Dr. Semmelweis and the Discovery of Handwashing

# Importing modules import pandas as pd # Read datasets/yearly_deaths_by_clinic.csv into yearly yearly = pd.read_csv('/work/Dr.-Semmelweis-and-the-Discovery-of-Handwashing/datasets/yearly_deaths_by_clinic.csv') # Print out yearly print(yearly)

    year  births  deaths    clinic
0   1841    3036     237  clinic 1
1   1842    3287     518  clinic 1
2   1843    3060     274  clinic 1
3   1844    3157     260  clinic 1
4   1845    3492     241  clinic 1
5   1846    4010     459  clinic 1
6   1841    2442      86  clinic 2
7   1842    2659     202  clinic 2
8   1843    2739     164  clinic 2
9   1844    2956      68  clinic 2
10  1845    3241      66  clinic 2
11  1846    3754     105  clinic 2

# Calculate proportion of deaths per no. births yearly['proportion_deaths'] = yearly['deaths'] / yearly['births'] # Extract Clinic 1 data into clinic_1 and Clinic 2 data into clinic_2 clinic_1 = yearly[yearly['clinic']=='clinic 1'] clinic_2 = yearly[yearly['clinic']=='clinic 2'] # Print out clinic_1 print(clinic_1)

   year  births  deaths    clinic  proportion_deaths
0  1841    3036     237  clinic 1           0.078063
1  1842    3287     518  clinic 1           0.157591
2  1843    3060     274  clinic 1           0.089542
3  1844    3157     260  clinic 1           0.082357
4  1845    3492     241  clinic 1           0.069015
5  1846    4010     459  clinic 1           0.114464

# Import matplotlib import matplotlib.pyplot as plt # This makes plots appear in the notebook %matplotlib inline # Plot yearly proportion of deaths at the two clinics ax = clinic_1.plot(x='year', y='proportion_deaths', label='Clinic 1') clinic_2.plot(x='year', y='proportion_deaths', label='Clinic 2', ax=ax) ax.set_ylabel('Proportion deaths')

# Read datasets/monthly_deaths.csv into monthly monthly = pd.read_csv('datasets/monthly_deaths.csv', parse_dates=['date']) # Calculate proportion of deaths per no. births monthly['proportion_deaths'] = monthly['deaths'] / monthly['births'] # Print out the first rows in monthly print(monthly.head())

        date  births  deaths  proportion_deaths
0 1841-01-01     254      37           0.145669
1 1841-02-01     239      18           0.075314
2 1841-03-01     277      12           0.043321
3 1841-04-01     255       4           0.015686
4 1841-05-01     255       2           0.007843

# Plot monthly proportion of deaths ax = monthly.plot(x='date', y='proportion_deaths') ax.set_ylabel('Proportion deaths')

# Date when handwashing was made mandatory handwashing_start = pd.to_datetime('1847-06-01') # Split monthly into before and after handwashing_start before_washing = monthly[monthly['date'] < handwashing_start] after_washing = monthly[monthly['date'] >= handwashing_start] # Plot monthly proportion of deaths before and after handwashing ax = before_washing.plot(x='date', y='proportion_deaths', label='Before washing') after_washing.plot(x='date', y='proportion_deaths', label='After washing', ax=ax) ax.set_ylabel('Proportion deaths')

# Difference in mean monthly proportion of deaths due to handwashing before_proportion = before_washing['proportion_deaths'] after_proportion = after_washing['proportion_deaths'] mean_diff = after_proportion.mean() - before_proportion.mean() mean_diff

# A bootstrap analysis of the reduction of deaths due to handwashing boot_mean_diff = [] for i in range(3000): boot_before = before_proportion.sample(frac=1, replace=True) boot_after = after_proportion.sample(frac=1, replace=True) boot_mean_diff.append( boot_after.mean() - boot_before.mean() ) # Calculating a 95% confidence interval from boot_mean_diff confidence_interval = pd.Series(boot_mean_diff).quantile([0.025, 0.975]) confidence_interval

# The data Semmelweis collected points to that: doctors_should_wash_their_hands = True