",\n",
    "    name = \"Fuel Efficiency\",\n",
    "    line_shape = \"spline\",\n",
    "))\n",
    "\n",
    "fig.update_layout(\n",
    "    polar = dict(\n",
    "        radialaxis = dict(\n",
    "            visible = True,\n",
    "            tickvals = [0,2,4,6,8,10,12],\n",
    "            range = [0,12]),\n",
    "        angularaxis = dict(\n",
    "            direction = \"clockwise\",\n",
    "            rotation = 90\n",
    ")))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a725425b",
   "metadata": {},
   "source": [
    "# Question 2\n",
    "\n",
    "The choropleth map below shows the percentage of each province’s population that are university or college students. Plotting relative data works well for spatial data, as plotting the absolute number of students would just highlight the most populous provinces. Instead, we can see that the proportion of students has only modest variations throughout Canada.\n",
    "\n",
    "Re-create the above choropleth map using GeoPandas and the students.csv data set. The map of Canada is given in the canada.shp file and associated files. The colour map uses the “Reds” colour map from ColorBrewer. Make sure to highlight the borders of each province and territory, and to include the title."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 264,
   "id": "6e6dfcd8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: geopandas in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (0.12.2)\n",
      "Requirement already satisfied: packaging in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from geopandas) (21.3)\n",
      "Requirement already satisfied: pandas>=1.0.0 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from geopandas) (1.4.4)\n",
      "Requirement already satisfied: fiona>=1.8 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from geopandas) (1.8.22)\n",
      "Requirement already satisfied: pyproj>=2.6.1.post1 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from geopandas) (3.4.1)\n",
      "Requirement already satisfied: shapely>=1.7 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from geopandas) (2.0.0)\n",
      "Requirement already satisfied: six>=1.7 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (1.16.0)\n",
      "Requirement already satisfied: click-plugins>=1.0 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
      "Requirement already satisfied: attrs>=17 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (21.4.0)\n",
      "Requirement already satisfied: click>=4.0 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (8.0.4)\n",
      "Requirement already satisfied: munch in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
      "Requirement already satisfied: setuptools in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (63.4.1)\n",
      "Requirement already satisfied: certifi in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (2022.9.24)\n",
      "Requirement already satisfied: cligj>=0.5 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
      "Requirement already satisfied: python-dateutil>=2.8.1 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (2022.7.1)\n",
      "Requirement already satisfied: numpy>=1.18.5 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from pandas>=1.0.0->geopandas) (1.21.5)\n",
      "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /Users/sharminrahi/opt/anaconda3/lib/python3.9/site-packages (from packaging->geopandas) (3.0.9)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install geopandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "5e92dae4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "71c5f85a",
   "metadata": {},
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>Total students</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>200382.0</td>\n",
       "      <td>4262635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>295488.0</td>\n",
       "      <td>5000879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Manitoba</td>\n",
       "      <td>63183.0</td>\n",
       "      <td>1342153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>26733.0</td>\n",
       "      <td>775610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>24777.0</td>\n",
       "      <td>510550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>54501.0</td>\n",
       "      <td>969383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>889287.0</td>\n",
       "      <td>14223942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>7428.0</td>\n",
       "      <td>154331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>530340.0</td>\n",
       "      <td>8501833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>59157.0</td>\n",
       "      <td>1132505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Territories</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>118160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Province  Total students  Population\n",
       "0                     Alberta        200382.0     4262635\n",
       "1            British Columbia        295488.0     5000879\n",
       "2                    Manitoba         63183.0     1342153\n",
       "3               New Brunswick         26733.0      775610\n",
       "4   Newfoundland and Labrador         24777.0      510550\n",
       "5                 Nova Scotia         54501.0      969383\n",
       "6                     Ontario        889287.0    14223942\n",
       "7        Prince Edward Island          7428.0      154331\n",
       "8                      Quebec        530340.0     8501833\n",
       "9                Saskatchewan         59157.0     1132505\n",
       "10                Territories          4038.0      118160"
      ]
     },
     "execution_count": 266,
     "metadata": {},
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    }
   ],
   "source": [
    "df1 = pd.read_csv(\"students.csv\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "c29358c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the percentage of each province's population that are university or college students\n",
    "df1['Student_Percentage'] = (df1['Total students'] / df1['Population']) * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "10fc8495",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>MULTIPOLYGON (((8307365.589 2582136.711, 83095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>MULTIPOLYGON (((8435711.754 1679935.966, 84274...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>MULTIPOLYGON (((8470851.646 1624745.011, 84748...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>MULTIPOLYGON (((8176224.537 1722553.460, 81786...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>MULTIPOLYGON (((8399709.494 2261445.703, 84007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>MULTIPOLYGON (((6378815.614 2295412.440, 63918...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manitoba</td>\n",
       "      <td>MULTIPOLYGON (((6039656.509 2636304.343, 60385...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>POLYGON ((5248633.914 2767057.263, 5452957.477...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>POLYGON ((5228304.177 2767597.891, 4890689.760...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>MULTIPOLYGON (((4018904.414 3410247.271, 41867...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yukon</td>\n",
