Lab5_Assignment-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab5 Feature Class\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# HOMEWORK ASSIGNMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Answer the following questions in the jupyter notebook file. (You can also use other IDEs to test your code) **"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Finish the tutorial (30 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow the __tutorial__ at https://medium.com/@vworri/simple-geospacial-mapping-with-geopandas-and-the-usual-suspects-77f46d40e807 to read and map spatial data using geopandas.\n",
    "\n",
    "Retype the code from the tutorial in this notebook and produce the final map in this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AGor/Re1Hj2p44omYkH5Wz55upkyxMXCgmw8+MHDwYHWD1LKllxdftFFSIvjn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\nmqHU0NDQCIJmKDU0NDSC8P8BvpiO3hiGbYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d3f1bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KNm6M9Hw1DRoaLFpaVHDp6acLcLsld9xhcvvtAXI93LI15tMZZX8KPCeE+H+A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\n8KEJlH5/ovFFKsKL2gAvvbQ6X5LyzlK1Nzaq3e3Fpkxh9+x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GnVtvDbJ2rWoWFe65XFW1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\nYmJikgYzUJqYmJik4f8DywPJ5Ws418IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1b5d2bb0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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SqqXZ36+jaaqMW2aU1ulpjenpyghMr9WjLBPVXNOxmrWvVRPRNOGjj3SuXClu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x243448b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           CMPLNT_FR_DT CMPLNT_FR_TM CMPLNT_TO_DT CMPLNT_TO_TM      RPT_DT  \\\n",
      "CMPLNT_NUM                                                                   \n",
      "479888864    12/21/2015      9:31:00   12/21/2015      9:47:00  12/21/2015   \n",
      "817223136    12/18/2015     15:30:00   12/18/2015     15:35:00  12/18/2015   \n",
      "144702604    12/17/2015     22:30:00   12/17/2015     22:56:00  12/17/2015   \n",
      "426614014     12/8/2015     20:50:00    12/8/2015     20:58:00   12/8/2015   \n",
      "598084040    11/27/2015     11:25:00   11/27/2015     11:30:00  11/27/2015   \n",
      "\n",
      "            KY_CD                OFNS_DESC  PD_CD  \\\n",
      "CMPLNT_NUM                                          \n",
      "479888864     106           FELONY ASSAULT  106.0   \n",
      "817223136     105                  ROBBERY  386.0   \n",
      "144702604     126  MISCELLANEOUS PENAL LAW  760.0   \n",
      "426614014     236        DANGEROUS WEAPONS  782.0   \n",
      "598084040     235          DANGEROUS DRUGS  567.0   \n",
      "\n",
      "                                       PD_DESC CRM_ATPT_CPTD_CD  \\\n",
      "CMPLNT_NUM                                                        \n",
      "479888864            ASSAULT 2,1,PEACE OFFICER        COMPLETED   \n",
      "817223136   ROBBERY,PERSONAL ELECTRONIC DEVICE        COMPLETED   \n",
      "144702604        BRIBERY,PUBLIC ADMINISTRATION        COMPLETED   \n",
      "426614014             WEAPONS, POSSESSION, ETC        COMPLETED   \n",
      "598084040          MARIJUANA, POSSESSION 4 & 5        COMPLETED   \n",
      "\n",
      "                        ...               ADDR_PCT_CD LOC_OF_OCCUR_DESC  \\\n",
      "CMPLNT_NUM              ...                                               \n",
      "479888864               ...                      76.0           REAR OF   \n",
      "817223136               ...                      88.0       OPPOSITE OF   \n",
      "144702604               ...                      49.0       OPPOSITE OF   \n",
      "426614014               ...                      32.0           REAR OF   \n",
      "598084040               ...                      75.0       OPPOSITE OF   \n",
      "\n",
      "                         PREM_TYP_DESC                          PARKS_NM  \\\n",
      "CMPLNT_NUM                                                                 \n",
      "479888864   RESIDENCE - PUBLIC HOUSING  NICHOLAS NAQUAN HEYWARD JR. PARK   \n",
      "817223136              PARK/PLAYGROUND              COMMODORE BARRY PARK   \n",
      "144702604              PARK/PLAYGROUND                        BRONX PARK   \n",
      "426614014              PARK/PLAYGROUND    COLONEL CHARLES YOUNG TRIANGLE   \n",
      "598084040   RESIDENCE - PUBLIC HOUSING                       LINDEN PARK   \n",
      "\n",
      "              HADEVELOPT X_COORD_CD Y_COORD_CD   Latitude  Longitude  \\\n",
      "CMPLNT_NUM                                                             \n",
      "479888864        GOWANUS   987442.0   188309.0  40.683549 -73.988492   \n",
      "817223136      INGERSOLL   989927.0   192766.0  40.695781 -73.979529   \n",
      "144702604       PARKSIDE  1020965.0   255753.0  40.868589 -73.867255   \n",
      "426614014   HARLEM RIVER  1001650.0   240575.0  40.826989 -73.937129   \n",
      "598084040         LINDEN  1015829.0   179159.0  40.658379 -73.886184   \n",
      "\n",
      "                                  Lat_Lon  \n",
      "CMPLNT_NUM                                 \n",
      "479888864    (40.68354911, -73.988492488)  \n",
      "817223136   (40.695781306, -73.979528908)  \n",
      "144702604      (40.86858934, -73.8672548)  \n",
      "426614014    (40.826989411, -73.93712941)  \n",
      "598084040   (40.658378671, -73.886184297)  \n",
      "\n",
      "[5 rows x 23 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15c8b710>"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AGor/Re1Hj2p44omYkH5Wz55upkyxMXCgmw8+MHDwYHWD1LKllxdftFFSIvjn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\nmqHU0NDQCIJmKDU0NDSC8P8BvpiO3hiGbYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a3ba390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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KgbHN2fmaxzZn52uOIK2Id82y/+wKeCxpbPolUcA2Z+drHtucna855r1VVTS6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\nvIc7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x21e81350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import geopandas\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.style.use('bmh')\n",
    "%matplotlib inline\n",
    "\n",
    "gdf = geopandas.GeoDataFrame.from_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\AccessBB\\geo_export_8c48c296-61a5-4088-8253-8e5238d052b7.shx\")\n",
    "gdf.head()\n",
    "\n",
    "gdf.plot()\n",
    "plt.show()\n",
    "\n",
    "hulls = gdf[\"geometry\"].convex_hull\n",
    "hulls.plot(ax=gdf.plot(), alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "envelope = geopandas.GeoSeries(hulls.envelope)\n",
    "envelope.plot(ax=gdf.plot(), alpha=0.5)\n",
    "plt.show()\n",
    "\n",
    "new_york_crime = pd.DataFrame.from_csv(\"C:\\Users\\casca\\Downloads\\SimpleAccess\\AccessCriNYC\\NYPD_Complaint_Data_Historic.csv\").dropna()\n",
