Lab5_Tasks_Completed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab5 Feature Class\n",
    "----------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "DataFrame and Data Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Import geopandas module\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Read build-in dataset using gpd.read_file()\n",
    "data = gpd.read_file(\"C:\\Users\\casca\\Downloads\\AccessBB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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>shape_leng</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>boro_code</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>330434.340939</td>\n",
       "      <td>1.623914e+09</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5.0</td>\n",
       "      <td>(POLYGON ((-74.05050806403247 40.5664220341608...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>361657.416718</td>\n",
       "      <td>6.366004e+08</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(POLYGON ((-74.01092841268032 40.6844914725429...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>741150.814455</td>\n",
       "      <td>1.937596e+09</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>(POLYGON ((-73.86706149472119 40.5820879767934...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895729.259626</td>\n",
       "      <td>3.044780e+09</td>\n",
       "      <td>Queens</td>\n",
       "      <td>4.0</td>\n",
       "      <td>(POLYGON ((-73.83668274106707 40.5949466970158...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>463454.790585</td>\n",
       "      <td>1.186619e+09</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>2.0</td>\n",
       "      <td>(POLYGON ((-73.89680883223774 40.7958084451597...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      shape_leng    shape_area      boro_name  boro_code  \\\n",
       "0  330434.340939  1.623914e+09  Staten Island        5.0   \n",
       "1  361657.416718  6.366004e+08      Manhattan        1.0   \n",
       "2  741150.814455  1.937596e+09       Brooklyn        3.0   \n",
       "3  895729.259626  3.044780e+09         Queens        4.0   \n",
       "4  463454.790585  1.186619e+09          Bronx        2.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  (POLYGON ((-74.05050806403247 40.5664220341608...  \n",
       "1  (POLYGON ((-74.01092841268032 40.6844914725429...  \n",
       "2  (POLYGON ((-73.86706149472119 40.5820879767934...  \n",
       "3  (POLYGON ((-73.83668274106707 40.5949466970158...  \n",
       "4  (POLYGON ((-73.89680883223774 40.7958084451597...  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the firs 5 rows of data\n",
    "data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the type of data using type()\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    (POLYGON ((-74.05050806403247 40.5664220341608...\n",
       "1    (POLYGON ((-74.01092841268032 40.6844914725429...\n",
       "2    (POLYGON ((-73.86706149472119 40.5820879767934...\n",
       "3    (POLYGON ((-73.83668274106707 40.5949466970158...\n",
       "4    (POLYGON ((-73.89680883223774 40.7958084451597...\n",
       "Name: geometry, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the geometry column by specifying the column name \n",
    "# within square brackets [], save it as a variable \"geoColumn\"\n",
    "# print the firs 5 rows of geoColumn\n",
    "geoColumn = data['geometry']\n",
    "geoColumn.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geoseries.GeoSeries"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the type of geoColumn using type()\n",
    "type(geoColumn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0.0160464654405\n",
      "1\n",
      "0.00630886205976\n",
      "2\n",
      "0.0191642375386\n",
      "3\n",
      "0.0301432839186\n",
      "4\n",
      "0.0117728754753\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.011772875475342993"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a selection that contains only the first five rows\n",
    "# of data, rename it as selection\n",
    "selection = data[0:5]\n",
    "# We can iterate over the selected rows using a specific .iterrows() \n",
    "# -function in (geo)pandas and print the x coordinate for each point\n",
    "for index, row in selection.iterrows():\n",
    "    print index\n",
    "    print row['geometry'].area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "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>shape_leng</th>\n",
       "      <th>shape_area</th>\n",
       "      <th>boro_name</th>\n",
       "      <th>boro_code</th>\n",
       "      <th>geometry</th>\n",
       "      <th>area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>330434.340939</td>\n",
       "      <td>1.623914e+09</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>5.0</td>\n",
