From f601232509bffdb86a534045bfa466c7bbe07721 Mon Sep 17 00:00:00 2001 From: Conengmo <33519926+Conengmo@users.noreply.github.com> Date: Wed, 6 Sep 2023 10:48:20 +0200 Subject: [PATCH] batch 1 --- examples/CheckZorder.ipynb | 4 +- ...th Jenks natural breaks optimization.ipynb | 4 +- examples/ClickEvents.ipynb | 503 +----- examples/Colormaps.ipynb | 1112 +----------- examples/ContinuousWorld.ipynb | 113 +- examples/ControlScale.ipynb | 41 +- examples/CustomIcon.ipynb | 52 +- examples/CustomPanes.ipynb | 170 +- examples/FeatureGroup.ipynb | 40 +- examples/Features.ipynb | 1552 +---------------- examples/FitOverlays.ipynb | 500 +----- examples/FloatImage.ipynb | 40 +- examples/GeoJSONMarker.ipynb | 346 +--- examples/GeoJSONWithoutTitles.ipynb | 146 +- examples/GeoJSON_and_choropleth.ipynb | 877 +--------- examples/GeoJsonPopupAndTooltip.ipynb | 955 +--------- examples/GeodedeticImageOverlay.ipynb | 202 +-- examples/Geopandas_and_geo_interface.ipynb | 373 +--- examples/Heatmap.ipynb | 47 +- examples/flask_example.py | 94 +- 20 files changed, 109 insertions(+), 7062 deletions(-) diff --git a/examples/CheckZorder.ipynb b/examples/CheckZorder.ipynb index f2b7aed4a..d502fb9c0 100644 --- a/examples/CheckZorder.ipynb +++ b/examples/CheckZorder.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "source": [ - "This notebook has been removed.\n", - "See the current documentation at https://python-visualization.github.io/folium/latest/." + "We've updated our documentation and removed this notebook.\n", + "You can find the current documentation at https://python-visualization.github.io/folium/latest/." ], "metadata": { "collapsed": false, diff --git a/examples/Choropleth with Jenks natural breaks optimization.ipynb b/examples/Choropleth with Jenks natural breaks optimization.ipynb index 57643e08d..82e89ed81 100644 --- a/examples/Choropleth with Jenks natural breaks optimization.ipynb +++ b/examples/Choropleth with Jenks natural breaks optimization.ipynb @@ -3,8 +3,8 @@ { "cell_type": "markdown", "source": [ - "This notebook has been moved to \n", - "https://python-visualization.github.io/folium/latest/advanced_guide/choropleth%20with%20Jenks%20natural%20breaks%20optimization.html." + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/choropleth%20with%20Jenks%20natural%20breaks%20optimization.html" ], "metadata": { "collapsed": false diff --git a/examples/ClickEvents.ipynb b/examples/ClickEvents.ipynb index 3f5d2aebf..f527c8df3 100644 --- a/examples/ClickEvents.ipynb +++ b/examples/ClickEvents.ipynb @@ -1,505 +1,16 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "6b3009b4", - "metadata": {}, - "outputs": [], - "source": [ - "import folium\n", - "from folium.features import ClickForLatLng, ClickForMarker, LatLngPopup" - ] - }, - { - "cell_type": "markdown", - "id": "df990137", - "metadata": {}, - "source": [ - "#### `ClickForMarker` Lets you create markers on each click" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "55e317b4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "folium.Map().add_child(ClickForMarker())" - ] - }, - { - "cell_type": "markdown", - "id": "ad4a4cfc", - "metadata": {}, - "source": [ - "*Click on the map to see the effects*" - ] - }, - { - "cell_type": "markdown", - "id": "9cecff8c", - "metadata": {}, - "source": [ - "You can customize the popup by providing a string, an IFrame object or an Html object. You can include the latitude and longitude of the marker by using `${lat}` and `${lng}`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "7755ae9b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "folium.Map().add_child(ClickForMarker(\"Lat: ${lat}
Lon: ${lng}\"))" - ] - }, - { - "cell_type": "markdown", - "id": "45c985b4", - "metadata": {}, - "source": [ - "*Click on the map to see the effects*" - ] - }, - { - "cell_type": "markdown", - "id": "76e5e9b9", - "metadata": {}, - "source": [ - "#### `LatLngPopup` lets you create a simple popup at each click" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "205a9f4a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "folium.Map().add_child(LatLngPopup())" - ] - }, - { - "cell_type": "markdown", - "id": "c7ed398a", - "metadata": {}, - "source": [ - "*Click on the map to see the effects*" - ] - }, { "cell_type": "markdown", - "id": "74717849", - "metadata": {}, - "source": [ - "#### `ClickForLatLng` lets you copy coordinates to your browser clipboard" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "5c564b6b", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "id": "6b3009b4", + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], - "source": [ - "folium.Map().add_child(ClickForLatLng(format_str='\"[\" + lat + \",\" + lng + \"]\"'))" - ] - }, - { - "cell_type": "markdown", - "id": "8f2809bd", - "metadata": {}, - "source": [ - "*Click on the map to see the effects*" - ] - }, - { - "cell_type": "markdown", - "id": "f90c1e9b", - "metadata": {}, + }, "source": [ - "If you want to collect back the information in python, you may (install and) import the [clipboard](https://github.com/terryyin/clipboard) library :\n", - "\n", - "```python\n", - "> import clipboard\n", - "> clipboard.paste()\n", - "```\n", - "```\n", - "[-43.580391,-123.824467]\n", - "```" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/features/click_related_classes.html" ] } ], diff --git a/examples/Colormaps.ipynb b/examples/Colormaps.ipynb index 1f195f39c..e42b8ea25 100644 --- a/examples/Colormaps.ipynb +++ b/examples/Colormaps.ipynb @@ -2,1115 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ - "# Using `folium.colormap`\n", - "\n", - "**A few examples of how to use `folium.colormap` in choropleths.**\n", - "\n", - "Let's load a GeoJSON file, and try to choropleth it." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import folium\n", - "import pandas as pd\n", - "import requests\n", - "\n", - "\n", - "url = (\n", - " \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data\"\n", - ")\n", - "us_states = f\"{url}/us-states.json\"\n", - "US_Unemployment_Oct2012 = f\"{url}/US_Unemployment_Oct2012.csv\"\n", - "\n", - "geo_json_data = json.loads(requests.get(us_states).text)\n", - "unemployment = pd.read_csv(US_Unemployment_Oct2012)\n", - "\n", - "unemployment_dict = unemployment.set_index(\"State\")[\"Unemployment\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Self-defined\n", - "\n", - "You can build a choropleth in using a self-defined function.\n", - "It has to output an hexadecimal color string of the form `#RRGGBB` or `#RRGGBBAA`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "def my_color_function(feature):\n", - " \"\"\"Maps low values to green and high values to red.\"\"\"\n", - " if unemployment_dict[feature[\"id\"]] > 6.5:\n", - " return \"#ff0000\"\n", - " else:\n", - " return \"#008000\"" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/colormaps.html" ], - "source": [ - "m = folium.Map([43, -100], tiles=\"cartodbpositron\", zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": my_color_function(feature),\n", - " \"color\": \"black\",\n", - " \"weight\": 2,\n", - " \"dashArray\": \"5, 5\",\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## StepColormap\n", - "\n", - "But to help you define your colormap, we've embedded `StepColormap` in `folium.colormap`.\n", - "\n", - "You can simply define the colors you want, and the `index` (*thresholds*) that correspond." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "310" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import branca.colormap as cm\n", - "\n", - "step = cm.StepColormap(\n", - " [\"green\", \"yellow\", \"red\"], vmin=3, vmax=10, index=[3, 4, 8, 10], caption=\"step\"\n", - ")\n", - "\n", - "step" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], tiles=\"cartodbpositron\", zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": step(unemployment_dict[feature[\"id\"]]),\n", - " \"color\": \"black\",\n", - " \"weight\": 2,\n", - " \"dashArray\": \"5, 5\",\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you specify no index, colors will be set uniformly." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "0.01.0" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.StepColormap([\"r\", \"y\", \"g\", \"c\", \"b\", \"m\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## LinearColormap\n", - "\n", - "But sometimes, you would prefer to have a *continuous* set of colors. This can be done by `LinearColormap`." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "310" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "linear = cm.LinearColormap([\"green\", \"yellow\", \"red\"], vmin=3, vmax=10)\n", - "\n", - "linear" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], tiles=\"cartodbpositron\", zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": linear(unemployment_dict[feature[\"id\"]]),\n", - " \"color\": \"black\",\n", - " \"weight\": 2,\n", - " \"dashArray\": \"5, 5\",\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, you can set the `index` if you want something irregular." