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    "\n",
    "# Creating a GeoDataFrame from a DataFrame with coordinates\n",
    "\n",
    "This example shows how to create a ``GeoDataFrame`` when starting from\n",
    "a *regular* ``DataFrame`` that has coordinates either WKT\n",
    "([well-known text](https://en.wikipedia.org/wiki/Well-known_text))\n",
    "format, or in\n",
    "two columns.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From longitudes and latitudes\n",
    "=============================\n",
    "\n",
    "First, let's consider a ``DataFrame`` containing cities and their respective\n",
    "longitudes and latitudes.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
    "     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
    "     'Latitude': [-34.58, -15.78, -33.45, 4.60, 10.48],\n",
    "     'Longitude': [-58.66, -47.91, -70.66, -74.08, -66.86]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A ``GeoDataFrame`` needs a ``shapely`` object. We use geopandas\n",
    "``points_from_xy()`` to transform **Longitude** and **Latitude** into a list\n",
    "of ``shapely.Point`` objects and set it as a ``geometry`` while creating the\n",
    "``GeoDataFrame``. (note that ``points_from_xy()`` is an enhanced wrapper for\n",
    "``[Point(x, y) for x, y in zip(df.Longitude, df.Latitude)]``)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = geopandas.GeoDataFrame(\n",
    "    df, geometry=geopandas.points_from_xy(df.Longitude, df.Latitude))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "``gdf`` looks like this :\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(gdf.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we plot the coordinates over a country-level map.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [],
   "source": [
    "world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))\n",
    "\n",
    "# We restrict to South America.\n",
    "ax = world[world.continent == 'South America'].plot(\n",
    "    color='white', edgecolor='black')\n",
    "\n",
    "# We can now plot our ``GeoDataFrame``.\n",
    "gdf.plot(ax=ax, color='red')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From WKT format\n",
    "===============\n",
    "Here, we consider a ``DataFrame`` having coordinates in WKT format.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(\n",
    "    {'City': ['Buenos Aires', 'Brasilia', 'Santiago', 'Bogota', 'Caracas'],\n",
    "     'Country': ['Argentina', 'Brazil', 'Chile', 'Colombia', 'Venezuela'],\n",
    "     'Coordinates': ['POINT(-58.66 -34.58)', 'POINT(-47.91 -15.78)',\n",
    "                     'POINT(-70.66 -33.45)', 'POINT(-74.08 4.60)',\n",
    "                     'POINT(-66.86 10.48)']})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use ``shapely.wkt`` sub-module to parse wkt format:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from shapely import wkt\n",
    "\n",
    "df['Coordinates'] = geopandas.GeoSeries.from_wkt(df['Coordinates'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``GeoDataFrame`` is constructed as follows :\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gdf = geopandas.GeoDataFrame(df, geometry='Coordinates')\n",
    "\n",
    "print(gdf.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, we can plot our ``GeoDataFrame``.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ax = world[world.continent == 'South America'].plot(\n",
    "    color='white', edgecolor='black')\n",
    "\n",
    "gdf.plot(ax=ax, color='red')\n",
    "\n",
    "plt.show()"
   ]
  }
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