       "      <td>MULTIPOLYGON (((4593284.320 4180399.606, 45086...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>MULTIPOLYGON (((4709778.780 4202808.109, 47095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nunavut</td>\n",
       "      <td>MULTIPOLYGON (((6105932.626 3935743.411, 61114...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Province  \\\n",
       "0   Newfoundland and Labrador   \n",
       "1        Prince Edward Island   \n",
       "2                 Nova Scotia   \n",
       "3               New Brunswick   \n",
       "4                      Quebec   \n",
       "5                     Ontario   \n",
       "6                    Manitoba   \n",
       "7                Saskatchewan   \n",
       "8                     Alberta   \n",
       "9            British Columbia   \n",
       "10                      Yukon   \n",
       "11      Northwest Territories   \n",
       "12                    Nunavut   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((8307365.589 2582136.711, 83095...  \n",
       "1   MULTIPOLYGON (((8435711.754 1679935.966, 84274...  \n",
       "2   MULTIPOLYGON (((8470851.646 1624745.011, 84748...  \n",
       "3   MULTIPOLYGON (((8176224.537 1722553.460, 81786...  \n",
       "4   MULTIPOLYGON (((8399709.494 2261445.703, 84007...  \n",
       "5   MULTIPOLYGON (((6378815.614 2295412.440, 63918...  \n",
       "6   MULTIPOLYGON (((6039656.509 2636304.343, 60385...  \n",
       "7   POLYGON ((5248633.914 2767057.263, 5452957.477...  \n",
       "8   POLYGON ((5228304.177 2767597.891, 4890689.760...  \n",
       "9   MULTIPOLYGON (((4018904.414 3410247.271, 41867...  \n",
       "10  MULTIPOLYGON (((4593284.320 4180399.606, 45086...  \n",
       "11  MULTIPOLYGON (((4709778.780 4202808.109, 47095...  \n",
       "12  MULTIPOLYGON (((6105932.626 3935743.411, 61114...  "
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the shapefile\n",
    "canada_gdf = gpd.read_file(\"canada.shp\")\n",
    "canada_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "cbf1467d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "canada_gdf.boundary.plot(ax = ax, linewidth = 0.8, edgecolor = 'black')\n",
    "ax.set_title('Students as Percentage of Population')\n",
    "ax.set_axis_off()\n",
    "ax.set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "968326e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Total students</th>\n",
       "      <th>Population</th>\n",
       "      <th>Student_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>MULTIPOLYGON (((8307365.589 2582136.711, 83095...</td>\n",
       "      <td>24777.0</td>\n",
       "      <td>510550.0</td>\n",
       "      <td>4.853002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>MULTIPOLYGON (((8435711.754 1679935.966, 84274...</td>\n",
       "      <td>7428.0</td>\n",
       "      <td>154331.0</td>\n",
       "      <td>4.813032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>MULTIPOLYGON (((8470851.646 1624745.011, 84748...</td>\n",
       "      <td>54501.0</td>\n",
       "      <td>969383.0</td>\n",
       "      <td>5.622236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>MULTIPOLYGON (((8176224.537 1722553.460, 81786...</td>\n",
       "      <td>26733.0</td>\n",
       "      <td>775610.0</td>\n",
       "      <td>3.446706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>MULTIPOLYGON (((8399709.494 2261445.703, 84007...</td>\n",
       "      <td>530340.0</td>\n",
       "      <td>8501833.0</td>\n",
       "      <td>6.237949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>MULTIPOLYGON (((6378815.614 2295412.440, 63918...</td>\n",
       "      <td>889287.0</td>\n",
       "      <td>14223942.0</td>\n",
       "      <td>6.252043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manitoba</td>\n",
       "      <td>MULTIPOLYGON (((6039656.509 2636304.343, 60385...</td>\n",
       "      <td>63183.0</td>\n",
       "      <td>1342153.0</td>\n",
       "      <td>4.707585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>POLYGON ((5248633.914 2767057.263, 5452957.477...</td>\n",
       "      <td>59157.0</td>\n",
       "      <td>1132505.0</td>\n",
       "      <td>5.223553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>POLYGON ((5228304.177 2767597.891, 4890689.760...</td>\n",
       "      <td>200382.0</td>\n",
       "      <td>4262635.0</td>\n",
       "      <td>4.700895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>MULTIPOLYGON (((4018904.414 3410247.271, 41867...</td>\n",
       "      <td>295488.0</td>\n",
       "      <td>5000879.0</td>\n",
       "      <td>5.908721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yukon</td>\n",
       "      <td>MULTIPOLYGON (((4593284.320 4180399.606, 45086...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>MULTIPOLYGON (((4709778.780 4202808.109, 47095...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nunavut</td>\n",
       "      <td>MULTIPOLYGON (((6105932.626 3935743.411, 61114...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Territories</td>\n",
       "      <td>None</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>118160.0</td>\n",
       "      <td>3.417400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Province  \\\n",
       "0   Newfoundland and Labrador   \n",
       "1        Prince Edward Island   \n",
       "2                 Nova Scotia   \n",
       "3               New Brunswick   \n",
       "4                      Quebec   \n",
       "5                     Ontario   \n",
       "6                    Manitoba   \n",
       "7                Saskatchewan   \n",
       "8                     Alberta   \n",
       "9            British Columbia   \n",
       "10                      Yukon   \n",
       "11      Northwest Territories   \n",
       "12                    Nunavut   \n",
       "13                Territories   \n",
       "\n",
       "                                             geometry  Total students  \\\n",
       "0   MULTIPOLYGON (((8307365.589 2582136.711, 83095...         24777.0   \n",
       "1   MULTIPOLYGON (((8435711.754 1679935.966, 84274...          7428.0   \n",
       "2   MULTIPOLYGON (((8470851.646 1624745.011, 84748...         54501.0   \n",
       "3   MULTIPOLYGON (((8176224.537 1722553.460, 81786...         26733.0   \n",
       "4   MULTIPOLYGON (((8399709.494 2261445.703, 84007...        530340.0   \n",
       "5   MULTIPOLYGON (((6378815.614 2295412.440, 63918...        889287.0   \n",
       "6   MULTIPOLYGON (((6039656.509 2636304.343, 60385...         63183.0   \n",
       "7   POLYGON ((5248633.914 2767057.263, 5452957.477...         59157.0   \n",
       "8   POLYGON ((5228304.177 2767597.891, 4890689.760...        200382.0   \n",
       "9   MULTIPOLYGON (((4018904.414 3410247.271, 41867...        295488.0   \n",
       "10  MULTIPOLYGON (((4593284.320 4180399.606, 45086...             NaN   \n",
       "11  MULTIPOLYGON (((4709778.780 4202808.109, 47095...             NaN   \n",
