notebook.ipynb

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    "## 1. Meet Dr. Ignaz Semmelweis\n",
    "<p><img style=\"float: left;margin:5px 20px 5px 1px\" src=\"https://assets.datacamp.com/production/project_20/img/ignaz_semmelweis_1860.jpeg\"></p>\n",
    "<!--\n",
    "<img style=\"float: left;margin:5px 20px 5px 1px\" src=\"https://assets.datacamp.com/production/project_20/datasets/ignaz_semmelweis_1860.jpeg\">\n",
    "-->\n",
    "<p>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 <em>childbed fever</em>: 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 <em>wash their hands</em>!</p>\n",
    "<p>In this notebook, we're going to reanalyze the data that made Semmelweis discover the importance of <em>handwashing</em>. Let's start by looking at the data that made Semmelweis realize that something was wrong with the procedures at Vienna General Hospital.</p>"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>births</th>\n",
       "      <th>deaths</th>\n",
       "      <th>clinic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1841</td>\n",
       "      <td>3036</td>\n",
       "      <td>237</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1842</td>\n",
       "      <td>3287</td>\n",
       "      <td>518</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1843</td>\n",
       "      <td>3060</td>\n",
       "      <td>274</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1844</td>\n",
       "      <td>3157</td>\n",
       "      <td>260</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1845</td>\n",
       "      <td>3492</td>\n",
       "      <td>241</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1846</td>\n",
       "      <td>4010</td>\n",
       "      <td>459</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1841</td>\n",
       "      <td>2442</td>\n",
       "      <td>86</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1842</td>\n",
       "      <td>2659</td>\n",
       "      <td>202</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1843</td>\n",
       "      <td>2739</td>\n",
       "      <td>164</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1844</td>\n",
       "      <td>2956</td>\n",
       "      <td>68</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1845</td>\n",
       "      <td>3241</td>\n",
       "      <td>66</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1846</td>\n",
       "      <td>3754</td>\n",
       "      <td>105</td>\n",
       "      <td>clinic 2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "    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"
      ]
     },
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   "source": [
    "# Importing modules\n",
    "# ... YOUR CODE FOR TASK 1 ...\n",
    "import pandas as pd\n",
    "# Read datasets/yearly_deaths_by_clinic.csv into yearly\n",
    "yearly = pd.read_csv(\"datasets/yearly_deaths_by_clinic.csv\")\n",
    "yearly\n",
    "# Print out yearly\n",
    "# ... YOUR CODE FOR TASK 1 ..."
   ]
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   "source": [
    "## 2. The alarming number of deaths\n",
    "<p>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 <em>alarming</em> number of women died as the result of childbirth, most of them from childbed fever.</p>\n",
    "<p>We see this more clearly if we look at the <em>proportion of deaths</em> out of the number of women giving birth. Let's zoom in on the proportion of deaths at Clinic 1.</p>"
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     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>births</th>\n",
       "      <th>deaths</th>\n",
       "      <th>clinic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1841</td>\n",
       "      <td>3036</td>\n",
       "      <td>237</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1842</td>\n",
       "      <td>3287</td>\n",
       "      <td>518</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1843</td>\n",
       "      <td>3060</td>\n",
       "      <td>274</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1844</td>\n",
       "      <td>3157</td>\n",
       "      <td>260</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1845</td>\n",
       "      <td>3492</td>\n",
       "      <td>241</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1846</td>\n",
       "      <td>4010</td>\n",
       "      <td>459</td>\n",
       "      <td>clinic 1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "   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"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Calculate proportion of deaths per no. births\n",
    "# ... YOUR CODE FOR TASK 2 ...\n",
    "\n",
    "# Extract Clinic 1 data into clinic_1 and Clinic 2 data into clinic_2\n",
    "clinic_1 = yearly[yearly[\"clinic\"] == 'clinic 1']\n",
    "clinic_2 = yearly[yearly[\"clinic\"] == 'clinic 2']\n",
    "clinic_1\n",
    "\n",
    "# Print out clinic_1\n",
    "# ... YOUR CODE FOR TASK 2 ..."
