text geopandas单位 - 可能-solution.ipynb

Posted 2021-05-02

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Example from Geopandas documentation](http://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POINT (-58.66 -34.58)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POINT (-47.91 -15.78)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>POINT (-70.66 -33.45)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>POINT (-74.08 4.6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>POINT (-66.86 10.48)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City    Country  Latitude  Longitude            Coordinates\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66  POINT (-58.66 -34.58)\n",
       "1      Brasilia     Brazil    -15.78     -47.91  POINT (-47.91 -15.78)\n",
       "2      Santiago      Chile    -33.45     -70.66  POINT (-70.66 -33.45)\n",
       "3        Bogota   Colombia      4.60     -74.08     POINT (-74.08 4.6)\n",
       "4       Caracas  Venezuela     10.48     -66.86   POINT (-66.86 10.48)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "\n",
    "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]})\n",
    "\n",
    "df['Coordinates']  = list(zip(df.Longitude, df.Latitude))\n",
    "df['Coordinates'] = df['Coordinates'].apply(Point)\n",
    "\n",
    "gdf = gpd.GeoDataFrame(df, geometry='Coordinates')\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the CRS on the GeoDataFrame to the WGS84 (EPSG:4326). This is the native CRS for the coordinates you have supplied. Once that's set, then use `to_crs` to transform them into EPSG:3395."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:3395', 'no_defs': True}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not in the example - add mercato projection, set units to meters\n",
    "gdf.crs = {'init': 'epsg:4326'}\n",
    "gdf = gdf.to_crs(epsg = 3395)\n",
    "gdf.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POINT (-6530001.329933428 -4082699.516779151)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POINT (-5333316.803905737 -1767646.178485063)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>POINT (-7865835.21945271 -3931636.078604394)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>POINT (-8246547.877965705 509196.2974916489)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>POINT (-7442821.154438271 1165421.424891677)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City    Country  Latitude  Longitude  \\\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66   \n",
       "1      Brasilia     Brazil    -15.78     -47.91   \n",
       "2      Santiago      Chile    -33.45     -70.66   \n",
       "3        Bogota   Colombia      4.60     -74.08   \n",
       "4       Caracas  Venezuela     10.48     -66.86   \n",
       "\n",
       "                                     Coordinates  \n",
       "0  POINT (-6530001.329933428 -4082699.516779151)  \n",
       "1  POINT (-5333316.803905737 -1767646.178485063)  \n",
       "2   POINT (-7865835.21945271 -3931636.078604394)  \n",
       "3   POINT (-8246547.877965705 509196.2974916489)  \n",
       "4   POINT (-7442821.154438271 1165421.424891677)  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Country</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Coordinates</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Buenos Aires</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-34.58</td>\n",
       "      <td>-58.66</td>\n",
       "      <td>POLYGON ((-6529991.329933428 -4082699.51677915...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Brasilia</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-15.78</td>\n",
       "      <td>-47.91</td>\n",
       "      <td>POLYGON ((-5333306.803905737 -1767646.17848506...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Santiago</td>\n",
       "      <td>Chile</td>\n",
       "      <td>-33.45</td>\n",
       "      <td>-70.66</td>\n",
       "      <td>POLYGON ((-7865825.21945271 -3931636.078604394...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bogota</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>4.60</td>\n",
       "      <td>-74.08</td>\n",
       "      <td>POLYGON ((-8246537.877965705 509196.2974916489...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Caracas</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>10.48</td>\n",
       "      <td>-66.86</td>\n",
       "      <td>POLYGON ((-7442811.154438271 1165421.424891677...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           City    Country  Latitude  Longitude  \\\n",
       "0  Buenos Aires  Argentina    -34.58     -58.66   \n",
       "1      Brasilia     Brazil    -15.78     -47.91   \n",
       "2      Santiago      Chile    -33.45     -70.66   \n",
       "3        Bogota   Colombia      4.60     -74.08   \n",
       "4       Caracas  Venezuela     10.48     -66.86   \n",
       "\n",
       "                                         Coordinates  \n",
       "0  POLYGON ((-6529991.329933428 -4082699.51677915...  \n",
       "1  POLYGON ((-5333306.803905737 -1767646.17848506...  \n",
       "2  POLYGON ((-7865825.21945271 -3931636.078604394...  \n",
       "3  POLYGON ((-8246537.877965705 509196.2974916489...  \n",
       "4  POLYGON ((-7442811.154438271 1165421.424891677...  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf['Coordinates'] = gdf.buffer(10)\n",
    "gdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    POLYGON ((-6529991.329933428 -4082699.51677915...\n",
       "1    POLYGON ((-5333306.803905737 -1767646.17848506...\n",
       "2    POLYGON ((-7865825.21945271 -3931636.078604394...\n",
       "3    POLYGON ((-8246537.877965705 509196.2974916489...\n",
       "4    POLYGON ((-7442811.154438271 1165421.424891677...\n",
       "Name: Coordinates, dtype: object"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e517dbd4a8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e51798c9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The buffers are  there, but because the data are millions (billions?) of meters apart and the polygons only 20m wide they don't show up. However if you isolate a single location..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1e519148160>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SkZxzYk+vS4kpFhoxJClJuHxCP6aO6s3fF2zmifdLqKlv9LqshJPTOZUbzx7C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\nH6nQH2JHgNy1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1e5192b2828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gdf2.loc[0:0].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "...you see one of your 10m buffered polygons."
   ]
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}