1_geopandas.ipynb

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    "### Geopandas: Introduction  \n",
    "see also: https://geopandas.readthedocs.io/en/stable/  \n",
    "Spatial data can be read easily with geopandas using gpd.from_file() -function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gp"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set filepath (fix path relative to yours)\n",
    "fp = \"data/DAMSELFISH_distributions.shp\"\n",
    "# Read file using gpd.read_file()\n",
    "data = gp.read_file(fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s see what datatype is our 'data' variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "geopandas.geodataframe.GeoDataFrame"
      ]
     },
     "execution_count": 3,
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   ],
   "source": [
    "type(data)"
   ]
  },
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   "source": [
    "Let’s take a look at our data and print the first 5 rows using the\n",
    "head() -function prints the first 5 rows by default"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s also take a look how our data looks like on a map. If you just want to explore your data on a map, you can use .plot() -function in geopandas that creates a simple map out of the data (uses matplotlib as a backend):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Writing a new Shapefile is also something that is needed frequently.\n",
    "Let’s select 50 first rows of the input data and write those into a new Shapefile by first selecting the data using index slicing and then write the selection into a Shapefile with gp.to_file() -function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a output path for the data\n",
    "out = \"data/DAMSELFISH_distributions_SELECTION.shp\"\n",
    "\n",
    "# Select first 50 rows\n",
    "selection = data[0:50]\n",
    "\n",
    "# Write those rows into a new Shapefile (the default output file format is Shapefile)\n",
    "selection.to_file(out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# It is possible to use only specific columns by specifying the column name within square brackets []\n",
    "data['geometry'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can iterate over the selected rows using a specific .iterrows() -function in (geo)pandas and print the area for each polygon:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in selection.iterrows():\n",
    "   poly_area = row['geometry'].area\n",
    "   print(\"Polygon area at index {0} is: {1:.3f}\".format(index, poly_area))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hence, as you might guess from here, all the functionalities of Pandas are available directly in Geopandas without the need to call pandas separately because Geopandas is an extension for Pandas.\n",
    "\n",
    "* Let’s next create a new column into our GeoDataFrame where we calculate\n",
    "\n",
    "and store the areas individual polygons. Calculating the areas of polygons is really easy in geopandas by using **GeoDataFrame.area** attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    19.396254\n",
       "1     6.145902\n",
       "Name: area, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['area'] = data.area\n",
    "data['area'].head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Course materials from: https://automating-gis-processes.github.io/CSC18/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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