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Posted to commits@superset.apache.org by ru...@apache.org on 2023/12/18 05:11:01 UTC
(superset) branch map-notebook-touchups updated: chore(maps): touchup notebook, update maps, add afghanistan, aland, albania, algeria
This is an automated email from the ASF dual-hosted git repository.
rusackas pushed a commit to branch map-notebook-touchups
in repository https://gitbox.apache.org/repos/asf/superset.git
The following commit(s) were added to refs/heads/map-notebook-touchups by this push:
new 255221336f chore(maps): touchup notebook, update maps, add afghanistan, aland, albania, algeria
255221336f is described below
commit 255221336f5ede67d45a2b77afc70def1ee3fa7d
Author: Evan Rusackas <ev...@rusackas.com>
AuthorDate: Sun Dec 17 22:10:48 2023 -0700
chore(maps): touchup notebook, update maps, add afghanistan, aland, albania, algeria
---
.../scripts/Country Map GeoJSON Generator.ipynb | 3942 ++++++++++----------
.../src/countries/afghanistan.geojson | 40 +
.../src/countries/aland.geojson | 17 +
.../src/countries/albania.geojson | 18 +
.../src/countries/algeria.geojson | 54 +
.../src/countries/china.geojson | 4 +-
.../src/countries/egypt.geojson | 2 +-
.../src/countries/finland.geojson | 2 +-
.../src/countries/france.geojson | 4 +-
.../src/countries/germany.geojson | 2 +-
.../src/countries/india.geojson | 4 +-
.../src/countries/iran.geojson | 2 +-
.../src/countries/malaysia.geojson | 2 +-
.../src/countries/myanmar.geojson | 2 +-
.../src/countries/norway.geojson | 2 +-
.../src/countries/poland.geojson | 2 +-
.../src/countries/portugal.geojson | 2 +-
.../src/countries/russia.geojson | 2 +-
.../src/countries/sweden.geojson | 2 +-
.../src/countries/syria.geojson | 12 +-
.../src/countries/thailand.geojson | 2 +-
.../src/countries/uk.geojson | 6 +-
.../src/countries/ukraine.geojson | 3 +-
23 files changed, 2136 insertions(+), 1992 deletions(-)
diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/scripts/Country Map GeoJSON Generator.ipynb b/superset-frontend/plugins/legacy-plugin-chart-country-map/scripts/Country Map GeoJSON Generator.ipynb
index 4f20bc93df..f508d11511 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/scripts/Country Map GeoJSON Generator.ipynb
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/scripts/Country Map GeoJSON Generator.ipynb
@@ -1,2087 +1,2103 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "65aIalqEt1LR"
- },
- "source": [
- "# Generate GeoJSON from Natural Earth Data"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "L4PY3Z15t1LS"
- },
- "source": [
- "## Install Dependencies"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "6_H7qbzIt1LS"
- },
- "source": [
- "```\n",
- "pip install geopandas shapely matplotlib\n",
- "```"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "hvA0SEXVt1LS"
- },
- "source": [
- "## Download Data\n",
- "\n",
- "Download datasets (_Admin 0 - Countries_ in [1:10](https://www.naturalearthdata.com/downloads/10m-cultural-vectors/), and _Admin 1 – States, Provinces_ in 1:10 and [1:50](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/)) from Natural Earch Data:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "VjGrqW4Kt1LS",
- "outputId": "2e2accda-5ee4-4270-872e-ecb78d0d02a2"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Done. \n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import requests\n",
- "\n",
- "data_dir = os.path.expanduser(\"~/Downloads\")\n",
- "if not os.path.exists(data_dir):\n",
- " os.mkdir(data_dir)\n",
- "\n",
- "def download_files(skip_existing=True):\n",
- " for url in [\n",
- " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip\",\n",
- " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip\",\n",
- " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_1_states_provinces.zip\"\n",
- " ]:\n",
- " file_name = url.split('/')[-1]\n",
- " full_file_name = f'{data_dir}/{file_name}'\n",
- " with requests.get(\n",
- " url,\n",
- " headers={\n",
- " \"accept-encoding\": \"gzip, deflate, br\",\n",
- " \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36\"\n",
- " },\n",
- " stream=True,\n",
- " ) as res:\n",
- " file_size = int(res.headers['content-length'])\n",
- " if res.status_code != 200:\n",
- " print(\"Error downloading files. Please open the URL to download them from browser manually.\")\n",
- " break\n",
- " if (\n",
- " skip_existing and\n",
- " os.path.exists(full_file_name) and\n",
- " file_size == os.path.getsize(full_file_name)\n",
- " ):\n",
- " print(f\"Skip {file_name} because it already exists\")\n",
- " continue\n",
- " print(f\"Downloading {file_name}... \\r\", end=\"\")\n",
- " with open(full_file_name, \"wb\") as fh:\n",
- " fh.write(res.content)\n",
- " print(\"Done. \")\n",
- "\n",
- "download_files(skip_existing=True)"
- ]
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "65aIalqEt1LR"
+ },
+ "source": [
+ "# Generate GeoJSON from Natural Earth Data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "L4PY3Z15t1LS"
+ },
+ "source": [
+ "## Install Dependencies"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6_H7qbzIt1LS"
+ },
+ "source": [
+ "```\n",
+ "pip install geopandas shapely matplotlib\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "hvA0SEXVt1LS"
+ },
+ "source": [
+ "## Download Data\n",
+ "\n",
+ "Download datasets (_Admin 0 - Countries_ in [1:10](https://www.naturalearthdata.com/downloads/10m-cultural-vectors/), and _Admin 1 – States, Provinces_ in 1:10 and [1:50](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/)) from Natural Earch Data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "VjGrqW4Kt1LS",
+ "outputId": "2e2accda-5ee4-4270-872e-ecb78d0d02a2"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "colab": {
- "base_uri": "https://localhost:8080/"
- },
- "id": "EL0e9DEVt1LT",
- "outputId": "16cd6450-d4a3-457a-b205-9797bbce33fc"
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "GeoJSON file for India downloaded and loaded successfully.\n"
- ]
- }
- ],
- "source": [
- "import os\n",
- "import geopandas as gpd\n",
- "import matplotlib.pyplot as plt\n",
- "import shapely\n",
- "\n",
- "df_admin0_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_0_countries.zip\")\n",
- "df_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_1_states_provinces.zip\")\n",
- "df_50m = gpd.read_file(f\"{data_dir}/ne_50m_admin_1_states_provinces.zip\")\n",
- "\n",
- "# Download and load the GeoJSON file for India\n",
- "india_geojson_url = \"https://github.com/geohacker/india/raw/bcb920c7d3c686f01d085f7661c9ba89bf9bf65e/state/india_state_kashmir_ladakh.geojson\"\n",
- "\n",
- "try:\n",
- " india_gdf = gpd.read_file(india_geojson_url)\n",
- " print(\"GeoJSON file for India downloaded and loaded successfully.\")\n",
- "except Exception as e:\n",
- " print(f\"Unable to download or load the GeoJSON file for India. Error: {str(e)}\")\n",
- " print(\"Please download the file from the URL and try again.\")"
- ]
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Skip ne_10m_admin_0_countries.zip because it already exists\n",
+ "Skip ne_10m_admin_1_states_provinces.zip because it already exists\n",
+ "Skip ne_50m_admin_1_states_provinces.zip because it already exists\n",
+ "Done. \n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import requests\n",
+ "\n",
+ "data_dir = os.path.expanduser(\"~/Downloads\")\n",
+ "if not os.path.exists(data_dir):\n",
+ " os.mkdir(data_dir)\n",
+ "\n",
+ "def download_files(skip_existing=True):\n",
+ " for url in [\n",
+ " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_0_countries.zip\",\n",
+ " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/10m/cultural/ne_10m_admin_1_states_provinces.zip\",\n",
+ " \"https://www.naturalearthdata.com/http//www.naturalearthdata.com/download/50m/cultural/ne_50m_admin_1_states_provinces.zip\"\n",
+ " ]:\n",
+ " file_name = url.split('/')[-1]\n",
+ " full_file_name = f'{data_dir}/{file_name}'\n",
+ " with requests.get(\n",
+ " url,\n",
+ " headers={\n",
+ " \"accept-encoding\": \"gzip, deflate, br\",\n",
+ " \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/97.0.4692.71 Safari/537.36\"\n",
+ " },\n",
+ " stream=True,\n",
+ " ) as res:\n",
+ " file_size = int(res.headers['content-length'])\n",
+ " if res.status_code != 200:\n",
+ " print(\"Error downloading files. Please open the URL to download them from browser manually.\")\n",
+ " break\n",
+ " if (\n",
+ " skip_existing and\n",
+ " os.path.exists(full_file_name) and\n",
+ " file_size == os.path.getsize(full_file_name)\n",
+ " ):\n",
+ " print(f\"Skip {file_name} because it already exists\")\n",
+ " continue\n",
+ " print(f\"Downloading {file_name}... \\r\", end=\"\")\n",
+ " with open(full_file_name, \"wb\") as fh:\n",
+ " fh.write(res.content)\n",
+ " print(\"Done. \")\n",
+ "\n",
+ "download_files(skip_existing=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
},
+ "id": "EL0e9DEVt1LT",
+ "outputId": "16cd6450-d4a3-457a-b205-9797bbce33fc"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "_PKEg8s1t1LT",
- "outputId": "9937eacd-7a05-491f-f356-790ad75a819f"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['featurecla', 'scalerank', 'adm1_code', 'diss_me', 'iso_3166_2',\n",
- " 'wikipedia', 'iso_a2', 'adm0_sr', 'name', 'name_alt',\n",
- " ...\n",
- " 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT', 'FCLASS_NL',\n",
- " 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'FCLASS_TLC', 'geometry'],\n",
- " dtype='object', length=122)"
- ]
- },
- "execution_count": 37,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_50m.columns"
- ]
- },
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GeoJSON file for India downloaded and loaded successfully.\n"
+ ]
+ }
+ ],
+ "source": [
+ "import os\n",
+ "import geopandas as gpd\n",
+ "import matplotlib.pyplot as plt\n",
+ "import shapely\n",
+ "\n",
+ "df_admin0_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_0_countries.zip\")\n",
+ "df_10m = gpd.read_file(f\"{data_dir}/ne_10m_admin_1_states_provinces.zip\")\n",
+ "df_50m = gpd.read_file(f\"{data_dir}/ne_50m_admin_1_states_provinces.zip\")\n",
+ "\n",
+ "# Download and load the GeoJSON file for India\n",
+ "india_geojson_url = \"https://github.com/geohacker/india/raw/bcb920c7d3c686f01d085f7661c9ba89bf9bf65e/state/india_state_kashmir_ladakh.geojson\"\n",
+ "\n",
+ "try:\n",
+ " india_gdf = gpd.read_file(india_geojson_url)\n",
+ " print(\"GeoJSON file for India downloaded and loaded successfully.\")\n",
+ "except Exception as e:\n",
+ " print(f\"Unable to download or load the GeoJSON file for India. Error: {str(e)}\")\n",
+ " print(\"Please download the file from the URL and try again.\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "id": "_PKEg8s1t1LT",
+ "outputId": "9937eacd-7a05-491f-f356-790ad75a819f"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "w8OD4nq2t1LT",
- "outputId": "b7164440-d6e0-4074-b357-47fd8a8d2884"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "Index(['featurecla', 'scalerank', 'labelrank', 'sovereignt', 'sov_a3',\n",
- " 'adm0_dif', 'level', 'type', 'tlc', 'admin',\n",
- " ...\n",
- " 'fclass_tr', 'fclass_id', 'fclass_pl', 'fclass_gr', 'fclass_it',\n",
- " 'fclass_nl', 'fclass_se', 'fclass_bd', 'fclass_ua', 'geometry'],\n",
- " dtype='object', length=169)"
- ]
- },
- "execution_count": 38,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_admin0_10m.columns = df_admin0_10m.columns.str.lower()\n",
- "df_admin0_10m.columns"
+ "data": {
+ "text/plain": [
+ "Index(['featurecla', 'scalerank', 'adm1_code', 'diss_me', 'iso_3166_2',\n",
+ " 'wikipedia', 'iso_a2', 'adm0_sr', 'name', 'name_alt',\n",
+ " ...\n",
+ " 'FCLASS_ID', 'FCLASS_PL', 'FCLASS_GR', 'FCLASS_IT', 'FCLASS_NL',\n",
+ " 'FCLASS_SE', 'FCLASS_BD', 'FCLASS_UA', 'FCLASS_TLC', 'geometry'],\n",
+ " dtype='object', length=122)"
]
- },
+ },
+ "execution_count": 23,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_50m.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "id": "w8OD4nq2t1LT",
+ "outputId": "b7164440-d6e0-4074-b357-47fd8a8d2884"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "DUrz04nYt1LT",
- "outputId": "18d7cdb0-8ab6-4238-e50c-925c5dc117b0"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
- "<div>\n",
- "<style scoped>\n",
- " .dataframe tbody tr th:only-of-type {\n",
- " vertical-align: middle;\n",
- " }\n",
- "\n",
- " .dataframe tbody tr th {\n",
- " vertical-align: top;\n",
- " }\n",
- "\n",
- " .dataframe thead th {\n",
- " text-align: right;\n",
- " }\n",
- "</style>\n",
- "<table border=\"1\" class=\"dataframe\">\n",
- " <thead>\n",
- " <tr style=\"text-align: right;\">\n",
- " <th></th>\n",
- " <th>featurecla</th>\n",
- " <th>scalerank</th>\n",
- " <th>adm1_code</th>\n",
- " <th>diss_me</th>\n",
- " <th>iso_3166_2</th>\n",
- " <th>wikipedia</th>\n",
- " <th>iso_a2</th>\n",
- " <th>adm0_sr</th>\n",
- " <th>name</th>\n",
- " <th>name_alt</th>\n",
- " <th>...</th>\n",
- " <th>FCLASS_ID</th>\n",
- " <th>FCLASS_PL</th>\n",
- " <th>FCLASS_GR</th>\n",
- " <th>FCLASS_IT</th>\n",
- " <th>FCLASS_NL</th>\n",
- " <th>FCLASS_SE</th>\n",
- " <th>FCLASS_BD</th>\n",
- " <th>FCLASS_UA</th>\n",
- " <th>FCLASS_TLC</th>\n",
- " <th>geometry</th>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>admin</th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " <th></th>\n",
- " </tr>\n",
- " </thead>\n",
- " <tbody>\n",
- " <tr>\n",
- " <th>Australia</th>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>0</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>0</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Brazil</th>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>0</td>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>27</td>\n",
- " <td>13</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>27</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Canada</th>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>13</td>\n",
- " <td>9</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>13</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>China</th>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>0</td>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>31</td>\n",
- " <td>30</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>31</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>India</th>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>0</td>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>36</td>\n",
- " <td>13</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>36</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Indonesia</th>\n",
- " <td>33</td>\n",
- " <td>33</td>\n",
- " <td>33</td>\n",
- " <td>33</td>\n",
- " <td>33</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>33</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>Russia</th>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>1</td>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>85</td>\n",
- " <td>84</td>\n",
- " <td>...</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>85</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>South Africa</th>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>0</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>9</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>0</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>9</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>United States of America</th>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>51</td>\n",
- " <td>...</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>0</td>\n",
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- " <td>0</td>\n",
- " <td>0</td>\n",