    "print new_york_crime.head()\n",
    "\n",
    "gdf.plot()\n",
    "new_york_crime.plot.scatter(x=\"Longitude\",y=\"Latitude\",alpha=0.5, c='r')\n",
    "#plt.show()\n",
    "\n",
    "#points don't display within the 5 boroughs.  Corrected: neglected to insert \"new_york_crime\" at beginning of plot.scatter..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Read and write shapefile (10 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find a shapefile of your own (may download from website). Read and display the shapefile, then write a selection (first 5 rows) of the file to a new shapefile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hint: Task 2 provide a good example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "import geopandas as gpd\n",
    "# Set filepath (fix path relative to yours)\n",
    "fp = r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\DataDamselFish\\Data\\DAMSELFISH_distributions.shp\"\n",
    "# Read file using gpd.read_file()\n",
    "data = gpd.read_file(fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "select5 = data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "select5.to_file(r'C:\\Users\\casca\\Downloads\\SimpleAccess\\select5.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Create Polygon from lists of coordinates (20 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Create a Polygon out of the the x and y coordinates that are provided.\n",
    "- insert the polygon into a GeoDataFrame\n",
    "- save the Polygon into a Shapefile.\n",
    "- plot out of the Polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import geopandas and necessary geometric objects from shapely module\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, Polygon\n",
    "import fiona\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# X -coordinates \n",
    "xcoords = [29.99671173095703, 31.58196258544922, 27.738052368164062, 26.50013542175293, 26.652359008789062, 25.921663284301758, 22.90027618408203, 23.257217407226562,\n",
    "           23.335693359375, 22.87444305419922, 23.08465003967285, 22.565473556518555, 21.452774047851562, 21.66388702392578, 21.065969467163086, 21.67659568786621,\n",
    "           21.496871948242188, 22.339998245239258, 22.288192749023438, 24.539581298828125, 25.444232940673828, 25.303749084472656, 24.669166564941406, 24.689163208007812,\n",
    "           24.174999237060547, 23.68471908569336, 24.000761032104492, 23.57332992553711, 23.76513671875, 23.430830001831055, 23.6597900390625, 20.580928802490234, 21.320831298828125,\n",
    "           22.398330688476562, 23.97638702392578, 24.934917449951172, 25.7611083984375, 25.95930290222168, 26.476804733276367, 27.91069221496582, 29.1027774810791, 29.29846954345703,\n",
    "           28.4355525970459, 28.817358016967773, 28.459857940673828, 30.028610229492188, 29.075136184692383, 30.13492774963379, 29.818885803222656, 29.640830993652344, 30.57735824584961,\n",
    "           29.99671173095703]\n",
    "\n",
    "# Y -coordinates\n",
    "ycoords = [63.748023986816406, 62.90789794921875, 60.511383056640625, 60.44499588012695, 60.646385192871094, 60.243743896484375, 59.806800842285156, 59.91944122314453,\n",
    "           60.02395248413086, 60.14555358886719, 60.3452033996582, 60.211936950683594, 60.56249237060547, 61.54027557373047, 62.59798049926758, 63.02013397216797,\n",
    "           63.20353698730469, 63.27652359008789, 63.525691986083984, 64.79915618896484, 64.9533920288086, 65.51513671875, 65.65470886230469, 65.89610290527344, 65.79151916503906,\n",
    "           66.26332092285156, 66.80228424072266, 67.1570053100586, 67.4168701171875, 67.47978210449219, 67.94589233398438, 69.060302734375, 69.32611083984375, 68.71110534667969,\n",
    "           68.83248901367188, 68.580810546875, 68.98916625976562, 69.68568420410156, 69.9363784790039, 70.08860778808594, 69.70597076416016, 69.48533630371094, 68.90263366699219,\n",
    "           68.84700012207031, 68.53485107421875, 67.69471740722656, 66.90360260009766, 65.70887756347656, 65.6533203125, 64.92096710205078, 64.22373962402344, 63.748023986816406]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<shapely.geometry.point.Point at 0x22179bb0>,\n",
       " <shapely.geometry.point.Point at 0x22179e50>,\n",
       " <shapely.geometry.point.Point at 0x22179c50>,\n",
       " <shapely.geometry.point.Point at 0x22179f70>,\n",
       " <shapely.geometry.point.Point at 0x22179e90>,\n",
       " <shapely.geometry.point.Point at 0x22179cd0>,\n",
       " <shapely.geometry.point.Point at 0x221799d0>,\n",
       " <shapely.geometry.point.Point at 0x22179b30>,\n",
       " <shapely.geometry.point.Point at 0x221793f0>,\n",
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       " <shapely.geometry.point.Point at 0x221797b0>,\n",
       " <shapely.geometry.point.Point at 0x22179750>,\n",
       " <shapely.geometry.point.Point at 0x22179450>,\n",
       " <shapely.geometry.point.Point at 0x22179150>,\n",
       " <shapely.geometry.point.Point at 0x221798f0>,\n",
       " <shapely.geometry.point.Point at 0x22179430>,\n",
       " <shapely.geometry.point.Point at 0x221790f0>,\n",
       " <shapely.geometry.point.Point at 0x22179a10>,\n",
       " <shapely.geometry.point.Point at 0x22179df0>,\n",
       " <shapely.geometry.point.Point at 0x22179170>,\n",
       " <shapely.geometry.point.Point at 0x22179630>,\n",
       " <shapely.geometry.point.Point at 0x22179c90>,\n",
       " <shapely.geometry.point.Point at 0x22179cb0>,\n",
       " <shapely.geometry.point.Point at 0x22179890>,\n",
       " <shapely.geometry.point.Point at 0x22179350>,\n",
       " <shapely.geometry.point.Point at 0x22179810>,\n",
       " <shapely.geometry.point.Point at 0x22179d90>,\n",
       " <shapely.geometry.point.Point at 0x22179a30>,\n",
       " <shapely.geometry.point.Point at 0x221795d0>,\n",
       " <shapely.geometry.point.Point at 0x22179490>,\n",
       " <shapely.geometry.point.Point at 0x22179950>,\n",
       " <shapely.geometry.point.Point at 0x22179ed0>,\n",
       " <shapely.geometry.point.Point at 0x221796b0>,\n",
       " <shapely.geometry.point.Point at 0x22179710>,\n",
       " <shapely.geometry.point.Point at 0x22179110>,\n",
       " <shapely.geometry.point.Point at 0x221795f0>,\n",
       " <shapely.geometry.point.Point at 0x221790d0>,\n",
       " <shapely.geometry.point.Point at 0x22179ad0>,\n",
       " <shapely.geometry.point.Point at 0x22179d30>,\n",
       " <shapely.geometry.point.Point at 0x22179cf0>,\n",
       " <shapely.geometry.point.Point at 0x22179a70>,\n",
       " <shapely.geometry.point.Point at 0x22179690>,\n",
       " <shapely.geometry.point.Point at 0x22179990>,\n",
       " <shapely.geometry.point.Point at 0x221791d0>,\n",
       " <shapely.geometry.point.Point at 0x221798b0>,\n",
       " <shapely.geometry.point.Point at 0x22179210>,\n",
       " <shapely.geometry.point.Point at 0x22179310>,\n",
       " <shapely.geometry.point.Point at 0x22179a50>,\n",
