Plot Maps in Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot maps in Python\n",
    "\n",
    "In this notebook, I'll explore various ways to plot data points with `latitude` and `longitude` values on Maps using various libraries. I took the dataset from [Kaggle](https://www.kaggle.com/darshank2019/business). I then downloaded the zip file, extracted the dataset and saved it as `dataset.csv` inside the `data` folder."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, I'll import the general libraries that are needed including `numpy`, `pandas` and `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll now import the dataset and use `head()` to see the various columns and their values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>address</th>\n",
       "      <th>name</th>\n",
       "      <th>stars</th>\n",
       "      <th>longitude</th>\n",
       "      <th>postal_code</th>\n",
       "      <th>business_id</th>\n",
       "      <th>latitude</th>\n",
       "      <th>review_count</th>\n",
       "      <th>categories</th>\n",
       "      <th>city</th>\n",
       "      <th>is_open</th>\n",
       "      <th>hours</th>\n",
       "      <th>attributes</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>b'2818 E Camino Acequia Drive'</td>\n",
       "      <td>b'Arizona Biltmore Golf Club'</td>\n",
       "      <td>3.0</td>\n",
       "      <td>-112.018481</td>\n",
       "      <td>b'85016'</td>\n",
       "      <td>b'1SWheh84yJXfytovILXOAQ'</td>\n",
       "      <td>33.522143</td>\n",
       "      <td>5</td>\n",
       "      <td>b'Golf, Active Life'</td>\n",
       "      <td>b'Phoenix'</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'GoodForKids': 'False'}</td>\n",
       "      <td>b'AZ'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>b'30 Eglinton Avenue W'</td>\n",
       "      <td>b'Emerald Chinese Restaurant'</td>\n",
       "      <td>2.5</td>\n",
       "      <td>-79.652289</td>\n",
       "      <td>b'L5R 3E7'</td>\n",
       "      <td>b'QXAEGFB4oINsVuTFxEYKFQ'</td>\n",
       "      <td>43.605499</td>\n",
       "      <td>128</td>\n",
       "      <td>b'Specialty Food, Restaurants, Dim Sum, Import...</td>\n",
       "      <td>b'Mississauga'</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W...</td>\n",
       "      <td>{'RestaurantsReservations': 'True', 'GoodForMe...</td>\n",
       "      <td>b'ON'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>b'10110 Johnston Rd, Ste 15'</td>\n",
       "      <td>b'Musashi Japanese Restaurant'</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-80.859132</td>\n",
       "      <td>b'28210'</td>\n",
       "      <td>b'gnKjwL_1w79qoiV3IC_xQQ'</td>\n",
       "      <td>35.092564</td>\n",
       "      <td>170</td>\n",
       "      <td>b'Sushi Bars, Restaurants, Japanese'</td>\n",
       "      <td>b'Charlotte'</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Monday': '17:30-21:30', 'Wednesday': '17:30-...</td>\n",
       "      <td>{'GoodForKids': 'True', 'NoiseLevel': \"u'avera...</td>\n",
       "      <td>b'NC'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>b'15655 W Roosevelt St, Ste 237'</td>\n",
       "      <td>b'Farmers Insurance - Paul Lorenz'</td>\n",
       "      <td>5.0</td>\n",
       "      <td>-112.395596</td>\n",
       "      <td>b'85338'</td>\n",
       "      <td>b'xvX2CttrVhyG2z1dFg_0xw'</td>\n",
       "      <td>33.455613</td>\n",
       "      <td>3</td>\n",
       "      <td>b'Insurance, Financial Services'</td>\n",
       "      <td>b'Goodyear'</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b'AZ'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>b'4209 Stuart Andrew Blvd, Ste F'</td>\n",
       "      <td>b'Queen City Plumbing'</td>\n",
       "      <td>4.0</td>\n",
       "      <td>-80.887223</td>\n",
       "      <td>b'28217'</td>\n",
       "      <td>b'HhyxOkGAM07SRYtlQ4wMFQ'</td>\n",
       "      <td>35.190012</td>\n",
       "      <td>4</td>\n",
       "      <td>b'Plumbing, Shopping, Local Services, Home Ser...</td>\n",
       "      <td>b'Charlotte'</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Monday': '7:0-23:0', 'Tuesday': '7:0-23:0', ...</td>\n",
       "      <td>{'BusinessAcceptsBitcoin': 'False', 'ByAppoint...</td>\n",
       "      <td>b'NC'</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             address                                name  \\\n",
       "0     b'2818 E Camino Acequia Drive'       b'Arizona Biltmore Golf Club'   \n",
       "1            b'30 Eglinton Avenue W'       b'Emerald Chinese Restaurant'   \n",
       "2       b'10110 Johnston Rd, Ste 15'      b'Musashi Japanese Restaurant'   \n",
       "3   b'15655 W Roosevelt St, Ste 237'  b'Farmers Insurance - Paul Lorenz'   \n",
       "4  b'4209 Stuart Andrew Blvd, Ste F'              b'Queen City Plumbing'   \n",
       "\n",
       "   stars   longitude postal_code                business_id   latitude  \\\n",