- " <td>51</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>9 rows × 121 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " featurecla scalerank adm1_code diss_me \\\n",
- "admin \n",
- "Australia 9 9 9 9 \n",
- "Brazil 27 27 27 27 \n",
- "Canada 13 13 13 13 \n",
- "China 31 31 31 31 \n",
- "India 36 36 36 36 \n",
- "Indonesia 33 33 33 33 \n",
- "Russia 85 85 85 85 \n",
- "South Africa 9 9 9 9 \n",
- "United States of America 51 51 51 51 \n",
- "\n",
- " iso_3166_2 wikipedia iso_a2 adm0_sr name \\\n",
- "admin \n",
- "Australia 9 0 9 9 9 \n",
- "Brazil 27 0 27 27 27 \n",
- "Canada 13 13 13 13 13 \n",
- "China 31 0 31 31 31 \n",
- "India 36 0 36 36 36 \n",
- "Indonesia 33 0 33 33 33 \n",
- "Russia 85 1 85 85 85 \n",
- "South Africa 9 0 9 9 9 \n",
- "United States of America 51 51 51 51 51 \n",
- "\n",
- " name_alt ... FCLASS_ID FCLASS_PL FCLASS_GR \\\n",
- "admin ... \n",
- "Australia 0 ... 0 0 0 \n",
- "Brazil 13 ... 0 0 0 \n",
- "Canada 9 ... 0 0 0 \n",
- "China 30 ... 0 0 0 \n",
- "India 13 ... 0 0 0 \n",
- "Indonesia 30 ... 0 0 0 \n",
- "Russia 84 ... 0 0 0 \n",
- "South Africa 9 ... 0 0 0 \n",
- "United States of America 51 ... 0 0 0 \n",
- "\n",
- " FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD \\\n",
- "admin \n",
- "Australia 0 0 0 0 \n",
- "Brazil 0 0 0 0 \n",
- "Canada 0 0 0 0 \n",
- "China 0 0 0 0 \n",
- "India 0 0 0 0 \n",
- "Indonesia 0 0 0 0 \n",
- "Russia 0 0 0 0 \n",
- "South Africa 0 0 0 0 \n",
- "United States of America 0 0 0 0 \n",
- "\n",
- " FCLASS_UA FCLASS_TLC geometry \n",
- "admin \n",
- "Australia 0 0 9 \n",
- "Brazil 0 0 27 \n",
- "Canada 0 0 13 \n",
- "China 0 0 31 \n",
- "India 0 0 36 \n",
- "Indonesia 0 0 33 \n",
- "Russia 0 0 85 \n",
- "South Africa 0 0 9 \n",
- "United States of America 0 0 51 \n",
- "\n",
- "[9 rows x 121 columns]"
- ]
- },
- "execution_count": 39,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_50m.groupby('admin').count()"
+ "data": {
+ "text/plain": [
+ "Index(['featurecla', 'scalerank', 'labelrank', 'sovereignt', 'sov_a3',\n",
+ " 'adm0_dif', 'level', 'type', 'tlc', 'admin',\n",
+ " ...\n",
+ " 'fclass_tr', 'fclass_id', 'fclass_pl', 'fclass_gr', 'fclass_it',\n",
+ " 'fclass_nl', 'fclass_se', 'fclass_bd', 'fclass_ua', 'geometry'],\n",
+ " dtype='object', length=169)"
]
- },
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_admin0_10m.columns = df_admin0_10m.columns.str.lower()\n",
+ "df_admin0_10m.columns"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "id": "DUrz04nYt1LT",
+ "outputId": "18d7cdb0-8ab6-4238-e50c-925c5dc117b0"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "colab": {
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- "height": 1000
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- {
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+ "data": {
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+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>featurecla</th>\n",
+ " <th>scalerank</th>\n",
+ " <th>adm1_code</th>\n",
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+ " <th></th>\n",
+ " <th></th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Australia</th>\n",
+ " <td>9</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Brazil</th>\n",
+ " <td>27</td>\n",
+ " <td>27</td>\n",
+ " <td>27</td>\n",
+ " <td>27</td>\n",
+ " <td>27</td>\n",
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+ " <th>Canada</th>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>13</td>\n",
+ " <td>9</td>\n",
+ " <td>...</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>13</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>China</th>\n",
+ " <td>31</td>\n",
+ " <td>31</td>\n",
+ " <td>31</td>\n",
+ " <td>31</td>\n",
+ " <td>31</td>\n",
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+ " <th>India</th>\n",
+ " <td>36</td>\n",
+ " <td>36</td>\n",
+ " <td>36</td>\n",
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+ " <td>36</td>\n",
+ " <td>0</td>\n",
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+ " <td>36</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>36</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Indonesia</th>\n",
+ " <td>33</td>\n",
+ " <td>33</td>\n",
+ " <td>33</td>\n",
+ " <td>33</td>\n",
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+ " <td>0</td>\n",
+ " <td>33</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Russia</th>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>1</td>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>85</td>\n",
+ " <td>84</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>South Africa</th>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>9</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>9</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>United States of America</th>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>51</td>\n",
+ " <td>...</td>\n",
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+ " <td>0</td>\n",
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+ " <td>0</td>\n",
+ " <td>0</td>\n",
+ " <td>51</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>9 rows × 121 columns</p>\n",
+ "</div>"
],
- "source": [
- "df_50m[df_50m.adm0_a3 == 'USA'].plot(figsize=(20,10))\n",
- "plt.show()\n",
- "india_gdf.plot(figsize=(20, 10))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "VvpqxOjMt1LU"
- },
- "source": [
- "Use 1:50m geometry for some large countries:"
+ "text/plain": [
+ " featurecla scalerank adm1_code diss_me \\\n",
+ "admin \n",
+ "Australia 9 9 9 9 \n",
+ "Brazil 27 27 27 27 \n",
+ "Canada 13 13 13 13 \n",
+ "China 31 31 31 31 \n",
+ "India 36 36 36 36 \n",
+ "Indonesia 33 33 33 33 \n",
+ "Russia 85 85 85 85 \n",
+ "South Africa 9 9 9 9 \n",
+ "United States of America 51 51 51 51 \n",
+ "\n",
+ " iso_3166_2 wikipedia iso_a2 adm0_sr name \\\n",
+ "admin \n",
+ "Australia 9 0 9 9 9 \n",
+ "Brazil 27 0 27 27 27 \n",
+ "Canada 13 13 13 13 13 \n",
+ "China 31 0 31 31 31 \n",
+ "India 36 0 36 36 36 \n",
+ "Indonesia 33 0 33 33 33 \n",
+ "Russia 85 1 85 85 85 \n",
+ "South Africa 9 0 9 9 9 \n",
+ "United States of America 51 51 51 51 51 \n",
+ "\n",
+ " name_alt ... FCLASS_ID FCLASS_PL FCLASS_GR \\\n",
+ "admin ... \n",
+ "Australia 0 ... 0 0 0 \n",
+ "Brazil 13 ... 0 0 0 \n",
+ "Canada 9 ... 0 0 0 \n",
+ "China 30 ... 0 0 0 \n",
+ "India 13 ... 0 0 0 \n",
+ "Indonesia 30 ... 0 0 0 \n",
+ "Russia 84 ... 0 0 0 \n",
+ "South Africa 9 ... 0 0 0 \n",
+ "United States of America 51 ... 0 0 0 \n",
+ "\n",
+ " FCLASS_IT FCLASS_NL FCLASS_SE FCLASS_BD \\\n",
+ "admin \n",
+ "Australia 0 0 0 0 \n",
+ "Brazil 0 0 0 0 \n",
+ "Canada 0 0 0 0 \n",
+ "China 0 0 0 0 \n",
+ "India 0 0 0 0 \n",
+ "Indonesia 0 0 0 0 \n",
+ "Russia 0 0 0 0 \n",
+ "South Africa 0 0 0 0 \n",
+ "United States of America 0 0 0 0 \n",
+ "\n",
+ " FCLASS_UA FCLASS_TLC geometry \n",
+ "admin \n",
+ "Australia 0 0 9 \n",
+ "Brazil 0 0 27 \n",
+ "Canada 0 0 13 \n",
+ "China 0 0 31 \n",
+ "India 0 0 36 \n",
+ "Indonesia 0 0 33 \n",
+ "Russia 0 0 85 \n",
+ "South Africa 0 0 9 \n",
+ "United States of America 0 0 51 \n",
+ "\n",
+ "[9 rows x 121 columns]"
]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_50m.groupby('admin').count()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
},
+ "id": "eUlJjdRkt1LT",
+ "outputId": "60df2dc3-800e-40ac-f151-696a7f91cff4"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "pr1jqM3kt1LU",
- "outputId": "7211a182-b64a-469b-fadb-af2148ec6852"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "array(['Australia', 'Brazil', 'Canada', 'United States of America',\n",
- " 'China', 'India', 'Indonesia', 'Russia', 'South Africa'],\n",
- " dtype=object)"
- ]
- },
- "execution_count": 41,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "df_50m['admin'].unique()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAABK4AAAMtCAYAAAC2GTmHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3ib1f338Y+m997xiu3svXcII0DYe7aMMktbRqGl5dfytNBB6QBK2S2bUvbeJIQQkkD2dIYz7HhvS56SLOn5w2Bi4iS2I1uy/X5dVy7iW0fn/grHTvTxOd9j8Hq9XgEAAAAAAAABxujvAgAAAAAAAIDOEFwBAAAAAAAgIBFcAQAAAAAAICARXAEAAAAAACAgEVwBAAAAAAAgIBFcAQAAAAAAICARXAEAAAAAACAgmf1dwPd5PB6VlJQoIiJCBoPB3+UAAAAAAADAh7xer+rr6zVkyBAZjYdfUxVw [...]
+ "text/plain": [
+ "<Figure size 2000x1000 with 1 Axes>"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "k-KuZ8L4t1LU"
- },
- "outputs": [],
- "source": [
- "import pandas as pd\n",
- "\n",
- "df = pd.concat([df_10m[~df_10m['admin'].isin(df_50m['admin'].unique())], df_50m])"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "06nhCSvTt1LU"
- },
- "source": [
- "## Adjust the Maps\n",
- "\n",
- "<span style=\"color: red; font-size: 1.5em\">TO SUPPORT NEW COUNTRIES, ADD COUNTRY NAME BELOW</span>"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAt8AAAMtCAYAAABHJx1iAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hc5ZU/8O+903tR7yNb7rZcZMuYTqgJIRBgQzYhkCykkmxC2CRLym+XbNqmVwhJYAktlAAhlEAoptuWJcuWbLlIsnqXpve55ffHSLJltZnRzNyZ0fk8D08iaXTvkayZOfe95z2HEUVRBCGEEEIIISTlWKkDIIQQQgghZLmg5JsQQgghhJA0oeSbEEIIIYSQNKHkmxBCCCGEkDSh5JsQQgghhJA0oeSbEEIIIYSQNKHkmxBCCCGEkDSRSx3AmQRBwODgIAwGAxiGkTocQgghhBBCFiWKIjweD0pL [...]
+ "text/plain": [
+ "<Figure size 2000x1000 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_50m[df_50m.adm0_a3 == 'USA'].plot(figsize=(20,10))\n",
+ "plt.show()\n",
+ "india_gdf.plot(figsize=(20, 10))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VvpqxOjMt1LU"
+ },
+ "source": [
+ "Use 1:50m geometry for some large countries:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "id": "pr1jqM3kt1LU",
+ "outputId": "7211a182-b64a-469b-fadb-af2148ec6852"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "-4uH5XaEt1LU"
- },
- "outputs": [],
- "source": [
- "# Country names used in file names\n",
- "countries = [\n",
- " 'aland',\n",
- " 'argentina',\n",
- " 'australia',\n",
- " 'belgium',\n",
- " 'bolivia',\n",
- " 'brazil',\n",
- " 'bulgaria',\n",
- " 'burundi',\n",
- " 'canada',\n",
- " 'chile',\n",
- " 'china',\n",
- " 'colombia',\n",
- " 'costa rica',\n",
- " 'cuba',\n",
- " 'denmark',\n",
- " 'dominican republic',\n",
- " 'ecuador',\n",
- " 'egypt',\n",
- " 'el salvador',\n",
- " 'estonia',\n",
- " 'ethiopia',\n",
- " 'france',\n",
- " 'finland',\n",
- " 'germany',\n",
- " 'guatemala',\n",
- " 'haiti',\n",
- " 'honduras',\n",
- " 'iceland',\n",
- " 'india',\n",
- " 'indonesia',\n",
- " 'iran',\n",
- " 'italy',\n",
- " 'japan',\n",
- " 'kazakhstan',\n",
- " 'kenya',\n",
- " 'korea',\n",
- " 'kyrgyzstan',\n",
- " 'latvia',\n",
- " 'liechtenstein',\n",
- " 'malaysia',\n",
- " 'mexico',\n",
- " 'morocco',\n",
- " 'myanmar',\n",
- " 'netherlands',\n",
- " 'nicaragua',\n",
- " 'nigeria',\n",
- " 'norway',\n",
- " 'panama',\n",
- " 'paraguay',\n",
- " 'portugal',\n",
- " 'poland',\n",
- " 'puerto rico',\n",
- " 'russia',\n",
- " 'rwanda',\n",
- " 'saint barthelemy',\n",
- " 'saint martin',\n",
- " 'singapore',\n",
- " 'slovenia',\n",
- " 'spain',\n",
- " 'sweden',\n",
- " 'switzerland',\n",
- " 'syria',\n",
- " 'tajikistan',\n",
- " 'tanzania',\n",
- " 'thailand',\n",
- " 'timorleste',\n",
- " 'turkmenistan',\n",
- " 'uganda',\n",
- " 'uk',\n",
- " 'ukraine',\n",
- " 'uruguay',\n",
- " 'usa',\n",
- " 'uzbekistan',\n",
- " 'venezuela',\n",
- " 'zambia',\n",
- "]\n",
- "\n",
- "# country name used in dataset\n",
- "country_name_aliases = {\n",
- " \"uk\": \"united kingdom\",\n",
- " \"usa\": \"united states of america\",\n",
- " \"korea\": \"south korea\",\n",
- " \"timorleste\": \"east timor\",\n",
- " \"tanzania\": \"united republic of tanzania\",\n",
- "}\n",
- "\n",
- "# Make sure all country names are covered:\n",
- "invalid_countries = [x for x in countries if country_name_aliases.get(x, x) not in df[\"admin\"].str.lower().unique()]\n",
- "if invalid_countries:\n",
- " print(f\"Following country names are not valid: {invalid_countries}\")"
+ "data": {
+ "text/plain": [
+ "array(['Australia', 'Brazil', 'Canada', 'United States of America',\n",
+ " 'China', 'India', 'Indonesia', 'Russia', 'South Africa'],\n",
+ " dtype=object)"
]
- },
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "df_50m['admin'].unique()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "id": "k-KuZ8L4t1LU"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "df = pd.concat([df_10m[~df_10m['admin'].isin(df_50m['admin'].unique())], df_50m])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "06nhCSvTt1LU"
+ },
+ "source": [
+ "## Adjust the Maps\n",
+ "\n",
+ "<span style=\"color: red; font-size: 1.5em\">TO SUPPORT NEW COUNTRIES, ADD COUNTRY NAME BELOW</span>"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "id": "-4uH5XaEt1LU"
+ },
+ "outputs": [],
+ "source": [
+ "# Country names used in file names\n",
+ "countries = [\n",
+ " 'afghanistan',\n",
+ " 'aland',\n",
+ " 'albania',\n",
+ " 'algeria',\n",
+ " 'argentina',\n",
+ " 'australia',\n",
+ " 'belgium',\n",
+ " 'bolivia',\n",
+ " 'brazil',\n",
+ " 'bulgaria',\n",
+ " 'burundi',\n",
+ " 'canada',\n",
+ " 'chile',\n",
+ " 'china',\n",
+ " 'colombia',\n",
+ " 'costa rica',\n",
+ " 'cuba',\n",
+ " 'denmark',\n",
+ " 'dominican republic',\n",
+ " 'ecuador',\n",
+ " 'egypt',\n",
+ " 'el salvador',\n",
+ " 'estonia',\n",
+ " 'ethiopia',\n",
+ " 'france',\n",
+ " 'finland',\n",
+ " 'germany',\n",
+ " 'guatemala',\n",
+ " 'haiti',\n",
+ " 'honduras',\n",
+ " 'iceland',\n",
+ " 'india',\n",
+ " 'indonesia',\n",
+ " 'iran',\n",
+ " 'italy',\n",
+ " 'japan',\n",
+ " 'kazakhstan',\n",
+ " 'kenya',\n",
+ " 'korea',\n",
+ " 'kyrgyzstan',\n",
+ " 'latvia',\n",
+ " 'liechtenstein',\n",
+ " 'malaysia',\n",
+ " 'mexico',\n",
+ " 'morocco',\n",
+ " 'myanmar',\n",
+ " 'netherlands',\n",
+ " 'nicaragua',\n",
+ " 'nigeria',\n",
+ " 'norway',\n",
+ " 'panama',\n",
+ " 'paraguay',\n",
+ " 'portugal',\n",
+ " 'poland',\n",
+ " 'puerto rico',\n",
+ " 'russia',\n",
+ " 'rwanda',\n",
+ " 'saint barthelemy',\n",
+ " 'saint martin',\n",
+ " 'singapore',\n",
+ " 'slovenia',\n",
+ " 'spain',\n",
+ " 'sweden',\n",
+ " 'switzerland',\n",
+ " 'syria',\n",
+ " 'tajikistan',\n",
+ " 'tanzania',\n",
+ " 'thailand',\n",
+ " 'timorleste',\n",
+ " 'turkmenistan',\n",
+ " 'uganda',\n",
+ " 'uk',\n",
+ " 'ukraine',\n",
+ " 'uruguay',\n",
+ " 'usa',\n",
+ " 'uzbekistan',\n",
+ " 'venezuela',\n",
+ " 'zambia',\n",
+ "]\n",
+ "\n",
+ "# country name used in dataset\n",
+ "country_name_aliases = {\n",
+ " \"uk\": \"united kingdom\",\n",
+ " \"usa\": \"united states of america\",\n",
+ " \"korea\": \"south korea\",\n",
+ " \"timorleste\": \"east timor\",\n",
+ " \"tanzania\": \"united republic of tanzania\",\n",
+ "}\n",
+ "\n",
+ "# Make sure all country names are covered:\n",
+ "invalid_countries = [x for x in countries if country_name_aliases.get(x, x) not in df[\"admin\"].str.lower().unique()]\n",
+ "if invalid_countries:\n",
+ " print(f\"Following country names are not valid: {invalid_countries}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7z--iQz4t1LU"
+ },
+ "source": [
+ "Preview all countries:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "id": "tJ_WNQl8t1LU",
+ "outputId": "4f601ce0-26e4-4a40-c36c-8449420e9406"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "7z--iQz4t1LU"
- },
- "source": [
- "Preview all countries:"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAB7wAAAbRCAYAAAA8yPiWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e/0kjLpjVQCBAKE3kRUFMXeu7uC+rNiw44Fu667dlexrGLDXrArNlCR3ntNI73OpE2/vz8CA0MmlYQEeD/Pk2edO2fuPZOFw73nPed9VYqiKAghhBBCCCGEEEIIIYQQQgghhBCHGHV3d0AIIYQQQgghhBBCCCGEEEIIIYToCAl4CyGEEEIIIYQQQgghhBBCCCGEOCRJwFsIIYQQQgghhBBCCCGEEEIIIcQhSQLeQgghhBBCCCGEEEIIIYQQQgghDkkS8BZCCCGEEEIIIYQQQgghhBBC [...]