       " <shapely.geometry.point.Point at 0x1824f150>,\n",
       " <shapely.geometry.point.Point at 0x22179c10>,\n",
       " <shapely.geometry.point.Point at 0x221798d0>,\n",
       " <shapely.geometry.point.Point at 0x1eb8d8d0>]"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1. Create a list of x and y coordinate pairs out of xcoords and ycoords\n",
    "# ------------------------------------------------------------------------\n",
    "# Coordinate pair can be either a tuple or a list.\n",
    "# The first coordinate pair in the 'coordpairs' -list should look like: (29.99671173095703, 63.748023986816406)\n",
    "# Hint: you might want to iterate over items in the lists using a for-loop\n",
    "\n",
    "#coordpairs = []\n",
    "#point_combine = []\n",
    "#coordconv = []\n",
    "\n",
    "#for i in range(len(xcoords)):\n",
    "#point_combine.append(Point(xcoords[i], ycoords[i]))\n",
    "#point_coord = point_combine.coords\n",
    "\n",
    "#polyList = []\n",
    "points = []\n",
    "for i in range(len(xcoords)):\n",
    "    points.append(Point(xcoords[i],ycoords[i]))\n",
    "points\n",
    "    \n",
    "    \n",
    "#pointList = [[p.x,p.y] for p in [points]]\n",
    "#point2 = Point(xcoords[1], ycoords[1])\n",
    "#point3 = Point(xcoords[2], ycoords[2])\n",
    "#polyList = [[p.x, p.y] for p in [point1, point2, point3]] \n",
    "#Polygon(polyList)\n",
    "#polyList = []\n",
    "\n",
    "#for i in range(len(xcoords)):\n",
    "#point = (Point(xcoords[0], ycoords[0]))\n",
    "#polyList = [[p.x,p.y] for p in [point]]\n",
    "#polyList.append(point)\n",
    "\n",
    "#polyList\n",
    "#    coordconv.append(Point(xcoords[i], ycoords[i]))\n",
    "#    coordpairs = coordconv\n",
    "#    print coordpairs    \n",
    "#len(coordpairs)\n",
    "#poly=Polygon([[p.x,p.y]for p in [point1,point2,point3]])\n",
    "#As we can see from above the xy -variable contains a tuple where x and y coordinates are stored inside numpy arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"20.1408874512 59.366759491 11.8811164856 11.1618896484\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,129.89540863)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"0.237622329712\" opacity=\"0.6\" d=\"M 29.996711731,63.7480239868 L 31.5819625854,62.9078979492 L 27.7380523682,60.5113830566 L 26.5001354218,60.4449958801 L 26.6523590088,60.6463851929 L 25.9216632843,60.2437438965 L 22.9002761841,59.8068008423 L 23.2572174072,59.9194412231 L 23.3356933594,60.0239524841 L 22.8744430542,60.1455535889 L 23.0846500397,60.3452033997 L 22.5654735565,60.2119369507 L 21.4527740479,60.5624923706 L 21.6638870239,61.5402755737 L 21.0659694672,62.5979804993 L 21.6765956879,63.0201339722 L 21.4968719482,63.2035369873 L 22.3399982452,63.2765235901 L 22.288192749,63.5256919861 L 24.5395812988,64.799156189 L 25.4442329407,64.9533920288 L 25.3037490845,65.5151367188 L 24.6691665649,65.6547088623 L 24.689163208,65.8961029053 L 24.1749992371,65.791519165 L 23.6847190857,66.2633209229 L 24.0007610321,66.8022842407 L 23.5733299255,67.1570053101 L 23.7651367188,67.4168701172 L 23.4308300018,67.4797821045 L 23.6597900391,67.945892334 L 20.5809288025,69.0603027344 L 21.3208312988,69.3261108398 L 22.3983306885,68.7111053467 L 23.9763870239,68.8324890137 L 24.93491745,68.5808105469 L 25.7611083984,68.9891662598 L 25.9593029022,69.6856842041 L 26.4768047333,69.936378479 L 27.910692215,70.0886077881 L 29.1027774811,69.7059707642 L 29.2984695435,69.4853363037 L 28.435552597,68.902633667 L 28.817358017,68.8470001221 L 28.4598579407,68.5348510742 L 30.0286102295,67.6947174072 L 29.0751361847,66.9036026001 L 30.1349277496,65.7088775635 L 29.8188858032,65.6533203125 L 29.6408309937,64.9209671021 L 30.5773582458,64.223739624 L 29.996711731,63.7480239868 z\" /></g></svg>"
      ],
      "text/plain": [
       "<shapely.geometry.polygon.Polygon at 0x1dc50f30>"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. Create a shapely Polygon using the 'coordpairs' -list\n",
    "# ------------------------------------------------------------------------\n",
    "#hint: used append and consider in coordinate tuple within a list.  Do not \"override\"\n",
    "poly = Polygon(zip(xcoords,ycoords))\n",
    "poly\n",
    "#error results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3. Create an empty GeoDataFrame\n",
    "# ---------------------------------\n",
    "geo = gpd.GeoDataFrame()\n",
    "#geo['geometry'] = None\n",
    "#print geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4. Insert our 'poly' -polygon into the 'geo' GeoDataFrame using a column name 'geometry'\n",
    "# ------------------------------------------------------------------------------------------\n",
    "# Hint: Take advantage of .loc -funtion\n",
    "geo.loc[0,'geometry'] = poly\n",
    "#Depends on 2 steps before/above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 5. Save the GeoDataFrame into a new Shapefile called 'polygon.shp'\n",
    "# --------------------------------------------------------------------\n",
    "# Note: you do not need to define the coordinate reference system at this time\n",
    "geo.to_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\lab5Q3.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x15ab8430>"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Q4hZVsBBCDBBCLBZCbBdCbBNCjBdCnCqEWC2E+FgIsV4IUR7z2XWivl77DlYGA5x1VoD5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\nYenqERcshg1r56ab+ra3pdW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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1dc50250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6. Plot the polygon using taking advantage of the .plot() -function in \n",
    "# GeoDataFrame. Don't forget %matplotlib inline\n",
    "# -----------------------------------------------------------------------------------------------------------------------------------------------------------------\n",
    "geo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Points to map (20 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The problem continues the process that we started last week, i.e. creating geometric point -objects and putting them into a map. Here our aim is to plot a set of x and y coordinates that we should read from a some_posts.csv comma separated file that contains following kind of data:\n",
    ">lat,lon,timestamp,userid  \n",
    "-24.980792492,31.484633302,2015-07-07 03:02,66487960\n",
    "-25.499224667,31.508905612,2015-07-07 03:18,65281761\n",
    "-24.342578456,30.930866066,2015-03-07 03:38,90916112\n",
    "-24.85461393,31.519718439,2015-10-07 05:04,37959089"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data has 81379 rows and consists of locations and times of social media posts inside Kruger national park in South Africa:\n",
    "\n",
    "|Column\t|Description  |\n",
    "|-------|-------------|\n",
    "|lat\t|y-coordinate of the post|\n",
    "|lon\t|x-coordinate of the post|\n",