       "      <td>(POLYGON ((-74.05050806403247 40.5664220341608...</td>\n",
       "      <td>0.016046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>361657.416718</td>\n",
       "      <td>6.366004e+08</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>(POLYGON ((-74.01092841268032 40.6844914725429...</td>\n",
       "      <td>0.006309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>741150.814455</td>\n",
       "      <td>1.937596e+09</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>3.0</td>\n",
       "      <td>(POLYGON ((-73.86706149472119 40.5820879767934...</td>\n",
       "      <td>0.019164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>895729.259626</td>\n",
       "      <td>3.044780e+09</td>\n",
       "      <td>Queens</td>\n",
       "      <td>4.0</td>\n",
       "      <td>(POLYGON ((-73.83668274106707 40.5949466970158...</td>\n",
       "      <td>0.030143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>463454.790585</td>\n",
       "      <td>1.186619e+09</td>\n",
       "      <td>Bronx</td>\n",
       "      <td>2.0</td>\n",
       "      <td>(POLYGON ((-73.89680883223774 40.7958084451597...</td>\n",
       "      <td>0.011773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      shape_leng    shape_area      boro_name  boro_code  \\\n",
       "0  330434.340939  1.623914e+09  Staten Island        5.0   \n",
       "1  361657.416718  6.366004e+08      Manhattan        1.0   \n",
       "2  741150.814455  1.937596e+09       Brooklyn        3.0   \n",
       "3  895729.259626  3.044780e+09         Queens        4.0   \n",
       "4  463454.790585  1.186619e+09          Bronx        2.0   \n",
       "\n",
       "                                            geometry      area  \n",
       "0  (POLYGON ((-74.05050806403247 40.5664220341608...  0.016046  \n",
       "1  (POLYGON ((-74.01092841268032 40.6844914725429...  0.006309  \n",
       "2  (POLYGON ((-73.86706149472119 40.5820879767934...  0.019164  \n",
       "3  (POLYGON ((-73.83668274106707 40.5949466970158...  0.030143  \n",
       "4  (POLYGON ((-73.89680883223774 40.7958084451597...  0.011773  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let’s next create a new column 'area' into our GeoDataFrame (data) to \n",
    "# store the area of each polygon\n",
    "data['area'] = data.area\n",
    "# print the first five row of data\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0301432839186\n",
      "0.0166871448866\n"
     ]
    }
   ],
   "source": [
    "# use panda functions to get the maximum and mean area of these polygon.\n",
    "# hint: .max(), .mean()\n",
    "print data['area'].max()\n",
    "print data['area'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read and write shapefile."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download data from this [link](https://github.com/Automating-GIS-processes/Lesson-2-Geo-DataFrames/raw/master/data/Data.zip). \n",
    "Unzip the file into your lab folder.  \n",
    "The Data folder includes a Shapefile called DAMSELFISH_distribution.shp (and files related to it)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "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": [
    {
     "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>ID_NO</th>\n",
       "      <th>BINOMIAL</th>\n",
       "      <th>ORIGIN</th>\n",
       "      <th>COMPILER</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>CITATION</th>\n",
       "      <th>SOURCE</th>\n",
       "      <th>DIST_COMM</th>\n",
       "      <th>ISLAND</th>\n",
       "      <th>SUBSPECIES</th>\n",
       "      <th>...</th>\n",
       "      <th>RL_UPDATE</th>\n",
       "      <th>KINGDOM_NA</th>\n",
       "      <th>PHYLUM_NAM</th>\n",
       "      <th>CLASS_NAME</th>\n",
       "      <th>ORDER_NAME</th>\n",
       "      <th>FAMILY_NAM</th>\n",
       "      <th>GENUS_NAME</th>\n",
       "      <th>SPECIES_NA</th>\n",
       "      <th>CATEGORY</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>183963.0</td>\n",
       "      <td>Stegastes leucorus</td>\n",
       "      <td>1</td>\n",
       "      <td>IUCN</td>\n",
       "      <td>2010</td>\n",
       "      <td>International Union for Conservation of Nature...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2012.1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>POMACENTRIDAE</td>\n",
       "      <td>Stegastes</td>\n",
       "      <td>leucorus</td>\n",
       "      <td>VU</td>\n",
       "      <td>POLYGON ((-115.6437454219999 29.71392059300007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183963.0</td>\n",
       "      <td>Stegastes leucorus</td>\n",
       "      <td>1</td>\n",
       "      <td>IUCN</td>\n",
       "      <td>2010</td>\n",