+ "text/plain": [
+ "<Figure size 2000x2000 with 78 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "alt_maps = dict()\n",
+ "\n",
+ "def get_gdf(country):\n",
+ " country_alias = country_name_aliases.get(country, country)\n",
+ " if country in alt_maps:\n",
+ " gdf = alt_maps[country]\n",
+ " else:\n",
+ " gdf = df[df[\"admin\"].str.lower() == country_alias]\n",
+ " return gdf.copy()\n",
+ "\n",
+ "def plot_all_countries():\n",
+ " plt.figure(figsize=(20, 20))\n",
+ "\n",
+ " for i, country in enumerate(countries):\n",
+ " # create subplot axes in a 3x3 grid\n",
+ " ax = plt.subplot(len(countries) // 5, 6, i + 1) # nrows, ncols, axes position\n",
+ " get_gdf(country).plot(ax=ax)\n",
+ " ax.set_title(country)\n",
+ " ax.set_aspect('equal', adjustable='datalim')\n",
+ "\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "plot_all_countries()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7Ab0_rHVt1LU"
+ },
+ "source": [
+ "### Handle countries with flying islands\n",
+ "\n",
+ "#### USA"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Z4y46Zuot1LU"
+ },
+ "source": [
+ "For countries with flying islands, we need to move the islands closer to the mainland."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "id": "xx8IbBKtt1LU",
+ "outputId": "025139d2-ba0b-43a9-e2ec-f4608e6ecad2"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tJ_WNQl8t1LU",
- "outputId": "4f601ce0-26e4-4a40-c36c-8449420e9406",
- "scrolled": false
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8AAAAdDCAYAAAAmZCF8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e/0SZ30Riqhd6QJCIq6gGvXxa5gwV0FXUVWwVUR67p2/dlXwYJdsVesqPTeIZDe26RMkqn390dgZMikl0nC+3kenmXu3HvnTFZOzj3vOe+rUhRFQQghhBBCCCGEEEIIIYQQQgghhOjh1L5ugBBCCCGEEEIIIYQQQgghhBBCCNERJAAuhBBCCCGEEEIIIYQQQgghhBCiV5AAuBBCCCGEEEIIIYQQQgghhBBCiF5BAuBCCCGEEEIIIYQQQgghhBBCCCF6BQmACyGEEEIIIYQQQg [...]
- "text/plain": [
- "<Figure size 2000x2000 with 69 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "alt_maps = dict()\n",
- "\n",
- "def get_gdf(country):\n",
- " country_alias = country_name_aliases.get(country, country)\n",
- " if country in alt_maps:\n",
- " gdf = alt_maps[country]\n",
- " else:\n",
- " gdf = df[df[\"admin\"].str.lower() == country_alias]\n",
- " return gdf.copy()\n",
- "\n",
- "def plot_all_countries():\n",
- " plt.figure(figsize=(20, 20))\n",
- "\n",
- " for i, country in enumerate(countries):\n",
- " # create subplot axes in a 3x3 grid\n",
- " ax = plt.subplot(len(countries) // 5, 6, i + 1) # nrows, ncols, axes position\n",
- " get_gdf(country).plot(ax=ax)\n",
- " ax.set_title(country)\n",
- " ax.set_aspect('equal', adjustable='datalim')\n",
- "\n",
- " plt.tight_layout()\n",
- " plt.show()\n",
- "\n",
- "plot_all_countries()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "7Ab0_rHVt1LU"
- },
- "source": [
- "### Handle countries with flying islands\n",
- "\n",
- "#### USA"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAF3CAYAAAAFEil7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABq+klEQVR4nO3dd3hc1bU28PdM7029FxdJlns3xhRjEGASCKZDQk3DkIAJCYaEclPghlxKvmACCYEQ4pAAAUIzAdvY4I7cm9xkS7J6maIy/Xx/yJItq81IU6X39zx6QHPOnNmzJeus2XvttQVRFEUQERERRYgk2g0gIiKi0YXBBxEREUUUgw8iIiKKKAYfREREFFEMPoiIiCiiGHwQERFRRDH4ICIiooiSRbsBZ/P7/aiuroZer4cgCNFuDhEREQVAFEU4HA6kp6dDIhl4bCPmgo/q6mpkZWVFuxlEREQ0BJWVlcjM [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "usa = df[df['adm0_a3'] == 'USA']\n",
+ "usa.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "id": "ixC6KENXt1LU",
+ "outputId": "8e63cb2d-d733-4a9f-caf6-ccc843f15b5d"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "Z4y46Zuot1LU"
- },
- "source": [
- "For countries with flying islands, we need to move the islands closer to the mainland."
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 39,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xx8IbBKtt1LU",
- "outputId": "025139d2-ba0b-43a9-e2ec-f4608e6ecad2"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 45,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAF3CAYAAAAFEil7AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABq+klEQVR4nO3dd3hc1bU28PdM7029FxdJlns3xhRjEGASCKZDQk3DkIAJCYaEclPghlxKvmACCYEQ4pAAAUIzAdvY4I7cm9xkS7J6maIy/Xx/yJItq81IU6X39zx6QHPOnNmzJeus2XvttQVRFEUQERERRYgk2g0gIiKi0YXBBxEREUUUgw8iIiKKKAYfREREFFEMPoiIiCiiGHwQERFRRDH4ICIiooiSRbsBZ/P7/aiuroZer4cgCNFuDhEREQVAFEU4HA6kp6dDIhl4bCPmgo/q6mpkZWVFuxlEREQ0BJ [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "usa = df[df['adm0_a3'] == 'USA']\n",
- "usa.plot()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAFvCAYAAAAFeQD2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACfgElEQVR4nOzdd1hb59k/8O/R3ou9p20MeGCMdxyPLGcnzptmtM1ezWiTjjRv+7ZNOpImv7YZbdK0TZM2sxlN0+xtxyM2GOOFDQbM3iC0t3R+f8hgY8AgkHSOxP25Li4bjaMHIaT7PM9z3zfDsiwLQgghhBBCIkDA9QAIIYQQQkj8omCTEEIIIYREDAWbhBBCCCEkYijYJIQQQgghEUPBJiGEEEIIiRgKNgkhhBBCSMRQsEkIIYQQQiKGgk1CCCGEEBIxIq4HcKpAIICuri6o1WowDMP1cAghhBBCyClYloXVakV6 [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def reposition(df, idx, xoff=None, yoff=None, xscale=None, yscale=None, simplify=None):\n",
+ "\n",
+ " def move_and_scale(series):\n",
+ " if xoff or yoff:\n",
+ " series = shapely.affinity.translate(series, xoff or 0, yoff or 0)\n",
+ " if xscale or yscale:\n",
+ " series = shapely.affinity.scale(series, xscale or 1, yscale or 1)\n",
+ " if simplify:\n",
+ " series = series.simplify(simplify, preserve_topology=False)\n",
+ " return series\n",
+ "\n",
+ " df.loc[idx, 'geometry'] = df.loc[idx, 'geometry'].apply(move_and_scale)\n",
+ "\n",
+ "\n",
+ "usa_copy = usa.copy()\n",
+ "reposition(usa_copy, usa.name == 'Hawaii', 51, 5.5)\n",
+ "reposition(usa_copy, usa.name == 'Alaska', 35, -34, 0.35, 0.35)\n",
+ "\n",
+ "usa_copy.plot(figsize=(8,8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d1p9cWNxt1LU"
+ },
+ "source": [
+ "#### China\n",
+ "\n",
+ "China claims sovereign over Taiwan. For disputed territories, we respect each country and give them what they want.\n",
+ "\n",
+ "In addition, Hong Kong and Macau should also be included in a China map."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "id": "vN4Ngpe7t1LU",
+ "outputId": "3bcdc612-cc01-49be-fe19-f6e08e833fca"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "ixC6KENXt1LU",
- "outputId": "8e63cb2d-d733-4a9f-caf6-ccc843f15b5d"
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- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
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- "execution_count": 46,
- "metadata": {},
- "output_type": "execute_result"
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- "<Figure size 800x800 with 1 Axes>"
- ]
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- "metadata": {},
- "output_type": "display_data"
- }
+ "data": {
+ "text/html": [
+ "<div>\n",
+ "<style scoped>\n",
+ " .dataframe tbody tr th:only-of-type {\n",
+ " vertical-align: middle;\n",
+ " }\n",
+ "\n",
+ " .dataframe tbody tr th {\n",
+ " vertical-align: top;\n",
+ " }\n",
+ "\n",
+ " .dataframe thead th {\n",
+ " text-align: right;\n",
+ " }\n",
+ "</style>\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>featurecla</th>\n",
+ " <th>scalerank</th>\n",
+ " <th>labelrank</th>\n",
+ " <th>sov_a3</th>\n",
+ " <th>type</th>\n",
+ " <th>admin</th>\n",
+ " <th>adm0_a3</th>\n",
+ " <th>gu_a3</th>\n",
+ " <th>name</th>\n",
+ " <th>abbrev</th>\n",
+ " <th>...</th>\n",
+ " <th>name_sv</th>\n",
+ " <th>name_tr</th>\n",
+ " <th>name_uk</th>\n",
+ " <th>name_ur</th>\n",
+ " <th>name_vi</th>\n",
+ " <th>name_zh_x</th>\n",
+ " <th>name_zht</th>\n",
+ " <th>geometry</th>\n",
+ " <th>name_zh_y</th>\n",
+ " <th>iso_3166_2</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>0</th>\n",
+ " <td>Admin-0 country</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " <td>CH1</td>\n",
+ " <td>Country</td>\n",
+ " <td>Hong Kong S.A.R.</td>\n",
+ " <td>HKG</td>\n",
+ " <td>HKG</td>\n",
+ " <td>Hong Kong</td>\n",
+ " <td>H.K.</td>\n",
+ " <td>...</td>\n",
+ " <td>Hongkong</td>\n",
+ " <td>Hong Kong</td>\n",
+ " <td>Гонконг</td>\n",
+ " <td>ہانگ کانگ</td>\n",
+ " <td>Hồng Kông</td>\n",
+ " <td>香港</td>\n",
+ " <td>香港</td>\n",
+ " <td>MULTIPOLYGON (((114.22983 22.55581, 114.23471 ...</td>\n",
+ " <td>香港特别行政区</td>\n",
+ " <td>CN-91</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>1</th>\n",
+ " <td>Admin-0 country</td>\n",
+ " <td>0</td>\n",
+ " <td>3</td>\n",
+ " <td>TWN</td>\n",
+ " <td>Sovereign country</td>\n",
+ " <td>Taiwan</td>\n",
+ " <td>TWN</td>\n",
+ " <td>TWN</td>\n",
+ " <td>Taiwan</td>\n",
+ " <td>Taiwan</td>\n",
+ " <td>...</td>\n",
+ " <td>Taiwan</td>\n",
+ " <td>Çin Cumhuriyeti</td>\n",
+ " <td>Республіка Китай</td>\n",
+ " <td>تائیوان</td>\n",
+ " <td>Đài Loan</td>\n",
+ " <td>中华民国</td>\n",
+ " <td>中華民國</td>\n",
+ " <td>MULTIPOLYGON (((121.90577 24.95010, 121.83473 ...</td>\n",
+ " <td>中国台湾</td>\n",
+ " <td>CN-71</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>2</th>\n",
+ " <td>Admin-0 country</td>\n",
+ " <td>0</td>\n",
+ " <td>4</td>\n",
+ " <td>CH1</td>\n",
+ " <td>Country</td>\n",
+ " <td>Macao S.A.R</td>\n",
+ " <td>MAC</td>\n",
+ " <td>MAC</td>\n",
+ " <td>Macao</td>\n",
+ " <td>Mac.</td>\n",
+ " <td>...</td>\n",
+ " <td>Macao</td>\n",
+ " <td>Makao</td>\n",
+ " <td>Аоминь</td>\n",
+ " <td>مکاؤ</td>\n",
+ " <td>Ma Cao</td>\n",
+ " <td>澳门</td>\n",
+ " <td>澳門</td>\n",
+ " <td>MULTIPOLYGON (((113.55860 22.16303, 113.56943 ...</td>\n",
+ " <td>澳门特别行政区</td>\n",
+ " <td>CN-92</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "<p>3 rows × 51 columns</p>\n",
+ "</div>"
],
- "source": [
- "def reposition(df, idx, xoff=None, yoff=None, xscale=None, yscale=None, simplify=None):\n",
- "\n",
- " def move_and_scale(series):\n",
- " if xoff or yoff:\n",
- " series = shapely.affinity.translate(series, xoff or 0, yoff or 0)\n",
- " if xscale or yscale:\n",
- " series = shapely.affinity.scale(series, xscale or 1, yscale or 1)\n",
- " if simplify:\n",
- " series = series.simplify(simplify, preserve_topology=False)\n",
- " return series\n",
- "\n",
- " df.loc[idx, 'geometry'] = df.loc[idx, 'geometry'].apply(move_and_scale)\n",
- "\n",
- "\n",
- "usa_copy = usa.copy()\n",
- "reposition(usa_copy, usa.name == 'Hawaii', 51, 5.5)\n",
- "reposition(usa_copy, usa.name == 'Alaska', 35, -34, 0.35, 0.35)\n",
- "\n",
- "usa_copy.plot(figsize=(8,8))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "id": "d1p9cWNxt1LU"
- },
- "source": [
- "#### China\n",
- "\n",
- "China claims sovereign over Taiwan. For disputed territories, we respect each country and give them what they want.\n",
- "\n",
- "In addition, Hong Kong and Macau should also be included in a China map."
