"
],
"text/plain": [
""
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43,-100], zoom_start=4)\n",
"\n",
"folium.GeoJson(open('us-states.json')).add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you provide only the filename, Folium will consider you don't want to embed the data in the map. You'll need to copy the file on the server if you want it to be rendered."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"
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" \n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43,-100], zoom_start=4)\n",
"\n",
"folium.GeoJson('us-states.json').add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
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"
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" \n",
"
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],
"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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
"
\n",
" \n",
"
"
],
"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' if 'e' in feature['properties']['name'].lower() 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": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
" \n",
"
\n",
"
\n",
"
State
\n",
"
Unemployment
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
AL
\n",
"
7.1
\n",
"
\n",
"
\n",
"
1
\n",
"
AK
\n",
"
6.8
\n",
"
\n",
"
\n",
"
2
\n",
"
AZ
\n",
"
8.1
\n",
"
\n",
"
\n",
"
3
\n",
"
AR
\n",
"
7.2
\n",
"
\n",
"
\n",
"
4
\n",
"
CA
\n",
"
10.1
\n",
"
\n",
" \n",
"
\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": [
"unemployment = pd.read_csv('./US_Unemployment_Oct2012.csv')\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": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"#c2e798\n"
]
},
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from folium.colormap import linear\n",
"\n",
"colormap = linear.YlGn.scale(\n",
" unemployment.Unemployment.min(),\n",
" unemployment.Unemployment.max())\n",
"\n",
"print(colormap(5.))\n",
"\n",
"colormap"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need also to convert the table into a dictionnary, in order to map a feature to it's unemployment value."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"7.0999999999999996"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"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": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"colormap.caption = 'Unemployment color scale'\n",
"colormap.add_to(m)\n",
"\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### GeoPandas\n",
"\n",
"Note that you can also create a GeoJson object in using a GeoPandas DataFrame. There's another notebook example for this. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using `choropleth` method\n",
"\n",
"Now if you want to get faster, you can use the `Map.choropleth` method. Have a look at it's docstring, it has several styling options.\n",
"\n",
"You can use it in providing a file name (`geo_path`) :"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
"
],
"text/plain": [
""
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43,-100], zoom_start=4)\n",
"\n",
"m.choropleth(geo_str=open('us-states.json').read())\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, in playing with keyword arguments, you can get a choropleth in (*almost*) one line :"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/journois/anaconda3/lib/python3.4/site-packages/ipykernel/__main__.py:8: FutureWarning: 'threshold_scale' default behavior has changed. Now you get a linear scale between the 'min' and the 'max' of your data. To get former behavior, use folium.utilities.split_six.\n"
]
},
{
"data": {
"text/html": [
"\n",
"
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"
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" \n",
"
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],
"text/plain": [
""
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"m = folium.Map([43,-100], zoom_start=4)\n",
"\n",
"m.choropleth(\n",
" geo_str=open('us-states.json').read(),\n",
" data=unemployment,\n",
" columns=['State', 'Unemployment'],\n",
" key_on='feature.id',\n",
" fill_color='YlGn',\n",
" )\n",
"m"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A cool thing: you can force the color scale to a given number of bins, in providing a `threshold_scale` argument."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"