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "0.01.0" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.LinearColormap([\"red\", \"orange\", \"yellow\", \"green\"], index=[0, 0.1, 0.9, 1.0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to transform a linear map into a *step* one, you can use the method `to_step`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "3.010.0" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "linear.to_step(6)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use more sophisticated rules to create the thresholds." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "31100" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "linear.to_step(\n", - " n=6,\n", - " data=[30.6, 50, 51, 52, 53, 54, 55, 60, 70, 100],\n", - " method=\"quantiles\",\n", - " round_method=\"int\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And the opposite is also possible with `to_linear`." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "310" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "step.to_linear()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Built-in\n", - "\n", - "For convenience, we provide a (small) set of built-in linear colormaps, in `folium.colormap.linear`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "0.01.0" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.linear.OrRd_09" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also use them to generate regular `StepColormap`." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "0.01.0" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.linear.PuBuGn_09.to_step(12)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course, you may need to scale the colormaps to your bounds. This is doable with `.scale`." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "312" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.linear.YlGnBu_09.scale(3, 12)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "5100" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.linear.RdGy_11.to_step(10).scale(5, 100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "At last, if you want to check them all, simply ask for `linear` in the notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
viridis0.01.0
Pastel1_030.01.0
Pastel1_050.01.0
Pastel1_040.01.0
Pastel1_070.01.0
YlOrRd_040.01.0
Pastel1_090.01.0
Pastel1_080.01.0
Spectral_070.01.0
RdYlBu_050.01.0
PuBuGn_030.01.0
Set1_080.01.0
PuBuGn_050.01.0
PuBuGn_040.01.0
PuBuGn_070.01.0
PuBuGn_060.01.0
PuBuGn_090.01.0
PuBuGn_080.01.0
YlOrBr_040.01.0
YlOrBr_050.01.0
Set1_070.01.0
YlOrBr_030.01.0
Set1_050.01.0
YlOrRd_030.01.0
PuOr_060.01.0
PuOr_070.01.0
PuOr_040.01.0
PuOr_050.01.0
PuOr_030.01.0
Purples_090.01.0
Set2_060.01.0
RdYlBu_110.01.0
PuOr_080.01.0
PuOr_090.01.0
Paired_030.01.0
RdBu_030.01.0
RdYlBu_100.01.0
Paired_070.01.0
Paired_060.01.0
Paired_050.01.0
Paired_040.01.0
Paired_090.01.0
Paired_080.01.0
RdGy_030.01.0
PiYG_040.01.0
Accent_030.01.0
BuGn_080.01.0
BuGn_090.01.0
BuGn_040.01.0
BuGn_050.01.0
BuGn_060.01.0
BuGn_070.01.0
BuGn_030.01.0
YlGnBu_070.01.0
YlGnBu_060.01.0
YlGnBu_050.01.0
YlGnBu_040.01.0
YlGnBu_030.01.0
RdBu_060.01.0
RdBu_050.01.0
RdBu_040.01.0
Accent_080.01.0
RdBu_090.01.0
RdBu_080.01.0
Set2_040.01.0
YlGnBu_090.01.0
YlGnBu_080.01.0
Blues_080.01.0
Blues_090.01.0
RdPu_090.01.0
RdPu_080.01.0
Set3_070.01.0
Set3_060.01.0
RdPu_050.01.0
RdPu_040.01.0
RdPu_070.01.0
RdPu_060.01.0
Blues_060.01.0
Blues_070.01.0
RdPu_030.01.0
Blues_050.01.0
Paired_100.01.0
Paired_110.01.0
Paired_120.01.0
PuBu_060.01.0
PuBu_070.01.0
PuBu_040.01.0
PuBu_050.01.0
PuRd_050.01.0
PuBu_030.01.0
PuRd_070.01.0
PuRd_060.01.0
PuRd_090.01.0
PuRd_080.01.0
Set2_070.01.0
PuBu_080.01.0
PuBu_090.01.0
RdBu_100.01.0
RdBu_110.01.0
Accent_060.01.0
Set3_030.01.0
Set3_050.01.0
Set3_120.01.0
Set3_100.01.0
Set3_040.01.0
RdGy_110.01.0
RdGy_100.01.0
Set1_030.01.0
Set1_090.01.0
Set3_090.01.0
BuPu_080.01.0
BuPu_090.01.0
RdYlGn_110.01.0
Blues_030.01.0
Set2_050.01.0
BuPu_030.01.0
BuPu_060.01.0
BuPu_070.01.0
BuPu_040.01.0
BuPu_050.01.0
Accent_040.01.0
YlOrRd_050.01.0
YlOrBr_080.01.0
Oranges_080.01.0
Oranges_090.01.0
Oranges_060.01.0
Oranges_070.01.0
Oranges_040.01.0
YlOrBr_090.01.0
Oranges_030.01.0
YlOrBr_060.01.0
Dark2_060.01.0
Blues_040.01.0
YlOrBr_070.01.0
RdYlGn_050.01.0
Set3_080.01.0
YlOrRd_060.01.0
Dark2_030.01.0
Accent_050.01.0
RdYlGn_080.01.0
RdYlGn_090.01.0
PuOr_110.01.0
YlOrRd_070.01.0
Spectral_110.01.0
RdGy_080.01.0
RdGy_090.01.0
RdGy_060.01.0
RdGy_070.01.0
RdGy_040.01.0
RdGy_050.01.0
RdYlGn_040.01.0
PiYG_090.01.0
RdYlGn_060.01.0
RdYlGn_070.01.0
Spectral_040.01.0
Spectral_050.01.0
Spectral_060.01.0
PiYG_080.01.0
Set2_030.01.0
Spectral_030.01.0
Reds_080.01.0
Set1_040.01.0
Spectral_080.01.0
Spectral_090.01.0
Set2_080.01.0
Reds_090.01.0
Greys_070.01.0
Greys_060.01.0
Greys_050.01.0
Greys_040.01.0
Greys_030.01.0
PuOr_100.01.0
Accent_070.01.0
Reds_060.01.0
Greys_090.01.0
Greys_080.01.0
Reds_070.01.0
RdYlBu_080.01.0
RdYlBu_090.01.0
BrBG_090.01.0
BrBG_080.01.0
BrBG_070.01.0
BrBG_060.01.0
BrBG_050.01.0
BrBG_040.01.0
BrBG_030.01.0
PiYG_060.01.0
Reds_030.01.0
Set3_110.01.0
Set1_060.01.0
PuRd_030.01.0
PiYG_070.01.0
RdBu_070.01.0
Pastel1_060.01.0
Spectral_100.01.0
PuRd_040.01.0
OrRd_030.01.0
PiYG_030.01.0
Oranges_050.01.0
OrRd_070.01.0
OrRd_060.01.0
OrRd_050.01.0
OrRd_040.01.0
Reds_040.01.0
Reds_050.01.0
OrRd_090.01.0
OrRd_080.01.0
BrBG_100.01.0
BrBG_110.01.0
PiYG_050.01.0
YlOrRd_080.01.0
GnBu_040.01.0
GnBu_050.01.0
GnBu_060.01.0
GnBu_070.01.0
Purples_080.01.0
GnBu_030.01.0
Purples_060.01.0
Purples_070.01.0
Purples_040.01.0
Purples_050.01.0
GnBu_080.01.0
GnBu_090.01.0
YlOrRd_090.01.0
Purples_030.01.0
RdYlBu_040.01.0
PRGn_090.01.0
PRGn_080.01.0
PRGn_070.01.0
PRGn_060.01.0
PRGn_050.01.0
PRGn_040.01.0
PRGn_030.01.0
RdYlBu_060.01.0
RdYlGn_100.01.0
YlGn_080.01.0
YlGn_090.01.0
RdYlBu_070.01.0
PiYG_100.01.0
PiYG_110.01.0
YlGn_030.01.0
YlGn_040.01.0
YlGn_050.01.0
YlGn_060.01.0
YlGn_070.01.0
Dark2_050.01.0
Dark2_040.01.0
Dark2_070.01.0
Pastel2_030.01.0
Pastel2_040.01.0
Pastel2_050.01.0
Pastel2_060.01.0
Pastel2_070.01.0
Pastel2_080.01.0
RdYlBu_030.01.0
Dark2_080.01.0
RdYlGn_030.01.0
PRGn_110.01.0
Greens_080.01.0
Greens_090.01.0
Greens_060.01.0
Greens_070.01.0
Greens_040.01.0
Greens_050.01.0
PRGn_100.01.0
Greens_030.01.0
\n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "cm.linear" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Draw a `ColorMap` on a map\n", - "\n", - "By the way, a ColorMap is also a Folium `Element` that you can draw on a map." - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(tiles=\"cartodbpositron\")\n", - "\n", - "colormap = cm.linear.Set1_09.scale(0, 35).to_step(10)\n", - "colormap.caption = \"A colormap caption\"\n", - "m.add_child(colormap)\n", - "\n", - "m" - ] + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/ContinuousWorld.ipynb b/examples/ContinuousWorld.ipynb index d2554d85b..3da78e8ac 100644 --- a/examples/ContinuousWorld.ipynb +++ b/examples/ContinuousWorld.ipynb @@ -2,116 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ - "## Defaults" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import folium\n", - "\n", - "\n", - "m = folium.Map(world_copy_jump=False, no_wrap=False)\n", - "\n", - "folium.Marker(\n", - " location=[0, 0], popup=\"I will disappear when moved outside the wrapped map domain.\"\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## World copy jump" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/world_copy.html" ], - "source": [ - "m = folium.Map(world_copy_jump=True, no_wrap=False)\n", - "\n", - "folium.Marker(\n", - " location=[0, 0],\n", - " popup=\"I will magically reappear when moved outside the wrapped map domain.\",\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## No wrap" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(\n", - " world_copy_jump=False,\n", - " no_wrap=True,\n", - ")\n", - "\n", - "folium.Marker(location=[0, 0], popup=\"The map domain here is not wrapped!