+ "text/plain": [
+ " featurecla scalerank labelrank sov_a3 type \\\n",
+ "0 Admin-0 country 0 4 CH1 Country \n",
+ "1 Admin-0 country 0 3 TWN Sovereign country \n",
+ "2 Admin-0 country 0 4 CH1 Country \n",
+ "\n",
+ " admin adm0_a3 gu_a3 name abbrev ... name_sv \\\n",
+ "0 Hong Kong S.A.R. HKG HKG Hong Kong H.K. ... Hongkong \n",
+ "1 Taiwan TWN TWN Taiwan Taiwan ... Taiwan \n",
+ "2 Macao S.A.R MAC MAC Macao Mac. ... Macao \n",
+ "\n",
+ " name_tr name_uk name_ur name_vi name_zh_x \\\n",
+ "0 Hong Kong Гонконг ہانگ کانگ Hồng Kông 香港 \n",
+ "1 Çin Cumhuriyeti Республіка Китай تائیوان Đài Loan 中华民国 \n",
+ "2 Makao Аоминь مکاؤ Ma Cao 澳门 \n",
+ "\n",
+ " name_zht geometry name_zh_y \\\n",
+ "0 香港 MULTIPOLYGON (((114.22983 22.55581, 114.23471 ... 香港特别行政区 \n",
+ "1 中華民國 MULTIPOLYGON (((121.90577 24.95010, 121.83473 ... 中国台湾 \n",
+ "2 澳門 MULTIPOLYGON (((113.55860 22.16303, 113.56943 ... 澳门特别行政区 \n",
+ "\n",
+ " iso_3166_2 \n",
+ "0 CN-91 \n",
+ "1 CN-71 \n",
+ "2 CN-92 \n",
+ "\n",
+ "[3 rows x 51 columns]"
]
- },
+ },
+ "execution_count": 40,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Chinese Special Administrative Regions\n",
+ "china_sars = df_admin0_10m.loc[\n",
+ " df_admin0_10m.name_en.isin(['Taiwan', 'Hong Kong', 'Macau']),\n",
+ " [x for x in df_admin0_10m.columns if x in df.columns]\n",
+ "]\n",
+ "china_sars = china_sars.merge(pd.DataFrame(\n",
+ " data={\n",
+ " \"name_en\": [\"Taiwan\", \"Hong Kong\", \"Macau\"],\n",
+ " \"name_zh\": [\"中国台湾\", \"香港特别行政区\", \"澳门特别行政区\"],\n",
+ " \"iso_3166_2\": [\"CN-71\", \"CN-91\", \"CN-92\"],\n",
+ " },\n",
+ "), on=\"name_en\", how=\"left\")\n",
+ "china_sars"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "id": "PP6E24eEt1LV",
+ "outputId": "2621d5f1-1edc-42fc-e8df-8afd6a525cc6",
+ "scrolled": true
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "vN4Ngpe7t1LU",
- "outputId": "3bcdc612-cc01-49be-fe19-f6e08e833fca"
- },
- "outputs": [
- {
- "data": {
- "text/html": [
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- " <td>H.K.</td>\n",
- " <td>...</td>\n",
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- " <td>Гонконг</td>\n",
- " <td>ہانگ کانگ</td>\n",
- " <td>Hồng Kông</td>\n",
- " <td>香港</td>\n",
- " <td>香港</td>\n",
- " <td>MULTIPOLYGON (((114.22983 22.55581, 114.23471 ...</td>\n",
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- " <td>Admin-0 country</td>\n",
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- " <td>Sovereign country</td>\n",
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- " <td>TWN</td>\n",
- " <td>TWN</td>\n",
- " <td>Taiwan</td>\n",
- " <td>Taiwan</td>\n",
- " <td>...</td>\n",
- " <td>Taiwan</td>\n",
- " <td>Çin Cumhuriyeti</td>\n",
- " <td>Республіка Китай</td>\n",
- " <td>تائیوان</td>\n",
- " <td>Đài Loan</td>\n",
- " <td>中华民国</td>\n",
- " <td>中華民國</td>\n",
- " <td>MULTIPOLYGON (((121.90577 24.95010, 121.83473 ...</td>\n",
- " <td>中国台湾</td>\n",
- " <td>CN-71</td>\n",
- " </tr>\n",
- " <tr>\n",
- " <th>2</th>\n",
- " <td>Admin-0 country</td>\n",
- " <td>0</td>\n",
- " <td>4</td>\n",
- " <td>CH1</td>\n",
- " <td>Country</td>\n",
- " <td>Macao S.A.R</td>\n",
- " <td>MAC</td>\n",
- " <td>MAC</td>\n",
- " <td>Macao</td>\n",
- " <td>Mac.</td>\n",
- " <td>...</td>\n",
- " <td>Macao</td>\n",
- " <td>Makao</td>\n",
- " <td>Аоминь</td>\n",
- " <td>مکاؤ</td>\n",
- " <td>Ma Cao</td>\n",
- " <td>澳门</td>\n",
- " <td>澳門</td>\n",
- " <td>MULTIPOLYGON (((113.55860 22.16303, 113.56943 ...</td>\n",
- " <td>澳门特别行政区</td>\n",
- " <td>CN-92</td>\n",
- " </tr>\n",
- " </tbody>\n",
- "</table>\n",
- "<p>3 rows × 51 columns</p>\n",
- "</div>"
- ],
- "text/plain": [
- " featurecla scalerank labelrank sov_a3 type \\\n",
- "0 Admin-0 country 0 4 CH1 Country \n",
- "1 Admin-0 country 0 3 TWN Sovereign country \n",
- "2 Admin-0 country 0 4 CH1 Country \n",
- "\n",
- " admin adm0_a3 gu_a3 name abbrev ... name_sv \\\n",
- "0 Hong Kong S.A.R. HKG HKG Hong Kong H.K. ... Hongkong \n",
- "1 Taiwan TWN TWN Taiwan Taiwan ... Taiwan \n",
- "2 Macao S.A.R MAC MAC Macao Mac. ... Macao \n",
- "\n",
- " name_tr name_uk name_ur name_vi name_zh_x \\\n",
- "0 Hong Kong Гонконг ہانگ کانگ Hồng Kông 香港 \n",
- "1 Çin Cumhuriyeti Республіка Китай تائیوان Đài Loan 中华民国 \n",
- "2 Makao Аоминь مکاؤ Ma Cao 澳门 \n",
- "\n",
- " name_zht geometry name_zh_y \\\n",
- "0 香港 MULTIPOLYGON (((114.22983 22.55581, 114.23471 ... 香港特别行政区 \n",
- "1 中華民國 MULTIPOLYGON (((121.90577 24.95010, 121.83473 ... 中国台湾 \n",
- "2 澳門 MULTIPOLYGON (((113.55860 22.16303, 113.56943 ... 澳门特别行政区 \n",
- "\n",
- " iso_3166_2 \n",
- "0 CN-91 \n",
- "1 CN-71 \n",
- "2 CN-92 \n",
- "\n",
- "[3 rows x 51 columns]"
- ]
- },
- "execution_count": 47,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# Chinese Special Administrative Regions\n",
- "china_sars = df_admin0_10m.loc[\n",
- " df_admin0_10m.name_en.isin(['Taiwan', 'Hong Kong', 'Macau']),\n",
- " [x for x in df_admin0_10m.columns if x in df.columns]\n",
- "]\n",
- "china_sars = china_sars.merge(pd.DataFrame(\n",
- " data={\n",
- " \"name_en\": [\"Taiwan\", \"Hong Kong\", \"Macau\"],\n",
- " \"name_zh\": [\"中国台湾\", \"香港特别行政区\", \"澳门特别行政区\"],\n",
- " \"iso_3166_2\": [\"CN-71\", \"CN-91\", \"CN-92\"],\n",
- " },\n",
- "), on=\"name_en\", how=\"left\")\n",
- "china_sars"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
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},
{
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- "metadata": {
- "id": "PP6E24eEt1LV",
- "outputId": "2621d5f1-1edc-42fc-e8df-8afd6a525cc6",
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\bryan\\AppData\\Local\\Temp\\ipykernel_31064\\3440732423.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " china_copy = china.append(china_sars)\n"
- ]
- },
- {
- "data": {
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- "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAALDCAYAAAASWYNXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5hkZZU/8O+tnFPn3D0550AWBERRDKCygDnHVdTVZdVV0FV3/a24RtR1xQCCKCAZyWlCT+7pyd3TOXdXzun+/uiZZno6Vb4Vvp/n4WGmq+reMzPdVffc97znCKIoiiAiIiIiIiKiBcmkDoCIiIiIiIioUDCJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkqQQuoAzhePxzE4OAij0QhBEKQOh4iIiIiIiI [...]
- "text/plain": [
- "<Figure size 1200x1200 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "china = df[df.admin == \"China\"]\n",
- "china_copy = china.append(china_sars)\n",
- "china_copy[\"name_zh\"] = china_copy[\"name_zh\"].combine_first(china_copy[\"name_zh_y\"])\n",
- "china_copy = china_copy.drop([\"name_zh_x\", \"name_zh_y\"], axis=1)\n",
- "china_copy.plot(figsize=(12, 12))"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9EAAALDCAYAAAASWYNXAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd5hkZZU/8O+tnFPn3D0550AWBERRDKCygDnHVdTVZdVV0FV3/a24RtR1xQCCKCAZyWlCT+7pyd3TOXdXzun+/uiZZno6Vb4Vvp/n4WGmq+reMzPdVffc97znCKIoiiAiIiIiIiKiBcmkDoCIiIiIiIioUDCJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkoQk2giIiIiIiKiBDGJJiIiIiIiIkqQQuoAzhePxzE4OAij0QhBEKQOh4iIiIiIiIqcKIrw [...]
+ "text/plain": [
+ "<Figure size 1200x1200 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "china = df[df.admin == \"China\"]\n",
+ "china_copy = pd.concat([china, china_sars], ignore_index=True)\n",
+ "# Combine the 'name_zh' columns\n",
+ "china_copy[\"name_zh\"] = china_copy[\"name_zh\"].combine_first(china_copy[\"name_zh_y\"])\n",
+ "# Drop the extra 'name_zh_x' and 'name_zh_y' columns, if they exist\n",
+ "china_copy = china_copy.drop([\"name_zh_x\", \"name_zh_y\"], axis=1)\n",
+ "# Plotting the DataFrame\n",
+ "china_copy.plot(figsize=(12, 12))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nqn5qsR-t1LV"
+ },
+ "source": [
+ "Note [ISO-3166-2:CN](https://en.wikipedia.org/wiki/ISO_3166-2:CN) has updated subdivisions to use letters instead of numbers (e.g. `CN-91` -> `CN-HK`). We kept the numeric code for backward compatibility."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iNnVk5dut1LV"
+ },
+ "source": [
+ "#### Finland\n",
+ "\n",
+ "The Åland Islands (ISO country code AX) is an autonomous region of Finland, and carries the ISO-3166 code FI-01."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "id": "LuNGgwiQt1LV"
+ },
+ "outputs": [],
+ "source": [
+ "finland_aland = df_admin0_10m.loc[\n",
+ " df_admin0_10m.name_en.isin(['Åland Islands']),\n",
+ " [x for x in df_admin0_10m.columns if x in df.columns]\n",
+ "]\n",
+ "finland_aland = finland_aland.merge(pd.DataFrame(\n",
+ " data={\n",
+ " \"name_en\": [\"Åland Islands\"],\n",
+ " \"name_fi\": [\"Ahvenanmaan maakunta\"],\n",
+ " \"iso_3166_2\": [\"FI-01\"],\n",
+ " },\n",
+ "), on=\"name_en\", how=\"left\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "id": "v8ig_jQDt1LV",
+ "outputId": "3f10b14d-dde2-46d9-f4f6-6f4311fb3e73"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "nqn5qsR-t1LV"
- },
- "source": [
- "Note [ISO-3166-2:CN](https://en.wikipedia.org/wiki/ISO_3166-2:CN) has updated subdivisions to use letters instead of numbers (e.g. `CN-91` -> `CN-HK`). We kept the numeric code for backward compatibility."
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 46,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "iNnVk5dut1LV"
- },
- "source": [
- "#### Finland\n",
- "\n",
- "The Åland Islands (ISO country code AX) is an autonomous region of Finland, and carries the ISO-3166 code FI-01."
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAPHCAYAAAB+ONNOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAADpCklEQVR4nOzdd5xkVZk+8KdyV+6uzrl78vTkLAxZFBBFgqAIhkVdQRRBf7om1t11BXFhRUVB0CVIFGHIOYfJOfdMz3TOoWJXrrq/P3popmd6pqu6q+rcW/V8PzuflZ7qqnc63KfOuee8RyVJkgQiIiKaMrXoAoiIiLIFQ5WIiChFGKpEREQpwlAlIiJKEYYqERFRijBUiYiIUoShSkRElCJa0QUcKx6Po6urC1arFSqVSnQ5RESUYyRJgtfrRUVFBdTq5MaesgvVrq4uVFdXiy6DiIhyXHt7O6qqqpL6HNmFqtVq [...]