       "12  MULTIPOLYGON (((6105932.626 3935743.411, 61114...             NaN   \n",
       "13                                               None          4038.0   \n",
       "\n",
       "    Population  Student_Percentage  \n",
       "0     510550.0            4.853002  \n",
       "1     154331.0            4.813032  \n",
       "2     969383.0            5.622236  \n",
       "3     775610.0            3.446706  \n",
       "4    8501833.0            6.237949  \n",
       "5   14223942.0            6.252043  \n",
       "6    1342153.0            4.707585  \n",
       "7    1132505.0            5.223553  \n",
       "8    4262635.0            4.700895  \n",
       "9    5000879.0            5.908721  \n",
       "10         NaN                 NaN  \n",
       "11         NaN                 NaN  \n",
       "12         NaN                 NaN  \n",
       "13    118160.0            3.417400  "
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the DataFrames\n",
    "merged_gdf = canada_gdf.merge(df1, on = 'Province', how='outer')\n",
    "merged_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "64eeb900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Total students</th>\n",
       "      <th>Population</th>\n",
       "      <th>Student_Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>MULTIPOLYGON (((8307365.589 2582136.711, 83095...</td>\n",
       "      <td>24777.0</td>\n",
       "      <td>510550.0</td>\n",
       "      <td>4.853002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>MULTIPOLYGON (((8435711.754 1679935.966, 84274...</td>\n",
       "      <td>7428.0</td>\n",
       "      <td>154331.0</td>\n",
       "      <td>4.813032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>MULTIPOLYGON (((8470851.646 1624745.011, 84748...</td>\n",
       "      <td>54501.0</td>\n",
       "      <td>969383.0</td>\n",
       "      <td>5.622236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>MULTIPOLYGON (((8176224.537 1722553.460, 81786...</td>\n",
       "      <td>26733.0</td>\n",
       "      <td>775610.0</td>\n",
       "      <td>3.446706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>MULTIPOLYGON (((8399709.494 2261445.703, 84007...</td>\n",
       "      <td>530340.0</td>\n",
       "      <td>8501833.0</td>\n",
       "      <td>6.237949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>MULTIPOLYGON (((6378815.614 2295412.440, 63918...</td>\n",
       "      <td>889287.0</td>\n",
       "      <td>14223942.0</td>\n",
       "      <td>6.252043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manitoba</td>\n",
       "      <td>MULTIPOLYGON (((6039656.509 2636304.343, 60385...</td>\n",
       "      <td>63183.0</td>\n",
       "      <td>1342153.0</td>\n",
       "      <td>4.707585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>POLYGON ((5248633.914 2767057.263, 5452957.477...</td>\n",
       "      <td>59157.0</td>\n",
       "      <td>1132505.0</td>\n",
       "      <td>5.223553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>POLYGON ((5228304.177 2767597.891, 4890689.760...</td>\n",
       "      <td>200382.0</td>\n",
       "      <td>4262635.0</td>\n",
       "      <td>4.700895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>MULTIPOLYGON (((4018904.414 3410247.271, 41867...</td>\n",
       "      <td>295488.0</td>\n",
       "      <td>5000879.0</td>\n",
       "      <td>5.908721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yukon</td>\n",
       "      <td>MULTIPOLYGON (((4593284.320 4180399.606, 45086...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>MULTIPOLYGON (((4709778.780 4202808.109, 47095...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nunavut</td>\n",
       "      <td>MULTIPOLYGON (((6105932.626 3935743.411, 61114...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Territories</td>\n",
       "      <td>None</td>\n",
       "      <td>4038.0</td>\n",
       "      <td>118160.0</td>\n",
       "      <td>3.417400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Province  \\\n",
       "0   Newfoundland and Labrador   \n",
       "1        Prince Edward Island   \n",
       "2                 Nova Scotia   \n",
       "3               New Brunswick   \n",
       "4                      Quebec   \n",
       "5                     Ontario   \n",
       "6                    Manitoba   \n",
       "7                Saskatchewan   \n",
       "8                     Alberta   \n",
       "9            British Columbia   \n",
       "10                      Yukon   \n",
       "11      Northwest Territories   \n",
       "12                    Nunavut   \n",
       "13                Territories   \n",
       "\n",
       "                                             geometry  Total students  \\\n",
       "0   MULTIPOLYGON (((8307365.589 2582136.711, 83095...         24777.0   \n",
       "1   MULTIPOLYGON (((8435711.754 1679935.966, 84274...          7428.0   \n",
       "2   MULTIPOLYGON (((8470851.646 1624745.011, 84748...         54501.0   \n",
       "3   MULTIPOLYGON (((8176224.537 1722553.460, 81786...         26733.0   \n",
       "4   MULTIPOLYGON (((8399709.494 2261445.703, 84007...        530340.0   \n",
       "5   MULTIPOLYGON (((6378815.614 2295412.440, 63918...        889287.0   \n",
       "6   MULTIPOLYGON (((6039656.509 2636304.343, 60385...         63183.0   \n",
       "7   POLYGON ((5248633.914 2767057.263, 5452957.477...         59157.0   \n",
       "8   POLYGON ((5228304.177 2767597.891, 4890689.760...        200382.0   \n",
       "9   MULTIPOLYGON (((4018904.414 3410247.271, 41867...        295488.0   \n",
       "10  MULTIPOLYGON (((4593284.320 4180399.606, 45086...             NaN   \n",
       "11  MULTIPOLYGON (((4709778.780 4202808.109, 47095...             NaN   \n",
       "12  MULTIPOLYGON (((6105932.626 3935743.411, 61114...             NaN   \n",
       "13                                               None          4038.0   \n",
       "\n",
       "    Population  Student_Percentage  \n",
       "0     510550.0            4.853002  \n",
       "1     154331.0            4.813032  \n",
       "2     969383.0            5.622236  \n",
       "3     775610.0            3.446706  \n",
       "4    8501833.0            6.237949  \n",
       "5   14223942.0            6.252043  \n",
       "6    1342153.0            4.707585  \n",
       "7    1132505.0            5.223553  \n",
       "8    4262635.0            4.700895  \n",
       "9    5000879.0            5.908721  \n",
       "10         NaN            0.000000  \n",
       "11         NaN            0.000000  \n",
       "12         NaN            0.000000  \n",
       "13    118160.0            3.417400  "
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_gdf['Student_Percentage'].fillna(0, inplace=True)\n",
    "merged_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "d9383922",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     Newfoundland and Labrador\n",
      "1          Prince Edward Island\n",
      "2                   Nova Scotia\n",
      "3                 New Brunswick\n",
      "4                        Quebec\n",
      "5                       Ontario\n",
      "6                      Manitoba\n",