\").add_to(m)\n", - "\n", - "m" - ] + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/ControlScale.ipynb b/examples/ControlScale.ipynb index 3df3d26ba..c161a3098 100644 --- a/examples/ControlScale.ipynb +++ b/examples/ControlScale.ipynb @@ -1,41 +1,14 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", "source": [ - "import folium\n", - "\n", - "\n", - "lon, lat = -38.625, -12.875\n", - "\n", - "zoom_start = 8\n", - "\n", - "m = folium.Map(\n", - " location=[lat, lon],\n", - " tiles=\"OpenStreetMap\",\n", - " zoom_start=zoom_start,\n", - " control_scale=True,\n", - ")\n", - "\n", - "m" - ] + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/map.html#Scale" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/CustomIcon.ipynb b/examples/CustomIcon.ipynb index 0cd8364c7..636eec657 100644 --- a/examples/CustomIcon.ipynb +++ b/examples/CustomIcon.ipynb @@ -1,53 +1,15 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], + }, "source": [ - "import folium\n", - "from folium.features import CustomIcon\n", - "\n", - "\n", - "m = folium.Map(location=[45.372, -121.6972], zoom_start=12, tiles=\"Stamen Terrain\")\n", - "\n", - "url = \"http://leafletjs.com/examples/custom-icons/{}\".format\n", - "icon_image = url(\"leaf-red.png\")\n", - "shadow_image = url(\"leaf-shadow.png\")\n", - "\n", - "icon = CustomIcon(\n", - " icon_image,\n", - " icon_size=(38, 95),\n", - " icon_anchor=(22, 94),\n", - " shadow_image=shadow_image,\n", - " shadow_size=(50, 64),\n", - " shadow_anchor=(4, 62),\n", - " popup_anchor=(-3, -76),\n", - ")\n", - "\n", - "marker = folium.Marker(\n", - " location=[45.3288, -121.6625], icon=icon, popup=\"Mt. Hood Meadows\"\n", - ")\n", - "\n", - "\n", - "m.add_child(marker)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/ui_elements/icons.html#Custom-icon" ] } ], diff --git a/examples/CustomPanes.ipynb b/examples/CustomPanes.ipynb index a547b9145..c44289705 100644 --- a/examples/CustomPanes.ipynb +++ b/examples/CustomPanes.ipynb @@ -2,175 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ - "Load GeoJSON as in [GeoJSON_and_choropleth.ipynb](https://github.com/python-visualization/folium/blob/main/examples/GeoJSON_and_choropleth.ipynb)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-05T22:38:12.549432Z", - "start_time": "2019-04-05T22:38:12.533446Z" - } - }, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import folium\n", - "import requests\n", - "\n", - "\n", - "url = (\n", - " \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data\"\n", - ")\n", - "us_states = f\"{url}/us-states.json\"\n", - "geo_json_data = json.loads(requests.get(us_states).text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using CustomPane to place labels above choropleth" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map without custom pane" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-05T22:38:12.758314Z", - "start_time": "2019-04-05T22:38:12.555438Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/custom_panes.html" ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4, tiles=\"stamentoner\")\n", - "\n", - "folium.GeoJson(geo_json_data).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map with custom pane" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "ExecuteTime": { - "end_time": "2019-04-05T22:38:13.237039Z", - "start_time": "2019-04-05T22:38:12.789303Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4, tiles=\"stamentonerbackground\")\n", - "\n", - "folium.GeoJson(geo_json_data).add_to(m)\n", - "\n", - "folium.map.CustomPane(\"labels\").add_to(m)\n", - "\n", - "# Final layer associated to custom pane via the appropriate kwarg\n", - "folium.TileLayer(\"stamentonerlabels\", pane=\"labels\").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Same, but with a different tileset" - ] - }, - { - "cell_type": "code", - "execution_count": 4, "metadata": { - "ExecuteTime": { - "end_time": "2019-04-05T22:38:13.543862Z", - "start_time": "2019-04-05T22:38:13.241037Z" - } - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4, tiles=\"CartoDBPositronNoLabels\")\n", - "\n", - "folium.GeoJson(geo_json_data).add_to(m)\n", - "\n", - "folium.map.CustomPane(\"labels\").add_to(m)\n", - "\n", - "# Final layer associated to custom pane via the appropriate kwarg\n", - "folium.TileLayer(\"CartoDBPositronOnlyLabels\", pane=\"labels\").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "More tile providers can be found at https://leaflet-extras.github.io/leaflet-providers/preview/." - ] + "collapsed": false + } } ], "metadata": { diff --git a/examples/FeatureGroup.ipynb b/examples/FeatureGroup.ipynb index 0179d335d..543ab5c3f 100644 --- a/examples/FeatureGroup.ipynb +++ b/examples/FeatureGroup.ipynb @@ -1,40 +1,14 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", "source": [ - "from folium import FeatureGroup, LayerControl, Map, Marker\n", - "\n", - "\n", - "m = Map(location=[45.372, -121.6972], zoom_start=12, tiles=\"Stamen Terrain\")\n", - "\n", - "feature_group = FeatureGroup(name=\"Some icons\")\n", - "Marker(location=[45.3288, -121.6625], popup=\"Mt. Hood Meadows\").add_to(feature_group)\n", - "\n", - "Marker(location=[45.3311, -121.7113], popup=\"Timberline Lodge\").add_to(feature_group)\n", - "\n", - "feature_group.add_to(m)\n", - "LayerControl().add_to(m)\n", - "\n", - "m" - ] + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/getting_started.html#Grouping-and-controlling" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/Features.ipynb b/examples/Features.ipynb index 974cf5565..d55d0dac7 100644 --- a/examples/Features.ipynb +++ b/examples/Features.ipynb @@ -2,1554 +2,14 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, - "source": [ - "## ColorLine" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "x = np.linspace(0, 2 * np.pi, 300)\n", - "\n", - "lats = 20 * np.cos(x)\n", - "lons = 20 * np.sin(x)\n", - "colors = np.sin(5 * x)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], - "source": [ - "import folium\n", - "from folium import features\n", - "\n", - "\n", - "m = folium.Map([0, 0], zoom_start=3)\n", - "\n", - "color_line = features.ColorLine(\n", - " positions=list(zip(lats, lons)),\n", - " colors=colors,\n", - " colormap=[\"y\", \"orange\", \"r\"],\n", - " weight=10,\n", - ")\n", - "\n", - "color_line.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Marker, Icon, Popup" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([0, 0], zoom_start=1)\n", - "mk = features.Marker([0, 0])\n", - "pp = folium.Popup(\"hello\")\n", - "ic = features.Icon(color=\"red\")\n", - "\n", - "mk.add_child(ic)\n", - "mk.add_child(pp)\n", - "m.add_child(mk)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vega popup" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import json\n", - "\n", - "import vincent\n", - "\n", - "N = 100\n", - "\n", - "multi_iter2 = {\n", - " \"x\": np.random.uniform(size=(N,)),\n", - " \"y\": np.random.uniform(size=(N,)),\n", - "}\n", - "\n", - "scatter = vincent.Scatter(multi_iter2, iter_idx=\"x\", height=100, width=200)\n", - "data = json.loads(scatter.to_json())\n", - "\n", - "m = folium.Map([0, 0], zoom_start=1)\n", - "mk = features.Marker([0, 0])\n", - "p = folium.Popup(\"Hello\")\n", - "v = features.Vega(data, width=\"100%\", height=\"100%\")\n", - "\n", - "mk.add_child(p)\n", - "p.add_child(v)\n", - "m.add_child(mk)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vega-Lite popup" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from altair import Chart\n", - "\n", - "import vega_datasets\n", - "\n", - "# load built-in dataset as a pandas DataFrame\n", - "cars = vega_datasets.data.cars()\n", - "\n", - "scatter = (\n", - " Chart(cars)\n", - " .mark_circle()\n", - " .encode(\n", - " x=\"Horsepower\",\n", - " y=\"Miles_per_Gallon\",\n", - " color=\"Origin\",\n", - " )\n", - ")\n", - "\n", - "vega = folium.features.VegaLite(\n", - " scatter,\n", - " width=\"100%\",\n", - " height=\"100%\",\n", - ")\n", - "\n", - "m = folium.Map(location=[-27.5717, -48.6256])\n", - "\n", - "marker = folium.features.Marker([-27.57, -48.62])\n", - "\n", - "popup = folium.Popup()\n", - "\n", - "vega.add_to(popup)\n", - "popup.add_to(marker)\n", - "\n", - "marker.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vega div and a Map\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import branca\n", - "\n", - "N = 100\n", - "\n", - "multi_iter2 = {\n", - " \"x\": np.random.uniform(size=(N,)),\n", - " \"y\": np.random.uniform(size=(N,)),\n", - "}\n", - "\n", - "scatter = vincent.Scatter(multi_iter2, iter_idx=\"x\", height=250, width=420)\n", - "data = json.loads(scatter.to_json())\n", - "\n", - "f = branca.element.Figure()\n", - "\n", - "# Create two maps.\n", - "m = folium.Map(\n", - " location=[0, 0],\n", - " tiles=\"stamenwatercolor\",\n", - " zoom_start=1,\n", - " position=\"absolute\",\n", - " left=\"0%\",\n", - " width=\"50%\",\n", - " height=\"50%\",\n", - ")\n", - "\n", - "m2 = folium.Map(\n", - " location=[46, 3],\n", - " tiles=\"OpenStreetMap\",\n", - " zoom_start=4,\n", - " position=\"absolute\",\n", - " left=\"50%\",\n", - " width=\"50%\",\n", - " height=\"50%\",\n", - " top=\"50%\",\n", - ")\n", - "\n", - "# Create two Vega.\n", - "v = features.Vega(data, position=\"absolute\", left=\"50%\", width=\"50%\", height=\"50%\")\n", - "\n", - "v2 = features.Vega(\n", - " data, position=\"absolute\", left=\"0%\", width=\"50%\", height=\"50%\", top=\"50%\"\n", - ")\n", - "\n", - "f.add_child(m)\n", - "f.add_child(m2)\n", - "f.add_child(v)\n", - "f.add_child(v2)\n", - "\n", - "f" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Vega-Lite div and a Map" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "N = 100\n", - "\n", - "multi_iter2 = pd.DataFrame(\n", - " {\n", - " \"x\": np.random.uniform(size=(N,)),\n", - " \"y\": np.random.uniform(size=(N,)),\n", - " }\n", - ")\n", - "\n", - "scatter = Chart(multi_iter2).mark_circle().encode(x=\"x\", y=\"y\")\n", - "scatter.width = 420\n", - "scatter.height = 250\n", - "data = json.loads(scatter.to_json())\n", - "\n", - "f = branca.element.Figure()\n", - "\n", - "# Create two maps.