+ "text/plain": [
+ "<Figure size 1200x1200 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import pandas as pd\n",
+ "\n",
+ "finland = df[df.admin == \"Finland\"]\n",
+ "# Concatenate the 'finland' DataFrame with 'finland_aland' DataFrame\n",
+ "finland_copy = pd.concat([finland, finland_aland], ignore_index=True)\n",
+ "# Combine 'name_fi' columns. However, since both columns are named 'name_fi', this might be redundant\n",
+ "# If you have two different columns for 'name_fi' values in each DataFrame, specify them as 'name_fi_x' and 'name_fi_y'\n",
+ "finland_copy[\"name_fi\"] = finland_copy[\"name_fi\"].combine_first(finland_copy[\"name_fi\"])\n",
+ "# Drop the 'name_fi' column, if that's intended. This will remove the 'name_fi' data entirely.\n",
+ "# If you meant to drop other columns (like 'name_fi_x' and 'name_fi_y'), update the column names accordingly\n",
+ "finland_copy = finland_copy.drop([\"name_fi\"], axis=1)\n",
+ "# Plotting the DataFrame\n",
+ "finland_copy.plot(figsize=(12, 12))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "UP0QB9BZt1LV"
+ },
+ "source": [
+ "#### Norway\n",
+ "\n",
+ "Remove NO-X01~ (The uninhabited Bouvet Island) and move Svalbard closer to mainland"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "metadata": {
+ "id": "8zBzSIqQt1LV",
+ "outputId": "cc8b6fbf-accb-44ba-b80a-a837df398c96"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "LuNGgwiQt1LV"
- },
- "outputs": [],
- "source": [
- "finland_aland = df_admin0_10m.loc[\n",
- " df_admin0_10m.name_en.isin(['Åland Islands']),\n",
- " [x for x in df_admin0_10m.columns if x in df.columns]\n",
- "]\n",
- "finland_aland = finland_aland.merge(pd.DataFrame(\n",
- " data={\n",
- " \"name_en\": [\"Åland Islands\"],\n",
- " \"name_fi\": [\"Ahvenanmaan maakunta\"],\n",
- " \"iso_3166_2\": [\"FI-01\"],\n",
- " },\n",
- "), on=\"name_en\", how=\"left\")\n"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 47,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "v8ig_jQDt1LV",
- "outputId": "3f10b14d-dde2-46d9-f4f6-6f4311fb3e73"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\bryan\\AppData\\Local\\Temp\\ipykernel_31064\\642512976.py:2: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
- " finland_copy = finland.append(finland_aland)\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 50,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdUAAAPHCAYAAAB+ONNOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAADpCklEQVR4nOzdd5xkVZk+8KdyV+6uzrl78vTkLAxZFBBFgqAIhkVdQRRBf7om1t11BXFhRUVB0CVIFGHIOYfJOfdMz3TOoWJXrrq/P3popmd6pqu6q+rcW/V8PzuflZ7qqnc63KfOuee8RyVJkgQiIiKaMrXoAoiIiLIFQ5WIiChFGKpEREQpwlAlIiJKEYYqERFRijBUiYiIUoShSkRElCJa0QUcKx6Po6urC1arFSqVSnQ5RESUYyRJgtfrRUVFBdTq5MaesgvVrq4uVFdXiy6DiIhyXHt7O6qqqpL6HN [...]
- "text/plain": [
- "<Figure size 1200x1200 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "finland = df[df.admin == \"Finland\"]\n",
- "finland_copy = finland.append(finland_aland)\n",
- "finland_copy[\"name_fi\"] = finland_copy[\"name_fi\"].combine_first(finland_copy[\"name_fi\"])\n",
- "finland_copy = finland_copy.drop([\"name_fi\"], axis=1)\n",
- "finland_copy.plot(figsize=(12, 12))"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKwAAAGdCAYAAACPYJhkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAkJUlEQVR4nO3de1xUdf7H8fdcDzM4M9xkBnBAvOKtMEwizbL4yZbbZrq13W+ubi62mT5a5VFq7Wa41raVj0zdfeS2ZavrttVqbeVS0baLmndRQQ0UBGYAgXMGmBsz398f1KnJS4NyGL7weT4e5/FgzhwO3/G8PJwZzsxRMcYYCOGEOtIDIKQrKFjCFQqWcIWCJVyhYAlXKFjCFQqWcIWCJVzRRnoAlyoYDKK2thYmkwkqlSrSwyEXiTEGl8uF5ORkqNXn349yH2xtbS3sdnukh0G6SXV1NQYNGnTe+7kP1mQyAeh8 [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "norway = df[df['adm0_a3'] == 'NOR']\n",
+ "norway.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "metadata": {
+ "id": "-LXcKKOjt1LV",
+ "outputId": "546a286e-9682-4f9a-c57e-b19250d88a34"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "UP0QB9BZt1LV"
- },
- "source": [
- "#### Norway\n",
- "\n",
- "Remove NO-X01~ (The uninhabited Bouvet Island) and move Svalbard closer to mainland"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 48,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8zBzSIqQt1LV",
- "outputId": "cc8b6fbf-accb-44ba-b80a-a837df398c96"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 51,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAKwAAAGdCAYAAACPYJhkAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAkJUlEQVR4nO3de1xUdf7H8fdcDzM4M9xkBnBAvOKtMEwizbL4yZbbZrq13W+ubi62mT5a5VFq7Wa41raVj0zdfeS2ZavrttVqbeVS0baLmndRQQ0UBGYAgXMGmBsz398f1KnJS4NyGL7weT4e5/FgzhwO3/G8PJwZzsxRMcYYCOGEOtIDIKQrKFjCFQqWcIWCJVyhYAlXKFjCFQqWcIWCJVzRRnoAlyoYDKK2thYmkwkqlSrSwyEXiTEGl8uF5ORkqNXn349yH2xtbS3sdnukh0G6SXV1NQYNGnTe+7kP1m [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "norway = df[df['adm0_a3'] == 'NOR']\n",
- "norway.plot()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAGdCAYAAABHM5ovAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACtAUlEQVR4nOzdd3iUVfbA8e/0TNqk9x4ILfTQpYpixd57byuKrgXddVfX1dXV1d+6a++rYkexgkhReq+B9N77JJlMn98fgYGQQhLSgPN5Hp41M+/c951skvfMveeeo3C5XC6EEEIIIbpB2d8XIIQQQogTlwQSQgghhOg2CSSEEEII0W0SSAghhBCi2ySQEEIIIUS3SSAhhBBCiG6TQEIIIYQQ3SaBhBBCCCG6Td3fF3A0p9NJcXExPj4+KBSK/r4cIYQQ4pTkcrmor68nIiICpbL9eYcBF0gUFxcTHR3d35chhBBC [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "norway_copy = norway.copy()\n",
+ "norway_copy = norway_copy[norway_copy[\"iso_3166_2\"] != \"NO-X01~\"]\n",
+ "reposition(norway_copy, norway.name == 'Svalbard', -12, -8, 0.5, 0.5)\n",
+ "#reposition(norway_copy, norway.name == 'Nordland', 10, 0, 2, 2)\n",
+ "\n",
+ "norway_copy.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NqdSwt2ct1LV"
+ },
+ "source": [
+ "#### Portugal"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {
+ "id": "mznw0XOgt1LV",
+ "outputId": "7e8085bc-abd9-4592-f047-62fa1a45eb01"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "-LXcKKOjt1LV",
- "outputId": "546a286e-9682-4f9a-c57e-b19250d88a34"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 52,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhIAAAGdCAYAAABHM5ovAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACtAUlEQVR4nOzdd3iUVfbA8e/0TNqk9x4ILfTQpYpixd57byuKrgXddVfX1dXV1d+6a++rYkexgkhReq+B9N77JJlMn98fgYGQQhLSgPN5Hp41M+/c951skvfMveeeo3C5XC6EEEIIIbpB2d8XIIQQQogTlwQSQgghhOg2CSSEEEII0W0SSAghhBCi2ySQEEIIIUS3SSAhhBBCiG6TQEIIIYQQ3SaBhBBCCCG6Td3fF3A0p9NJcXExPj4+KBSK/r4cIYQQ4pTkcrmor68nIiICpbL9eYcBF0gUFxcTHR3d35 [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "norway_copy = norway.copy()\n",
- "norway_copy = norway_copy[norway_copy[\"iso_3166_2\"] != \"NO-X01~\"]\n",
- "reposition(norway_copy, norway.name == 'Svalbard', -12, -8, 0.5, 0.5)\n",
- "#reposition(norway_copy, norway.name == 'Nordland', 10, 0, 2, 2)\n",
- "\n",
- "norway_copy.plot()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 49,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "NqdSwt2ct1LV"
- },
- "source": [
- "#### Portugal"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAFUCAYAAAAOBceiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5GUlEQVR4nO3deXxU5b0/8M/sk8yWTPZlskAgIUDYlyBaQVap4i1W788F21IVS/EqbUXaWq9FhZ/aS71W0etWfrVcKlgUF0RcwKosIRAJIAgSSMhKmGRmMsns5/dHZCSSZSYzSc5MPu/XK69XZs45z3znODifPOc5zyMRBEEAERERkYhIB7oAIiIiou9jQCEiIiLRYUAhIiIi0WFAISIiItFhQCEiIiLRYUAhIiIi0WFAISIiItFhQCEiIiLRkQ90Ad/n8/lQU1MDnU4HiUQy0OUQERFRAARBgM1mQ3p6OqTS0Ps/ [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "portugal = df[df.admin == 'Portugal']\n",
+ "portugal.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "id": "yfAO1qFrt1LV",
+ "outputId": "9151ce8f-2412-415b-da73-eeec613276d8"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "mznw0XOgt1LV",
- "outputId": "7e8085bc-abd9-4592-f047-62fa1a45eb01"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 53,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAFUCAYAAAAOBceiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA5GUlEQVR4nO3deXxU5b0/8M/sk8yWTPZlskAgIUDYlyBaQVap4i1W788F21IVS/EqbUXaWq9FhZ/aS71W0etWfrVcKlgUF0RcwKosIRAJIAgSSMhKmGRmMsns5/dHZCSSZSYzSc5MPu/XK69XZs45z3znODifPOc5zyMRBEEAERERkYhIB7oAIiIiou9jQCEiIiLRYUAhIiIi0WFAISIiItFhQCEiIiLRYUAhIiIi0WFAISIiItFhQCEiIiLRkQ90Ad/n8/lQU1MDnU4HiUQy0OUQERFRAARBgM1mQ3p6Oq [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "portugal = df[df.admin == 'Portugal']\n",
- "portugal.plot()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 50,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "yfAO1qFrt1LV",
- "outputId": "9151ce8f-2412-415b-da73-eeec613276d8"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 54,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAHRCAYAAAAluXLzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABoAklEQVR4nO3deXhU5d0+8Hv2mcw+2fcEAmENq0BwRxYpVVx+2sWKtdZWS/u6dFFefau2tdDS19a+Vlyrtkq1arVaF1xxQYQQtgAStoSELGSdJZPZ5/z+CARCttnOzCTcn+vKVXLmzHO+c4rJzXOeRSIIggAiIiIiIhFIE10AEREREY1eDJtEREREJBqGTSIiIiISDcMmEREREYmGYZOIiIiIRMOwSURERESiYdgkIiIiItHIE13AmYLBIBobG6HX6yGRSBJdDhERERGdQRAEOBwO5OTkQCoduu8y6cJmY2 [...]
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "portugal_copy = portugal.copy()\n",
- "reposition(portugal_copy, portugal.name == 'Azores', 11, 0)\n",
- "reposition(portugal_copy, portugal.name == 'Madeira', 6, 2, simplify=0.015)\n",
- "portugal_copy.plot(figsize=(8, 8))"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAHRCAYAAAAluXLzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABoAklEQVR4nO3deXhU5d0+8Hv2mcw+2fcEAmENq0BwRxYpVVx+2sWKtdZWS/u6dFFefau2tdDS19a+Vlyrtkq1arVaF1xxQYQQtgAStoSELGSdJZPZ5/z+CARCttnOzCTcn+vKVXLmzHO+c4rJzXOeRSIIggAiIiIiIhFIE10AEREREY1eDJtEREREJBqGTSIiIiISDcMmEREREYmGYZOIiIiIRMOwSURERESiYdgkIiIiItHIE13AmYLBIBobG6HX6yGRSBJdDhERERGdQRAEOBwO5OTkQCoduu8y6cJmY2Mj8vPz [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "portugal_copy = portugal.copy()\n",
+ "reposition(portugal_copy, portugal.name == 'Azores', 11, 0)\n",
+ "reposition(portugal_copy, portugal.name == 'Madeira', 6, 2, simplify=0.015)\n",
+ "portugal_copy.plot(figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "jJyypJbJt1LV"
+ },
+ "source": [
+ "#### Spain"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 51,
+ "metadata": {
+ "id": "bbyDCO0Qt1LV",
+ "outputId": "f2a0594d-999b-4573-d008-5158f898a1c6"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "jJyypJbJt1LV"
- },
- "source": [
- "#### Spain"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 51,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "bbyDCO0Qt1LV",
- "outputId": "f2a0594d-999b-4573-d008-5158f898a1c6"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 55,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdEAAAGdCAYAAABNWUmMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABrWklEQVR4nO3deXicZbk/8O87+75l35M2bdK0TfeUFFmEQqmIoCgcRSgczlE4BYSKS8/RI6hYfqKCCwJHEXHhsHmKqCACWhAoXdKFdF+z78ssmX15f39MMzTNJJl5Z7J/P9eV6yKTd573mWnIPc9234IoiiKIiIgoabLJ7gAREdF0xSBKREQkEYMoERGRRAyiREREEjGIEhERScQgSkREJBGDKBERkUQMokRERBIpJrsD54pEImhra4PRaIQgCJPdHSIimmVEUYTL5UJ+fj5kstHHmlMuiLa1taGoqGiyu0 [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "spain = df[df.admin == 'Spain']\n",
- "spain.plot()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAdEAAAGdCAYAAABNWUmMAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABrWklEQVR4nO3deXicZbk/8O87+75l35M2bdK0TfeUFFmEQqmIoCgcRSgczlE4BYSKS8/RI6hYfqKCCwJHEXHhsHmKqCACWhAoXdKFdF+z78ssmX15f39MMzTNJJl5Z7J/P9eV6yKTd573mWnIPc9234IoiiKIiIgoabLJ7gAREdF0xSBKREQkEYMoERGRRAyiREREEjGIEhERScQgSkREJBGDKBERkUQMokRERBIpJrsD54pEImhra4PRaIQgCJPdHSIimmVEUYTL5UJ+fj5kstHHmlMuiLa1taGoqGiyu0FERLNc [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "spain = df[df.admin == 'Spain']\n",
+ "spain.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 52,
+ "metadata": {
+ "id": "yJ_Ueh7Rt1LV",
+ "outputId": "16fe59db-4be4-4e02-d37b-3098bdfa945a"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "yJ_Ueh7Rt1LV",
- "outputId": "16fe59db-4be4-4e02-d37b-3098bdfa945a"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 56,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAGjCAYAAABuTa5+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACvZklEQVR4nOzdd3hkZ3k34N/p08409T7a1e5qtStt02q9tgE3MMaYbiCAIZSEEDohEPIRAqEHEgiBAKEFEhwCBoztEIwpNja2V9t7X/UuTe8zZ873x0iz6ppypknPfV1ciUcz57wqKz3zvk9hVFVVQQghhBBCSB6xxV4AIYQQQghZ/yjoJIQQQggheUdBJyGEEEIIyTsKOgkhhBBCSN5R0EkIIYQQQvKOgk5CCCGEEJJ3FHQSQgghhJC8o6CTEEIIIYTkHV/sBSyWSCQwOjoKWZbBMEyxl0MIIYQQQhZRVR [...]