       "0    3.0 -112.018481    b'85016'  b'1SWheh84yJXfytovILXOAQ'  33.522143   \n",
       "1    2.5  -79.652289  b'L5R 3E7'  b'QXAEGFB4oINsVuTFxEYKFQ'  43.605499   \n",
       "2    4.0  -80.859132    b'28210'  b'gnKjwL_1w79qoiV3IC_xQQ'  35.092564   \n",
       "3    5.0 -112.395596    b'85338'  b'xvX2CttrVhyG2z1dFg_0xw'  33.455613   \n",
       "4    4.0  -80.887223    b'28217'  b'HhyxOkGAM07SRYtlQ4wMFQ'  35.190012   \n",
       "\n",
       "   review_count                                         categories  \\\n",
       "0             5                               b'Golf, Active Life'   \n",
       "1           128  b'Specialty Food, Restaurants, Dim Sum, Import...   \n",
       "2           170               b'Sushi Bars, Restaurants, Japanese'   \n",
       "3             3                   b'Insurance, Financial Services'   \n",
       "4             4  b'Plumbing, Shopping, Local Services, Home Ser...   \n",
       "\n",
       "             city  is_open                                              hours  \\\n",
       "0      b'Phoenix'        0                                                NaN   \n",
       "1  b'Mississauga'        1  {'Monday': '9:0-0:0', 'Tuesday': '9:0-0:0', 'W...   \n",
       "2    b'Charlotte'        1  {'Monday': '17:30-21:30', 'Wednesday': '17:30-...   \n",
       "3     b'Goodyear'        1  {'Monday': '8:0-17:0', 'Tuesday': '8:0-17:0', ...   \n",
       "4    b'Charlotte'        1  {'Monday': '7:0-23:0', 'Tuesday': '7:0-23:0', ...   \n",
       "\n",
       "                                          attributes  state  \n",
       "0                           {'GoodForKids': 'False'}  b'AZ'  \n",
       "1  {'RestaurantsReservations': 'True', 'GoodForMe...  b'ON'  \n",
       "2  {'GoodForKids': 'True', 'NoiseLevel': \"u'avera...  b'NC'  \n",
       "3                                                NaN  b'AZ'  \n",
       "4  {'BusinessAcceptsBitcoin': 'False', 'ByAppoint...  b'NC'  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv('data/dataset.csv')\n",
    "dataset.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The latitude and lognitude values can be directly extracted from the respective columns. I'll also extract the states from the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "latitudes = dataset.loc[:, 'latitude']\n",
    "longitudes = dataset.loc[:, 'longitude']\n",
    "states = dataset['state']\n",
    "unique_states = states.unique()\n",
    "names = dataset['address']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'll also need to know the minimum and maximum values for the latitudes and longitudes so that if the map is a world map, I can zoom to the area where the locations exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_latitude = latitudes.min()\n",
    "max_latitude = latitudes.max()\n",
    "min_longitude = longitudes.min()\n",
    "max_longitude = longitudes.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gmplot\n",
    "\n",
    "Using `gmplot`, we can use Google Maps to plot the data and save the file as an HTML which we can then open in the browser. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gmplot \n",
    "\n",
    "gmap = gmplot.GoogleMapPlotter(35, -102, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I begin by plotting the first 1,000 points from the dataset on the map."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmap.scatter(latitudes[:1000], longitudes[:1000], 'red', size = 10)\n",
    "gmap.draw('data/gmplot.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now open the html in the browser. As we can see, the points are plotted on the map but the whole map is covered with the text `For development purposes only`. However, if we want to remove that, we need to supply an API Key which we can get after registering for Google APIs access and adding billing information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmap.apikey = '<API-KEY>'\n",
    "gmap.draw('data/gmplot.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The text is now gone and the darkended background is also removed. Next, let's try to plot all the points in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmap.scatter(latitudes, longitudes, 'blue', size = 10)\n",
    "gmap.draw('data/gmplot.html')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even though the `html` page was generated. Each time I tried to open it, it would load for a few seconds and then get unresponsive. The page is not able to handle so many points at the same time."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geopandas\n",
    "\n",
    "It's an opensource library that lets us make geo plots. It extends the `pandas` datatypes further for its spacial use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import descartes\n",