\n", - "m = folium.Map(\n", - " location=[0, 0],\n", - " tiles=\"stamenwatercolor\",\n", - " zoom_start=1,\n", - " position=\"absolute\",\n", - " left=\"0%\",\n", - " width=\"50%\",\n", - " height=\"50%\",\n", - ")\n", - "\n", - "m2 = folium.Map(\n", - " location=[46, 3],\n", - " tiles=\"OpenStreetMap\",\n", - " zoom_start=4,\n", - " position=\"absolute\",\n", - " left=\"50%\",\n", - " width=\"50%\",\n", - " height=\"50%\",\n", - " top=\"50%\",\n", - ")\n", - "\n", - "\n", - "# Create two Vega.\n", - "v = features.VegaLite(data, position=\"absolute\", left=\"50%\", width=\"50%\", height=\"50%\")\n", - "\n", - "v2 = features.VegaLite(\n", - " data, position=\"absolute\", left=\"0%\", width=\"50%\", height=\"50%\", top=\"50%\"\n", - ")\n", - "\n", - "f.add_child(m)\n", - "f.add_child(m2)\n", - "f.add_child(v)\n", - "f.add_child(v2)\n", - "\n", - "f" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### GeoJson" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "N = 1000\n", - "\n", - "lons = +5 - np.random.normal(size=N)\n", - "lats = 48 - np.random.normal(size=N)\n", - "\n", - "data = {\n", - " \"type\": \"FeatureCollection\",\n", - " \"features\": [\n", - " {\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"MultiPoint\",\n", - " \"coordinates\": [[lon, lat] for (lat, lon) in zip(lats, lons)],\n", - " },\n", - " \"properties\": {\"prop0\": \"value0\"},\n", - " },\n", - " ],\n", - "}\n", - "\n", - "m = folium.Map([48, 5], zoom_start=6)\n", - "m.add_child(features.GeoJson(data))\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Div" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "N = 100\n", - "\n", - "multi_iter2 = {\n", - " \"x\": np.random.uniform(size=(N,)),\n", - " \"y\": np.random.uniform(size=(N,)),\n", - "}\n", - "\n", - "scatter = vincent.Scatter(multi_iter2, iter_idx=\"x\", height=250, width=420)\n", - "data = json.loads(scatter.to_json())\n", - "\n", - "f = branca.element.Figure()\n", - "\n", - "d1 = f.add_subplot(1, 2, 1)\n", - "d2 = f.add_subplot(1, 2, 2)\n", - "\n", - "d1.add_child(folium.Map([0, 0], tiles=\"stamenwatercolor\", zoom_start=1))\n", - "d2.add_child(folium.Map([46, 3], tiles=\"OpenStreetMap\", zoom_start=5))\n", - "\n", - "f" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### LayerControl" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + }, "source": [ - "m = folium.Map(tiles=None)\n", - "\n", - "folium.raster_layers.TileLayer(\"OpenStreetMap\").add_to(m)\n", - "folium.raster_layers.TileLayer(\"stamentoner\", show=False).add_to(m)\n", - "\n", - "folium.LayerControl(collapsed=False).add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide.html" ] } ], diff --git a/examples/FitOverlays.ipynb b/examples/FitOverlays.ipynb index bb52359cd..ab85d1bc4 100644 --- a/examples/FitOverlays.ipynb +++ b/examples/FitOverlays.ipynb @@ -3,502 +3,14 @@ { "cell_type": "markdown", "id": "14a617e3", - "metadata": {}, - "source": [ - "# FitOverlays\n", - "\n", - "When you add this class to your map, the map will pan and zoom to fit the enabled overlays." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "45275ca3", - "metadata": {}, - "outputs": [], - "source": [ - "from folium import Map, LayerControl, FeatureGroup, Marker, FitOverlays" - ] - }, - { - "cell_type": "markdown", - "id": "fda0a32d", - "metadata": {}, - "source": [ - "In this first map we placed two markers, but they are not in view. You have to pan or zoom out to find them." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "1467934c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "def get_map_with_markers():\n", - " m = Map((52, 0), tiles='cartodbpositron', zoom_start=8)\n", - "\n", - " fg1 = FeatureGroup().add_to(m)\n", - " Marker((52, 5)).add_to(fg1)\n", - "\n", - " fg2 = FeatureGroup(show=False).add_to(m)\n", - " Marker((52, 5.1)).add_to(fg2)\n", - "\n", - " LayerControl(collapsed=False).add_to(m)\n", - " \n", - " return m\n", - "\n", - "get_map_with_markers()" - ] - }, - { - "cell_type": "markdown", - "id": "ea497a79", - "metadata": {}, - "source": [ - "Now we add the `FitOverlays` class, which automatically pans and zooms to show the enabled overlays. We show only the first marker by default, if we enable the second marker, the view changes to include it." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "fc47cce2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], - "source": [ - "m = get_map_with_markers()\n", - "\n", - "FitOverlays().add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "id": "099d40d3", - "metadata": {}, - "source": [ - "`FitOverlays` has a couple options:\n", - "- `padding` adds pixels around the bounds.\n", - "- `max_zoom` can be used to prevent zooming in too far.\n", - "- `fly` enables a smoother, longer animation, so you can see how the view changes.\n", - "- `fit_on_map_load` can be used to disable the fitting that happens when the map loads.\n", - "\n", - "Note that `padding` and `max_zoom` can achieve the same effect." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "11910053", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + }, "source": [ - "m = get_map_with_markers()\n", - "\n", - "FitOverlays(\n", - " padding=0,\n", - " max_zoom=10,\n", - " fly=True,\n", - " fit_on_map_load=False,\n", - ").add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/features/fit_overlays.html" ] } ], diff --git a/examples/FloatImage.ipynb b/examples/FloatImage.ipynb index 40d4a6ddc..7bd65133b 100644 --- a/examples/FloatImage.ipynb +++ b/examples/FloatImage.ipynb @@ -1,40 +1,14 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "cell_type": "markdown", "source": [ - "import folium\n", - "from folium.plugins import FloatImage\n", - "\n", - "\n", - "url = (\n", - " \"https://raw.githubusercontent.com/ocefpaf/secoora_assets_map/\"\n", - " \"a250729bbcf2ddd12f46912d36c33f7539131bec/secoora_icons/rose.png\"\n", - ")\n", - "\n", - "m = folium.Map([-13, -38.15], zoom_start=10)\n", - "\n", - "FloatImage(url, bottom=40, left=65).add_to(m)\n", - "\n", - "m" - ] + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/plugins/float_image.html" + ], + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/GeoJSONMarker.ipynb b/examples/GeoJSONMarker.ipynb index ca9637f1c..d5818ae19 100644 --- a/examples/GeoJSONMarker.ipynb +++ b/examples/GeoJSONMarker.ipynb @@ -2,350 +2,14 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using GeoJSON Point Features with Markers" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import folium\n", - "import geopandas as gpd" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "rootpath = os.path.abspath(os.getcwd())" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = gpd.read_file(os.path.join(rootpath, \"data\", \"subwaystations.geojson\"))" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
nameurllineobjectidnotesgeometry
0Astor Plhttp://web.mta.info/nyct/service/4-6-6 Express14 nights, 6-all times, 6 Express-weekdays AM s...POINT (-73.99107 40.73005)
1Canal Sthttp://web.mta.info/nyct/service/4-6-6 Express24 nights, 6-all times, 6 Express-weekdays AM s...POINT (-74.00019 40.71880)
250th Sthttp://web.mta.info/nyct/service/1-231-all times, 2-nightsPOINT (-73.98385 40.76173)
3Bergen Sthttp://web.mta.info/nyct/service/2-3-444-nights, 3-all other times, 2-all timesPOINT (-73.97500 40.68086)
4Pennsylvania Avehttp://web.mta.info/nyct/service/3-454-nights, 3-all other timesPOINT (-73.89489 40.66471)
\n", - "
" - ], - "text/plain": [ - " name url line \\\n", - "0 Astor Pl http://web.mta.info/nyct/service/ 4-6-6 Express \n", - "1 Canal St http://web.mta.info/nyct/service/ 4-6-6 Express \n", - "2 50th St http://web.mta.info/nyct/service/ 1-2 \n", - "3 Bergen St http://web.mta.info/nyct/service/ 2-3-4 \n", - "4 Pennsylvania Ave http://web.mta.info/nyct/service/ 3-4 \n", - "\n", - " objectid notes \\\n", - "0 1 4 nights, 6-all times, 6 Express-weekdays AM s... \n", - "1 2 4 nights, 6-all times, 6 Express-weekdays AM s... \n", - "2 3 1-all times, 2-nights \n", - "3 4 4-nights, 3-all other times, 2-all times \n", - "4 5 4-nights, 3-all other times \n", - "\n", - " geometry \n", - "0 POINT (-73.99107 40.73005) \n", - "1 POINT (-74.00019 40.71880) \n", - "2 POINT (-73.98385 40.76173) \n", - "3 POINT (-73.97500 40.68086) \n", - "4 POINT (-73.89489 40.66471) " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [], - "source": [ - "gdf['href'] = '' + gdf.url + \"\"\n", - "gdf['service_level'] = gdf.notes.str.split(', ').apply(lambda x: len([v for v in x if \"all\" in v]))\n", - "gdf['lines_served'] = gdf.line.str.split('-').apply(lambda x: len(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3, 0]" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "service_levels = gdf.service_level.unique().tolist()\n", - "service_levels" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [], - "source": [ - "colors = [\"orange\", \"yellow\", \"green\", \"blue\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Use a Circle as a Marker" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(location=[40.75, -73.95], zoom_start=13)\n", - "\n", - "folium.GeoJson(\n", - " gdf,\n", - " name=\"Subway Stations\",\n", - " marker=folium.Circle(radius=4, fill_color=\"orange\", fill_opacity=0.4, color=\"black\", weight=1),\n", - " tooltip=folium.GeoJsonTooltip(fields=[\"name\", \"line\", \"notes\"]),\n", - " popup=folium.GeoJsonPopup(fields=[\"name\", \"line\", \"href\", \"notes\"]),\n", - " style_function=lambda x: {\n", - " \"fillColor\": colors[x['properties']['service_level']],\n", - " \"radius\": (x['properties']['lines_served'])*30,\n", - " },\n", - " highlight_function=lambda x: {\"fillOpacity\": 0.8},\n", - " zoom_on_click=True,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Or use a DivIcon" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(location=[40.75, -73.95], zoom_start=13)\n", - "\n", - "\n", - "def style_function(feature):\n", - " props = feature.get('properties')\n", - " markup = f\"\"\"\n", - " \n", - "