    "|timestamp\t|Time when the post was uploaded|\n",
    "|userid\t|userid|\n",
    "\n",
    "Note: although the data is based on real social media data, it is heavily anonymized. Userids and timestamps have been randomized, i.e. they do not not match with real ones, also spatial accuracy of the data have been lowered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is called 'some_posts.csv' provided in the assignment attachement files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules first\n",
    "# e.g. fiona, import geopandas as gpd;\n",
    "# from shapely.geometry import Point,LineString, Polygon\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Read the data with Pandas into a DataFrame use pandas.read_csv()\n",
    "my_data = pd.read_csv(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\some_posts.csv\")\n",
    "#my_data\n",
    "#pd.DataFrame.from_csv(\"NYPD_Complaint_Data_Historic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Create an empty column called geometry in my_data where you will store shapely Point objects\n",
    "my_data['geometry'] = None\n",
    "#my_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>userid</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-24.980792</td>\n",
       "      <td>31.484633</td>\n",
       "      <td>7/7/2015 3:02</td>\n",
       "      <td>66487960</td>\n",
       "      <td>POINT (-24.98079249 31.4846333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-25.499225</td>\n",
       "      <td>31.508906</td>\n",
       "      <td>7/7/2015 3:18</td>\n",
       "      <td>65281761</td>\n",
       "      <td>POINT (-25.49922467 31.50890561)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-24.342578</td>\n",
       "      <td>30.930866</td>\n",
       "      <td>3/7/2015 3:38</td>\n",
       "      <td>90916112</td>\n",
       "      <td>POINT (-24.34257846 30.93086607)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-24.854614</td>\n",
       "      <td>31.519718</td>\n",
       "      <td>10/7/2015 5:04</td>\n",
       "      <td>37959089</td>\n",
       "      <td>POINT (-24.85461393 31.51971844)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-24.921069</td>\n",
       "      <td>31.520836</td>\n",
       "      <td>10/7/2015 5:19</td>\n",
       "      <td>27793716</td>\n",
       "      <td>POINT (-24.92106889 31.52083556)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-24.047833</td>\n",
       "      <td>31.687000</td>\n",
       "      <td>5/7/2015 5:55</td>\n",
       "      <td>88751696</td>\n",
       "      <td>POINT (-24.04783333 31.687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-24.044000</td>\n",
       "      <td>31.687000</td>\n",
       "      <td>3/7/2015 5:57</td>\n",
       "      <td>88751696</td>\n",
       "      <td>POINT (-24.044 31.687)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-25.498371</td>\n",
       "      <td>30.983844</td>\n",
       "      <td>9/7/2015 6:21</td>\n",
       "      <td>52431146</td>\n",
       "      <td>POINT (-25.49837105 30.98384409)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-24.342279</td>\n",
       "      <td>30.990728</td>\n",
       "      <td>7/7/2015 7:15</td>\n",
       "      <td>11505530</td>\n",
       "      <td>POINT (-24.34227949 30.99072848)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-25.413386</td>\n",
       "      <td>31.159930</td>\n",
       "      <td>11/7/2015 7:17</td>\n",
       "      <td>24990235</td>\n",
       "      <td>POINT (-25.4133857 31.15993)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-24.517680</td>\n",
       "      <td>31.162064</td>\n",
       "      <td>9/7/2015 7:22</td>\n",
       "      <td>39790157</td>\n",
       "      <td>POINT (-24.51767986 31.16206385)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-25.469772</td>\n",
       "      <td>30.956835</td>\n",
       "      <td>5/7/2015 7:24</td>\n",
       "      <td>39646946</td>\n",
       "      <td>POINT (-25.469772 30.956835)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-25.470622</td>\n",
       "      <td>30.993113</td>\n",
       "      <td>12/7/2015 7:27</td>\n",
       "      <td>54372438</td>\n",
       "      <td>POINT (-25.47062201 30.99311259)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-25.470622</td>\n",
       "      <td>30.993113</td>\n",
       "      <td>12/7/2015 7:27</td>\n",
       "      <td>54372438</td>\n",
       "      <td>POINT (-25.47062201 30.99311259)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-25.470520</td>\n",
       "      <td>30.979195</td>\n",
       "      <td>8/7/2015 7:38</td>\n",
       "      <td>2518415</td>\n",
       "      <td>POINT (-25.47052022 30.9791946)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-25.182072</td>\n",
       "      <td>31.112934</td>\n",
       "      <td>5/7/2015 7:55</td>\n",
       "      <td>28143688</td>\n",
       "      <td>POINT (-25.1820724 31.1129338)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-24.384113</td>\n",
       "      <td>31.046408</td>\n",
       "      <td>3/7/2015 8:08</td>\n",
       "      <td>68184135</td>\n",
       "      <td>POINT (-24.38411333 31.04640833)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-23.937441</td>\n",
       "      <td>31.027850</td>\n",
       "      <td>12/7/2015 8:38</td>\n",
       "      <td>61978646</td>\n",
       "      <td>POINT (-23.93744078 31.02785013)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-25.498773</td>\n",
       "      <td>31.173228</td>\n",
       "      <td>4/7/2015 8:47</td>\n",
       "      <td>98277872</td>\n",
       "      <td>POINT (-25.49877312 31.17322846)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-25.437894</td>\n",
       "      <td>31.960251</td>\n",
       "      <td>11/7/2015 9:02</td>\n",
       "      <td>19151287</td>\n",
       "      <td>POINT (-25.43789368 31.96025054)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-25.289637</td>\n",
       "      <td>31.055169</td>\n",
       "      <td>1/7/2015 9:08</td>\n",
       "      <td>34680030</td>\n",
       "      <td>POINT (-25.28963669 31.05516887)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-25.437894</td>\n",
       "      <td>31.960251</td>\n",
       "      <td>8/7/2015 9:09</td>\n",
       "      <td>19151287</td>\n",
       "      <td>POINT (-25.43789368 31.96025054)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-23.521025</td>\n",
       "      <td>31.395103</td>\n",
       "      <td>6/7/2015 9:15</td>\n",
       "      <td>37106688</td>\n",
       "      <td>POINT (-23.521025 31.39510333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-24.859702</td>\n",
       "      <td>31.401844</td>\n",
       "      <td>3/7/2015 9:23</td>\n",
       "      <td>27793716</td>\n",
       "      <td>POINT (-24.859702 31.40184394)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-25.492772</td>\n",
       "      <td>30.971141</td>\n",
       "      <td>3/7/2015 9:44</td>\n",
       "      <td>34589050</td>\n",
