{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we use the new Sunburst plot by [plotly](http://plot.ly/) to illustrate how the World population\n", "is splitted among regions and countries. The data set illustrated here originates from the\n", "[World Bank](https://data.worldbank.org). This notebook is also a quick demo for the \n", "[world_bank_data](https://github.com/mwouts/world_bank_data/blob/master/README.md) Python package." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/vnd.plotly.v1+html": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import pandas as pd\n", "import mock\n", "import plotly.offline as offline\n", "import world_bank_data as wb\n", "\n", "try:\n", " # Python 3.6\n", " from urllib.request import urlopen\n", "except ImportError:\n", " # Python 2.7\n", " from urllib import urlopen\n", "\n", "# Only show head and tail of dataframes\n", "pd.set_option('display.max_rows', 6)\n", "\n", "\n", "# Plotly.js in version 1.46.1\n", "def get_latest_plotlyjs(url='https://cdn.plot.ly/plotly-1.46.1.min.js'):\n", " return urlopen(url).read().decode('utf-8')\n", "\n", "\n", "with mock.patch('plotly.offline.offline.get_plotlyjs', get_latest_plotlyjs):\n", " offline.init_notebook_mode()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | iso2Code | \n", "name | \n", "region | \n", "adminregion | \n", "incomeLevel | \n", "lendingType | \n", "capitalCity | \n", "longitude | \n", "latitude | \n", "
---|---|---|---|---|---|---|---|---|---|
id | \n", "\n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " | \n", " |
ABW | \n", "AW | \n", "Aruba | \n", "Latin America & Caribbean | \n", "\n", " | High income | \n", "Not classified | \n", "Oranjestad | \n", "-70.0167 | \n", "12.5167 | \n", "
AFG | \n", "AF | \n", "Afghanistan | \n", "South Asia | \n", "South Asia | \n", "Low income | \n", "IDA | \n", "Kabul | \n", "69.1761 | \n", "34.5228 | \n", "
AFR | \n", "A9 | \n", "Africa | \n", "Aggregates | \n", "\n", " | Aggregates | \n", "Aggregates | \n", "\n", " | NaN | \n", "NaN | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
ZAF | \n", "ZA | \n", "South Africa | \n", "Sub-Saharan Africa | \n", "Sub-Saharan Africa (excluding high income) | \n", "Upper middle income | \n", "IBRD | \n", "Pretoria | \n", "28.1871 | \n", "-25.7460 | \n", "
ZMB | \n", "ZM | \n", "Zambia | \n", "Sub-Saharan Africa | \n", "Sub-Saharan Africa (excluding high income) | \n", "Lower middle income | \n", "IDA | \n", "Lusaka | \n", "28.2937 | \n", "-15.3982 | \n", "
ZWE | \n", "ZW | \n", "Zimbabwe | \n", "Sub-Saharan Africa | \n", "Sub-Saharan Africa (excluding high income) | \n", "Low income | \n", "Blend | \n", "Harare | \n", "31.0672 | \n", "-17.8312 | \n", "
304 rows × 9 columns
\n", "\n", " | region | \n", "country | \n", "population | \n", "
---|---|---|---|
id | \n", "\n", " | \n", " | \n", " |
ABW | \n", "Latin America & Caribbean | \n", "Aruba | \n", "105264.0 | \n", "
AFG | \n", "South Asia | \n", "Afghanistan | \n", "35530081.0 | \n", "
AGO | \n", "Sub-Saharan Africa | \n", "Angola | \n", "29784193.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "
ZAF | \n", "Sub-Saharan Africa | \n", "South Africa | \n", "56717156.0 | \n", "
ZMB | \n", "Sub-Saharan Africa | \n", "Zambia | \n", "17094130.0 | \n", "
ZWE | \n", "Sub-Saharan Africa | \n", "Zimbabwe | \n", "16529904.0 | \n", "
218 rows × 3 columns
\n", "\n", " | parents | \n", "labels | \n", "values | \n", "text | \n", "
---|---|---|---|---|
0 | \n", "Latin America & Caribbean | \n", "Aruba | \n", "105264.0 | \n", "105,264 | \n", "
1 | \n", "South Asia | \n", "Afghanistan | \n", "35530081.0 | \n", "35,530,081 | \n", "
2 | \n", "Sub-Saharan Africa | \n", "Angola | \n", "29784193.0 | \n", "29,784,193 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
223 | \n", "World | \n", "South Asia | \n", "0.0 | \n", "1,788,388,852 | \n", "
224 | \n", "World | \n", "Sub-Saharan Africa | \n", "0.0 | \n", "1,056,038,890 | \n", "
225 | \n", "\n", " | World | \n", "0.0 | \n", "7,530,360,149 | \n", "
226 rows × 4 columns
\n", "