    "from shapely.geometry import Point"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It expects the latitude and longitude values as `Point` values. Using colors is the easiest for geopandas as you can simply pass a list of colors from matplotlib and it automatically assigns the colors to each point respectively.\n",
    "\n",
    "I'll create a list of colors for each state and save it to `colors` variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_points = [Point(xy) for xy in zip(longitudes, latitudes)]\n",
    "colormap = cm.rainbow(np.linspace(0, 1, len(unique_states)))\n",
    "\n",
    "colors = []\n",
    "for i, state in enumerate(states):\n",
    "    colors.append(colormap[np.where(unique_states == state)[0][0]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `gpd.datasets.get_path('naturalearth_lowres')` we can generate the worldmap. I've set its background as `lightgrey` and lines as `black`. Using `GeoDataFrame`, we can plot the points. It requires the `crs` to be `{'init': 'epsg:4326'}`. Finally, I disabled the axis and plot the max and min values for the plot based on extreme latitude and longitude values we calculated earlier. I'll plot the first 1000 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23.204642003000004, 56.2999426916)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "ax = world.plot(color = 'lightgrey', linewidth = 0.5, edgecolor = 'black', figsize = (20, 12))\n",
    "\n",
    "geo_df = gpd.GeoDataFrame(dataset.iloc[:1000,:], crs = {'init': 'epsg:4326'}, geometry = data_points[:1000])\n",
    "geo_df.plot(markersize = 10, c = colors[:1000], ax = ax)\n",
    "\n",
    "ax.axis('off')\n",
    "ax.set_xlim(min_longitude - 10, max_longitude + 10)\n",
    "ax.set_ylim(min_latitude - 10, max_latitude + 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each location is plot on the map and the colors are grouped according to their `state` values. Next, let's plot all the points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23.204642003000004, 56.2999426916)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "ax = world.plot(color = 'lightgrey', linewidth = 0.5, edgecolor = 'black', figsize = (20, 12))\n",
    "\n",
    "geo_df = gpd.GeoDataFrame(dataset, crs = {'init': 'epsg:4326'}, geometry = data_points)\n",
    "geo_df.plot(markersize = 10, c = colors, ax = ax)\n",
    "\n",
    "ax.axis('off')\n",
    "ax.set_xlim(min_longitude - 10, max_longitude + 10)\n",
    "ax.set_ylim(min_latitude - 10, max_latitude + 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data points have increased incredibly. Some colors appear to be same but that is primarily due to how the list of colors was generated in the firsy place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotly\n",
    "\n",
    "`plotly` is yet another library that plots highly interactive charts and lets the user interact with the charts as well. However, it requires username and an API Key to work properly. We will have to pass it in credentials to make it work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "plotly.tools.set_credentials_file(username='<USERNAME>', api_key='<API-KEY>')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the points, we can directly add the latitude and longitude values in the `Scattergeo` parameters `lat` and `long`. We also need to define the layout such as the colors, scope, title and more. Finally, the `Figure()` creates the object which we can plot. I'll first plot the first 1000 data points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/k.bhanot/Personal/Important Work/Plot-Maps-in-Python/lib/python3.7/site-packages/IPython/core/display.py:689: UserWarning:\n",
      "\n",
      "Consider using IPython.display.IFrame instead\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~karanbhanot100/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [ go.Scattergeo(\n",
    "        locationmode = 'USA-states',\n",
    "        lon = longitudes[:1000],\n",
    "        lat = latitudes[:1000],\n",
    "        text = names[:1000],\n",
    "        mode = 'markers',\n",
    "        marker = dict( \n",
    "            size = 10,\n",
    "            opacity = 0.8\n",
    "        ))]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Business locations', \n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict(type = 'albers usa'),\n",
    "            showland = True,\n",
    "            landcolor = \"rgb(250, 250, 250)\",\n",
    "            subunitcolor = \"rgb(217, 217, 217)\",\n",
    "            countrycolor = \"rgb(217, 217, 217)\",\n",
    "            countrywidth = 0.5,\n",
    "            subunitwidth = 0.5        \n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "py.iplot(fig, filename='business')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, I'll try to plot all the points at once."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/k.bhanot/Personal/Important Work/Plot-Maps-in-Python/lib/python3.7/site-packages/chart_studio/plotly/plotly.py:248: UserWarning:\n",
