{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.8.3+52.g2758dc7.dirty\n"
]
}
],
"source": [
"import os\n",
"import folium\n",
"\n",
"print(folium.__version__)"
]
},
{
"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": 2,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import pandas as pd\n",
"import requests\n",
"\n",
"\n",
"url = 'https://raw.githubusercontent.com/python-visualization/folium/master/examples/data'\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": 3,
"metadata": {},
"outputs": [],
"source": [
"def my_color_function(feature):\n",
" \"\"\"Maps low values to green and hugh values to red.\"\"\"\n",
" if unemployment_dict[feature['id']] > 6.5:\n",
" return '#ff0000'\n",
" else:\n",
" return '#008000'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 4,
"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': my_color_function(feature),\n",
" 'color': 'black',\n",
" 'weight': 2,\n",
" 'dashArray': '5, 5'\n",
" }\n",
").add_to(m)\n",
"\n",
"m.save(os.path.join('results', 'Colormaps_0.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## StepColormap\n",
"\n",
"But to help you define you 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": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import branca.colormap as cm\n",
"\n",
"\n",
"step = cm.StepColormap(\n",
" ['green', 'yellow', 'red'],\n",
" vmin=3, vmax=10,\n",
" index=[3, 4, 8, 10],\n",
" caption='step'\n",
")\n",
"\n",
"step"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 6,
"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.save(os.path.join('results', 'Colormaps_1.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you specify no index, colors will be set uniformely."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 7,
"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": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"linear = cm.LinearColormap(\n",
" ['green', 'yellow', 'red'],\n",
" vmin=3, vmax=10\n",
")\n",
"\n",
"linear"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"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.save(os.path.join('results', 'Colormaps_2.html'))\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Again, you can set the `index` if you want something irregular."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cm.LinearColormap(\n",
" ['red', 'orange', 'yellow', 'green'],\n",
" index=[0, 0.1, 0.9, 1.0]\n",
")"
]
},
{
"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": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 11,
"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": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 12,
"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": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"step.to_linear()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Build-in\n",
"\n",
"For convenience, we provide a (small) set of built-in linear colormaps, in `folium.colormap.linear`."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 14,
"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": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 15,
"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": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cm.linear.YlGnBu_09.scale(3, 12)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 17,
"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": 18,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
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" \n",
" | Set3_11 | |
\n",
" \n",
" | Set1_06 | |
\n",
" \n",
" | PuRd_03 | |
\n",
" \n",
" | PiYG_07 | |
\n",
" \n",
" | RdBu_07 | |
\n",
" \n",
" | Pastel1_06 | |
\n",
" \n",
" | Spectral_10 | |
\n",
" \n",
" | PuRd_04 | |
\n",
" \n",
" | OrRd_03 | |
\n",
" \n",
" | PiYG_03 | |
\n",
" \n",
" | Oranges_05 | |
\n",
" \n",
" | OrRd_07 | |
\n",
" \n",
" | OrRd_06 | |
\n",
" \n",
" | OrRd_05 | |
\n",
" \n",
" | OrRd_04 | |
\n",
" \n",
" | Reds_04 | |
\n",
" \n",
" | Reds_05 | |
\n",
" \n",
" | OrRd_09 | |
\n",
" \n",
" | OrRd_08 | |
\n",
" \n",
" | BrBG_10 | |
\n",
" \n",
" | BrBG_11 | |
\n",
" \n",
" | PiYG_05 | |
\n",
" \n",
" | YlOrRd_08 | |
\n",
" \n",
" | GnBu_04 | |
\n",
" \n",
" | GnBu_05 | |
\n",
" \n",
" | GnBu_06 | |
\n",
" \n",
" | GnBu_07 | |
\n",
" \n",
" | Purples_08 | |
\n",
" \n",
" | GnBu_03 | |
\n",
" \n",
" | Purples_06 | |
\n",
" \n",
" | Purples_07 | |
\n",
" \n",
" | Purples_04 | |
\n",
" \n",
" | Purples_05 | |
\n",
" \n",
" | GnBu_08 | |
\n",
" \n",
" | GnBu_09 | |
\n",
" \n",
" | YlOrRd_09 | |
\n",
" \n",
" | Purples_03 | |
\n",
" \n",
" | RdYlBu_04 | |
\n",
" \n",
" | PRGn_09 | |
\n",
" \n",
" | PRGn_08 | |
\n",
" \n",
" | PRGn_07 | |
\n",
" \n",
" | PRGn_06 | |
\n",
" \n",
" | PRGn_05 | |
\n",
" \n",
" | PRGn_04 | |
\n",
" \n",
" | PRGn_03 | |
\n",
" \n",
" | RdYlBu_06 | |
\n",
" \n",
" | RdYlGn_10 | |
\n",
" \n",
" | YlGn_08 | |
\n",
" \n",
" | YlGn_09 | |
\n",
" \n",
" | RdYlBu_07 | |
\n",
" \n",
" | PiYG_10 | |
\n",
" \n",
" | PiYG_11 | |
\n",
" \n",
" | YlGn_03 | |
\n",
" \n",
" | YlGn_04 | |
\n",
" \n",
" | YlGn_05 | |
\n",
" \n",
" | YlGn_06 | |
\n",
" \n",
" | YlGn_07 | |
\n",
" \n",
" | Dark2_05 | |
\n",
" \n",
" | Dark2_04 | |
\n",
" \n",
" | Dark2_07 | |
\n",
" \n",
" | Pastel2_03 | |
\n",
" \n",
" | Pastel2_04 | |
\n",
" \n",
" | Pastel2_05 | |
\n",
" \n",
" | Pastel2_06 | |
\n",
" \n",
" | Pastel2_07 | |
\n",
" \n",
" | Pastel2_08 | |
\n",
" \n",
" | RdYlBu_03 | |
\n",
" \n",
" | Dark2_08 | |
\n",
" \n",
" | RdYlGn_03 | |
\n",
" \n",
" | PRGn_11 | |
\n",
" \n",
" | Greens_08 | |
\n",
" \n",
" | Greens_09 | |
\n",
" \n",
" | Greens_06 | |
\n",
" \n",
" | Greens_07 | |
\n",
" \n",
" | Greens_04 | |
\n",
" \n",
" | Greens_05 | |
\n",
" \n",
" | PRGn_10 | |
\n",
" \n",
" | Greens_03 | |
\n",
"
\n",
" "
],
"text/plain": [
""
]
},
"execution_count": 18,
"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": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 19,
"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.save(os.path.join('results', 'Colormaps_3.html'))\n",
"\n",
"m"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 1
}