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "spain_copy = spain.copy()\n",
- "reposition(spain_copy, spain.name.isin(['Las Palmas', 'Santa Cruz de Tenerife']), 3, 7, 1, 1)\n",
- "spain_copy.plot(figsize=(8, 8))"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 52,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "-SNb1b-Et1LV"
- },
- "source": [
- "#### Russia"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAp0AAAGjCAYAAABuTa5+AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACvZklEQVR4nOzdd3hkZ3k34N/p08409T7a1e5qtStt02q9tgE3MMaYbiCAIZSEEDohEPIRAqEHEgiBAKEFEhwCBoztEIwpNja2V9t7X/UuTe8zZ873x0iz6ppypknPfV1ciUcz57wqKz3zvk9hVFVVQQghhBBCSB6xxV4AIYQQQghZ/yjoJIQQQggheUdBJyGEEEIIyTsKOgkhhBBCSN5R0EkIIYQQQvKOgk5CCCGEEJJ3FHQSQgghhJC8o6CTEEIIIYTkHV/sBSyWSCQwOjoKWZbBMEyxl0MIIYQQQhZRVRU+nw/1 [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "spain_copy = spain.copy()\n",
+ "reposition(spain_copy, spain.name.isin(['Las Palmas', 'Santa Cruz de Tenerife']), 3, 7, 1, 1)\n",
+ "spain_copy.plot(figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-SNb1b-Et1LV"
+ },
+ "source": [
+ "#### Russia"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 53,
+ "metadata": {
+ "id": "60UpJMNwt1LV",
+ "outputId": "1c9ff3fa-83e6-411e-9dc3-0c718ee97d39"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "60UpJMNwt1LV",
- "outputId": "1c9ff3fa-83e6-411e-9dc3-0c718ee97d39"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 57,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAChCAYAAABnAt39AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAABT8klEQVR4nO3dd3ykVbnA8d87vbf0XrZle+8sdWVB4IIgIKIUERRQRBAE76VaQFDwqhRBL0UUBBUEpQjLLggs23sv6XVTJjOZTJ/3/jHJbCYzyaZnsznfz4cPu++88845k+y8z5zznOdIsizLCIIgCIIgjBDFaDdAEARBEITxRQQfgiAIgiCMKBF8CIIgCIIwokTwIQiCIAjCiBLBhyAIgiAII0oEH4IgCIIgjCgRfAiCIAiCMKJE8CEIgiAIwohSjXYDuotEItTU1GA2m5EkabSbIwiCIAhCH8iyjNvtJj [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "russia = df[df.admin == 'Russia']\n",
- "russia.plot()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 53,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "IOuQ_OzMt1LW"
- },
- "source": [
- "Russia looks off because of Chukchi runs across E180. We need to move the parts on the other side of the map to the right."
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAChCAYAAABnAt39AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAABT8klEQVR4nO3dd3ykVbnA8d87vbf0XrZle+8sdWVB4IIgIKIUERRQRBAE76VaQFDwqhRBL0UUBBUEpQjLLggs23sv6XVTJjOZTJ/3/jHJbCYzyaZnsznfz4cPu++88845k+y8z5zznOdIsizLCIIgCIIgjBDFaDdAEARBEITxRQQfgiAIgiCMKBF8CIIgCIIwokTwIQiCIAjCiBLBhyAIgiAII0oEH4IgCIIgjCgRfAiCIAiCMKJE8CEIgiAIwohSjXYDuotEItTU1GA2m5EkabSbIwiCIAhCH8iyjNvtJjs7G4Wi [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "russia = df[df.admin == 'Russia']\n",
+ "russia.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IOuQ_OzMt1LW"
+ },
+ "source": [
+ "Russia looks off because of Chukchi runs across E180. We need to move the parts on the other side of the map to the right."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 58,
+ "metadata": {
+ "id": "rfBkQf78t1LW",
+ "outputId": "8342e4b8-2483-4aac-8a79-e88d455297e2",
+ "scrolled": true
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "rfBkQf78t1LW",
- "outputId": "8342e4b8-2483-4aac-8a79-e88d455297e2"
- },
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "C:\\Users\\bryan\\AppData\\Local\\Temp\\ipykernel_31064\\127064943.py:6: ShapelyDeprecationWarning: Iteration over multi-part geometries is deprecated and will be removed in Shapely 2.0. Use the `geoms` property to access the constituent parts of a multi-part geometry.\n",
- " for item in splitted_geom:\n"
- ]
- },
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 58,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAABj0AAAMyCAYAAAA2aQPiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXhk5fUH8O+4zySZuMsmm9WsuwKLu7v9sBZaKC0tFCm0SCkUt0JxpzgLLLDCuls2nmzcfTTj9/dHlkDYZGMzmcj38zw82czc+94z7CaT3POec0SCIAggIiIiIiIiIiIiIiIa5cTBDoCIiIiIiIiIiIiIiMgfmPQgIiIiIiIiIiIiIqIxgUkPIiIiIiIiIiIiIiIaE5j0ICIiIiIiIiIiIiKiMYFJDyIiIiIiIiIiIiIiGhOY9CAiIiIiIiIiIiIiojGBSQ8iIiIiIiIiIiIiIhoTmPQgIi [...]
- "text/plain": [
- "<Figure size 2000x2000 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def shift_geom(geom, cutoff=0):\n",
- " border = shapely.geometry.LineString([(cutoff,-90),(cutoff, 90)])\n",
- " splitted_geom = shapely.ops.split(geom, border)\n",
- " moved_geom = []\n",
- "\n",
- " for item in splitted_geom:\n",
- " minx, miny, maxx, maxy = item.bounds\n",
- " if minx < cutoff:\n",
- " moved_geom.append(shapely.affinity.translate(item, xoff=cutoff + 360))\n",
- " else:\n",
- " moved_geom.append(item)\n",
- "\n",
- " # got `moved_geom` as the moved geometry\n",
- " return shapely.ops.unary_union(moved_geom)\n",
- "\n",
- "russia_copy = russia.copy()\n",
- "russia_copy.loc[\n",
- " russia.name == 'Chukchi Autonomous Okrug', 'geometry'\n",
- "] = russia_copy.loc[\n",
- " russia.name == 'Chukchi Autonomous Okrug', 'geometry'\n",
- "].apply(shift_geom)\n",
- "\n",
- "russia_copy = russia_copy\n",
- "\n",
- "russia_copy.plot(figsize=(20, 20))"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 58,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "aYFQYe8-t1LW"
- },
- "source": [
- "#### France"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAABj0AAAMyCAYAAAA2aQPiAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzddXhk5fUH8O+4zySZuMsmm9WsuwKLu7v9sBZaKC0tFCm0SCkUt0JxpzgLLLDCuls2nmzcfTTj9/dHlkDYZGMzmcj38zw82czc+94z7CaT3POec0SCIAggIiIiIiIiIiIiIiIa5cTBDoCIiIiIiIiIiIiIiMgfmPQgIiIiIiIiIiIiIqIxgUkPIiIiIiIiIiIiIiIaE5j0ICIiIiIiIiIiIiKiMYFJDyIiIiIiIiIiIiIiGhOY9CAiIiIiIiIiIiIiojGBSQ8iIiIiIiIiIiIiIhoTmPQgIiIiIiIi [...]
+ "text/plain": [
+ "<Figure size 2000x2000 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import shapely.geometry\n",
+ "import shapely.ops\n",
+ "import shapely.affinity\n",
+ "\n",
+ "def shift_geom(geom, cutoff=0):\n",
+ " border = shapely.geometry.LineString([(cutoff, -90), (cutoff, 90)])\n",
+ " splitted_geom = shapely.ops.split(geom, border)\n",
+ "\n",
+ " # Create a list to store moved geometries\n",
+ " moved_geom = []\n",
+ "\n",
+ " # Check if the split operation returned a GeometryCollection\n",
+ " if isinstance(splitted_geom, shapely.geometry.GeometryCollection):\n",
+ " # Iterate over each geometry in the GeometryCollection\n",
+ " for item in splitted_geom.geoms:\n",
+ " minx, miny, maxx, maxy = item.bounds\n",
+ " if minx < cutoff:\n",
+ " # Translate the geometry\n",
+ " moved_geom.append(shapely.affinity.translate(item, xoff=360 - cutoff))\n",
+ " else:\n",
+ " moved_geom.append(item)\n",
+ " else:\n",
+ " # If the result is not a GeometryCollection, it means no split occurred\n",
+ " moved_geom.append(geom)\n",
+ "\n",
+ " # Combine all moved geometries into a single geometry\n",
+ " return shapely.ops.unary_union(moved_geom)\n",
+ "\n",
+ "# Applying the function to the DataFrame\n",
+ "russia_copy = russia.copy()\n",
+ "russia_copy.loc[\n",
+ " russia.name == 'Chukchi Autonomous Okrug', 'geometry'\n",
+ "] = russia_copy.loc[\n",
+ " russia.name == 'Chukchi Autonomous Okrug', 'geometry'\n",
+ "].apply(shift_geom)\n",
+ "\n",
+ "# Plotting\n",
+ "russia_copy.plot(figsize=(20, 20))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aYFQYe8-t1LW"
+ },
+ "source": [
+ "#### France"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 59,
+ "metadata": {
+ "id": "AcT31Diyt1LW",
+ "outputId": "cd6cc6ef-43ba-478e-b183-84eb7e003e17"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "AcT31Diyt1LW",
- "outputId": "cd6cc6ef-43ba-478e-b183-84eb7e003e17"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 59,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAFnCAYAAABqwnnlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxQUlEQVR4nO3de3RU9b3//9fcc53J/UYSCIICIogoGG/1kkqpbbWi39baViw/rDZaEdqjHK3W3zoWlvXUVlsvbS3aUxWL56cercrxYIvt1yAQ5RIUBLkkIZkESDKTC5nJzHx+f0RHcwBLkMlkm+djrb0W89l79n7PR2Rea8/n89k2Y4wRAADAMGdPdgEAAABHg9ACAAAsgdACAAAsgdACAAAsgdACAAAsgdACAAAsgdACAAAswZnsAj6rWCympqYmZWZmymazJbscAABwFIwx6uzsVElJiez2o7uHYvnQ0t [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "france = df[df.admin == 'France']\n",
- "france.plot()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 59,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "p7Y4Vf6pt1LW"
- },
- "source": [
- "Ignore all the oversea islands"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi0AAAFnCAYAAABqwnnlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAxQUlEQVR4nO3de3RU9b3//9fcc53J/UYSCIICIogoGG/1kkqpbbWi39baViw/rDZaEdqjHK3W3zoWlvXUVlsvbS3aUxWL56cercrxYIvt1yAQ5RIUBLkkIZkESDKTC5nJzHx+f0RHcwBLkMlkm+djrb0W89l79n7PR2Rea8/n89k2Y4wRAADAMGdPdgEAAABHg9ACAAAsgdACAAAsgdACAAAsgdACAAAsgdACAAAsgdACAAAswZnsAj6rWCympqYmZWZmymazJbscAABwFIwx6uzsVElJiez2o7uHYvnQ0tTUpLKy [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "france = df[df.admin == 'France']\n",
+ "france.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "p7Y4Vf6pt1LW"
+ },
+ "source": [
+ "Ignore all the oversea islands"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 60,
+ "metadata": {
+ "id": "yjKX9Pbbt1LW",
+ "outputId": "14caae01-b1b0-4775-a00e-a9e4f30fdf73"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "yjKX9Pbbt1LW",
- "outputId": "14caae01-b1b0-4775-a00e-a9e4f30fdf73"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 60,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAJ8CAYAAACx02peAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gkaXUv4F+lrs5RUiurNUmTk2Y0m3eBJZgcLtgsLCYYYwzYhosDvjaOGMzFGHMxOWMWWHJYWGAX2LyjyVmaoJxD5xyq7h8t9SirQ1V3tXTe51mbkbqrS1KHU993AiPLsgxCCCGEEEJUwFb6BAghhBBCyMZFwSYhhBBCCFENBZuEEEIIIUQ1FGwSQgghhBDVULBJCCGEEEJUQ8EmIYQQQghRDQWbhBBCCCFENXylT2ApSZIwNjYGi8UChmEqfTqEEEIIIWQJWZYRCoXQ2NgIll177VJzwe [...]
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "def apply_bounds(df, northwest, southeast):\n",
- " x1, y1 = northwest\n",
- " x2, y2 = southeast\n",
- " boundry = shapely.geometry.Polygon([(x1, y1),(x1, y2), (x2, y2), (x2, y1)])\n",
- " df = df.copy()\n",
- " return df[df.geometry.apply(lambda x: boundry.contains(x))]\n",
- "\n",
- "france_copy = apply_bounds(france, (-20, 60), (20, 20))\n",
- "france_copy.plot(figsize=(8, 8))"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 60,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "d1T6jfJPt1LW"
- },
- "source": [
- "#### Netherlands"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAApsAAAJ8CAYAAACx02peAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3gkaXUv4F+lrs5RUiurNUmTk2Y0m3eBJZgcLtgsLCYYYwzYhosDvjaOGMzFGHMxOWMWWHJYWGAX2LyjyVmaoJxD5xyq7h8t9SirQ1V3tXTe51mbkbqrS1KHU993AiPLsgxCCCGEEEJUwFb6BAghhBBCyMZFwSYhhBBCCFENBZuEEEIIIUQ1FGwSQgghhBDVULBJCCGEEEJUQ8EmIYQQQghRDQWbhBBCCCFENXylT2ApSZIwNjYGi8UChmEqfTqEEEIIIWQJWZYRCoXQ2NgIll177VJzwebY2Bha [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def apply_bounds(df, northwest, southeast):\n",
+ " x1, y1 = northwest\n",
+ " x2, y2 = southeast\n",
+ " boundry = shapely.geometry.Polygon([(x1, y1),(x1, y2), (x2, y2), (x2, y1)])\n",
+ " df = df.copy()\n",
+ " return df[df.geometry.apply(lambda x: boundry.contains(x))]\n",
+ "\n",
+ "france_copy = apply_bounds(france, (-20, 60), (20, 20))\n",
+ "france_copy.plot(figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "d1T6jfJPt1LW"
+ },
+ "source": [
+ "#### Netherlands"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 61,
+ "metadata": {
+ "id": "IS5Gcxgct1LW",
+ "outputId": "b8dbb05f-4ca9-4884-83ac-a7c169a9830a"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "IS5Gcxgct1LW",
- "outputId": "b8dbb05f-4ca9-4884-83ac-a7c169a9830a"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 61,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAFmCAYAAAC/V5rzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlV0lEQVR4nO3df3RU9Z3/8dfk15CQzOT3L5kExApomq+KNYw/WJRUFlyqa9zTo1TFL8uKJ2IF2mXzlVbhuzZ89RRc2YW6tYuelUhLD5T1J0XQeKyJ0pgYwJIKBRIMCVXMTAhkEsjn+4dlykhQJgl8yOT5OOeew3w+937m/Zlo7uvc+dwbhzHGCAAAwJIo2wUAAIChjTACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArIoJZ+fHHntMixcvDmkbM2aMdu3aJUmaNG [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "netherlands = df[df.admin == 'Netherlands']\n",
- "netherlands.plot()"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 61,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "nwUGucQ1t1LW",
- "outputId": "26c5aede-c587-4d88-cfe0-30ecaec9ede3"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 62,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAKTCAYAAAD1xWeKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC1YklEQVR4nOzdd3hc1bU34N+Z3pt6by5yL7IlG0zHYJMChOoACQk3kEZCCCH1Jibhfk5yCZc0ShqENCAEU0INxcbgIhfZli1LVhv1Or3X8/0hW1i2yvRzZma9z6PH9mh0ZsmWZ9bsvfZaDMuyLAghhBBCspCA6wAIIYQQQrhCiRAhhBBCshYlQoQQQgjJWpQIEUIIISRrUSJECCGEkKxFiRAhhBBCshYlQoQQQgjJWiKuA0iEcDiMwcFBqNVqMAzDdTiEEEII4RDLsnA4HCguLoZAMPuaT0YkQoODgygrK+ [...]
