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Make it possible to use Choropleth layer with data containing NaNs. The NaN regions have a dedicated color referred to as nan_color (black is set as default value) and a dedicated opacity referred to as nan_opacity (set to 0.4 as default value).
1 parent 201be19 commit 079ee97

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6 files changed

+178
-20
lines changed

6 files changed

+178
-20
lines changed

‎examples/Choropleth.ipynb

Lines changed: 3 additions & 3 deletions
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@@ -162,7 +162,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
@@ -176,9 +176,9 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.2"
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎examples/Choropleth_with_NANS.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ipyleaflet\n",
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"from ipyleaflet import Map, LegendControl\n",
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"import json\n",
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"import pandas as pd\n",
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"from ipywidgets import link, FloatSlider\n",
14+
"from branca.colormap import linear\n",
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"import random\n",
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"\n",
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"colormap_choice = linear.YlOrRd_04\n",
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"geo_json_data = json.load(open(\"us-states.json\"))\n",
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"m = ipyleaflet.Map(center=(43, -100), zoom=4)\n",
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"unemployment = pd.read_csv(\"US_Unemployment_Oct2012_with_NANS.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_unemployment = dict(\n",
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" zip(unemployment[\"State\"].tolist(), unemployment[\"Unemployment\"].tolist())\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"layer = ipyleaflet.Choropleth(\n",
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" geo_data=geo_json_data,\n",
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" choro_data=data_unemployment,\n",
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" colormap=colormap_choice,\n",
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" style={\"dashArray\": \"5, 5\"}\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# To display the colormap bar on the map\n",
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"from ipywidgets import Output\n",
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"from ipyleaflet import WidgetControl\n",
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"\n",
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"out = Output()\n",
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"\n",
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"with out:\n",
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" colormap = colormap_choice.scale(layer.value_min,layer.value_max)\n",
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" display(colormap)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"m.add_layer(layer)\n",
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"m.add_control(WidgetControl(widget=out, position='topright'))\n",
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"m"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}

‎examples/LegendControl.ipynb

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@@ -1,8 +1,10 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Legend: How to use"
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]
@@ -145,18 +147,11 @@
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"a_legend.positioning = \"topright\" # set positioning : possible values are topleft, topright, bottomleft, bottomright\n",
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"a_legend.positioning # get current positioning"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.9"
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"version": "3.9.12"
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}
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},
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"nbformat": 4,

‎examples/US_Unemployment_Oct2012_with_NANS.csv

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State,Unemployment
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AL,7.1
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AK,6.8
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AZ,8.1
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AR,7.2
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CA,nan
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CO,7.7
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CT,8.4
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DE,7.1
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FL,8.2
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GA,8.8
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HI,nan
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ID,6.6
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IL,8.8
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IN,8.4
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IA,nan
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KS,5.6
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KY,8.1
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LA,5.9
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ME,7.2
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MD,nan
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MA,6.7
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MI,9.1
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MN,5.6
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MS,9.1
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MO,nan
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MT,5.8
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NE,3.9
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NV,10.3
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NH,5.7
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NJ,nan
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NM,6.8
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NY,8.4
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NC,9.4
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ND,3.2
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OH,6.9
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OK,5.2
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OR,8.5
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PA,8
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RI,10.1
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SC,8.8
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SD,4.4
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TN,7.8
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TX,nan
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UT,5.5
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VT,5
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VA,nan
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WA,7.8
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WV,7.5
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WI,6.8
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WY,5.1

‎examples/WidgetControl.ipynb

Lines changed: 3 additions & 3 deletions
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@@ -97,7 +97,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.1"
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"version": "3.9.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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"nbformat_minor": 4
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}

‎ipyleaflet/leaflet.py

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Original file line numberDiff line numberDiff line change
@@ -7,6 +7,7 @@
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import json
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import xyzservices
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from datetime import date, timedelta
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from math import isnan
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from ipywidgets import (
1213
Widget, DOMWidget, Box, Color, CallbackDispatcher, widget_serialization,
@@ -1330,6 +1331,12 @@ class Choropleth(GeoJSON):
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The colormap used for the effect.
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key_on: string, default "id"
13321333
The feature key to use for the colormap effect.
1334+
nan_color: string, default "black"
1335+
The color used for filling polygons with NaN-values data.
1336+
nan_opacity : float, default 0.4
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The opacity used for NaN data polygons, between 0. (fully transparent) and 1. (fully opaque).
1338+
default_opacity: float, default 1.0
1339+
The opacity used for well-defined data (non-NaN values), between 0. (fully transparent) and 1. (fully opaque).
13331340
"""
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13351342
geo_data = Dict()
@@ -1338,8 +1345,11 @@ class Choropleth(GeoJSON):
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value_max = CFloat(None, allow_none=True)
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colormap = Any()
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key_on = Unicode('id')
1348+
nan_color = Unicode('black')
1349+
nan_opacity = CFloat(0.4)
1350+
default_opacity = CFloat(1.0)
13411351

1342-
@observe('style', 'style_callback', 'value_min', 'value_max', 'geo_data', 'choro_data', 'colormap')
1352+
@observe('style', 'style_callback', 'value_min', 'value_max', 'nan_color', 'nan_opacity', 'default_opacity', 'geo_data', 'choro_data', 'colormap')
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def _update_data(self, change):
13441354
self.data = self._get_data()
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@@ -1355,7 +1365,8 @@ def _default_colormap(self):
13551365
def _default_style_callback(self):
13561366
def compute_style(feature, colormap, choro_data):
13571367
return dict(
1358-
fillColor=colormap(choro_data),
1368+
fillColor=self.nan_color if isnan(choro_data) else colormap(choro_data),
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fillOpacity=self.nan_opacity if isnan(choro_data) else self.default_opacity,
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color='black',
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weight=0.9
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)
@@ -1366,12 +1377,15 @@ def _get_data(self):
13661377
if not self.geo_data:
13671378
return {}
13681379

1380+
choro_data_values_list = [x for x in self.choro_data.values() if not math.isnan(x)]
1381+
13691382
if self.value_min is None:
1370-
self.value_min = min(self.choro_data.items(), key=lambda x: x[1])[1]
1383+
self.value_min = min(choro_data_values_list)
13711384
if self.value_max is None:
1372-
self.value_max = max(self.choro_data.items(), key=lambda x: x[1])[1]
1385+
self.value_max = max(choro_data_values_list)
13731386

13741387
colormap = self.colormap.scale(self.value_min, self.value_max)
1388+
13751389
data = copy.deepcopy(self.geo_data)
13761390

13771391
for feature in data['features']:

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