      "7                  Saskatchewan\n",
      "8                       Alberta\n",
      "9              British Columbia\n",
      "10                        Yukon\n",
      "11        Northwest Territories\n",
      "12                      Nunavut\n",
      "Name: Province, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(canada_gdf['Province'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "8aa90b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                       Alberta\n",
      "1              British Columbia\n",
      "2                      Manitoba\n",
      "3                 New Brunswick\n",
      "4     Newfoundland and Labrador\n",
      "5                   Nova Scotia\n",
      "6                       Ontario\n",
      "7          Prince Edward Island\n",
      "8                        Quebec\n",
      "9                  Saskatchewan\n",
      "10                  Territories\n",
      "Name: Province, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df1['Province'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "46b10678",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the choropleth map\n",
    "fig, ax = plt.subplots(1, 1, figsize = (10, 6))\n",
    "merged_gdf.plot(column = 'Student_Percentage', cmap = 'Reds', \n",
    "                linewidth = 0.8, edgecolor = 'black', legend = False, ax = ax)\n",
    "ax.set_title('Students as Percentage of Population')\n",
    "ax.set_axis_off()\n",
    "ax.set_aspect('equal')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "861c5f95",
   "metadata": {},
   "source": [
    "# Question 3:\n",
    "\n",
    "The map below shows the location of a number of universities throughout Canada. Each university is represented by a point, and the size of each point corresponds to the number of students enrolled at that university. Inset into the map is a close-up view of Southern Ontario, where there is a high density of universities.\n",
    "\n",
    "Using the universities.geojson data set and map of Canada shapefiles, re-create the above visualization with GeoPandas. The universities data set uses the world geodetic system (EPSG:4326) as its coordinate reference system, which is based on latitude and longitude. The Canada shapefiles use the North American Datum geodetic system with Lambert conformal conic projection (EPSG:3347). Its units are in metres. You will need to reconcile the differences between these coordinate reference systems.\n",
    "\n",
    "Make sure to include the inset with a zoom-in view of Southern Ontario. It contains the 6 universities of The University of Toronto, the University of Waterloo, the University of Guelph, McMaster University, Queen’s University, and the University of Western Ontario. The point size for each university should be scaled to the total number of students. The background colour of Canada is a pale yellow (#FFFFE5) and the universities are red (#A50F15). Make sure to highlight the borders of each province and territory. Include the titles for the visualization and Ontario zoom-in."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4d88449",
   "metadata": {},
   "source": [
    "# Load the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "id": "3464d40f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !brew install proj geos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "7e367070",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install cartopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "7eb0b2cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.patches import Rectangle\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "# import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "de7aebb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Institution</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>Total students</th>\n",
       "      <th>Established</th>\n",
       "      <th>Sponsored research income</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>University of Toronto</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>97400</td>\n",
       "      <td>1827</td>\n",
       "      <td>1111035000</td>\n",
       "      <td>43.741667</td>\n",
       "      <td>-79.373333</td>\n",
       "      <td>POINT (-79.37333 43.74167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>University of British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>70340</td>\n",
       "      <td>1908</td>\n",
       "      <td>618024000</td>\n",
       "      <td>49.261111</td>\n",
       "      <td>-123.113889</td>\n",
       "      <td>POINT (-123.11389 49.26111)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>University of Ottawa</td>\n",
       "      <td>Ottawa</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>48800</td>\n",
       "      <td>1848</td>\n",
       "      <td>314609000</td>\n",
       "      <td>45.424722</td>\n",
       "      <td>-75.695000</td>\n",
       "      <td>POINT (-75.69500 45.42472)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Université Laval</td>\n",
       "      <td>Quebec City</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>47690</td>\n",
       "      <td>1663</td>\n",
       "      <td>403841000</td>\n",
       "      <td>46.813889</td>\n",
       "      <td>-71.208056</td>\n",
       "      <td>POINT (-71.20806 46.81389)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Université de Montréal</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>45800</td>\n",
       "      <td>1878</td>\n",
       "      <td>567065000</td>\n",
       "      <td>45.508889</td>\n",
       "      <td>-73.554167</td>\n",
       "      <td>POINT (-73.55417 45.50889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>University of Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>43490</td>\n",
       "      <td>1908</td>\n",
       "      <td>493824000</td>\n",
       "      <td>53.534444</td>\n",
       "      <td>-113.490278</td>\n",
       "      <td>POINT (-113.49028 53.53444)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>University of Western Ontario</td>\n",
       "      <td>London</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>43190</td>\n",
       "      <td>1878</td>\n",
       "      <td>260740000</td>\n",
       "      <td>42.967500</td>\n",
       "      <td>-81.232500</td>\n",
       "      <td>POINT (-81.23250 42.96750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>University of Waterloo</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>42200</td>\n",
       "      <td>1956</td>\n",
       "      <td>212649000</td>\n",
       "      <td>43.466667</td>\n",
       "      <td>-80.516667</td>\n",
       "      <td>POINT (-80.51667 43.46667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>McMaster University</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>37330</td>\n",
       "      <td>1887</td>\n",
       "      <td>391573000</td>\n",
       "      <td>43.256667</td>\n",
       "      <td>-79.869167</td>\n",
       "      <td>POINT (-79.86917 43.25667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>University of Calgary</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>36330</td>\n",
       "      <td>1966</td>\n",
