The Beginner's Guide to Choropleth Maps in Python: How to Read the Data?

choropleth_maps_reading_the_data.ipynb

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     "text": [
      "['naturalearth_lowres', 'naturalearth_cities', 'nybb']\n"
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "print(gpd.datasets.available)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
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   "outputs": [
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     "text": [
      "Number of rows: 177\n"
     ]
    }
   ],
   "source": [
    "gdf = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n",
    "print('Number of rows:', len(gdf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_est</th>\n",
       "      <th>continent</th>\n",
       "      <th>name</th>\n",
       "      <th>iso_a3</th>\n",
       "      <th>gdp_md_est</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>920938</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>FJI</td>\n",
       "      <td>8374.0</td>\n",
       "      <td>MULTIPOLYGON (((180.00000 -16.06713, 180.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>53950935</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>TZA</td>\n",
       "      <td>150600.0</td>\n",
       "      <td>POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>603253</td>\n",
       "      <td>Africa</td>\n",
       "      <td>W. Sahara</td>\n",
       "      <td>ESH</td>\n",
       "      <td>906.5</td>\n",
       "      <td>POLYGON ((-8.66559 27.65643, -8.66512 27.58948...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35623680</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>CAN</td>\n",
       "      <td>1674000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -122.9742...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>326625791</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States of America</td>\n",
       "      <td>USA</td>\n",
       "      <td>18560000.0</td>\n",
       "      <td>MULTIPOLYGON (((-122.84000 49.00000, -120.0000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "     pop_est      continent                      name iso_a3  gdp_md_est  \\\n",
       "0     920938        Oceania                      Fiji    FJI      8374.0   \n",
       "1   53950935         Africa                  Tanzania    TZA    150600.0   \n",
       "2     603253         Africa                 W. Sahara    ESH       906.5   \n",
       "3   35623680  North America                    Canada    CAN   1674000.0   \n",
       "4  326625791  North America  United States of America    USA  18560000.0   \n",
       "\n",
       "                                            geometry  \n",
       "0  MULTIPOLYGON (((180.00000 -16.06713, 180.00000...  \n",
       "1  POLYGON ((33.90371 -0.95000, 34.07262 -1.05982...  \n",
       "2  POLYGON ((-8.66559 27.65643, -8.66512 27.58948...  \n",
       "3  MULTIPOLYGON (((-122.84000 49.00000, -122.9742...  \n",
       "4  MULTIPOLYGON (((-122.84000 49.00000, -120.0000...  "
      ]
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdf.head()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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