       "      <td>POINT (-25.49277169 30.97114069)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-23.949790</td>\n",
       "      <td>31.151078</td>\n",
       "      <td>1/7/2015 9:49</td>\n",
       "      <td>94490818</td>\n",
       "      <td>POINT (-23.94978964 31.15107752)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-25.456545</td>\n",
       "      <td>30.998739</td>\n",
       "      <td>12/7/2015 10:07</td>\n",
       "      <td>36870228</td>\n",
       "      <td>POINT (-25.45654532 30.99873878)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-25.493817</td>\n",
       "      <td>30.985831</td>\n",
       "      <td>9/7/2015 10:14</td>\n",
       "      <td>4770267</td>\n",
       "      <td>POINT (-25.49381657 30.98583063)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-25.488667</td>\n",
       "      <td>31.017167</td>\n",
       "      <td>8/7/2015 10:39</td>\n",
       "      <td>86295905</td>\n",
       "      <td>POINT (-25.48866667 31.01716667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-25.507035</td>\n",
       "      <td>30.982462</td>\n",
       "      <td>5/7/2015 10:54</td>\n",
       "      <td>17095038</td>\n",
       "      <td>POINT (-25.5070354 30.98246192)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>-25.452000</td>\n",
       "      <td>30.880667</td>\n",
       "      <td>9/30/2015 14:11</td>\n",
       "      <td>21104631</td>\n",
       "      <td>POINT (-25.452 30.88066667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>-25.473150</td>\n",
       "      <td>30.980428</td>\n",
       "      <td>9/30/2015 14:15</td>\n",
       "      <td>93275103</td>\n",
       "      <td>POINT (-25.47315 30.980428)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>-23.097000</td>\n",
       "      <td>31.397333</td>\n",
       "      <td>9/30/2015 14:17</td>\n",
       "      <td>95395153</td>\n",
       "      <td>POINT (-23.097 31.39733333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>-25.155498</td>\n",
       "      <td>31.198091</td>\n",
       "      <td>9/30/2015 14:18</td>\n",
       "      <td>19044147</td>\n",
       "      <td>POINT (-25.15549843 31.19809118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>-25.473150</td>\n",
       "      <td>30.980428</td>\n",
       "      <td>1/30/2015 14:20</td>\n",
       "      <td>93275103</td>\n",
       "      <td>POINT (-25.47315 30.980428)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>-24.872175</td>\n",
       "      <td>31.545572</td>\n",
       "      <td>3/30/2015 14:36</td>\n",
       "      <td>37959089</td>\n",
       "      <td>POINT (-24.87217481 31.54557205)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1976</th>\n",
       "      <td>-25.379850</td>\n",
       "      <td>31.718936</td>\n",
       "      <td>2015-02-30 14:44</td>\n",
       "      <td>94123653</td>\n",
       "      <td>POINT (-25.37985008 31.71893638)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1977</th>\n",
       "      <td>-25.056167</td>\n",
       "      <td>30.988833</td>\n",
       "      <td>3/30/2015 15:06</td>\n",
       "      <td>53340298</td>\n",
       "      <td>POINT (-25.05616667 30.98883333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>-25.035303</td>\n",
       "      <td>31.114082</td>\n",
       "      <td>10/30/2015 15:12</td>\n",
       "      <td>64818554</td>\n",
       "      <td>POINT (-25.03530327 31.11408174)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1979</th>\n",
       "      <td>-25.473295</td>\n",
       "      <td>30.969345</td>\n",
       "      <td>6/30/2015 15:30</td>\n",
       "      <td>41418953</td>\n",
       "      <td>POINT (-25.47329535 30.969345)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980</th>\n",
       "      <td>-25.331828</td>\n",
       "      <td>30.996908</td>\n",
       "      <td>10/30/2015 15:43</td>\n",
       "      <td>85739127</td>\n",
       "      <td>POINT (-25.33182833 30.99690833)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1981</th>\n",
       "      <td>-24.872175</td>\n",
       "      <td>31.545572</td>\n",
       "      <td>7/30/2015 15:44</td>\n",
       "      <td>37959089</td>\n",
       "      <td>POINT (-24.87217481 31.54557205)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1982</th>\n",
       "      <td>-24.220687</td>\n",
       "      <td>30.973517</td>\n",
       "      <td>9/30/2015 15:46</td>\n",
       "      <td>74498061</td>\n",
       "      <td>POINT (-24.22068667 30.97351667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1983</th>\n",
       "      <td>-24.393738</td>\n",
       "      <td>31.780053</td>\n",
       "      <td>12/30/2015 16:22</td>\n",
       "      <td>91614500</td>\n",
       "      <td>POINT (-24.39373833 31.78005333)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1984</th>\n",
       "      <td>-24.223025</td>\n",
       "      <td>30.958667</td>\n",
       "      <td>2015-02-30 16:35</td>\n",
       "      <td>74498061</td>\n",
       "      <td>POINT (-24.223025 30.95866667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1985</th>\n",
       "      <td>-25.356635</td>\n",
       "      <td>31.682165</td>\n",
       "      <td>6/30/2015 16:40</td>\n",
       "      <td>22013943</td>\n",
       "      <td>POINT (-25.35663523 31.68216512)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>-22.652883</td>\n",
       "      <td>30.926728</td>\n",
       "      <td>8/30/2015 16:42</td>\n",
       "      <td>64336802</td>\n",
       "      <td>POINT (-22.65288333 30.92672833)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987</th>\n",
       "      <td>-25.470157</td>\n",
       "      <td>30.950590</td>\n",
       "      <td>2015-02-30 17:22</td>\n",
       "      <td>86267392</td>\n",
       "      <td>POINT (-25.47015673 30.95059002)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988</th>\n",
       "      <td>-25.470289</td>\n",
       "      <td>30.950547</td>\n",
       "      <td>8/30/2015 17:35</td>\n",
       "      <td>86267392</td>\n",
       "      <td>POINT (-25.47028866 30.95054685)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1989</th>\n",
       "      <td>-25.475435</td>\n",
       "      <td>30.969687</td>\n",
       "      <td>8/30/2015 17:39</td>\n",
       "      <td>86267392</td>\n",
       "      <td>POINT (-25.47543533 30.96968651)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <td>-25.155498</td>\n",
       "      <td>31.198091</td>\n",
       "      <td>10/30/2015 18:23</td>\n",
       "      <td>73766394</td>\n",
       "      <td>POINT (-25.15549843 31.19809118)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1991</th>\n",
       "      <td>-25.033090</td>\n",
       "      <td>31.089304</td>\n",
       "      <td>10/30/2015 18:31</td>\n",
       "      <td>38691411</td>\n",
       "      <td>POINT (-25.03309011 31.08930402)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1992</th>\n",
       "      <td>-25.313167</td>\n",
       "      <td>31.097667</td>\n",