       "      <td>International Union for Conservation of Nature...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2012.1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>POMACENTRIDAE</td>\n",
       "      <td>Stegastes</td>\n",
       "      <td>leucorus</td>\n",
       "      <td>VU</td>\n",
       "      <td>POLYGON ((-105.589950704 21.89339825500002, -1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>183963.0</td>\n",
       "      <td>Stegastes leucorus</td>\n",
       "      <td>1</td>\n",
       "      <td>IUCN</td>\n",
       "      <td>2010</td>\n",
       "      <td>International Union for Conservation of Nature...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2012.1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>POMACENTRIDAE</td>\n",
       "      <td>Stegastes</td>\n",
       "      <td>leucorus</td>\n",
       "      <td>VU</td>\n",
       "      <td>POLYGON ((-111.159618439 19.01535626700007, -1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>183793.0</td>\n",
       "      <td>Chromis intercrusma</td>\n",
       "      <td>1</td>\n",
       "      <td>IUCN</td>\n",
       "      <td>2010</td>\n",
       "      <td>International Union for Conservation of Nature...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2012.1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>POMACENTRIDAE</td>\n",
       "      <td>Chromis</td>\n",
       "      <td>intercrusma</td>\n",
       "      <td>LC</td>\n",
       "      <td>POLYGON ((-80.86500229899997 -0.77894492099994...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>183793.0</td>\n",
       "      <td>Chromis intercrusma</td>\n",
       "      <td>1</td>\n",
       "      <td>IUCN</td>\n",
       "      <td>2010</td>\n",
       "      <td>International Union for Conservation of Nature...</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>...</td>\n",
       "      <td>2012.1</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>PERCIFORMES</td>\n",
       "      <td>POMACENTRIDAE</td>\n",
       "      <td>Chromis</td>\n",
       "      <td>intercrusma</td>\n",
       "      <td>LC</td>\n",
       "      <td>POLYGON ((-67.33922225599997 -55.6761029239999...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ID_NO             BINOMIAL  ORIGIN COMPILER  YEAR  \\\n",
       "0  183963.0   Stegastes leucorus       1     IUCN  2010   \n",
       "1  183963.0   Stegastes leucorus       1     IUCN  2010   \n",
       "2  183963.0   Stegastes leucorus       1     IUCN  2010   \n",
       "3  183793.0  Chromis intercrusma       1     IUCN  2010   \n",
       "4  183793.0  Chromis intercrusma       1     IUCN  2010   \n",
       "\n",
       "                                            CITATION SOURCE DIST_COMM ISLAND  \\\n",
       "0  International Union for Conservation of Nature...                           \n",
       "1  International Union for Conservation of Nature...                           \n",
       "2  International Union for Conservation of Nature...                           \n",
       "3  International Union for Conservation of Nature...                           \n",
       "4  International Union for Conservation of Nature...                           \n",
       "\n",
       "  SUBSPECIES                        ...                         RL_UPDATE  \\\n",
       "0                                   ...                            2012.1   \n",
       "1                                   ...                            2012.1   \n",
       "2                                   ...                            2012.1   \n",
       "3                                   ...                            2012.1   \n",
       "4                                   ...                            2012.1   \n",
       "\n",
       "  KINGDOM_NA  PHYLUM_NAM      CLASS_NAME   ORDER_NAME     FAMILY_NAM  \\\n",
       "0   ANIMALIA    CHORDATA  ACTINOPTERYGII  PERCIFORMES  POMACENTRIDAE   \n",
       "1   ANIMALIA    CHORDATA  ACTINOPTERYGII  PERCIFORMES  POMACENTRIDAE   \n",
       "2   ANIMALIA    CHORDATA  ACTINOPTERYGII  PERCIFORMES  POMACENTRIDAE   \n",
       "3   ANIMALIA    CHORDATA  ACTINOPTERYGII  PERCIFORMES  POMACENTRIDAE   \n",
       "4   ANIMALIA    CHORDATA  ACTINOPTERYGII  PERCIFORMES  POMACENTRIDAE   \n",
       "\n",
       "  GENUS_NAME   SPECIES_NA CATEGORY  \\\n",
       "0  Stegastes     leucorus       VU   \n",
       "1  Stegastes     leucorus       VU   \n",
       "2  Stegastes     leucorus       VU   \n",
       "3    Chromis  intercrusma       LC   \n",
       "4    Chromis  intercrusma       LC   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-115.6437454219999 29.71392059300007...  \n",
       "1  POLYGON ((-105.589950704 21.89339825500002, -1...  \n",
       "2  POLYGON ((-111.159618439 19.01535626700007, -1...  \n",
       "3  POLYGON ((-80.86500229899997 -0.77894492099994...  \n",