       "      <td>421761000</td>\n",
       "      <td>51.050000</td>\n",
       "      <td>-114.066667</td>\n",
       "      <td>POINT (-114.06667 51.05000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Queen's University</td>\n",
       "      <td>Kingston</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>33800</td>\n",
       "      <td>1841</td>\n",
       "      <td>175929000</td>\n",
       "      <td>44.234722</td>\n",
       "      <td>-76.510833</td>\n",
       "      <td>POINT (-76.51083 44.23472)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>University of Guelph</td>\n",
       "      <td>Guelph</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>31180</td>\n",
       "      <td>1964</td>\n",
       "      <td>149829000</td>\n",
       "      <td>43.535833</td>\n",
       "      <td>-80.228889</td>\n",
       "      <td>POINT (-80.22889 43.53583)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>University of Manitoba</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>29800</td>\n",
       "      <td>1877</td>\n",
       "      <td>212295000</td>\n",
       "      <td>49.884444</td>\n",
       "      <td>-97.146389</td>\n",
       "      <td>POINT (-97.14639 49.88444)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>University of Saskatchewan</td>\n",
       "      <td>Saskatoon</td>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>22390</td>\n",
       "      <td>1907</td>\n",
       "      <td>175057000</td>\n",
       "      <td>52.139722</td>\n",
       "      <td>-106.686111</td>\n",
       "      <td>POINT (-106.68611 52.13972)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>University of Victoria</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>22060</td>\n",
       "      <td>1903</td>\n",
       "      <td>118599000</td>\n",
       "      <td>48.428333</td>\n",
       "      <td>-123.364722</td>\n",
       "      <td>POINT (-123.36472 48.42833)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dalhousie University</td>\n",
       "      <td>Halifax</td>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>21320</td>\n",
       "      <td>1818</td>\n",
       "      <td>141311000</td>\n",
       "      <td>44.647500</td>\n",
       "      <td>-63.590556</td>\n",
       "      <td>POINT (-63.59056 44.64750)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Memorial University of Newfoundland</td>\n",
       "      <td>St. John's</td>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>18910</td>\n",
       "      <td>1925</td>\n",
       "      <td>116796000</td>\n",
       "      <td>47.482222</td>\n",
       "      <td>-52.796944</td>\n",
       "      <td>POINT (-52.79694 47.48222)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>University of New Brunswick</td>\n",
       "      <td>Fredericton</td>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>10030</td>\n",
       "      <td>1785</td>\n",
       "      <td>38694000</td>\n",
       "      <td>45.963611</td>\n",
       "      <td>-66.643056</td>\n",
       "      <td>POINT (-66.64306 45.96361)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>University of Prince Edward Island</td>\n",
       "      <td>Charlottetown</td>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>5540</td>\n",
       "      <td>1969</td>\n",
       "      <td>11363000</td>\n",
       "      <td>46.240278</td>\n",
       "      <td>-63.134722</td>\n",
       "      <td>POINT (-63.13472 46.24028)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Institution           City  \\\n",
       "0                 University of Toronto        Toronto   \n",
       "1        University of British Columbia      Vancouver   \n",
       "2                  University of Ottawa         Ottawa   \n",
       "3                      Université Laval    Quebec City   \n",
       "4                Université de Montréal       Montreal   \n",
       "5                 University of Alberta       Edmonton   \n",
       "6         University of Western Ontario         London   \n",
       "7                University of Waterloo       Waterloo   \n",
       "8                   McMaster University       Hamilton   \n",
       "9                 University of Calgary        Calgary   \n",
       "10                   Queen's University       Kingston   \n",
       "11                 University of Guelph         Guelph   \n",
       "12               University of Manitoba       Winnipeg   \n",
       "13           University of Saskatchewan      Saskatoon   \n",
       "14               University of Victoria       Victoria   \n",
       "15                 Dalhousie University        Halifax   \n",
       "16  Memorial University of Newfoundland     St. John's   \n",
       "17          University of New Brunswick    Fredericton   \n",
       "18   University of Prince Edward Island  Charlottetown   \n",
       "\n",
       "                     Province  Total students  Established  \\\n",
       "0                     Ontario           97400         1827   \n",
       "1            British Columbia           70340         1908   \n",
       "2                     Ontario           48800         1848   \n",
       "3                      Quebec           47690         1663   \n",
       "4                      Quebec           45800         1878   \n",
       "5                     Alberta           43490         1908   \n",
       "6                     Ontario           43190         1878   \n",
       "7                     Ontario           42200         1956   \n",
       "8                     Ontario           37330         1887   \n",
       "9                     Alberta           36330         1966   \n",
       "10                    Ontario           33800         1841   \n",
       "11                    Ontario           31180         1964   \n",
       "12                   Manitoba           29800         1877   \n",
       "13               Saskatchewan           22390         1907   \n",
       "14           British Columbia           22060         1903   \n",
       "15                Nova Scotia           21320         1818   \n",
       "16  Newfoundland and Labrador           18910         1925   \n",
       "17              New Brunswick           10030         1785   \n",
       "18       Prince Edward Island            5540         1969   \n",
       "\n",
       "    Sponsored research income   Latitude   Longitude  \\\n",
       "0                  1111035000  43.741667  -79.373333   \n",
       "1                   618024000  49.261111 -123.113889   \n",
       "2                   314609000  45.424722  -75.695000   \n",
       "3                   403841000  46.813889  -71.208056   \n",
       "4                   567065000  45.508889  -73.554167   \n",
       "5                   493824000  53.534444 -113.490278   \n",
       "6                   260740000  42.967500  -81.232500   \n",
       "7                   212649000  43.466667  -80.516667   \n",
       "8                   391573000  43.256667  -79.869167   \n",
       "9                   421761000  51.050000 -114.066667   \n",
       "10                  175929000  44.234722  -76.510833   \n",