   ]
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   "source": [
    "## 3. Death at the clinics\n",
    "<p>If we now plot the proportion of deaths at both Clinic 1 and Clinic 2  we'll see a curious pattern…</p>"
   ]
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     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='year', ylabel='deaths'>"
      ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import matplotlib\n",
    "import matplotlib as plt\n",
    "\n",
    "# This makes plots appear in the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "ax = clinic_1.plot(x=\"year\", \n",
    "    y=\"deaths\", \n",
    "    label=\"Clinic 1\")\n",
    "clinic_2.plot(x=\"year\", y=\"deaths\", label=\"Clinic 2\", ax=ax, ylabel=\"deaths\")\n",
    "\n",
    "\n",
    "# Plot yearly proportion of deaths at the two clinics\n",
    "# ... YOUR CODE FOR TASK 3 ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "0c9fdbf550"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 4. The handwashing begins\n",
    "<p>Why is the proportion of deaths consistently so much higher in Clinic 1? Semmelweis saw the same pattern and was puzzled and distressed. The only difference between the clinics was that many medical students served at Clinic 1, while mostly midwife students served at Clinic 2. While the midwives only tended to the women giving birth, the medical students also spent time in the autopsy rooms examining corpses. </p>\n",
    "<p>Semmelweis started to suspect that something on the corpses spread from the hands of the medical students, caused childbed fever. So in a desperate attempt to stop the high mortality rates, he decreed: <em>Wash your hands!</em> This was an unorthodox and controversial request, nobody in Vienna knew about bacteria at this point in time. </p>\n",
    "<p>Let's load in monthly data from Clinic 1 to see if the handwashing had any effect.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "dc": {
     "key": "0c9fdbf550"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>births</th>\n",
       "      <th>deaths</th>\n",
       "      <th>proportion_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1841-01-01</td>\n",
       "      <td>254</td>\n",
       "      <td>37</td>\n",
       "      <td>0.145669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1841-02-01</td>\n",
       "      <td>239</td>\n",
       "      <td>18</td>\n",
       "      <td>0.075314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1841-03-01</td>\n",
       "      <td>277</td>\n",
       "      <td>12</td>\n",
       "      <td>0.043321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1841-04-01</td>\n",
       "      <td>255</td>\n",
       "      <td>4</td>\n",
       "      <td>0.015686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1841-05-01</td>\n",
       "      <td>255</td>\n",
       "      <td>2</td>\n",
       "      <td>0.007843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date  births  deaths  proportion_deaths\n",
       "0 1841-01-01     254      37           0.145669\n",
       "1 1841-02-01     239      18           0.075314\n",
       "2 1841-03-01     277      12           0.043321\n",
       "3 1841-04-01     255       4           0.015686\n",
       "4 1841-05-01     255       2           0.007843"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read datasets/monthly_deaths.csv into monthly\n",
    "monthly = pd.read_csv(\"datasets/monthly_deaths.csv\", parse_dates=[\"date\"])\n",
    "\n",
    "# Calculate proportion of deaths per no. births\n",
    "monthly[\"proportion_deaths\"] = monthly[\"deaths\"] / monthly[\"births\"]\n",
    "\n",
    "# Print out the first rows in monthly\n",
    "monthly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "2da2a84119"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 5. The effect of handwashing\n",
    "<p>With the data loaded we can now look at the proportion of deaths over time. In the plot below we haven't marked where obligatory handwashing started, but it reduced the proportion of deaths to such a degree that you should be able to spot it!</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "dc": {
     "key": "2da2a84119"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot monthly proportion of deaths\n",
    "ax = monthly.plot(x=\"date\", y=\"proportion_deaths\", ylabel=\"Proportion deaths\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "518e95acc5"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 6. The effect of handwashing highlighted\n",
    "<p>Starting from the summer of 1847 the proportion of deaths is drastically reduced and, yes, this was when Semmelweis made handwashing obligatory. </p>\n",