\n", - "
\n", - "
\n", - " {props.get('name')}\n", - "
\n", - " \n", - " \"\"\"\n", - " return {\"html\": markup}\n", - "\n", - "\n", - "folium.GeoJson(\n", - " gdf,\n", - " name=\"Subway Stations\",\n", - " marker=folium.Marker(icon=folium.DivIcon()),\n", - " tooltip=folium.GeoJsonTooltip(fields=[\"name\", \"line\", \"notes\"]),\n", - " popup=folium.GeoJsonPopup(fields=[\"name\", \"line\", \"href\", \"notes\"]),\n", - " style_function=style_function,\n", - " zoom_on_click=True,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Use a Marker" - ] - }, - { - "cell_type": "code", - "execution_count": 36, "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" + "pycharm": { + "name": "#%% md\n" } - ], + }, "source": [ - "m = folium.Map(location=[40.75, -73.95], zoom_start=13)\n", - "\n", - "marker_colors = [\"red\", \"orange\", \"green\", \"blue\"]\n", - "\n", - "folium.GeoJson(\n", - " gdf,\n", - " name=\"Subway Stations\",\n", - " zoom_on_click=True,\n", - " marker=folium.Marker(icon=folium.Icon(icon='star')),\n", - " tooltip=folium.GeoJsonTooltip(fields=[\"name\", \"line\", \"notes\"]),\n", - " popup=folium.GeoJsonPopup(fields=[\"name\", \"line\", \"href\", \"notes\"]),\n", - " style_function=lambda x: {\n", - " 'markerColor': marker_colors[x['properties']['service_level']],\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/geojson/geojson_marker.html" ] } ], diff --git a/examples/GeoJSONWithoutTitles.ipynb b/examples/GeoJSONWithoutTitles.ipynb index b4a52510a..815e11034 100644 --- a/examples/GeoJSONWithoutTitles.ipynb +++ b/examples/GeoJSONWithoutTitles.ipynb @@ -1,147 +1,15 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import json\n", - "\n", - "\n", - "us_states = os.path.join(\"data\", \"us-states.json\")\n", - "\n", - "with open(us_states) as f:\n", - " states = json.load(f)\n", - "\n", - "kw = {\"location\": [48, -102], \"zoom_start\": 3}" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import folium\n", - "from folium import GeoJson\n", - "\n", - "\n", - "m = folium.Map(tiles=None, **kw)\n", - "\n", - "GeoJson(states).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import branca\n", - "\n", - "# Create a white image of 4 pixels, and embed it in a url.\n", - "white_tile = branca.utilities.image_to_url([[1, 1], [1, 1]])\n", - "\n", - "# Create a map using this url for each tile.\n", - "m = folium.Map(tiles=white_tile, attr=\"white tile\", **kw)\n", - "\n", - "GeoJson(states).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "images = [[(-1) ** ((i + j) // 30) for i in range(300)] for j in range(300)]\n", - "\n", - "tiles = branca.utilities.image_to_url(images)\n", - "\n", - "m = folium.Map(tiles=tiles, attr=\"Just because we can\", **kw)\n", - "\n", - "GeoJson(states).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], + }, "source": [ - "images = [[(-1) ** ((i // 30 + j // 30)) for i in range(300)] for j in range(300)]\n", - "\n", - "tiles = branca.utilities.image_to_url(images)\n", - "\n", - "m = folium.Map(tiles=tiles, attr=\"Just because we can\", **kw)\n", - "\n", - "GeoJson(states).add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/custom_tiles.html" ] } ], diff --git a/examples/GeoJSON_and_choropleth.ipynb b/examples/GeoJSON_and_choropleth.ipynb index 2ae897ea2..6be025d54 100644 --- a/examples/GeoJSON_and_choropleth.ipynb +++ b/examples/GeoJSON_and_choropleth.ipynb @@ -2,882 +2,13 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, "source": [ - "# GeoJSON and choropleth\n", - "\n", - "**A few examples of how to do that with `folium`.**\n", - "\n", - "\n", - "## Using `GeoJson`\n", - "\n", - "### Loading data\n", - "\n", - "Let us load a GeoJSON file representing the US states." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "\n", - "import requests\n", - "\n", - "url = (\n", - " \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data\"\n", - ")\n", - "us_states = f\"{url}/us-states.json\"\n", - "\n", - "geo_json_data = json.loads(requests.get(us_states).text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It is a classical GeoJSON `FeatureCollection` (see https://en.wikipedia.org/wiki/GeoJSON) of the form :\n", - "\n", - " {\n", - " \"type\": \"FeatureCollection\",\n", - " \"features\": [\n", - " {\n", - " \"properties\": {\"name\": \"Alabama\"},\n", - " \"id\": \"AL\",\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"Polygon\",\n", - " \"coordinates\": [[[-87.359296, 35.00118], ...]]\n", - " }\n", - " },\n", - " {\n", - " \"properties\": {\"name\": \"Alaska\"},\n", - " \"id\": \"AK\",\n", - " \"type\": \"Feature\",\n", - " \"geometry\": {\n", - " \"type\": \"MultiPolygon\",\n", - " \"coordinates\": [[[[-131.602021, 55.117982], ... ]]]\n", - " }\n", - " },\n", - " ...\n", - " ]\n", - " }\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A first way of drawing it on a map, is simply to use `folium.GeoJson` :" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import folium\n", - "\n", - "\n", - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(geo_json_data).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that you can avoid loading the file on yourself ; in simply providing a file path." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(us_states).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can pass a geopandas object." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import geopandas\n", - "\n", - "gdf = geopandas.read_file(us_states)\n", - "\n", - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " gdf,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Click on zoom\n", - "\n", - "You can enable an option that if you click on a part of the geometry the map will zoom in to that.\n", - "\n", - "Try it on the map below:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(geo_json_data, zoom_on_click=True).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Styling\n", - "\n", - "Now this is cool and simple, but we may be willing to choose the style of the data.\n", - "\n", - "You can provide a function of the form `lambda feature: {}` that sets the style of each feature.\n", - "\n", - "For possible options, see:\n", - "\n", - "* For `Point` and `MultiPoint`, see http://leafletjs.com/reference.html#marker\n", - "* For other features, see http://leafletjs.com/reference.html#path-options and http://leafletjs.com/reference.html#polyline-options\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": \"#ffff00\",\n", - " \"color\": \"black\",\n", - " \"weight\": 2,\n", - " \"dashArray\": \"5, 5\",\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "What's cool in providing a function, is that you can specify a style depending on the feature. For example, if you want to visualize in green all states whose name contains the letter 'E', just do:" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": \"green\"\n", - " if \"e\" in feature[\"properties\"][\"name\"].lower()\n", - " else \"#ffff00\",\n", - " \"color\": \"black\",\n", - " \"weight\": 2,\n", - " \"dashArray\": \"5, 5\",\n", - " },\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Wow, this looks almost like a choropleth. To do one, we just need to compute a color for each state.\n", - "\n", - "Let's imagine we want to draw a choropleth of unemployment in the US.\n", - "\n", - "First, we may load the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StateUnemployment
0AL7.1
1AK6.8
2AZ8.1
3AR7.2
4CA10.1
\n", - "