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "netherlands_copy = apply_bounds(netherlands, (-20, 60), (20, 20))\n",
- "netherlands_copy.plot(figsize=(8, 8))"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiMAAAFmCAYAAAC/V5rzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAlV0lEQVR4nO3df3RU9Z3/8dfk15CQzOT3L5kExApomq+KNYw/WJRUFlyqa9zTo1TFL8uKJ2IF2mXzlVbhuzZ89RRc2YW6tYuelUhLD5T1J0XQeKyJ0pgYwJIKBRIMCVXMTAhkEsjn+4dlykhQJgl8yOT5OOeew3w+937m/Zlo7uvc+dwbhzHGCAAAwJIo2wUAAIChjTACAACsIowAAACrCCMAAMAqwggAALCKMAIAAKwijAAAAKsIIwAAwCrCCAAAsIowAgAArIoJZ+fHHntMixcvDmkbM2aMdu3aJUmaNGmSKisr [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "netherlands = df[df.admin == 'Netherlands']\n",
+ "netherlands.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {
+ "id": "nwUGucQ1t1LW",
+ "outputId": "26c5aede-c587-4d88-cfe0-30ecaec9ede3"
+ },
+ "outputs": [
{
- "cell_type": "markdown",
- "metadata": {
- "id": "tTpJe28jt1LW"
- },
- "source": [
- "#### UK"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 62,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "xfMx6gJmt1LW",
- "outputId": "5278dfc3-3f51-4c21-84cc-922251b1d0cb"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 63,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAGdCAYAAAAomHm2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAACafElEQVR4nOzddXxkZ7348c8Zd4t7spvdZN1361SoAYUWb6G4S6HIvXB/cKEXKRcpXNylQCmlLaVQL3VZ981K3H3c7fz+mGQ2k0ySiW2y2+f9eu2LzciZM6H7nWee8xVJlmUZQRAEYdEoFvsEBEEQXulEIBYEQVhkIhALgiAsMhGIBUEQFpkIxIIgCItMBGJBEIRFJgKxIAjCIhOBWBAEYZGpFvsExksmk/T09GA2m5EkabFPRxAEIYMsy/h8PkpLS1Eo5mctu+QCcU9PDxUVFYt9GoIgCFPq7OykvLx8Xo [...]
- "text/plain": [
- "<Figure size 640x480 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "uk = df[df.admin == 'United Kingdom']\n",
- "uk.plot()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAKTCAYAAAD1xWeKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC1YklEQVR4nOzdd3hc1bU34N+Z3pt6by5yL7IlG0zHYJMChOoACQk3kEZCCCH1Jibhfk5yCZc0ShqENCAEU0INxcbgIhfZli1LVhv1Or3X8/0hW1i2yvRzZma9z6PH9mh0ZsmWZ9bsvfZaDMuyLAghhBBCspCA6wAIIYQQQrhCiRAhhBBCshYlQoQQQgjJWpQIEUIIISRrUSJECCGEkKxFiRAhhBBCshYlQoQQQgjJWiKuA0iEcDiMwcFBqNVqMAzDdTiEEEII4RDLsnA4HCguLoZAMPuaT0YkQoODgygrK+M6DEII [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "netherlands_copy = apply_bounds(netherlands, (-20, 60), (20, 20))\n",
+ "netherlands_copy.plot(figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "tTpJe28jt1LW"
+ },
+ "source": [
+ "#### UK"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "metadata": {
+ "id": "xfMx6gJmt1LW",
+ "outputId": "5278dfc3-3f51-4c21-84cc-922251b1d0cb"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "28VU40f9t1LW",
- "outputId": "45585067-de13-4e02-8147-053ef0115d2d"
- },
- "outputs": [
- {
- "data": {
- "text/plain": [
- "<AxesSubplot:>"
- ]
- },
- "execution_count": 64,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAKTCAYAAABSLqyAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xb1dkH8N+92nt47x07HkkcJ86GEMLeu2GvAqWM0gKFtrxA4YX2LQXKKmWVUSCMsjdhZMdOnOmVeO+tvdd9/5CtWLYkS7Zsyfb5fj79fBrpSjoOzn10znnO81AMwzAgCIIgiChBR3oABEEQBDEaCUwEQRBEVCGBiSAIgogqJDARBEEQUYUEJoIgCCKqkMBEEARBRBUSmAiCIIiowo70AMZyuVzo7u6GRCIBRVGRHg5BEAQRBgzDQK/XIzk5GTQdeE4UdYGpu7sbaWlpkR4GQRAEMQ06Oj [...]
- "text/plain": [
- "<Figure size 800x800 with 1 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "uk_copy = apply_bounds(uk, (-10, 60), (20, 20))\n",
- "uk_copy.plot(figsize=(8, 8))"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 63,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "markdown",
- "metadata": {
- "id": "Fb58eGlIt1LW"
- },
- "source": [
- "## Output GeoJSON"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWIAAAGdCAYAAAAomHm2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAACafElEQVR4nOzddXxkZ7348c8Zd4t7spvdZN1361SoAYUWb6G4S6HIvXB/cKEXKRcpXNylQCmlLaVQL3VZ981K3H3c7fz+mGQ2k0ySiW2y2+f9eu2LzciZM6H7nWee8xVJlmUZQRAEYdEoFvsEBEEQXulEIBYEQVhkIhALgiAsMhGIBUEQFpkIxIIgCItMBGJBEIRFJgKxIAjCIhOBWBAEYZGpFvsExksmk/T09GA2m5EkabFPRxAEIYMsy/h8PkpLS1Eo5mctu+QCcU9PDxUVFYt9GoIgCFPq7OykvLx8Xo615AKx [...]
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "uk = df[df.admin == 'United Kingdom']\n",
+ "uk.plot()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 64,
+ "metadata": {
+ "id": "28VU40f9t1LW",
+ "outputId": "45585067-de13-4e02-8147-053ef0115d2d"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "5xOVyzXCt1LW"
- },
- "outputs": [],
- "source": [
- "alt_maps = {\n",
- " \"finland\": finland_copy,\n",
- " \"china\": china_copy,\n",
- " \"usa\": usa_copy,\n",
- " \"france\": france_copy,\n",
- " \"netherlands\": netherlands_copy,\n",
- " \"norway\": norway_copy,\n",
- " \"uk\": uk_copy,\n",
- " \"russia\": russia_copy,\n",
- " \"spain\": spain_copy,\n",
- " \"portugal\": portugal_copy,\n",
- "}"
+ "data": {
+ "text/plain": [
+ "<Axes: >"
]
+ },
+ "execution_count": 64,
+ "metadata": {},
+ "output_type": "execute_result"
},
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "tM1F5d0Vt1LW",
- "outputId": "75abad9b-9442-4279-d66d-a0cd5fb97198"
- },
- "outputs": [
- {
- "data": {
- "image/png": "iVBORw0KGgoAAAANSUhEUgAAB8AAAAdDCAYAAAAmZCF8AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/NK7nSAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e/0SZ30Riqhd6QJCIq6gGvXxa5gwV0FXUVWwVUR67p2/dlXwYJdsVesqPTeIZDe26RMkqn390dgZMikl0nC+3kenmXu3HvnTFZOzj3vOe+rUhRFQQghhBBCCCGEEEIIIYQQQgghhOjh1L5ugBBCCCGEEEIIIYQQQgghhBBCCNERJAAuhBBCCCGEEEIIIYQQQgghhBCiV5AAuBBCCCGEEEIIIYQQQgghhBBCiF5BAuBCCCGEEEIIIYQQQgghhBBCCCF6BQmACyGEEEIIIYQQQg [...]
- "text/plain": [
- "<Figure size 2000x2000 with 69 Axes>"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "plot_all_countries()"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaYAAAKTCAYAAABSLqyAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xb1dkH8N+92nt47x07HkkcJ86GEMLeu2GvAqWM0gKFtrxA4YX2LQXKKmWVUSCMsjdhZMdOnOmVeO+tvdd9/5CtWLYkS7Zsyfb5fj79fBrpSjoOzn10znnO81AMwzAgCIIgiChBR3oABEEQBDEaCUwEQRBEVCGBiSAIgogqJDARBEEQUYUEJoIgCCKqkMBEEARBRBUSmAiCIIiowo70AMZyuVzo7u6GRCIBRVGRHg5BEAQRBgzDQK/XIzk5GTQdeE4UdYGpu7sbaWlpkR4GQRAEMQ06OjqQmpoa [...]
+ "text/plain": [
+ "<Figure size 800x800 with 1 Axes>"
]
- },
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "uk_copy = apply_bounds(uk, (-10, 60), (20, 20))\n",
+ "uk_copy.plot(figsize=(8, 8))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fb58eGlIt1LW"
+ },
+ "source": [
+ "## Output GeoJSON"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 65,
+ "metadata": {
+ "id": "5xOVyzXCt1LW"
+ },
+ "outputs": [],
+ "source": [
+ "alt_maps = {\n",
+ " \"finland\": finland_copy,\n",
+ " \"china\": china_copy,\n",
+ " \"usa\": usa_copy,\n",
+ " \"france\": france_copy,\n",
+ " \"netherlands\": netherlands_copy,\n",
+ " \"norway\": norway_copy,\n",
+ " \"uk\": uk_copy,\n",
+ " \"russia\": russia_copy,\n",
+ " \"spain\": spain_copy,\n",
+ " \"portugal\": portugal_copy,\n",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 66,
+ "metadata": {
+ "id": "tM1F5d0Vt1LW",
+ "outputId": "75abad9b-9442-4279-d66d-a0cd5fb97198"
+ },
+ "outputs": [
{
- "cell_type": "code",
- "execution_count": null,
- "metadata": {
- "id": "8U3S1PUbt1LW",
- "outputId": "cfb8d229-ffdf-473f-d516-6aa136e41a60",
- "scrolled": false
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "aland\tSize 0.913\tSaving geojson for aland...\n",
- "argentina\tSize 662.347\tSaving geojson for argentina...\n",
- "australia\tSaving geojson for australia...\n",
- "belgium\tSize 7.709\tSaving geojson for belgium...\n",
- "bolivia\tSize 161.264\tSaving geojson for bolivia...\n",
- "brazil\tSaving geojson for brazil...\n",
- "bulgaria\tSize 18.715\tSaving geojson for bulgaria...\n",
- "burundi\tSize 3.99\tSaving geojson for burundi...\n",
- "canada\tSaving geojson for canada...\n",
- "chile\tSize 1652.977\tSaving geojson for chile...\n",
- "china\tSaving geojson for china...\n",
- "colombia\tSize 264.526\tSaving geojson for colombia...\n",
- "costa rica\tSize 25.939\tSaving geojson for costa rica...\n",
- "cuba\tSize 37.185\tSaving geojson for cuba...\n",
- "denmark\tSize 22.461\tSaving geojson for denmark...\n",
- "dominican republic\tSize 8.806\tSaving geojson for dominican republic...\n",
- "ecuador\tSize 112.048\tSaving geojson for ecuador...\n",
- "egypt\tSize 117.982\tSaving geojson for egypt...\n",
- "el salvador\tSize 3.116\tSaving geojson for el salvador...\n",
- "estonia\tSize 13.694\tSaving geojson for estonia...\n",
- "ethiopia\tSize 172.021\tSaving geojson for ethiopia...\n",
- "france\tSize 142.834\tSaving geojson for france...\n",
- "finland\tSize 112.354\tSaving geojson for finland...\n",
- "germany\tSize 71.47\tSaving geojson for germany...\n",
- "guatemala\tSize 16.442\tSaving geojson for guatemala...\n",
- "haiti\tSize 5.882\tSaving geojson for haiti...\n",
- "honduras\tSize 27.669\tSaving geojson for honduras...\n",
- "iceland\tSize 34.959\tSaving geojson for iceland...\n",
- "india\tSaving geojson for india...\n",
- "indonesia\tSaving geojson for indonesia...\n",
- "iran\tSize 284.014\tSaving geojson for iran...\n",
- "italy\tSize 138.162\tSaving geojson for italy...\n",
- "japan\tSize 661.569\tSaving geojson for japan...\n",
- "kenya\tSize 77.61\tSaving geojson for kenya...\n",
- "korea\tSize 34.227\tSaving geojson for korea...\n",
- "liechtenstein\tSize 0.029\tSaving geojson for liechtenstein...\n",
- "malaysia\tSize 127.7\tSaving geojson for malaysia...\n",
- "mexico\tSize 575.302\tSaving geojson for mexico...\n",
- "morocco\tSize 231.84\tSaving geojson for morocco...\n",
- "myanmar\tSize 168.709\tSaving geojson for myanmar...\n",
- "netherlands\tSize 10.818\tSaving geojson for netherlands...\n",
- "nicaragua\tSize 21.415\tSaving geojson for nicaragua...\n",
- "nigeria\tSize 115.287\tSaving geojson for nigeria...\n",
- "norway\tSize 530.052\tSaving geojson for norway...\n",
- "panama\tSize 14.275\tSaving geojson for panama...\n",
- "paraguay\tSize 69.763\tSaving geojson for paraguay...\n",
- "portugal\tSize 105.727\tSaving geojson for portugal...\n",
- "poland\tSize 58.556\tSaving geojson for poland...\n",
- "puerto rico\tSize 1.616\tSaving geojson for puerto rico...\n",
- "russia\tSaving geojson for russia...\n",
- "rwanda\tSize 3.59\tSaving geojson for rwanda...\n",
- "saint barthelemy\tSize 0.004\tSaving geojson for saint barthelemy...\n",
- "saint martin\tSize 0.012\tSaving geojson for saint martin...\n",
- "singapore\tSize 0.067\tSaving geojson for singapore...\n",
- "slovenia\tSize 4.537\tSaving geojson for slovenia...\n",
- "spain\tSize 178.488\tSaving geojson for spain...\n",
- "sweden\tSize 178.774\tSaving geojson for sweden...\n",
- "switzerland\tSize 8.935\tSaving geojson for switzerland...\n",
- "syria\tSize 33.348\tSaving geojson for syria...\n",
- "tanzania\tSize 119.579\tSaving geojson for tanzania...\n",
- "thailand\tSize 122.959\tSaving geojson for thailand...\n",
- "timorleste\tSize 4.486\tSaving geojson for timorleste...\n",
- "uganda\tSize 31.083\tSaving geojson for uganda...\n",
- "uk\tSaving geojson for uk...\n",
- "ukraine\tSize 128.988\tSaving geojson for ukraine...\n",
- "uruguay\tSize 25.985\tSaving geojson for uruguay...\n",
- "usa\tSaving geojson for usa...\n",
- "venezuela\tSize 204.361\tSaving geojson for venezuela...\n",
- "zambia\tSize 115.483\tSaving geojson for zambia...\n",
- "Done. \n"
- ]
- }
- ],
- "source": [
- "simplify_factors = {\n",
- " \"uk\": 0.005,\n",
- "}\n",
- "useful_columns = [\"ISO\", \"NAME_1\", \"geometry\"]\n",
- "\n",
- "def get_simplify_factor_by_size(gdf):\n",
- " xmin, ymin, xmax, ymax = shapely.ops.unary_union(gdf[\"geometry\"]).bounds\n",
- " size = (xmax - xmin) * (ymax - ymin)\n",
- " print(\"Size\", round(size, 3), end=\"\\t\")\n",
- " if size > 1000: return 0.03\n",
- " if size > 300: return 0.02\n",
- " if size > 100: return 0.01\n",
- " return 0\n",
- "\n",
- "def simplify_if_needed(country, gdf):\n",
- " \"\"\"Simplify the maps based on country size\"\"\"\n",
- " country_alias = country_name_aliases.get(country, country)\n",
- " if country_alias in df_50m[\"admin\"].str.lower().unique():\n",
- " return\n",
- "\n",
- " factor = simplify_factors.get(country) or get_simplify_factor_by_size(gdf)\n",
- "\n",
- " if factor:\n",
- " gdf[\"geometry\"] = gdf.simplify(factor)\n",
- "\n",
- "def save_geojson(country):\n",
- " gdf = get_gdf(country)\n",
- " print(country, end=\"\\t\")\n",
- "\n",
- " # For backward compatibility\n",
- " gdf[\"ISO\"] = gdf[\"iso_3166_2\"]\n",
- " gdf[\"NAME_1\"] = gdf[\"name\"]\n",
- "\n",
- " simplify_if_needed(country, gdf)\n",
- "\n",
- " print(f'Saving geojson for {country}...')\n",
- " gdf[useful_columns].to_file(f\"../src/countries/{country}.geojson\", driver=\"GeoJSON\")\n",
- "\n",
- "for country in countries:\n",
- " save_geojson(country)\n",
- "\n",
- "print(\"Done. \")"
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAB7wAAAbRCAYAAAA8yPiWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e/0kjLpjVQCBAKE3kRUFMXeu7uC+rNiw44Fu667dlexrGLDXrArNlCR3ntNI73OpE2/vz8CA0MmlYQEeD/Pk2edO2fuPZOFw73nPed9VYqiKAghhBBCCCGEEEIIIYQQQgghhBCHGHV3d0AIIYQQQgghhBBCCCGEEEIIIYToCAl4CyGEEEIIIYQQQgghhBBCCCGEOCRJwFsIIYQQQgghhBBCCCGEEEIIIcQhSQLeQgghhBBCCCGEEEIIIYQQQgghDkkS8BZCCCGEEEIIIYQQQgghhBBC [...]