       "      <td>8/30/2015 18:58</td>\n",
       "      <td>15525879</td>\n",
       "      <td>POINT (-25.31316667 31.09766667)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1993</th>\n",
       "      <td>-24.662218</td>\n",
       "      <td>31.048188</td>\n",
       "      <td>10/30/2015 19:02</td>\n",
       "      <td>31944101</td>\n",
       "      <td>POINT (-24.66221819 31.04818763)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1994</th>\n",
       "      <td>-24.786132</td>\n",
       "      <td>31.428992</td>\n",
       "      <td>9/30/2015 20:00</td>\n",
       "      <td>56523514</td>\n",
       "      <td>POINT (-24.78613216 31.42899182)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1995</th>\n",
       "      <td>-23.921780</td>\n",
       "      <td>31.024793</td>\n",
       "      <td>4/30/2015 20:21</td>\n",
       "      <td>54918463</td>\n",
       "      <td>POINT (-23.92178018 31.02479311)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>-24.794730</td>\n",
       "      <td>31.344255</td>\n",
       "      <td>2/1/2015 2:04</td>\n",
       "      <td>15284579</td>\n",
       "      <td>POINT (-24.79473 31.344255)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>-24.502593</td>\n",
       "      <td>30.891696</td>\n",
       "      <td>5/1/2015 2:14</td>\n",
       "      <td>41807690</td>\n",
       "      <td>POINT (-24.50259291 30.89169567)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>-24.349882</td>\n",
       "      <td>30.948071</td>\n",
       "      <td>1/1/2015 2:39</td>\n",
       "      <td>29890436</td>\n",
       "      <td>POINT (-24.34988171 30.94807142)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>-24.345782</td>\n",
       "      <td>30.979891</td>\n",
       "      <td>5/1/2015 2:43</td>\n",
       "      <td>29890436</td>\n",
       "      <td>POINT (-24.34578244 30.97989099)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2000 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            lat        lon         timestamp    userid  \\\n",
       "0    -24.980792  31.484633     7/7/2015 3:02  66487960   \n",
       "1    -25.499225  31.508906     7/7/2015 3:18  65281761   \n",
       "2    -24.342578  30.930866     3/7/2015 3:38  90916112   \n",
       "3    -24.854614  31.519718    10/7/2015 5:04  37959089   \n",
       "4    -24.921069  31.520836    10/7/2015 5:19  27793716   \n",
       "5    -24.047833  31.687000     5/7/2015 5:55  88751696   \n",
       "6    -24.044000  31.687000     3/7/2015 5:57  88751696   \n",
       "7    -25.498371  30.983844     9/7/2015 6:21  52431146   \n",
       "8    -24.342279  30.990728     7/7/2015 7:15  11505530   \n",
       "9    -25.413386  31.159930    11/7/2015 7:17  24990235   \n",
       "10   -24.517680  31.162064     9/7/2015 7:22  39790157   \n",
       "11   -25.469772  30.956835     5/7/2015 7:24  39646946   \n",
       "12   -25.470622  30.993113    12/7/2015 7:27  54372438   \n",
       "13   -25.470622  30.993113    12/7/2015 7:27  54372438   \n",
       "14   -25.470520  30.979195     8/7/2015 7:38   2518415   \n",
       "15   -25.182072  31.112934     5/7/2015 7:55  28143688   \n",
       "16   -24.384113  31.046408     3/7/2015 8:08  68184135   \n",
       "17   -23.937441  31.027850    12/7/2015 8:38  61978646   \n",
       "18   -25.498773  31.173228     4/7/2015 8:47  98277872   \n",
       "19   -25.437894  31.960251    11/7/2015 9:02  19151287   \n",
       "20   -25.289637  31.055169     1/7/2015 9:08  34680030   \n",
       "21   -25.437894  31.960251     8/7/2015 9:09  19151287   \n",
       "22   -23.521025  31.395103     6/7/2015 9:15  37106688   \n",
       "23   -24.859702  31.401844     3/7/2015 9:23  27793716   \n",
       "24   -25.492772  30.971141     3/7/2015 9:44  34589050   \n",
       "25   -23.949790  31.151078     1/7/2015 9:49  94490818   \n",
       "26   -25.456545  30.998739   12/7/2015 10:07  36870228   \n",
       "27   -25.493817  30.985831    9/7/2015 10:14   4770267   \n",
       "28   -25.488667  31.017167    8/7/2015 10:39  86295905   \n",
       "29   -25.507035  30.982462    5/7/2015 10:54  17095038   \n",
       "...         ...        ...               ...       ...   \n",
       "1970 -25.452000  30.880667   9/30/2015 14:11  21104631   \n",
       "1971 -25.473150  30.980428   9/30/2015 14:15  93275103   \n",
       "1972 -23.097000  31.397333   9/30/2015 14:17  95395153   \n",
       "1973 -25.155498  31.198091   9/30/2015 14:18  19044147   \n",
       "1974 -25.473150  30.980428   1/30/2015 14:20  93275103   \n",
       "1975 -24.872175  31.545572   3/30/2015 14:36  37959089   \n",
       "1976 -25.379850  31.718936  2015-02-30 14:44  94123653   \n",
       "1977 -25.056167  30.988833   3/30/2015 15:06  53340298   \n",
       "1978 -25.035303  31.114082  10/30/2015 15:12  64818554   \n",
       "1979 -25.473295  30.969345   6/30/2015 15:30  41418953   \n",
       "1980 -25.331828  30.996908  10/30/2015 15:43  85739127   \n",
       "1981 -24.872175  31.545572   7/30/2015 15:44  37959089   \n",
       "1982 -24.220687  30.973517   9/30/2015 15:46  74498061   \n",
       "1983 -24.393738  31.780053  12/30/2015 16:22  91614500   \n",
       "1984 -24.223025  30.958667  2015-02-30 16:35  74498061   \n",
       "1985 -25.356635  31.682165   6/30/2015 16:40  22013943   \n",
       "1986 -22.652883  30.926728   8/30/2015 16:42  64336802   \n",
       "1987 -25.470157  30.950590  2015-02-30 17:22  86267392   \n",
       "1988 -25.470289  30.950547   8/30/2015 17:35  86267392   \n",
       "1989 -25.475435  30.969687   8/30/2015 17:39  86267392   \n",
       "1990 -25.155498  31.198091  10/30/2015 18:23  73766394   \n",
       "1991 -25.033090  31.089304  10/30/2015 18:31  38691411   \n",
       "1992 -25.313167  31.097667   8/30/2015 18:58  15525879   \n",
       "1993 -24.662218  31.048188  10/30/2015 19:02  31944101   \n",
       "1994 -24.786132  31.428992   9/30/2015 20:00  56523514   \n",
       "1995 -23.921780  31.024793   4/30/2015 20:21  54918463   \n",
       "1996 -24.794730  31.344255     2/1/2015 2:04  15284579   \n",
       "1997 -24.502593  30.891696     5/1/2015 2:14  41807690   \n",
       "1998 -24.349882  30.948071     1/1/2015 2:39  29890436   \n",
       "1999 -24.345782  30.979891     5/1/2015 2:43  29890436   \n",
       "\n",
       "                              geometry  \n",
       "0      POINT (-24.98079249 31.4846333)  \n",
       "1     POINT (-25.49922467 31.50890561)  \n",
       "2     POINT (-24.34257846 30.93086607)  \n",
       "3     POINT (-24.85461393 31.51971844)  \n",
       "4     POINT (-24.92106889 31.52083556)  \n",
       "5          POINT (-24.04783333 31.687)  \n",
       "6               POINT (-24.044 31.687)  \n",
       "7     POINT (-25.49837105 30.98384409)  \n",
       "8     POINT (-24.34227949 30.99072848)  \n",
       "9         POINT (-25.4133857 31.15993)  \n",
       "10    POINT (-24.51767986 31.16206385)  \n",
       "11        POINT (-25.469772 30.956835)  \n",