" - ], - "text/plain": [ - " State Unemployment\n", - "0 AL 7.1\n", - "1 AK 6.8\n", - "2 AZ 8.1\n", - "3 AR 7.2\n", - "4 CA 10.1" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "US_Unemployment_Oct2012 = f\"{url}/US_Unemployment_Oct2012.csv\"\n", - "unemployment = pd.read_csv(US_Unemployment_Oct2012)\n", - "\n", - "unemployment.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we need to create a function that maps one value to a RGB color (of the form `#RRGGBB`).\n", - "For this, we'll use colormap tools from `folium.colormap`." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "#d8f0a3ff\n" - ] - }, - { - "data": { - "text/html": [ - "3.210.3" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from branca.colormap import linear\n", - "\n", - "colormap = linear.YlGn_09.scale(\n", - " unemployment.Unemployment.min(), unemployment.Unemployment.max()\n", - ")\n", - "\n", - "print(colormap(5.0))\n", - "\n", - "colormap" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We need also to convert the table into a dictionary, in order to map a feature to it's unemployment value." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7.1" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/geojson.html" ], - "source": [ - "unemployment_dict = unemployment.set_index(\"State\")[\"Unemployment\"]\n", - "\n", - "unemployment_dict[\"AL\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can do the choropleth." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " name=\"unemployment\",\n", - " style_function=lambda feature: {\n", - " \"fillColor\": colormap(unemployment_dict[feature[\"id\"]]),\n", - " \"color\": \"black\",\n", - " \"weight\": 1,\n", - " \"dashArray\": \"5, 5\",\n", - " \"fillOpacity\": 0.9,\n", - " },\n", - ").add_to(m)\n", - "\n", - "folium.LayerControl().add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Of course, if you can create and/or use a dictionary providing directly the good color. Thus, the finishing seems faster:" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "color_dict = {key: colormap(unemployment_dict[key]) for key in unemployment_dict.keys()}" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.GeoJson(\n", - " geo_json_data,\n", - " style_function=lambda feature: {\n", - " \"fillColor\": color_dict[feature[\"id\"]],\n", - " \"color\": \"black\",\n", - " \"weight\": 1,\n", - " \"dashArray\": \"5, 5\",\n", - " \"fillOpacity\": 0.9,\n", - " },\n", - ").add_to(m)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note that adding a color legend may be a good idea." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "colormap.caption = \"Unemployment color scale\"\n", - "colormap.add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Using `Choropleth` class\n", - "\n", - "Now if you want to get faster, you can use the `Choropleth` class. Have a look at it's docstring, it has several styling options.\n", - "\n", - "Just like the `GeoJson` class you can provide it a filename, a dict, or a geopandas object." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.Choropleth(\n", - " geo_data=us_states,\n", - " fill_opacity=0.3,\n", - " line_weight=2,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, in playing with keyword arguments, you can get a choropleth in a few lines:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.Choropleth(\n", - " geo_data=us_states,\n", - " data=unemployment,\n", - " columns=[\"State\", \"Unemployment\"],\n", - " key_on=\"feature.id\",\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can force the color scale to a given number of bins (or directly list the bins you would like), by providing the `bins` argument." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "folium.Choropleth(\n", - " geo_data=us_states,\n", - " data=unemployment,\n", - " columns=[\"State\", \"Unemployment\"],\n", - " key_on=\"feature.id\",\n", - " fill_color=\"YlGn\",\n", - " bins=[3, 4, 9, 11],\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also enable the highlight function, to enable highlight functionality when you hover over each area." - ] - }, - { - "cell_type": "code", - "execution_count": 18, "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "state_data = pd.read_csv(US_Unemployment_Oct2012)\n", - "\n", - "m = folium.Map(location=[48, -102], zoom_start=3)\n", - "folium.Choropleth(\n", - " geo_data=us_states,\n", - " data=state_data,\n", - " columns=[\"State\", \"Unemployment\"],\n", - " key_on=\"feature.id\",\n", - " fill_color=\"YlGn\",\n", - " fill_opacity=0.7,\n", - " line_opacity=0.2,\n", - " legend_name=\"Unemployment Rate (%)\",\n", - " highlight=True,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can customize the way missing and `nan` values are displayed on your map using the two parameters `nan_fill_color` and `nan_fill_opacity`." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "messed_up_data = unemployment.drop(0)\n", - "messed_up_data.loc[4, \"Unemployment\"] = float(\"nan\")\n", - "\n", - "folium.Choropleth(\n", - " geo_data=us_states,\n", - " data=messed_up_data,\n", - " columns=[\"State\", \"Unemployment\"],\n", - " nan_fill_color=\"purple\",\n", - " nan_fill_opacity=0.4,\n", - " key_on=\"feature.id\",\n", - " fill_color=\"YlGn\",\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Internally Choropleth uses the `GeoJson` or `TopoJson` class, depending on your settings, and the `StepColormap` class. Both objects are attributes of your `Choropleth` object called `geojson` and `color_scale`. You can make changes to them, but for regular things you won't have to. For example setting a name for in the layer controls or disabling showing the layer on opening the map is possible in `Choropleth` itself." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - }, - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([43, -100], zoom_start=4)\n", - "\n", - "choropleth = folium.Choropleth(\n", - " geo_data=us_states,\n", - " data=state_data,\n", - " columns=[\"State\", \"Unemployment\"],\n", - " key_on=\"feature.id\",\n", - " fill_color=\"YlGn\",\n", - " name=\"Unenployment\",\n", - " show=False,\n", - ").add_to(m)\n", - "\n", - "# The underlying GeoJson and StepColormap objects are reachable\n", - "print(type(choropleth.geojson))\n", - "print(type(choropleth.color_scale))\n", - "\n", - "folium.LayerControl(collapsed=False).add_to(m)\n", - "\n", - "m" - ] + "collapsed": false + } } ], "metadata": { diff --git a/examples/GeoJsonPopupAndTooltip.ipynb b/examples/GeoJsonPopupAndTooltip.ipynb index 6b7366e5c..dac3ddee9 100644 --- a/examples/GeoJsonPopupAndTooltip.ipynb +++ b/examples/GeoJsonPopupAndTooltip.ipynb @@ -1,956 +1,15 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "\n", - "income = pd.read_csv(\n", - " \"https://raw.githubusercontent.com/pri-data/50-states/master/data/income-counties-states-national.csv\",\n", - " dtype={\"fips\": str},\n", - ")\n", - "income[\"income-2015\"] = pd.to_numeric(income[\"income-2015\"], errors=\"coerce\")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
fipscountystateincome-2015income-1989aincome-1989bchange
000000USUS55775.02890653367.281024.316843
101000AlabamaAL44833.02220240990.118778.571546
201001Autauga CountyAL56580.02689849660.0403012.230399
301003Baldwin CountyAL52387.02404344389.0381815.267074
401005Barbour CountyAL31433.01867334474.75398-9.676945
\n", - "
" - ], - "text/plain": [ - " fips county state income-2015 income-1989a income-1989b \n", - "0 00000 US US 55775.0 28906 53367.28102 \\\n", - "1 01000 Alabama AL 44833.0 22202 40990.11877 \n", - "2 01001 Autauga County AL 56580.0 26898 49660.04030 \n", - "3 01003 Baldwin County AL 52387.0 24043 44389.03818 \n", - "4 01005 Barbour County AL 31433.0 18673 34474.75398 \n", - "\n", - " change \n", - "0 4.316843 \n", - "1 8.571546 \n", - "2 12.230399 \n", - "3 15.267074 \n", - "4 -9.676945 " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "income.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometryname
0POLYGON ((-87.35930 35.00118, -85.60667 34.984...Alabama
1MULTIPOLYGON (((-131.60202 55.11798, -131.5691...Alaska
2POLYGON ((-109.04250 37.00026, -109.04798 31.3...Arizona
3POLYGON ((-94.47384 36.50186, -90.15254 36.496...Arkansas
4POLYGON ((-123.23326 42.00619, -122.37885 42.0...California
\n", - "
" - ], - "text/plain": [ - " geometry name\n", - "0 POLYGON ((-87.35930 35.00118, -85.60667 34.984... Alabama\n", - "1 MULTIPOLYGON (((-131.60202 55.11798, -131.5691... Alaska\n", - "2 POLYGON ((-109.04250 37.00026, -109.04798 31.3... Arizona\n", - "3 POLYGON ((-94.47384 36.50186, -90.15254 36.496... Arkansas\n", - "4 POLYGON ((-123.23326 42.00619, -122.37885 42.0... California" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import requests\n", - "import geopandas\n", - "\n", - "\n", - "response = requests.get(\n", - " \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data/us-states.json\"\n", - ")\n", - "data = response.json()\n", - "states = geopandas.GeoDataFrame.from_features(data, crs=\"EPSG:4326\")\n", - "\n", - "states.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
namealpha-2
0AlabamaAL
1AlaskaAK
2ArizonaAZ
\n", - "
" - ], - "text/plain": [ - " name alpha-2\n", - "0 Alabama AL\n", - "1 Alaska AK\n", - "2 Arizona AZ" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "response = requests.get(\n", - " \"https://gist.githubusercontent.com/tvpmb/4734703/raw/\"\n", - " \"b54d03154c339ed3047c66fefcece4727dfc931a/US%2520State%2520List\"\n", - ")\n", - "abbrs = pd.read_json(response.text)\n", - "\n", - "abbrs.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometrynamealpha-2
0POLYGON ((-87.35930 35.00118, -85.60667 34.984...AlabamaAL
1MULTIPOLYGON (((-131.60202 55.11798, -131.5691...AlaskaAK
2POLYGON ((-109.04250 37.00026, -109.04798 31.3...ArizonaAZ
3POLYGON ((-94.47384 36.50186, -90.15254 36.496...ArkansasAR