       "11                  149829000  43.535833  -80.228889   \n",
       "12                  212295000  49.884444  -97.146389   \n",
       "13                  175057000  52.139722 -106.686111   \n",
       "14                  118599000  48.428333 -123.364722   \n",
       "15                  141311000  44.647500  -63.590556   \n",
       "16                  116796000  47.482222  -52.796944   \n",
       "17                   38694000  45.963611  -66.643056   \n",
       "18                   11363000  46.240278  -63.134722   \n",
       "\n",
       "                       geometry  \n",
       "0    POINT (-79.37333 43.74167)  \n",
       "1   POINT (-123.11389 49.26111)  \n",
       "2    POINT (-75.69500 45.42472)  \n",
       "3    POINT (-71.20806 46.81389)  \n",
       "4    POINT (-73.55417 45.50889)  \n",
       "5   POINT (-113.49028 53.53444)  \n",
       "6    POINT (-81.23250 42.96750)  \n",
       "7    POINT (-80.51667 43.46667)  \n",
       "8    POINT (-79.86917 43.25667)  \n",
       "9   POINT (-114.06667 51.05000)  \n",
       "10   POINT (-76.51083 44.23472)  \n",
       "11   POINT (-80.22889 43.53583)  \n",
       "12   POINT (-97.14639 49.88444)  \n",
       "13  POINT (-106.68611 52.13972)  \n",
       "14  POINT (-123.36472 48.42833)  \n",
       "15   POINT (-63.59056 44.64750)  \n",
       "16   POINT (-52.79694 47.48222)  \n",
       "17   POINT (-66.64306 45.96361)  \n",
       "18   POINT (-63.13472 46.24028)  "
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the universities GeoJSON file\n",
    "universities_gdf = gpd.read_file(\"universities.geojson\")\n",
    "universities_gdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "e0329440",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Institution                  0\n",
       "City                         0\n",
       "Province                     0\n",
       "Total students               0\n",
       "Established                  0\n",
       "Sponsored research income    0\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "geometry                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universities_gdf.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "3f7eebe8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Convert the universities GeoDataFrame to EPSG:3347\n",
    "universities_gdf1 = universities_gdf.to_crs(epsg=3347)\n",
    "\n",
    "# Convert the Canada GeoDataFrame to EPSG:3347\n",
    "canada_gdf1 = canada_gdf.to_crs(epsg=3347)\n",
    "\n",
    "#universities_gdf = universities_gdf.to_crs(epsg=3347)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "094361ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Institution</th>\n",
       "      <th>City</th>\n",
       "      <th>Province</th>\n",
       "      <th>Total students</th>\n",
       "      <th>Established</th>\n",
       "      <th>Sponsored research income</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>University of Toronto</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>97400</td>\n",
       "      <td>1827</td>\n",
       "      <td>1111035000</td>\n",
       "      <td>43.741667</td>\n",
       "      <td>-79.373333</td>\n",
       "      <td>POINT (7223652.871 938729.334)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>University of British Columbia</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>70340</td>\n",
       "      <td>1908</td>\n",
       "      <td>618024000</td>\n",
       "      <td>49.261111</td>\n",
       "      <td>-123.113889</td>\n",
       "      <td>POINT (4018139.391 2004988.791)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>University of Ottawa</td>\n",
       "      <td>Ottawa</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>48800</td>\n",
       "      <td>1848</td>\n",
       "      <td>314609000</td>\n",
       "      <td>45.424722</td>\n",
       "      <td>-75.695000</td>\n",
       "      <td>POINT (7471319.555 1191039.904)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Université Laval</td>\n",
       "      <td>Quebec City</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>47690</td>\n",
       "      <td>1663</td>\n",
       "      <td>403841000</td>\n",
       "      <td>46.813889</td>\n",
       "      <td>-71.208056</td>\n",
       "      <td>POINT (7763100.367 1440936.052)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Université de Montréal</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>Quebec</td>\n",
       "      <td>45800</td>\n",
       "      <td>1878</td>\n",
       "      <td>567065000</td>\n",
       "      <td>45.508889</td>\n",
       "      <td>-73.554167</td>\n",
       "      <td>POINT (7632529.305 1245697.899)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>University of Alberta</td>\n",
       "      <td>Edmonton</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>43490</td>\n",
       "      <td>1908</td>\n",
       "      <td>493824000</td>\n",
       "      <td>53.534444</td>\n",
       "      <td>-113.490278</td>\n",
       "      <td>POINT (4814940.032 2167169.341)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>University of Western Ontario</td>\n",
       "      <td>London</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>43190</td>\n",
       "      <td>1878</td>\n",
       "      <td>260740000</td>\n",
       "      <td>42.967500</td>\n",
       "      <td>-81.232500</td>\n",
       "      <td>POINT (7087549.999 824015.562)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>University of Waterloo</td>\n",
       "      <td>Waterloo</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>42200</td>\n",
       "      <td>1956</td>\n",
       "      <td>212649000</td>\n",
       "      <td>43.466667</td>\n",
       "      <td>-80.516667</td>\n",
       "      <td>POINT (7136575.929 890357.389)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>McMaster University</td>\n",
       "      <td>Hamilton</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>37330</td>\n",
       "      <td>1887</td>\n",
       "      <td>391573000</td>\n",
       "      <td>43.256667</td>\n",
       "      <td>-79.869167</td>\n",
       "      <td>POINT (7193875.555 876654.071)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>University of Calgary</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Alberta</td>\n",
       "      <td>36330</td>\n",
       "      <td>1966</td>\n",
       "      <td>421761000</td>\n",
       "      <td>51.050000</td>\n",
       "      <td>-114.066667</td>\n",
       "      <td>POINT (4686047.866 1923058.299)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Queen's University</td>\n",
       "      <td>Kingston</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>33800</td>\n",
       "      <td>1841</td>\n",
       "      <td>175929000</td>\n",
       "      <td>44.234722</td>\n",
       "      <td>-76.510833</td>\n",
       "      <td>POINT (7440663.631 1044381.343)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>University of Guelph</td>\n",
       "      <td>Guelph</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>31180</td>\n",
       "      <td>1964</td>\n",
       "      <td>149829000</td>\n",
       "      <td>43.535833</td>\n",