       "12    POINT (-25.47062201 30.99311259)  \n",
       "13    POINT (-25.47062201 30.99311259)  \n",
       "14     POINT (-25.47052022 30.9791946)  \n",
       "15      POINT (-25.1820724 31.1129338)  \n",
       "16    POINT (-24.38411333 31.04640833)  \n",
       "17    POINT (-23.93744078 31.02785013)  \n",
       "18    POINT (-25.49877312 31.17322846)  \n",
       "19    POINT (-25.43789368 31.96025054)  \n",
       "20    POINT (-25.28963669 31.05516887)  \n",
       "21    POINT (-25.43789368 31.96025054)  \n",
       "22      POINT (-23.521025 31.39510333)  \n",
       "23      POINT (-24.859702 31.40184394)  \n",
       "24    POINT (-25.49277169 30.97114069)  \n",
       "25    POINT (-23.94978964 31.15107752)  \n",
       "26    POINT (-25.45654532 30.99873878)  \n",
       "27    POINT (-25.49381657 30.98583063)  \n",
       "28    POINT (-25.48866667 31.01716667)  \n",
       "29     POINT (-25.5070354 30.98246192)  \n",
       "...                                ...  \n",
       "1970       POINT (-25.452 30.88066667)  \n",
       "1971       POINT (-25.47315 30.980428)  \n",
       "1972       POINT (-23.097 31.39733333)  \n",
       "1973  POINT (-25.15549843 31.19809118)  \n",
       "1974       POINT (-25.47315 30.980428)  \n",
       "1975  POINT (-24.87217481 31.54557205)  \n",
       "1976  POINT (-25.37985008 31.71893638)  \n",
       "1977  POINT (-25.05616667 30.98883333)  \n",
       "1978  POINT (-25.03530327 31.11408174)  \n",
       "1979    POINT (-25.47329535 30.969345)  \n",
       "1980  POINT (-25.33182833 30.99690833)  \n",
       "1981  POINT (-24.87217481 31.54557205)  \n",
       "1982  POINT (-24.22068667 30.97351667)  \n",
       "1983  POINT (-24.39373833 31.78005333)  \n",
       "1984    POINT (-24.223025 30.95866667)  \n",
       "1985  POINT (-25.35663523 31.68216512)  \n",
       "1986  POINT (-22.65288333 30.92672833)  \n",
       "1987  POINT (-25.47015673 30.95059002)  \n",
       "1988  POINT (-25.47028866 30.95054685)  \n",
       "1989  POINT (-25.47543533 30.96968651)  \n",
       "1990  POINT (-25.15549843 31.19809118)  \n",
       "1991  POINT (-25.03309011 31.08930402)  \n",
       "1992  POINT (-25.31316667 31.09766667)  \n",
       "1993  POINT (-24.66221819 31.04818763)  \n",
       "1994  POINT (-24.78613216 31.42899182)  \n",
       "1995  POINT (-23.92178018 31.02479311)  \n",
       "1996       POINT (-24.79473 31.344255)  \n",
       "1997  POINT (-24.50259291 30.89169567)  \n",
       "1998  POINT (-24.34988171 30.94807142)  \n",
       "1999  POINT (-24.34578244 30.97989099)  \n",
       "\n",
       "[2000 rows x 5 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Iterate over the rows of the DataFrame and insert Point objects into column\n",
    "## geometry (you need to use .loc indexer to update the row, see materials\n",
    "for index, row in my_data.iterrows():\n",
    "    point = Point(row[0], row[1])\n",
    "    my_data.loc[index, 'geometry'] = point\n",
    "my_data    \n",
    "#...'geometry'] = my_data.loc[index,['lat','lon']]    \n",
    "#error    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Convert that DataFrame into a GeoDataFrame\n",
    "## hint: gpd.GeoDataFrame(DataFrame,geometry = 'geometry column name')\n",
    "geo = gpd.GeoDataFrame(my_data, geometry = 'geometry')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  Update the CRS for coordinate system as WGS84 (i.e. epsg code: 4326)\n",
    "from fiona.crs import from_epsg\n",
    "geo.crs = from_epsg(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "##  Save the data into a Shapefile called Kruger_posts.shp\n",
    "geo.to_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\Kruger_posts.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2049d9f0>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H1WrF7/fT1tbGxMQEUkra2toYHh6mvr4egEAgwFxwnutbwkSkwGOL8bHWMANB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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2049d870>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "##  Create a simple map of those points using a GIS software or using .plot() -funtion in Python.\n",
    "geo.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Create buffers around shopping centers (20 points)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate a 5 km buffer around the shooping center points (shopping_centers.shp)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules\n",
    "# e.g. import pandas as pd, geopandas as gpd;\n",
    "# from shapely.geometry import Point,LineString, Polygon\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import fiona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read the shopping_centers.shp in data folder using gpd.readfile()\n",
    "shpctrs = gpd.read_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\ShoCen\\shopping_centers.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address_x</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>address_y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>buffer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7400 San Pedro Ave, San Antonio, TX 78216, USA</td>\n",
       "      <td>1</td>\n",
       "      <td>North Star Mall</td>\n",
       "      <td>7400 San Pedro Ave, San Antonio, TX 78216</td>\n",
       "      <td>POINT (2128310.54719041 13736229.85452912)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15900 La Cantera Pkwy, San Antonio, TX 78256, USA</td>\n",
       "      <td>2</td>\n",
       "      <td>The Shops at La Cantera</td>\n",
       "      <td>15900 La Cantera Pkwy, San Antonio, TX 78256</td>\n",
       "      <td>POINT (2090561.70545312 13763128.71206174)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6301 NW Loop 410, San Antonio, TX 78238, USA</td>\n",
       "      <td>3</td>\n",
       "      <td>Ingram Park Mall</td>\n",
       "      <td>6301 NW Loop 410, San Antonio, TX 78238</td>\n",
       "      <td>POINT (2089050.909919086 13718295.44962042)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>849 E Commerce St, San Antonio, TX 78205, USA</td>\n",
       "      <td>4</td>\n",
       "      <td>Shops at Rivercenter</td>\n",
       "      <td>849 E Commerce St, San Antonio, TX 78205</td>\n",
       "      <td>POINT (2132618.022841336 13701765.55606672)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2310 SW Military Dr, San Antonio, TX 78224, USA</td>\n",
       "      <td>5</td>\n",
       "      <td>South Park Mall</td>\n",
       "      <td>2310 SW Military Dr, San Antonio, TX 78224</td>\n",
       "      <td>POINT (2117776.095366633 13676107.3635996)</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           address_x  id  \\\n",
       "0     7400 San Pedro Ave, San Antonio, TX 78216, USA   1   \n",
       "1  15900 La Cantera Pkwy, San Antonio, TX 78256, USA   2   \n",
       "2       6301 NW Loop 410, San Antonio, TX 78238, USA   3   \n",
       "3      849 E Commerce St, San Antonio, TX 78205, USA   4   \n",