4POLYGON ((-123.23326 42.00619, -120.00186 41.9...CaliforniaCA
\n", - "
" - ], - "text/plain": [ - " geometry name alpha-2\n", - "0 POLYGON ((-87.35930 35.00118, -85.60667 34.984... Alabama AL\n", - "1 MULTIPOLYGON (((-131.60202 55.11798, -131.5691... Alaska AK\n", - "2 POLYGON ((-109.04250 37.00026, -109.04798 31.3... Arizona AZ\n", - "3 POLYGON ((-94.47384 36.50186, -90.15254 36.496... Arkansas AR\n", - "4 POLYGON ((-123.23326 42.00619, -120.00186 41.9... California CA" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statesmerge = states.merge(abbrs, how=\"left\", left_on=\"name\", right_on=\"name\")\n", - "statesmerge[\"geometry\"] = statesmerge.geometry.simplify(0.05)\n", - "\n", - "statesmerge.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "state\n", - "AK 62561.5\n", - "AL 38721.5\n", - "AR 37890.0\n", - "AZ 43810.0\n", - "CA 53341.0\n", - "Name: income-2015, dtype: float64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], - "source": [ - "income.groupby(\"state\")[\"income-2015\"].median().head()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "statesmerge[\"medianincome\"] = statesmerge.merge(\n", - " income.groupby(\"state\")[\"income-2015\"].median(),\n", - " how=\"left\",\n", - " left_on=\"alpha-2\",\n", - " right_on=\"state\",\n", - ")[\"income-2015\"]\n", - "statesmerge[\"change\"] = statesmerge.merge(\n", - " income.groupby(\"state\")[\"change\"].median(),\n", - " how=\"left\",\n", - " left_on=\"alpha-2\",\n", - " right_on=\"state\",\n", - ")[\"change\"]" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
geometrynamealpha-2medianincomechange
0POLYGON ((-87.35930 35.00118, -85.60667 34.984...AlabamaAL38721.52.779114
1MULTIPOLYGON (((-131.60202 55.11798, -131.5691...AlaskaAK62561.514.758367
2POLYGON ((-109.04250 37.00026, -109.04798 31.3...ArizonaAZ43810.0NaN
3POLYGON ((-94.47384 36.50186, -90.15254 36.496...ArkansasAR37890.010.897394
4POLYGON ((-123.23326 42.00619, -120.00186 41.9...CaliforniaCA53341.06.716596
\n", - "
" - ], - "text/plain": [ - " geometry name alpha-2 \n", - "0 POLYGON ((-87.35930 35.00118, -85.60667 34.984... Alabama AL \\\n", - "1 MULTIPOLYGON (((-131.60202 55.11798, -131.5691... Alaska AK \n", - "2 POLYGON ((-109.04250 37.00026, -109.04798 31.3... Arizona AZ \n", - "3 POLYGON ((-94.47384 36.50186, -90.15254 36.496... Arkansas AR \n", - "4 POLYGON ((-123.23326 42.00619, -120.00186 41.9... California CA \n", - "\n", - " medianincome change \n", - "0 38721.5 2.779114 \n", - "1 62561.5 14.758367 \n", - "2 43810.0 NaN \n", - "3 37890.0 10.897394 \n", - "4 53341.0 6.716596 " - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statesmerge.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "43969.375" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "statesmerge[\"medianincome\"].quantile(0.25)" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "import branca\n", - "\n", - "\n", - "colormap = branca.colormap.LinearColormap(\n", - " vmin=statesmerge[\"change\"].quantile(0.0),\n", - " vmax=statesmerge[\"change\"].quantile(1),\n", - " colors=[\"red\", \"orange\", \"lightblue\", \"green\", \"darkgreen\"],\n", - " caption=\"State Level Median County Household Income (%)\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + }, "source": [ - "import folium\n", - "from folium.features import GeoJsonPopup, GeoJsonTooltip\n", - "\n", - "\n", - "m = folium.Map(location=[35.3, -97.6], zoom_start=4)\n", - "\n", - "popup = GeoJsonPopup(\n", - " fields=[\"name\", \"change\"],\n", - " aliases=[\"State\", \"% Change\"],\n", - " localize=True,\n", - " labels=True,\n", - " style=\"background-color: yellow;\",\n", - ")\n", - "\n", - "tooltip = GeoJsonTooltip(\n", - " fields=[\"name\", \"medianincome\", \"change\"],\n", - " aliases=[\"State:\", \"2015 Median Income(USD):\", \"Median % Change:\"],\n", - " localize=True,\n", - " sticky=False,\n", - " labels=True,\n", - " style=\"\"\"\n", - " background-color: #F0EFEF;\n", - " border: 2px solid black;\n", - " border-radius: 3px;\n", - " box-shadow: 3px;\n", - " \"\"\",\n", - " max_width=800,\n", - ")\n", - "\n", - "\n", - "g = folium.GeoJson(\n", - " statesmerge,\n", - " style_function=lambda x: {\n", - " \"fillColor\": colormap(x[\"properties\"][\"change\"])\n", - " if x[\"properties\"][\"change\"] is not None\n", - " else \"transparent\",\n", - " \"color\": \"black\",\n", - " \"fillOpacity\": 0.4,\n", - " },\n", - " tooltip=tooltip,\n", - " popup=popup,\n", - ").add_to(m)\n", - "\n", - "colormap.add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/geojson/geojson_popup_and_tooltip.html" ] } ], diff --git a/examples/GeodedeticImageOverlay.ipynb b/examples/GeodedeticImageOverlay.ipynb index 348c553ce..dd9580857 100644 --- a/examples/GeodedeticImageOverlay.ipynb +++ b/examples/GeodedeticImageOverlay.ipynb @@ -1,206 +1,14 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "def sample_data(shape=(73, 145)):\n", - " nlats, nlons = shape\n", - " lats = np.linspace(-np.pi / 2, np.pi / 2, nlats)\n", - " lons = np.linspace(0, 2 * np.pi, nlons)\n", - " lons, lats = np.meshgrid(lons, lats)\n", - " wave = 0.75 * (np.sin(2 * lats) ** 8) * np.cos(4 * lons)\n", - " mean = 0.5 * np.cos(2 * lats) * ((np.sin(2 * lats)) ** 2 + 2)\n", - "\n", - " lats = np.rad2deg(lats)\n", - " lons = np.rad2deg(lons)\n", - " data = wave + mean\n", - "\n", - " return lons, lats, data\n", - "\n", - "\n", - "lon, lat, data = sample_data(shape=(73, 145))\n", - "lon -= 180" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "\n", - "import matplotlib\n", - "\n", - "cm = matplotlib.cm.get_cmap(\"cubehelix\")\n", - "\n", - "normed_data = (data - data.min()) / (data.max() - data.min())\n", - "colored_data = cm(normed_data)" - ] - }, { "cell_type": "markdown", - "metadata": {}, "source": [ - "# Bad" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/advanced_guide/geodedetic_image_overlay.html" ], - "source": [ - "import folium\n", - "\n", - "\n", - "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", - "\n", - "folium.raster_layers.ImageOverlay(\n", - " image=colored_data,\n", - " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", - " opacity=0.25,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Good" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", - "\n", - "folium.raster_layers.ImageOverlay(\n", - " image=colored_data,\n", - " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", - " mercator_project=True,\n", - " opacity=0.25,\n", - ").add_to(m)\n", - "\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Same as above but with cartopy" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import cartopy.crs as ccrs\n", - "from cartopy.img_transform import warp_array\n", - "\n", - "source_extent = [lon.min(), lon.max(), lat.min(), lat.max()]\n", - "\n", - "new_data = warp_array(\n", - " colored_data,\n", - " target_proj=ccrs.GOOGLE_MERCATOR,\n", - " source_proj=ccrs.PlateCarree(),\n", - " target_res=data.shape,\n", - " source_extent=source_extent,\n", - " target_extent=None,\n", - " mask_extrapolated=False,\n", - ")\n", - "\n", - "\n", - "m = folium.Map(location=[lat.mean(), lon.mean()], zoom_start=1)\n", - "\n", - "folium.raster_layers.ImageOverlay(\n", - " image=new_data[0],\n", - " bounds=[[lat.min(), lon.min()], [lat.max(), lon.max()]],\n", - " opacity=0.25,\n", - ").add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "TODO: Try [rasterio](https://github.com/mapbox/rasterio/blob/ca75cf0a842943c1b3da4522e6ea3500215130fd/docs/reproject.rst). Rasterio can warp images and arrays." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compare to original" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "From https://scitools.org.uk/cartopy/docs/latest/gallery/waves.html\n", - "\n", - "![](https://scitools.org.uk/cartopy/docs/latest/_images/sphx_glr_waves_001.png)" - ] + "metadata": { + "collapsed": false + } } ], "metadata": { diff --git a/examples/Geopandas_and_geo_interface.ipynb b/examples/Geopandas_and_geo_interface.ipynb index 93a870819..3225e08a5 100644 --- a/examples/Geopandas_and_geo_interface.ipynb +++ b/examples/Geopandas_and_geo_interface.ipynb @@ -2,375 +2,14 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, - "source": [ - "# How to draw a GeoPandas.GeoDataFrame into folium\n", - "\n", - "GeoPandas is a project to add support for geographic data to [pandas](http://pandas.pydata.org) objects.\n", - "(See https://github.com/geopandas/geopandas)\n", - "\n", - "It provides (among other cool things) a `GeoDataFrame` object that represents a Feature collection.\n", - "When you have one, you may be willing to use it on a folium map. Here's the simplest way to do so." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example, we'll use the same file as GeoPandas demo ; it's containing\n", - "[the boroughs of New York City](http://www.nyc.gov/html/dcp/download/bytes/nybb_14aav.zip)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BoroCodeBoroNameShape_LengShape_Areageometry
05Staten Island330454.1759331.623847e+09MULTIPOLYGON (((970217.022 145643.332, 970227....
