# Importing modules import pandas as pd # Read datasets/yearly_deaths_by_clinic.csv into yearly yearly = pd.read_csv('datasets/yearly_deaths_by_clinic.csv') # Print out yearly print(yearly)

# 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.query("clinic == 'clinic 1'") clinic_2 = yearly.query("clinic == 'clinic 2'") # Print out clinic_1 print(clinic_1)

# 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', ax=ax, label='clinic 2', 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())

# Plot monthly proportion of deaths ax = monthly.plot(x='date', y='proportion_deaths', 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', ax=ax, label='after washing', 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 = False