      "\n",
      "Woah there! Look at all those points! Due to browser limitations, the Plotly SVG drawing functions have a hard time graphing more than 500k data points for line charts, or 40k points for other types of charts. Here are some suggestions:\n",
      "(1) Use the `plotly.graph_objs.Scattergl` trace object to generate a WebGl graph.\n",
      "(2) Trying using the image API to return an image instead of a graph URL\n",
      "(3) Use matplotlib\n",
      "(4) See if you can create your visualization with fewer data points\n",
      "\n",
      "If the visualization you're using aggregates points (e.g., box plot, histogram, etc.) you can disregard this warning.\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~karanbhanot100/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<chart_studio.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = [ go.Scattergeo(\n",
    "        locationmode = 'USA-states',\n",
    "        lon = longitudes,\n",
    "        lat = latitudes,\n",
    "        text = names,\n",
    "        mode = 'markers',\n",
    "        marker = dict( \n",
    "            size = 10, \n",
    "            opacity = 0.8\n",
    "        ))]\n",
    "\n",
    "layout = dict(\n",
    "        title = 'Business locations', \n",
    "        geo = dict(\n",
    "            scope='usa',\n",
    "            projection=dict(type='albers usa'),\n",
    "            showland = True,\n",
    "            landcolor = \"rgb(250, 250, 250)\",\n",
    "            subunitcolor = \"rgb(217, 217, 217)\",\n",
    "            countrycolor = \"rgb(217, 217, 217)\",\n",
    "            countrywidth = 0.5,\n",
    "            subunitwidth = 0.5        \n",
    "        ),\n",
    "    )\n",
    "\n",
    "fig = go.Figure(data = data, layout = layout)\n",
    "py.iplot(fig, filename='business')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While I was able to plot all the points, the rendering is very slow and any interaction takes a lot of time to respond. Even the warning message suggests to use other methods to plot such graph."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bokeh\n",
    "\n",
    "It is yet another library to plot beautiful visualizations. For maps, it uses Google maps and hence requires an API Key to work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh.io import output_file, show\n",
    "from bokeh.models import ColumnDataSource, GMapOptions\n",
    "from bokeh.plotting import gmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once, the required libraries are loaded, using `gmap` I create the basic map centered around USA. I then plot all the points as circles on the map.To begin with I plot 1000 points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file(\"data/bokeh.html\")\n",
    "\n",
    "map_options = GMapOptions(lat = 35, lng = -102, map_type = \"roadmap\", zoom = 4)\n",
    "bokeh_plot = gmap(\"<API-KEY>\", map_options, title = \"Business locations\")\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data = dict(lat = latitudes[:1000],\n",
    "              lon = longitudes[:1000])\n",
    ")\n",
    "bokeh_plot.circle(x = \"lon\", y = \"lat\", size = 10, fill_alpha = 0.8, source = source)\n",
    "show(bokeh_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It opens up a new tab with the file with the map. Let's also try to plot all points."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_file(\"data/bokeh.html\")\n",
    "\n",
    "map_options = GMapOptions(lat = 35, lng = -102, map_type = \"roadmap\", zoom = 4)\n",
    "bokeh_plot = gmap(\"<API-KEY>\", map_options, title = \"Business locations\")\n",
    "\n",
    "source = ColumnDataSource(\n",
    "    data = dict(lat = latitudes,\n",
    "              lon = longitudes)\n",
    ")\n",
    "bokeh_plot.circle(x = \"lon\", y = \"lat\", size = 10, fill_alpha = 0.8, source = source)\n",
    "show(bokeh_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is again based on Google Maps API and hence, again fails to load the complete set of data points."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots above, I identified that all libraries are really useful for plotting the data points and my observations were as follows:\n",
    "1. The most easiest way to get started is with gmplot. It requires very minimal code to start and is able to produce good results too.\n",
    "2. Plotting points with different colors based on their state was easiest in GeoPandas. It was easy to plot all points but probably because the plot is not interactive.\n",
    "3. The most visually interactive are Plotly plots. They were able to produce plots with all points but were less responsive then and suggested other means of plotting.\n",
    "4. Bokeh was able to produce really nice Google Maps plot with very minimal code. However, it also failed at plotting all points."
   ]
  }
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