13Brooklyn741227.3370731.937810e+09MULTIPOLYGON (((1021176.479 151374.797, 102100...
24Queens896875.3964493.045079e+09MULTIPOLYGON (((1029606.077 156073.814, 102957...
31Manhattan358400.9128366.364308e+08MULTIPOLYGON (((981219.056 188655.316, 980940....
42Bronx464475.1456511.186822e+09MULTIPOLYGON (((1012821.806 229228.265, 101278...
\n", - "
" - ], - "text/plain": [ - " BoroCode BoroName Shape_Leng Shape_Area \\\n", - "0 5 Staten Island 330454.175933 1.623847e+09 \n", - "1 3 Brooklyn 741227.337073 1.937810e+09 \n", - "2 4 Queens 896875.396449 3.045079e+09 \n", - "3 1 Manhattan 358400.912836 6.364308e+08 \n", - "4 2 Bronx 464475.145651 1.186822e+09 \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((970217.022 145643.332, 970227.... \n", - "1 MULTIPOLYGON (((1021176.479 151374.797, 102100... \n", - "2 MULTIPOLYGON (((1029606.077 156073.814, 102957... \n", - "3 MULTIPOLYGON (((981219.056 188655.316, 980940.... \n", - "4 MULTIPOLYGON (((1012821.806 229228.265, 101278... " - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], - "source": [ - "import geopandas\n", - "\n", - "url = (\n", - " \"https://raw.githubusercontent.com/python-visualization/folium/main/examples/data\"\n", - ")\n", - "nybb = f\"{url}/nybb.zip\"\n", - "boros = geopandas.read_file(nybb)\n", - "\n", - "boros" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To create a map with these features, simply put them in a `GeoJson`:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import folium\n", - "\n", - "\n", - "m = folium.Map([40.7, -74], zoom_start=10, tiles=\"cartodbpositron\")\n", - "\n", - "folium.GeoJson(boros).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Quite easy.\n", - "\n", - "Well, you can also take advantage of your `GeoDataFrame` structure to set the style of the data. For this, just create a column `style` containing each feature's style in a dictionary." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
BoroCodeBoroNameShape_LengShape_Areageometrystyle
05Staten Island330454.1759331.623847e+09MULTIPOLYGON (((970217.022 145643.332, 970227....{'fillColor': '#ff0000', 'weight': 2, 'color':...
13Brooklyn741227.3370731.937810e+09MULTIPOLYGON (((1021176.479 151374.797, 102100...{'fillColor': '#00ff00', 'weight': 2, 'color':...
24Queens896875.3964493.045079e+09MULTIPOLYGON (((1029606.077 156073.814, 102957...{'fillColor': '#0000ff', 'weight': 2, 'color':...
31Manhattan358400.9128366.364308e+08MULTIPOLYGON (((981219.056 188655.316, 980940....{'fillColor': '#ffff00', 'weight': 2, 'color':...
42Bronx464475.1456511.186822e+09MULTIPOLYGON (((1012821.806 229228.265, 101278...{'fillColor': '#00ffff', 'weight': 2, 'color':...
\n", - "
" - ], - "text/plain": [ - " BoroCode BoroName Shape_Leng Shape_Area \\\n", - "0 5 Staten Island 330454.175933 1.623847e+09 \n", - "1 3 Brooklyn 741227.337073 1.937810e+09 \n", - "2 4 Queens 896875.396449 3.045079e+09 \n", - "3 1 Manhattan 358400.912836 6.364308e+08 \n", - "4 2 Bronx 464475.145651 1.186822e+09 \n", - "\n", - " geometry \\\n", - "0 MULTIPOLYGON (((970217.022 145643.332, 970227.... \n", - "1 MULTIPOLYGON (((1021176.479 151374.797, 102100... \n", - "2 MULTIPOLYGON (((1029606.077 156073.814, 102957... \n", - "3 MULTIPOLYGON (((981219.056 188655.316, 980940.... \n", - "4 MULTIPOLYGON (((1012821.806 229228.265, 101278... \n", - "\n", - " style \n", - "0 {'fillColor': '#ff0000', 'weight': 2, 'color':... \n", - "1 {'fillColor': '#00ff00', 'weight': 2, 'color':... \n", - "2 {'fillColor': '#0000ff', 'weight': 2, 'color':... \n", - "3 {'fillColor': '#ffff00', 'weight': 2, 'color':... \n", - "4 {'fillColor': '#00ffff', 'weight': 2, 'color':... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "boros[\"style\"] = [\n", - " {\"fillColor\": \"#ff0000\", \"weight\": 2, \"color\": \"black\"},\n", - " {\"fillColor\": \"#00ff00\", \"weight\": 2, \"color\": \"black\"},\n", - " {\"fillColor\": \"#0000ff\", \"weight\": 2, \"color\": \"black\"},\n", - " {\"fillColor\": \"#ffff00\", \"weight\": 2, \"color\": \"black\"},\n", - " {\"fillColor\": \"#00ffff\", \"weight\": 2, \"color\": \"black\"},\n", - "]\n", - "\n", - "boros" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "m = folium.Map([40.7, -74], zoom_start=10, tiles=\"cartodbpositron\")\n", - "\n", - "folium.GeoJson(boros).add_to(m)\n", - "\n", - "m" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Folium should work with any object that implements the `__geo_interface__` but be aware that sometimes you may need to convert your data to `epsg='4326'` before sending it to `folium`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + }, "source": [ - "import fiona\n", - "import shapely\n", - "\n", - "fname = f\"{url}/2014_08_05_farol.gpx\"\n", - "with fiona.open(fname, \"r\", layer=\"tracks\") as records:\n", - " tracks = [shapely.geometry.shape(record[\"geometry\"]) for record in records]\n", - "\n", - "track = tracks[0]\n", - "\n", - "m = folium.Map(tiles=\"cartodbpositron\")\n", - "folium.GeoJson(track).add_to(m)\n", - "\n", - "m.fit_bounds(m.get_bounds())\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/geojson/geopandas_and_geo_interface.html" ] } ], diff --git a/examples/Heatmap.ipynb b/examples/Heatmap.ipynb index 1e7082371..8034f621b 100644 --- a/examples/Heatmap.ipynb +++ b/examples/Heatmap.ipynb @@ -1,48 +1,15 @@ { "cells": [ { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "\n", - "\n", - "data = (\n", - " np.random.normal(size=(100, 3)) * np.array([[1, 1, 1]]) + np.array([[48, 5, 1]])\n", - ").tolist()" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
Make this Notebook Trusted to load map: File -> Trust Notebook
" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" } - ], + }, "source": [ - "import folium\n", - "from folium.plugins import HeatMap\n", - "\n", - "\n", - "m = folium.Map([48.0, 5.0], tiles=\"stamentoner\", zoom_start=6)\n", - "\n", - "HeatMap(data).add_to(m)\n", - "\n", - "m" + "We've updated our documentation. You can find the new version of this notebook here:\n", + "https://python-visualization.github.io/folium/latest/user_guide/plugins/heatmap.html" ] } ], diff --git a/examples/flask_example.py b/examples/flask_example.py index 47d4273cf..9491936e8 100644 --- a/examples/flask_example.py +++ b/examples/flask_example.py @@ -1,92 +1,4 @@ -""" flask_example.py - - Required packages: - - flask - - folium - - Usage: - - Start the flask server by running: - - $ python flask_example.py - - And then head to http://127.0.0.1:5000/ in your browser to see the map displayed - """ - -from flask import Flask, render_template_string - -import folium - -app = Flask(__name__) - - -@app.route("/") -def fullscreen(): - """Simple example of a fullscreen map.""" - m = folium.Map() - return m.get_root().render() - - -@app.route("/iframe") -def iframe(): - """Embed a map as an iframe on a page.""" - m = folium.Map() - - # set the iframe width and height - m.get_root().width = "800px" - m.get_root().height = "600px" - iframe = m.get_root()._repr_html_() - - return render_template_string( - """ - - - - -

Using an iframe

- {{ iframe|safe }} - - - """, - iframe=iframe, - ) - - -@app.route("/components") -def components(): - """Extract map components and put those on a page.""" - m = folium.Map( - width=800, - height=600, - ) - - m.get_root().render() - header = m.get_root().header.render() - body_html = m.get_root().html.render() - script = m.get_root().script.render() - - return render_template_string( - """ - - - - {{ header|safe }} - - -

Using components

- {{ body_html|safe }} - - - - """, - header=header, - body_html=body_html, - script=script, - ) - - -if __name__ == "__main__": - app.run(debug=True) +We've updated our documentation. You can find the new version of this example here: +https://python-visualization.github.io/folium/latest/advanced_guide/flask.html +"""