{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dr. Semmelweis and the Discovery of Handwashing\n", "> A Summary of project in datacamp\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp]\n", "- image: images/Ignaz_Semmelweis_1860.jpg" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "13f090f9f0" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 1. Meet Dr. Ignaz Semmelweis\n", "
\n", "\n", "This is Dr. Ignaz Semmelweis, a Hungarian physician born in 1818 and active at the Vienna General Hospital. If Dr. Semmelweis looks troubled it's probably because he's thinking about childbed fever: A deadly disease affecting women that just have given birth. He is thinking about it because in the early 1840s at the Vienna General Hospital as many as 10% of the women giving birth die from it. He is thinking about it because he knows the cause of childbed fever: It's the contaminated hands of the doctors delivering the babies. And they won't listen to him and wash their hands!
\n", "In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of handwashing. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "dc": { "key": "13f090f9f0" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " year births deaths clinic\n", "0 1841 3036 237 clinic 1\n", "1 1842 3287 518 clinic 1\n", "2 1843 3060 274 clinic 1\n", "3 1844 3157 260 clinic 1\n", "4 1845 3492 241 clinic 1\n", "5 1846 4010 459 clinic 1\n", "6 1841 2442 86 clinic 2\n", "7 1842 2659 202 clinic 2\n", "8 1843 2739 164 clinic 2\n", "9 1844 2956 68 clinic 2\n", "10 1845 3241 66 clinic 2\n", "11 1846 3754 105 clinic 2\n" ] } ], "source": [ "# importing modules\n", "import pandas as pd\n", "\n", "# Read datasets/yearly_deaths_by_clinic.csv into yearly\n", "yearly = pd.read_csv('./dataset/yearly_deaths_by_clinic.csv')\n", "\n", "# Print out yearly\n", "print(yearly)" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "45ea098e15" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 2. The alarming number of deaths\n", "The table above shows the number of women giving birth at the two clinics at the Vienna General Hospital for the years 1841 to 1846. You'll notice that giving birth was very dangerous; an alarming number of women died as the result of childbirth, most of them from childbed fever.
\n", "We see this more clearly if we look at the proportion of deaths out of the number of women giving birth. Let's zoom in on the proportion of deaths at Clinic 1.
" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "dc": { "key": "45ea098e15" }, "tags": [ "sample_code" ] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " year births deaths clinic proportion_deaths\n", "0 1841 3036 237 clinic 1 0.078063\n", "1 1842 3287 518 clinic 1 0.157591\n", "2 1843 3060 274 clinic 1 0.089542\n", "3 1844 3157 260 clinic 1 0.082357\n", "4 1845 3492 241 clinic 1 0.069015\n", "5 1846 4010 459 clinic 1 0.114464\n" ] } ], "source": [ "# Calculate proportion of deaths per no. births\n", "yearly['proportion_deaths'] = yearly['deaths'] / yearly['births']\n", "\n", "# Extract clinic 1 data into yearly1 and clinic 2 data into yearly2\n", "yearly1 = yearly[yearly['clinic'] == 'clinic 1']\n", "yearly2 = yearly[yearly['clinic'] == 'clinic 2']\n", "\n", "# Print out yearly1\n", "print(yearly1)" ] }, { "cell_type": "markdown", "metadata": { "dc": { "key": "2bc9206960" }, "deletable": false, "editable": false, "run_control": { "frozen": true }, "tags": [ "context" ] }, "source": [ "## 3. Death at the clinics\n", "If we now plot the proportion of deaths at both clinic 1 and clinic 2 we'll see a curious pattern...
" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "dc": { "key": "2bc9206960" }, "tags": [ "sample_code" ] }, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Proportion deaths')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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