{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### About\n",
    "\n",
    "This notebook is for the following functions:\n",
    "\n",
    "  - turn entered coordinate data into a plottable, GeoDataFrame\n",
    "  - read geospatial data into GeoPandas\n",
    "  - perform basic geospatial data operations\n",
    "  - get introduced to other useful geospatial data python packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import fiona\n",
    "import shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Start with some data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Restaurant</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pizza Amoré</td>\n",
       "      <td>-64.91485</td>\n",
       "      <td>18.32928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pizza Hut</td>\n",
       "      <td>-64.87370</td>\n",
       "      <td>18.33795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>-64.95922</td>\n",
       "      <td>18.33785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant      long       lat\n",
       "0     Pizza Amoré -64.91485  18.32928\n",
       "1       Pizza Hut -64.87370  18.33795\n",
       "2  Domino's Pizza -64.95922  18.33785"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here are some longs and lats for pizza places on St. Thomas\n",
    "\n",
    "data = [['Pizza Amoré', -64.914850, 18.329280],\\\n",
    "        ['Pizza Hut', -64.873700, 18.337950],\\\n",
    "        [\"Domino's Pizza\", -64.959220, 18.337850]]\n",
    "\n",
    "# Create a pandas dataframe for pizza places on St. Thomas\n",
    "pizza_df = pd.DataFrame(data, columns = ['Restaurant','long', 'lat'])\n",
    "display(pizza_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Convert the pandas dataframe to a geopandas dataframe\n",
    "A GeoDataFrame is just like a dataframe, it just has...geometry! GeoDataFrames have a geometry column formatted for easy reading and writing across python geospatial packages. To create a GeoDataFrame, we must first define what this geometry will be. We can use Shapely (an established geospatial python packge for geospatial analysis) to create a formatted point geometry column from our substation dataframe's long and lat colums."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geometry = [shapely.geometry.Point(xy) for xy in zip(pizza_df['long'], pizza_df['lat'])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "GeoDataFrames are also special because they have geospatial metadata like a \n",
    "coordinate reference system (CRS) for data projection. Setting the CRS of your GeoDataFrame isn't always necessary, but it is a good practice as some python packages (such as folium) cannot make sense of the data without it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>Restaurant</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Pizza Amoré</td>\n",
       "      <td>-64.91485</td>\n",
       "      <td>18.32928</td>\n",
       "      <td>POINT (-64.91485 18.32928)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pizza Hut</td>\n",
       "      <td>-64.87370</td>\n",
       "      <td>18.33795</td>\n",
       "      <td>POINT (-64.87370 18.33795)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>-64.95922</td>\n",
       "      <td>18.33785</td>\n",
       "      <td>POINT (-64.95922 18.33785)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant      long       lat                    geometry\n",
       "0     Pizza Amoré -64.91485  18.32928  POINT (-64.91485 18.32928)\n",
       "1       Pizza Hut -64.87370  18.33795  POINT (-64.87370 18.33795)\n",
       "2  Domino's Pizza -64.95922  18.33785  POINT (-64.95922 18.33785)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pizza_gdf = gpd.GeoDataFrame(pizza_df, geometry = geometry, crs = {'init': 'epsg:4326'})\n",
    "# Now we can construct a geopandas dataframe with geometry\n",
    "display(pizza_gdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The \"geometry\" we just established works similarly to other data structures in python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pizza_gdf.geometry[0] is type: <class 'shapely.geometry.point.Point'> \n",
      "and has value: POINT (-64.91485 18.32928) \n",
      "\n",
      "pizza_gdf.geometry[0].x is type: <class 'float'> \n",
      "and has value: -64.91485 \n",
      "\n",
      "-64.91485 18.32928\n",
      "-64.8737 18.33795\n",
      "-64.95922 18.33785\n"
     ]
    }
   ],
   "source": [
    "type1=type(pizza_gdf.geometry[0])\n",
    "value1=pizza_gdf.geometry[0]\n",
    "print('pizza_gdf.geometry[0] is type: %s \\nand has value: %s'%(type1,value1),'\\n')\n",
    "\n",
    "type2=type(pizza_gdf.geometry[0].x)\n",
    "value2=pizza_gdf.geometry[0].x\n",
    "print('pizza_gdf.geometry[0].x is type: %s \\nand has value: %s'%(type2,value2),'\\n')\n",
    "\n",
    "#we can iterate through all of our points\n",
    "for point in range(len(pizza_gdf.geometry)):\n",
    "    print(pizza_gdf.geometry[point].x, pizza_gdf.geometry[point].y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can also perform Pandas-style operations on GeoDataFrames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Restaurant</th>\n",
       "      <th>long</th>\n",
       "      <th>lat</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Domino's Pizza</td>\n",
       "      <td>-64.95922</td>\n",
       "      <td>18.33785</td>\n",
       "      <td>POINT (-64.95922 18.33785)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Pizza Amoré</td>\n",
       "      <td>-64.91485</td>\n",
       "      <td>18.32928</td>\n",
       "      <td>POINT (-64.91485 18.32928)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Pizza Hut</td>\n",
       "      <td>-64.87370</td>\n",
       "      <td>18.33795</td>\n",
       "      <td>POINT (-64.87370 18.33795)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Restaurant      long       lat                    geometry\n",
       "0  Domino's Pizza -64.95922  18.33785  POINT (-64.95922 18.33785)\n",
       "1     Pizza Amoré -64.91485  18.32928  POINT (-64.91485 18.32928)\n",
       "2       Pizza Hut -64.87370  18.33795  POINT (-64.87370 18.33795)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-64.95922\n"
     ]
    }
   ],
   "source": [
    "pizza_gdf.sort_values(by=['Restaurant'], ignore_index=True, inplace=True)\n",
    "display(pizza_gdf)\n",
    "print(pizza_gdf['long'][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Let's plot the points from our GeoDataFrame!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RVK0PUv2J/EXgq1Tf4VO27Foj4mdUF5Xvl/Q01XWCjjTsT4F1qX8f1UAp+099FFVrJ/ATSc8CjwIdEfGTi563/M+FmZmNVb4SMTOzbA4RMzPL5hAxM7NsDhEzM8vmEDEzs2wOETMzy+YQMTOzbP8HiiDZrwtjwTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pizza_gdf.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ...that isn't super exciting. Let's put it on a basemap!\n",
    "First we need to import a file to use as our basemap. \n",
    "Good thing you can import just about any geospatial data file into a GeoDataFrame with `geopandas.read_file`!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stt = gpd.read_file('stt_example_basemap.geojson')\n",
    "basemap = stt.plot(color='#00447c', linewidth=0.5, edgecolor='white', figsize=(15,5))\n",
    "pizza_gdf.plot(markersize=100, color='#fed402', alpha=1.0, ax=basemap);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Packages Related to GeoPandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shapely\n",
    "Shapely is useful for creating vector data. It has built in functions for generating\n",
    "formatted objects that all geospatial software knows how to read.\n",
    "The shapely.geometry package has classes and factories for accessing and generating\n",
    "geospatial data stored in a geopandas dataframe (GeoDataFrame.geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(shapely.geometry)\n",
    "help(shapely.geometry.Point)\n",
    "#help(shapely.geometry.LineString)\n",
    "#help(shapely.geometry.Polygon)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fiona\n",
    "Fiona is an API that links python to OGR (i.e., GDAL) libraries and utilities for \n",
    "geospatial data processing. Its especially useful for projections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "help(fiona)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}