+ "text/plain": [
+ "<Figure size 2000x2000 with 78 Axes>"
]
+ },
+ "metadata": {},
+ "output_type": "display_data"
}
- ],
- "metadata": {
- "colab": {
- "provenance": []
- },
- "kernelspec": {
- "display_name": "Python 3.10.11 64-bit",
- "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": "3.10.11"
- },
- "vscode": {
- "interpreter": {
- "hash": "bd385fe162c5ca0c84973b7dd5c518456272446b2b64e67c2a69f949ca7a1754"
- }
+ ],
+ "source": [
+ "plot_all_countries()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {
+ "id": "8U3S1PUbt1LW",
+ "outputId": "cfb8d229-ffdf-473f-d516-6aa136e41a60"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "afghanistan\tSize 130.904\tSaving geojson for afghanistan...\n",
+ "aland\tSize 0.913\tSaving geojson for aland...\n",
+ "albania\tSize 5.325\tSaving geojson for albania...\n",
+ "algeria\tSize 374.167\tSaving geojson for algeria...\n",
+ "argentina\tSize 662.347\tSaving geojson for argentina...\n",
+ "australia\tSaving geojson for australia...\n",
+ "belgium\tSize 7.709\tSaving geojson for belgium...\n",
+ "bolivia\tSize 161.264\tSaving geojson for bolivia...\n",
+ "brazil\tSaving geojson for brazil...\n",
+ "bulgaria\tSize 18.715\tSaving geojson for bulgaria...\n",
+ "burundi\tSize 3.99\tSaving geojson for burundi...\n",
+ "canada\tSaving geojson for canada...\n",
+ "chile\tSize 1652.977\tSaving geojson for chile...\n",
+ "china\tSaving geojson for china...\n",
+ "colombia\tSize 264.526\tSaving geojson for colombia...\n",
+ "costa rica\tSize 25.939\tSaving geojson for costa rica...\n",
+ "cuba\tSize 37.185\tSaving geojson for cuba...\n",
+ "denmark\tSize 22.461\tSaving geojson for denmark...\n",
+ "dominican republic\tSize 8.806\tSaving geojson for dominican republic...\n",
+ "ecuador\tSize 112.048\tSaving geojson for ecuador...\n",
+ "egypt\tSize 117.982\tSaving geojson for egypt...\n",
+ "el salvador\tSize 3.116\tSaving geojson for el salvador...\n",
+ "estonia\tSize 13.694\tSaving geojson for estonia...\n",
+ "ethiopia\tSize 172.021\tSaving geojson for ethiopia...\n",
+ "france\tSize 142.834\tSaving geojson for france...\n",
+ "finland\tSize 112.354\tSaving geojson for finland...\n",
+ "germany\tSize 71.47\tSaving geojson for germany...\n",
+ "guatemala\tSize 16.442\tSaving geojson for guatemala...\n",
+ "haiti\tSize 5.882\tSaving geojson for haiti...\n",
+ "honduras\tSize 27.669\tSaving geojson for honduras...\n",
+ "iceland\tSize 34.959\tSaving geojson for iceland...\n",
+ "india\tSaving geojson for india...\n",
+ "indonesia\tSaving geojson for indonesia...\n",
+ "iran\tSize 284.014\tSaving geojson for iran...\n",
+ "italy\tSize 138.162\tSaving geojson for italy...\n",
+ "japan\tSize 661.569\tSaving geojson for japan...\n",
+ "kazakhstan\tSize 606.552\tSaving geojson for kazakhstan...\n",
+ "kenya\tSize 77.61\tSaving geojson for kenya...\n",
+ "korea\tSize 34.227\tSaving geojson for korea...\n",
+ "kyrgyzstan\tSize 44.924\tSaving geojson for kyrgyzstan...\n",
+ "latvia\tSize 17.456\tSaving geojson for latvia...\n",
+ "liechtenstein\tSize 0.029\tSaving geojson for liechtenstein...\n",
+ "malaysia\tSize 127.7\tSaving geojson for malaysia...\n",
+ "mexico\tSize 575.302\tSaving geojson for mexico...\n",
+ "morocco\tSize 231.84\tSaving geojson for morocco...\n",
+ "myanmar\tSize 168.709\tSaving geojson for myanmar...\n",
+ "netherlands\tSize 10.818\tSaving geojson for netherlands...\n",
+ "nicaragua\tSize 21.415\tSaving geojson for nicaragua...\n",
+ "nigeria\tSize 115.287\tSaving geojson for nigeria...\n",
+ "norway\tSize 530.052\tSaving geojson for norway...\n",
+ "panama\tSize 14.275\tSaving geojson for panama...\n",
+ "paraguay\tSize 69.763\tSaving geojson for paraguay...\n",
+ "portugal\tSize 105.727\tSaving geojson for portugal...\n",
+ "poland\tSize 58.556\tSaving geojson for poland...\n",
+ "puerto rico\tSize 1.616\tSaving geojson for puerto rico...\n",
+ "russia\tSaving geojson for russia...\n",
+ "rwanda\tSize 3.59\tSaving geojson for rwanda...\n",
+ "saint barthelemy\tSize 0.004\tSaving geojson for saint barthelemy...\n",
+ "saint martin\tSize 0.012\tSaving geojson for saint martin...\n",
+ "singapore\tSize 0.067\tSaving geojson for singapore...\n",
+ "slovenia\tSize 4.537\tSaving geojson for slovenia...\n",
+ "spain\tSize 178.488\tSaving geojson for spain...\n",
+ "sweden\tSize 178.774\tSaving geojson for sweden...\n",
+ "switzerland\tSize 8.935\tSaving geojson for switzerland...\n",
+ "syria\tSize 33.348\tSaving geojson for syria...\n",
+ "tajikistan\tSize 34.112\tSaving geojson for tajikistan...\n",
+ "tanzania\tSize 119.579\tSaving geojson for tanzania...\n",
+ "thailand\tSize 122.959\tSaving geojson for thailand...\n",
+ "timorleste\tSize 4.486\tSaving geojson for timorleste...\n",
+ "turkmenistan\tSize 108.7\tSaving geojson for turkmenistan...\n",
+ "uganda\tSize 31.083\tSaving geojson for uganda...\n",
+ "uk\tSaving geojson for uk...\n",
+ "ukraine\tSize 128.988\tSaving geojson for ukraine...\n",
+ "uruguay\tSize 25.985\tSaving geojson for uruguay...\n",
+ "usa\tSaving geojson for usa...\n",
+ "uzbekistan\tSize 143.798\tSaving geojson for uzbekistan...\n",
+ "venezuela\tSize 204.361\tSaving geojson for venezuela...\n",
+ "zambia\tSize 115.483\tSaving geojson for zambia...\n",
+ "Done. \n"
+ ]
}
+ ],
+ "source": [
+ "simplify_factors = {\n",
+ " \"uk\": 0.005,\n",
+ "}\n",
+ "useful_columns = [\"ISO\", \"NAME_1\", \"geometry\"]\n",
+ "\n",
+ "def get_simplify_factor_by_size(gdf):\n",
+ " xmin, ymin, xmax, ymax = shapely.ops.unary_union(gdf[\"geometry\"]).bounds\n",
+ " size = (xmax - xmin) * (ymax - ymin)\n",
+ " print(\"Size\", round(size, 3), end=\"\\t\")\n",
+ " if size > 1000: return 0.03\n",
+ " if size > 300: return 0.02\n",
+ " if size > 100: return 0.01\n",
+ " return 0\n",
+ "\n",
+ "def simplify_if_needed(country, gdf):\n",
+ " \"\"\"Simplify the maps based on country size\"\"\"\n",
+ " country_alias = country_name_aliases.get(country, country)\n",
+ " if country_alias in df_50m[\"admin\"].str.lower().unique():\n",
+ " return\n",
+ "\n",
+ " factor = simplify_factors.get(country) or get_simplify_factor_by_size(gdf)\n",
+ "\n",
+ " if factor:\n",
+ " gdf[\"geometry\"] = gdf.simplify(factor)\n",
+ "\n",
+ "def save_geojson(country):\n",
+ " gdf = get_gdf(country)\n",
+ " print(country, end=\"\\t\")\n",
+ "\n",
+ " # For backward compatibility\n",
+ " gdf[\"ISO\"] = gdf[\"iso_3166_2\"]\n",
+ " gdf[\"NAME_1\"] = gdf[\"name\"]\n",
+ "\n",
+ " simplify_if_needed(country, gdf)\n",
+ "\n",
+ " print(f'Saving geojson for {country}...')\n",
+ " gdf[useful_columns].to_file(f\"../src/countries/{country}.geojson\", driver=\"GeoJSON\")\n",
+ "\n",
+ "for country in countries:\n",
+ " save_geojson(country)\n",
+ "\n",
+ "print(\"Done. \")"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.11.5"
},
- "nbformat": 4,
- "nbformat_minor": 0
+ "vscode": {
+ "interpreter": {
+ "hash": "bd385fe162c5ca0c84973b7dd5c518456272446b2b64e67c2a69f949ca7a1754"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
}
diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/afghanistan.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/afghanistan.geojson
new file mode 100644
index 0000000000..0acf64dd71
--- /dev/null
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/afghanistan.geojson
@@ -0,0 +1,40 @@
+{
+"type": "FeatureCollection",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/aland.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/aland.geojson
new file mode 100644
index 0000000000..745faf1ee9
--- /dev/null
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/aland.geojson
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/albania.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/albania.geojson
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/algeria.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/algeria.geojson
new file mode 100644
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/china.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/china.geojson
index 8d9208b502..6614a0b105 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/china.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/china.geojson
@@ -33,8 +33,8 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/egypt.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/egypt.geojson
index 2ce3628b18..442249da0b 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/egypt.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/egypt.geojson
@@ -4,7 +4,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/finland.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/finland.geojson
index 556d82ce2c..cf5d779bc3 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/finland.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/finland.geojson
@@ -7,7 +7,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/france.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/france.geojson
index 378d6e95a6..ef559e11ab 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/france.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/france.geojson
@@ -19,7 +19,7 @@
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@@ -96,7 +96,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/germany.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/germany.geojson
index f298f781de..b4f893aac2 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/germany.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/germany.geojson
@@ -3,7 +3,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/india.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/india.geojson
index ec93b675e8..9f708a9133 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/india.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/india.geojson
@@ -4,12 +4,12 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/iran.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/iran.geojson
index 44ed6417b8..53e1b40cbb 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/iran.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/iran.geojson
@@ -2,7 +2,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/malaysia.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/malaysia.geojson
index 5e9b14d5ed..68d110d7d9 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/malaysia.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/malaysia.geojson
@@ -2,7 +2,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/myanmar.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/myanmar.geojson
index a02bfeb9fb..f5af5a084a 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/myanmar.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/myanmar.geojson
@@ -10,7 +10,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/norway.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/norway.geojson
index 666530e0c3..9d0a49dcbf 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/norway.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/norway.geojson
@@ -18,7 +18,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/poland.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/poland.geojson
index 881302ffa0..3aa45743b0 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/poland.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/poland.geojson
@@ -7,7 +7,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/portugal.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/portugal.geojson
index aa0e8eec51..485c04ac40 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/portugal.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/portugal.geojson
@@ -19,7 +19,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/russia.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/russia.geojson
index 6a57b978b0..97ff809205 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/russia.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/russia.geojson
@@ -9,7 +9,7 @@
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index 03c4ea9eca..08f1516d44 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/sweden.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/sweden.geojson
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/syria.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/syria.geojson
index b070872428..c842943f43 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/syria.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/syria.geojson
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/thailand.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/thailand.geojson
index 3ef8dff637..a317e57099 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/thailand.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/thailand.geojson
@@ -22,7 +22,7 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/uk.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/uk.geojson
index 334285f4ed..44ef8f7d1e 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/uk.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/uk.geojson
@@ -7,9 +7,9 @@
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diff --git a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/ukraine.geojson b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/ukraine.geojson
index 6d2c915122..a4b3c47b99 100644
--- a/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/ukraine.geojson
+++ b/superset-frontend/plugins/legacy-plugin-chart-country-map/src/countries/ukraine.geojson
@@ -26,7 +26,6 @@
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