       "      <td>-80.228889</td>\n",
       "      <td>POINT (7158627.359 902392.953)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>University of Manitoba</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>Manitoba</td>\n",
       "      <td>29800</td>\n",
       "      <td>1877</td>\n",
       "      <td>212295000</td>\n",
       "      <td>49.884444</td>\n",
       "      <td>-97.146389</td>\n",
       "      <td>POINT (5822258.685 1542875.396)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>University of Saskatchewan</td>\n",
       "      <td>Saskatoon</td>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>22390</td>\n",
       "      <td>1907</td>\n",
       "      <td>175057000</td>\n",
       "      <td>52.139722</td>\n",
       "      <td>-106.686111</td>\n",
       "      <td>POINT (5205599.444 1892631.978)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>University of Victoria</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>British Columbia</td>\n",
       "      <td>22060</td>\n",
       "      <td>1903</td>\n",
       "      <td>118599000</td>\n",
       "      <td>48.428333</td>\n",
       "      <td>-123.364722</td>\n",
       "      <td>POINT (3958037.462 1932089.542)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Dalhousie University</td>\n",
       "      <td>Halifax</td>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>21320</td>\n",
       "      <td>1818</td>\n",
       "      <td>141311000</td>\n",
       "      <td>44.647500</td>\n",
       "      <td>-63.590556</td>\n",
       "      <td>POINT (8411532.325 1440510.093)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Memorial University of Newfoundland</td>\n",
       "      <td>St. John's</td>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>18910</td>\n",
       "      <td>1925</td>\n",
       "      <td>116796000</td>\n",
       "      <td>47.482222</td>\n",
       "      <td>-52.796944</td>\n",
       "      <td>POINT (8979957.202 2141390.807)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>University of New Brunswick</td>\n",
       "      <td>Fredericton</td>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>10030</td>\n",
       "      <td>1785</td>\n",
       "      <td>38694000</td>\n",
       "      <td>45.963611</td>\n",
       "      <td>-66.643056</td>\n",
       "      <td>POINT (8128868.407 1476838.232)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>University of Prince Edward Island</td>\n",
       "      <td>Charlottetown</td>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>5540</td>\n",
       "      <td>1969</td>\n",
       "      <td>11363000</td>\n",
       "      <td>46.240278</td>\n",
       "      <td>-63.134722</td>\n",
       "      <td>POINT (8366289.897 1618187.115)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Institution           City  \\\n",
       "0                 University of Toronto        Toronto   \n",
       "1        University of British Columbia      Vancouver   \n",
       "2                  University of Ottawa         Ottawa   \n",
       "3                      Université Laval    Quebec City   \n",
       "4                Université de Montréal       Montreal   \n",
       "5                 University of Alberta       Edmonton   \n",
       "6         University of Western Ontario         London   \n",
       "7                University of Waterloo       Waterloo   \n",
       "8                   McMaster University       Hamilton   \n",
       "9                 University of Calgary        Calgary   \n",
       "10                   Queen's University       Kingston   \n",
       "11                 University of Guelph         Guelph   \n",
       "12               University of Manitoba       Winnipeg   \n",
       "13           University of Saskatchewan      Saskatoon   \n",
       "14               University of Victoria       Victoria   \n",
       "15                 Dalhousie University        Halifax   \n",
       "16  Memorial University of Newfoundland     St. John's   \n",
       "17          University of New Brunswick    Fredericton   \n",
       "18   University of Prince Edward Island  Charlottetown   \n",
       "\n",
       "                     Province  Total students  Established  \\\n",
       "0                     Ontario           97400         1827   \n",
       "1            British Columbia           70340         1908   \n",
       "2                     Ontario           48800         1848   \n",
       "3                      Quebec           47690         1663   \n",
       "4                      Quebec           45800         1878   \n",
       "5                     Alberta           43490         1908   \n",
       "6                     Ontario           43190         1878   \n",
       "7                     Ontario           42200         1956   \n",
       "8                     Ontario           37330         1887   \n",
       "9                     Alberta           36330         1966   \n",
       "10                    Ontario           33800         1841   \n",
       "11                    Ontario           31180         1964   \n",
       "12                   Manitoba           29800         1877   \n",
       "13               Saskatchewan           22390         1907   \n",
       "14           British Columbia           22060         1903   \n",
       "15                Nova Scotia           21320         1818   \n",
       "16  Newfoundland and Labrador           18910         1925   \n",
       "17              New Brunswick           10030         1785   \n",
       "18       Prince Edward Island            5540         1969   \n",
       "\n",
       "    Sponsored research income   Latitude   Longitude  \\\n",
       "0                  1111035000  43.741667  -79.373333   \n",
       "1                   618024000  49.261111 -123.113889   \n",
       "2                   314609000  45.424722  -75.695000   \n",
       "3                   403841000  46.813889  -71.208056   \n",
       "4                   567065000  45.508889  -73.554167   \n",
       "5                   493824000  53.534444 -113.490278   \n",
       "6                   260740000  42.967500  -81.232500   \n",
       "7                   212649000  43.466667  -80.516667   \n",
       "8                   391573000  43.256667  -79.869167   \n",
       "9                   421761000  51.050000 -114.066667   \n",
       "10                  175929000  44.234722  -76.510833   \n",
       "11                  149829000  43.535833  -80.228889   \n",
       "12                  212295000  49.884444  -97.146389   \n",
       "13                  175057000  52.139722 -106.686111   \n",
       "14                  118599000  48.428333 -123.364722   \n",
       "15                  141311000  44.647500  -63.590556   \n",
       "16                  116796000  47.482222  -52.796944   \n",
       "17                   38694000  45.963611  -66.643056   \n",
       "18                   11363000  46.240278  -63.134722   \n",
       "\n",
       "                           geometry  \n",
       "0    POINT (7223652.871 938729.334)  \n",
       "1   POINT (4018139.391 2004988.791)  \n",
