{ "cells": [ { "cell_type": "code", "execution_count": 10, "id": "55d4d6b4-a4eb-4fb6-bfbb-e65cbe360ac8", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "id": "b9d92396-7239-4dfc-90ad-83699c9ef520", "metadata": {}, "source": [ "# More Advanced Visualizations" ] }, { "cell_type": "markdown", "id": "e65315a9-a783-4bb3-b03e-b8aa2678f60d", "metadata": {}, "source": [ "This chapter shall discuss some more advanced visualization techniques. Let us begin by loading a World Bank dataset on education." ] }, { "cell_type": "code", "execution_count": 2, "id": "f78e96bc-03f2-4db3-adab-ce4a8ec7de14", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Continent | \n", "Country | \n", "Primary completion rate: Male: % of relevant age group: 2015 | \n", "Primary completion rate: Female: % of relevant age group: 2015 | \n", "Lower secondary completion rate: Male: % of relevant age group: 2015 | \n", "Lower secondary completion rate: Female: % of relevant age group: 2015 | \n", "Youth literacy rate: Male: % of ages 15-24: 2005-14 | \n", "Youth literacy rate: Female: % of ages 15-24: 2005-14 | \n", "Adult literacy rate: Male: % ages 15 and older: 2005-14 | \n", "Adult literacy rate: Female: % ages 15 and older: 2005-14 | \n", "... | \n", "Access to improved sanitation facilities: % of population: 1990 | \n", "Access to improved sanitation facilities: % of population: 2015 | \n", "Child immunization rate: Measles: % of children ages 12-23 months: 2015 | \n", "Child immunization rate: DTP3: % of children ages 12-23 months: 2015 | \n", "Children with acute respiratory infection taken to health provider: % of children under age 5 with ARI: 2009-2016 | \n", "Children with diarrhea who received oral rehydration and continuous feeding: % of children under age 5 with diarrhea: 2009-2016 | \n", "Children sleeping under treated bed nets: % of children under age 5: 2009-2016 | \n", "Children with fever receiving antimalarial drugs: % of children under age 5 with fever: 2009-2016 | \n", "Tuberculosis: Treatment success rate: % of new cases: 2014 | \n", "Tuberculosis: Cases detection rate: % of new estimated cases: 2015 | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "Africa | \n", "Algeria | \n", "106.0 | \n", "105.0 | \n", "68.0 | \n", "85.0 | \n", "96.0 | \n", "92.0 | \n", "83.0 | \n", "68.0 | \n", "... | \n", "80.0 | \n", "88.0 | \n", "95.0 | \n", "95.0 | \n", "66.0 | \n", "42.0 | \n", "NaN | \n", "NaN | \n", "88.0 | \n", "80.0 | \n", "
1 | \n", "Africa | \n", "Angola | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "79.0 | \n", "67.0 | \n", "82.0 | \n", "60.0 | \n", "... | \n", "22.0 | \n", "52.0 | \n", "55.0 | \n", "64.0 | \n", "NaN | \n", "NaN | \n", "25.9 | \n", "28.3 | \n", "34.0 | \n", "64.0 | \n", "
2 | \n", "Africa | \n", "Benin | \n", "83.0 | \n", "73.0 | \n", "50.0 | \n", "37.0 | \n", "55.0 | \n", "31.0 | \n", "41.0 | \n", "18.0 | \n", "... | \n", "7.0 | \n", "20.0 | \n", "75.0 | \n", "79.0 | \n", "23.0 | \n", "33.0 | \n", "72.7 | \n", "25.9 | \n", "89.0 | \n", "61.0 | \n", "
3 | \n", "Africa | \n", "Botswana | \n", "98.0 | \n", "101.0 | \n", "86.0 | \n", "87.0 | \n", "96.0 | \n", "99.0 | \n", "87.0 | \n", "89.0 | \n", "... | \n", "39.0 | \n", "63.0 | \n", "97.0 | \n", "95.0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "77.0 | \n", "62.0 | \n", "
4 | \n", "Africa | \n", "Burundi | \n", "58.0 | \n", "66.0 | \n", "35.0 | \n", "30.0 | \n", "90.0 | \n", "88.0 | \n", "89.0 | \n", "85.0 | \n", "... | \n", "42.0 | \n", "48.0 | \n", "93.0 | \n", "94.0 | \n", "55.0 | \n", "43.0 | \n", "53.8 | \n", "25.4 | \n", "91.0 | \n", "51.0 | \n", "
... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "... | \n", "
161 | \n", "S. America | \n", "Guyana | \n", "87.0 | \n", "81.0 | \n", "NaN | \n", "NaN | \n", "92.0 | \n", "94.0 | \n", "82.0 | \n", "87.0 | \n", "... | \n", "76.0 | \n", "84.0 | \n", "99.0 | \n", "95.0 | \n", "84.0 | \n", "29.0 | \n", "7.4 | \n", "7.4 | \n", "69.0 | \n", "80.0 | \n", "
162 | \n", "S. America | \n", "Paraguay | \n", "89.0 | \n", "90.0 | \n", "71.0 | \n", "77.0 | \n", "99.0 | \n", "98.0 | \n", "96.0 | \n", "94.0 | \n", "... | \n", "52.0 | \n", "89.0 | \n", "83.0 | \n", "93.0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "71.0 | \n", "87.0 | \n", "
163 | \n", "S. America | \n", "Peru | \n", "99.0 | \n", "100.0 | \n", "84.0 | \n", "87.0 | \n", "99.0 | \n", "99.0 | \n", "97.0 | \n", "90.0 | \n", "... | \n", "53.0 | \n", "76.0 | \n", "92.0 | \n", "90.0 | \n", "60.0 | \n", "57.0 | \n", "NaN | \n", "NaN | \n", "87.0 | \n", "80.0 | \n", "
164 | \n", "S. America | \n", "Suriname | \n", "90.0 | \n", "99.0 | \n", "36.0 | \n", "65.0 | \n", "98.0 | \n", "99.0 | \n", "95.0 | \n", "94.0 | \n", "... | \n", "NaN | \n", "79.0 | \n", "94.0 | \n", "89.0 | \n", "76.0 | \n", "61.0 | \n", "43.4 | \n", "0.0 | \n", "77.0 | \n", "80.0 | \n", "
165 | \n", "S. America | \n", "Uruguay | \n", "103.0 | \n", "104.0 | \n", "54.0 | \n", "68.0 | \n", "98.0 | \n", "99.0 | \n", "98.0 | \n", "99.0 | \n", "... | \n", "92.0 | \n", "96.0 | \n", "96.0 | \n", "95.0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "NaN | \n", "75.0 | \n", "87.0 | \n", "
166 rows × 47 columns
\n", "