       "4  POLYGON ((-67.33922225599997 -55.6761029239999...  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let’s take a look at the data and print the first 5 rows\n",
    "# hint: head()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x115c20b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Evx+y/dhxPt6Rzb9+M6FL+/u6O3N0hTW6Z9zy73h23RGmRvvj7GQgLtSLRy8YTULMibA/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\nZ43gzGg/6k0a72/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11510c30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Let’s also take a look how our data looks like on a map. If you just\n",
    "# want to explore your data on a map, you can use .plot() -function in geopandas \n",
    "# that creates a simple map out of the data\n",
    "data.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let’s select first 10 rows of the input data, save it as data10\n",
    "data10 = data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# write the selection into a Shapefile (Frist10.shp) with geodataframe.to_file() -function\n",
    "data10.to_file(r'C:\\Users\\casca\\Downloads\\DataDamselFish\\Data\\first10.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a shapefile with 3 point features from scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import necessary modules first\n",
    "# e.g. geopandas, shapely geometries, fiona\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point, LineString, Polygon\n",
    "import fiona\n",
    "\n",
    "# Create an empty geopandas GeoDataFrame using GeoDataFrame()\n",
    "newdata = gpd.GeoDataFrame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Empty GeoDataFrame\n",
      "Columns: [geometry]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "# Create a new column called 'geometry' to the GeoDataFrame\n",
    "newdata['geometry']=None\n",
    "print newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create 3 Shapely points with random coordinates using Point()\n",
    "point1 = Point(2,2, 4.2)\n",
    "point2 = Point(7.2, -25.1)\n",
    "point3 = Point(9.26, -2.456)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "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>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT Z (2 2 4.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (7.2 -25.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (9.26 -2.456)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              geometry\n",
       "0    POINT Z (2 2 4.2)\n",
       "1    POINT (7.2 -25.1)\n",
       "2  POINT (9.26 -2.456)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Insert the points into 'geometry' -column at index 0-2\n",
    "# using Geodataframe.loc[rowindex, columnindex] = your point\n",
    "newdata.loc[0,'geometry'] = point1\n",
    "newdata.loc[1,'geometry'] = point2\n",
    "newdata.loc[2,'geometry'] = point3\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>geometry</th>\n",
       "      <th>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>POINT Z (2 2 4.2)</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>POINT (7.2 -25.1)</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>POINT (9.26 -2.456)</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              geometry name\n",
       "0    POINT Z (2 2 4.2)    A\n",
       "1    POINT (7.2 -25.1)    B\n",
       "2  POINT (9.26 -2.456)    C"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a new column called name and insert data\n",
    "newdata.loc[0:2, 'name'] = list(\"ABC\")\n",
    "newdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import specific function 'from_epsg' from fiona module\n",
    "from fiona.crs import from_epsg\n",
    "# Set the GeoDataFrame's coordinate system to WGS84\n",
    "newdata.crs = from_epsg(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# # write the newfile into a Shapefile with geodataframe.to_file() -function\n",
    "out = r\"C:\\Users\\casca\\Downloads\\AdvProGISt4\\NewCreatedShpTask.shp\"\n",
    "newdata.to_file(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\casca\\\\Downloads\\\\AdvProGISt4\\\\NewCreatedShpTask.shp'"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x116d73b0>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAGgAAAD8CAYAAACb6+H0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1169e750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newdata.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.10"
  },
  "toc": {
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   "toc_position": {
    "height": "326px",
    "left": "791px",
    "right": "20px",
    "top": "110.5px",
    "width": "144px"
   },
   "toc_section_display": "block",
   "toc_window_display": true
  }
 },
 "nbformat": 4,
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}