       "2   POINT (7471319.555 1191039.904)  \n",
       "3   POINT (7763100.367 1440936.052)  \n",
       "4   POINT (7632529.305 1245697.899)  \n",
       "5   POINT (4814940.032 2167169.341)  \n",
       "6    POINT (7087549.999 824015.562)  \n",
       "7    POINT (7136575.929 890357.389)  \n",
       "8    POINT (7193875.555 876654.071)  \n",
       "9   POINT (4686047.866 1923058.299)  \n",
       "10  POINT (7440663.631 1044381.343)  \n",
       "11   POINT (7158627.359 902392.953)  \n",
       "12  POINT (5822258.685 1542875.396)  \n",
       "13  POINT (5205599.444 1892631.978)  \n",
       "14  POINT (3958037.462 1932089.542)  \n",
       "15  POINT (8411532.325 1440510.093)  \n",
       "16  POINT (8979957.202 2141390.807)  \n",
       "17  POINT (8128868.407 1476838.232)  \n",
       "18  POINT (8366289.897 1618187.115)  "
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "universities_gdf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "f66a9bd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Newfoundland and Labrador</td>\n",
       "      <td>MULTIPOLYGON (((8307365.589 2582136.711, 83095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Prince Edward Island</td>\n",
       "      <td>MULTIPOLYGON (((8435711.754 1679935.966, 84274...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Nova Scotia</td>\n",
       "      <td>MULTIPOLYGON (((8470851.646 1624745.011, 84748...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>New Brunswick</td>\n",
       "      <td>MULTIPOLYGON (((8176224.537 1722553.460, 81786...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Quebec</td>\n",
       "      <td>MULTIPOLYGON (((8399709.494 2261445.703, 84007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Ontario</td>\n",
       "      <td>MULTIPOLYGON (((6378815.614 2295412.440, 63918...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Manitoba</td>\n",
       "      <td>MULTIPOLYGON (((6039656.509 2636304.343, 60385...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Saskatchewan</td>\n",
       "      <td>POLYGON ((5248633.914 2767057.263, 5452957.477...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alberta</td>\n",
       "      <td>POLYGON ((5228304.177 2767597.891, 4890689.760...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>British Columbia</td>\n",
       "      <td>MULTIPOLYGON (((4018904.414 3410247.271, 41867...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Yukon</td>\n",
       "      <td>MULTIPOLYGON (((4593284.320 4180399.606, 45086...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>MULTIPOLYGON (((4709778.780 4202808.109, 47095...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Nunavut</td>\n",
       "      <td>MULTIPOLYGON (((6105932.626 3935743.411, 61114...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Province  \\\n",
       "0   Newfoundland and Labrador   \n",
       "1        Prince Edward Island   \n",
       "2                 Nova Scotia   \n",
       "3               New Brunswick   \n",
       "4                      Quebec   \n",
       "5                     Ontario   \n",
       "6                    Manitoba   \n",
       "7                Saskatchewan   \n",
       "8                     Alberta   \n",
       "9            British Columbia   \n",
       "10                      Yukon   \n",
       "11      Northwest Territories   \n",
       "12                    Nunavut   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((8307365.589 2582136.711, 83095...  \n",
       "1   MULTIPOLYGON (((8435711.754 1679935.966, 84274...  \n",
       "2   MULTIPOLYGON (((8470851.646 1624745.011, 84748...  \n",
       "3   MULTIPOLYGON (((8176224.537 1722553.460, 81786...  \n",
       "4   MULTIPOLYGON (((8399709.494 2261445.703, 84007...  \n",
       "5   MULTIPOLYGON (((6378815.614 2295412.440, 63918...  \n",
       "6   MULTIPOLYGON (((6039656.509 2636304.343, 60385...  \n",
       "7   POLYGON ((5248633.914 2767057.263, 5452957.477...  \n",
       "8   POLYGON ((5228304.177 2767597.891, 4890689.760...  \n",
       "9   MULTIPOLYGON (((4018904.414 3410247.271, 41867...  \n",
       "10  MULTIPOLYGON (((4593284.320 4180399.606, 45086...  \n",
       "11  MULTIPOLYGON (((4709778.780 4202808.109, 47095...  \n",
       "12  MULTIPOLYGON (((6105932.626 3935743.411, 61114...  "
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canada_gdf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e4af5ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "7dc0e682",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
    "from shapely.geometry import box\n",
    "\n",
    "# Filter the universities GeoDataFrame to include only the universities of interest\n",
    "universities_of_interest = ['University of Toronto', \n",
    "                            'University of Waterloo', 'University of Guelph',\n",
    "                            'McMaster University', \"Queen's University\", 'University of Western Ontario']\n",
    "filtered_universities_gdf = universities_gdf1[universities_gdf1['Institution'].isin(universities_of_interest)]\n",
    "\n",
    "# Calculate the bounding box for the universities of interest\n",
    "minx, miny, maxx, maxy = filtered_universities_gdf.total_bounds\n",
    "buffer = 50000  # Add a buffer to the bounding box\n",
    "x1, x2, y1, y2 = minx - buffer, maxx + buffer, miny - buffer, maxy + buffer\n",
    "\n",
    "# Plot the base map of Canada\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "canada_gdf1.plot(ax=ax, edgecolor='black', facecolor='#FFFFE5', linewidth=0.8)\n",
    "\n",
    "# Plot the universities on the base map\n",
    "universities_gdf1.plot(ax=ax, \n",
    "                       markersize=universities_gdf1['Total students']/100, \n",
    "                       color='#A50F15', edgecolor='black', linewidth=0.5, alpha=0.8)\n",
    "ax.set_title('Canadian Universities by Student Population')\n",
    "\n",
    "# Create an inset map focusing on Southern Ontario\n",
    "axins = inset_axes(ax, width=\"30%\", height=\"30%\",bbox_to_anchor=(-0.1, 0.01, 1, 1), bbox_transform=ax.transAxes,loc='upper left')\n",
    "canada_gdf1.plot(ax=axins, edgecolor='black', facecolor='#FFFFE5', linewidth=0.8)\n",
    "filtered_universities_gdf.plot(ax=axins, \n",
    "                               markersize=filtered_universities_gdf['Total students'] / 100, \n",
    "                               color='#A50F15', edgecolor='black', linewidth=0.5, alpha=0.7)\n",
    "\n",
    "# Set the extent of the inset map\n",
    "axins.set_xlim(x1, x2)\n",
    "axins.set_ylim(y1, y2)\n",
    "axins.set_axis_off()\n",
    "\n",
    "# Add a title to the inset map\n",
    "axins.set_title('Ontario Zoom-In', fontsize=10)\n",
    "\n",
    "rect = Rectangle((0, 0), 1, 1, transform=axins.transAxes, \n",
    "                 zorder=1000, facecolor='none', edgecolor='black', linewidth=1)\n",
    "\n",
    "axins.add_artist(rect)\n",
    "ax.set_axis_off()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dfeda0b4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "My kernel",
   "language": "python",
   "name": "sharminrahi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}