    "<p>The effect of handwashing is made even more clear if we highlight this in the graph.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "dc": {
     "key": "518e95acc5"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date', ylabel='Proportion deaths'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Date when handwashing was made mandatory\n",
    "handwashing_start = pd.to_datetime('1847-06-01')\n",
    "\n",
    "# Split monthly into before and after handwashing_start\n",
    "before_washing = monthly[monthly[\"date\"] < handwashing_start]\n",
    "after_washing = monthly[monthly[\"date\"] >= handwashing_start]\n",
    "\n",
    "# Plot monthly proportion of deaths before and after handwashing\n",
    "ax = before_washing.plot(x=\"date\", y=\"proportion_deaths\",\n",
    "                         label=\"Before handwashing\")\n",
    "after_washing.plot(x=\"date\", y=\"proportion_deaths\",\n",
    "                   label=\"After handwashing\", ax=ax, ylabel=\"Proportion deaths\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "586a9f9803"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 7. More handwashing, fewer deaths?\n",
    "<p>Again, the graph shows that handwashing had a huge effect. How much did it reduce the monthly proportion of deaths on average?</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "dc": {
     "key": "586a9f9803"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.08395660751183336"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Difference in mean monthly proportion of deaths due to handwashing\n",
    "before_proportion = before_washing[\"proportion_deaths\"]\n",
    "after_proportion = after_washing[\"proportion_deaths\"]\n",
    "mean_diff = after_proportion.mean() - before_proportion.mean()\n",
    "mean_diff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "d8ff65292a"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 8. A Bootstrap analysis of Semmelweis handwashing data\n",
    "<p>It reduced the proportion of deaths by around 8 percentage points! From 10% on average to just 2% (which is still a high number by modern standards). </p>\n",
    "<p>To get a feeling for the uncertainty around how much handwashing reduces mortalities we could look at a confidence interval (here calculated using the bootstrap method).</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "dc": {
     "key": "d8ff65292a"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.025   -0.101818\n",
       "0.975   -0.067088\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A bootstrap analysis of the reduction of deaths due to handwashing\n",
    "boot_mean_diff = []\n",
    "for i in range(3000):\n",
    "    boot_before = before_proportion.sample(frac=1, replace=True)\n",
    "    boot_after = after_proportion.sample(frac=1, replace=True)\n",
    "    boot_mean_diff.append( boot_after.mean() - boot_before.mean() )\n",
    "\n",
    "# Calculating a 95% confidence interval from boot_mean_diff \n",
    "confidence_interval = pd.Series(boot_mean_diff).quantile([0.025, 0.975])\n",
    "confidence_interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "dc": {
     "key": "0645423069"
    },
    "deletable": false,
    "editable": false,
    "run_control": {
     "frozen": true
    },
    "tags": [
     "context"
    ]
   },
   "source": [
    "## 9. The fate of Dr. Semmelweis\n",
    "<p>So handwashing reduced the proportion of deaths by between 6.7 and 10 percentage points, according to a 95% confidence interval. All in all, it would seem that Semmelweis had solid evidence that handwashing was a simple but highly effective procedure that could save many lives.</p>\n",
    "<p>The tragedy is that, despite the evidence, Semmelweis' theory — that childbed fever was caused by some \"substance\" (what we today know as <em>bacteria</em>) from autopsy room corpses — was ridiculed by contemporary scientists. The medical community largely rejected his discovery and in 1849 he was forced to leave the Vienna General Hospital for good.</p>\n",
    "<p>One reason for this was that statistics and statistical arguments were uncommon in medical science in the 1800s. Semmelweis only published his data as long tables of raw data, but he didn't show any graphs nor confidence intervals. If he would have had access to the analysis we've just put together he might have been more successful in getting the Viennese doctors to wash their hands.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "dc": {
     "key": "0645423069"
    },
    "tags": [
     "sample_code"
    ]
   },
   "outputs": [],
   "source": [
    "# The data Semmelweis collected points to that:\n",
    "doctors_should_wash_their_hands = True"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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