       "4    2310 SW Military Dr, San Antonio, TX 78224, USA   5   \n",
       "\n",
       "                      name                                     address_y  \\\n",
       "0          North Star Mall     7400 San Pedro Ave, San Antonio, TX 78216   \n",
       "1  The Shops at La Cantera  15900 La Cantera Pkwy, San Antonio, TX 78256   \n",
       "2         Ingram Park Mall       6301 NW Loop 410, San Antonio, TX 78238   \n",
       "3     Shops at Rivercenter      849 E Commerce St, San Antonio, TX 78205   \n",
       "4          South Park Mall    2310 SW Military Dr, San Antonio, TX 78224   \n",
       "\n",
       "                                      geometry buffer  \n",
       "0   POINT (2128310.54719041 13736229.85452912)   None  \n",
       "1   POINT (2090561.70545312 13763128.71206174)   None  \n",
       "2  POINT (2089050.909919086 13718295.44962042)   None  \n",
       "3  POINT (2132618.022841336 13701765.55606672)   None  \n",
       "4   POINT (2117776.095366633 13676107.3635996)   None  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new empty column called 'buffer' to your shopping-centers GeoDataFrame\n",
    "shpctrs['buffer'] = None\n",
    "shpctrs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>address_x</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>address_y</th>\n",
       "      <th>geometry</th>\n",
       "      <th>buffer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7400 San Pedro Ave, San Antonio, TX 78216, USA</td>\n",
       "      <td>1</td>\n",
       "      <td>North Star Mall</td>\n",
       "      <td>7400 San Pedro Ave, San Antonio, TX 78216</td>\n",
       "      <td>POINT (2128310.54719041 13736229.85452912)</td>\n",
       "      <td>POLYGON ((2144714.74719041 13736229.85452912, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15900 La Cantera Pkwy, San Antonio, TX 78256, USA</td>\n",
       "      <td>2</td>\n",
       "      <td>The Shops at La Cantera</td>\n",
       "      <td>15900 La Cantera Pkwy, San Antonio, TX 78256</td>\n",
       "      <td>POINT (2090561.70545312 13763128.71206174)</td>\n",
       "      <td>POLYGON ((2106965.905453119 13763128.71206174,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6301 NW Loop 410, San Antonio, TX 78238, USA</td>\n",
       "      <td>3</td>\n",
       "      <td>Ingram Park Mall</td>\n",
       "      <td>6301 NW Loop 410, San Antonio, TX 78238</td>\n",
       "      <td>POINT (2089050.909919086 13718295.44962042)</td>\n",
       "      <td>POLYGON ((2105455.109919087 13718295.44962042,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>849 E Commerce St, San Antonio, TX 78205, USA</td>\n",
       "      <td>4</td>\n",
       "      <td>Shops at Rivercenter</td>\n",
       "      <td>849 E Commerce St, San Antonio, TX 78205</td>\n",
       "      <td>POINT (2132618.022841336 13701765.55606672)</td>\n",
       "      <td>POLYGON ((2149022.222841336 13701765.55606672,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2310 SW Military Dr, San Antonio, TX 78224, USA</td>\n",
       "      <td>5</td>\n",
       "      <td>South Park Mall</td>\n",
       "      <td>2310 SW Military Dr, San Antonio, TX 78224</td>\n",
       "      <td>POINT (2117776.095366633 13676107.3635996)</td>\n",
       "      <td>POLYGON ((2134180.295366633 13676107.3635996, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           address_x  id  \\\n",
       "0     7400 San Pedro Ave, San Antonio, TX 78216, USA   1   \n",
       "1  15900 La Cantera Pkwy, San Antonio, TX 78256, USA   2   \n",
       "2       6301 NW Loop 410, San Antonio, TX 78238, USA   3   \n",
       "3      849 E Commerce St, San Antonio, TX 78205, USA   4   \n",
       "4    2310 SW Military Dr, San Antonio, TX 78224, USA   5   \n",
       "\n",
       "                      name                                     address_y  \\\n",
       "0          North Star Mall     7400 San Pedro Ave, San Antonio, TX 78216   \n",
       "1  The Shops at La Cantera  15900 La Cantera Pkwy, San Antonio, TX 78256   \n",
       "2         Ingram Park Mall       6301 NW Loop 410, San Antonio, TX 78238   \n",
       "3     Shops at Rivercenter      849 E Commerce St, San Antonio, TX 78205   \n",
       "4          South Park Mall    2310 SW Military Dr, San Antonio, TX 78224   \n",
       "\n",
       "                                      geometry  \\\n",
       "0   POINT (2128310.54719041 13736229.85452912)   \n",
       "1   POINT (2090561.70545312 13763128.71206174)   \n",
       "2  POINT (2089050.909919086 13718295.44962042)   \n",
       "3  POINT (2132618.022841336 13701765.55606672)   \n",
       "4   POINT (2117776.095366633 13676107.3635996)   \n",
       "\n",
       "                                              buffer  \n",
       "0  POLYGON ((2144714.74719041 13736229.85452912, ...  \n",
       "1  POLYGON ((2106965.905453119 13763128.71206174,...  \n",
       "2  POLYGON ((2105455.109919087 13718295.44962042,...  \n",
       "3  POLYGON ((2149022.222841336 13701765.55606672,...  \n",
       "4  POLYGON ((2134180.295366633 13676107.3635996, ...  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Iterate over the rows of the DataFrame and update the buffer column with a \n",
    "## 5 km buffer Polygon (because the unit is feet, so 16404.2 is the buffer distance)\n",
    "## You need to use .loc indexer to update the row.\n",
    "## Use Shapely's buffer function to create the buffer \n",
    "## (see http://toblerity.org/shapely/manual.html#object.buffer for details how to use it)\n",
    "## You only need to use the distance -parameter, don't care about the other parameters.\n",
    "#.buffer(16404.2)\n",
    "\n",
    "for i, row in shpctrs.iterrows():\n",
    "    pointbuff = shpctrs.loc[i, 'geometry']\n",
    "    shpctrs.loc[i,'buffer'] = pointbuff.buffer(16404.2)\n",
    "shpctrs\n",
    "#need to clear buffer row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Replace the values in geometry column with the values of buffer column, and drop the buffer column.\n",
    "shpctrs['geometry'] = shpctrs['buffer'] \n",
    "shpctrs = shpctrs.drop(['buffer', 'address_y'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1eb708d0>"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1eb70f30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Have a look of the data you created by using GeoDataFrame.plot() -funtion.\n",
    "%matplotlib inline\n",
    "shpctrs.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##  Save the data into a Shapefile called shopping_centers_5kmbuffer.shp\n",
    "shpctrs.to_file(r\"C:\\Users\\casca\\Downloads\\SimpleAccess\\shopping_centers_5kmbuffer.shp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": false,
   "sideBar": false,
   "skip_h1_title": true,
   "toc_cell": false,
   "toc_position": {
    "height": "40px",
    "left": "578px",
    "right": "20px",
    "top": "147px",
    "width": "380px"
   },
   "toc_section_display": "none",
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}