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Merge pull request #422 from martinRenou/add_wealth_of_nations_notebook
Add wealth of nations notebook
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‎README.md

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@@ -32,6 +32,10 @@ A Jupyter / Leaflet bridge enabling interactive maps in the Jupyter notebook.
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![Choropleth Screencast](choropleth.gif)
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**Widget control**
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![Widget Control](widget_control.gif)
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## Installation
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Using conda:

‎examples/WealthOfNations.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 os\n",
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"import json\n",
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"\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"from ipywidgets import Dropdown\n",
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"\n",
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"from bqplot import Lines, Figure, LinearScale, DateScale, Axis\n",
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"\n",
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"from ipyleaflet import Map, GeoJSON, WidgetControl"
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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 = pd.read_json(os.path.abspath('nations.json'))"
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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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"def clean_data(data):\n",
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" for column in ['income', 'lifeExpectancy', 'population']:\n",
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" data = data.drop(data[data[column].apply(len) <= 4].index)\n",
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" return data\n",
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"\n",
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"def extrap_interp(data):\n",
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" data = np.array(data)\n",
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" x_range = np.arange(1800, 2009, 1.)\n",
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" y_range = np.interp(x_range, data[:, 0], data[:, 1])\n",
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" return y_range\n",
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"\n",
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"def extrap_data(data):\n",
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" for column in ['income', 'lifeExpectancy', 'population']:\n",
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" data[column] = data[column].apply(extrap_interp)\n",
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" return data"
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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 = clean_data(data)\n",
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"data = extrap_data(data)"
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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"
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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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"date_start = pd.datetime(1800, 12, 31)\n",
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"date_end = pd.datetime(2009, 12, 31)\n",
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"\n",
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"date_scale = DateScale(min=date_start, max=date_end)"
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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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"date_data = pd.date_range(start=date_start, end=date_end, freq='A', normalize=True)"
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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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"country_name = 'Angola'\n",
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"data_name = 'income'\n",
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"\n",
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"x_data = data[data.name == country_name][data_name].values[0]"
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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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"x_scale = LinearScale()\n",
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"\n",
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"lines = Lines(x=date_data, y=x_data, scales={'x': date_scale, 'y': x_scale})\n",
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"\n",
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"ax_x = Axis(label='Year', scale=date_scale, num_ticks=10, tick_format='%Y')\n",
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"ax_y = Axis(label=data_name.capitalize(), scale=x_scale, orientation='vertical', side='left')\n",
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"\n",
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"figure = Figure(axes=[ax_x, ax_y], title=country_name, marks=[lines], animation_duration=500,\n",
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" layout={'max_height': '250px', 'max_width': '400px'})\n",
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"figure"
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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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"def update_figure(country_name, data_name):\n",
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" lines.y = data[data.name == country_name][data_name].values[0]\n",
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" ax_y.label = data_name.capitalize()\n",
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" figure.title = country_name"
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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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"country_name = 'Benin'\n",
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"data_name = 'income'\n",
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"\n",
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"update_figure(country_name, data_name)"
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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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"country_name = 'Angola'\n",
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"data_name = 'population'\n",
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"\n",
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"update_figure(country_name, data_name)"
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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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"with open('./countries.geo.json') as f:\n",
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" countries = json.load(f)"
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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 = Map(zoom=3)\n",
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"\n",
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"geo = GeoJSON(data=countries, style={'fillColor': 'white', 'weight': 0.5}, hover_style={'fillColor': '#1f77b4'}, name='Countries')\n",
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"m.add_layer(geo)\n",
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"\n",
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"m"
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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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"widget_control1 = WidgetControl(widget=figure, position='bottomright')\n",
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"\n",
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"m.add_control(widget_control1)\n",
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"\n",
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"def on_hover(event, feature, **kwargs):\n",
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" global country_name\n",
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"\n",
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" country_name = feature['properties']['name']\n",
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" update_figure(country_name, data_name)\n",
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"\n",
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"geo.on_hover(on_hover)"
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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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"dropdown = Dropdown(\n",
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" options=['income', 'population', 'lifeExpectancy'],\n",
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" value=data_name,\n",
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" description='Plotting:'\n",
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")\n",
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"\n",
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"def on_click(change):\n",
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" global data_name\n",
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"\n",
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" data_name = change['new']\n",
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" update_figure(country_name, data_name)\n",
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"\n",
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"dropdown.observe(on_click, 'value')\n",
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"\n",
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"widget_control2 = WidgetControl(widget=dropdown, position='bottomleft')\n",
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"\n",
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"m.add_control(widget_control2)"
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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",
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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": "3.7.3"
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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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}

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