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# 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[yearly['clinic'] == "clinic 1"]
clinic_2 = yearly[yearly['clinic'] == "clinic 2"]

# Print out clinic_1
print(clinic_1)
print(clinic_2)
import matplotlib.pyplot as plt

# Plot yearly proportion of deaths at the two clinics
%matplotlib inline
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, 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
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 handwashing")
after_washing.plot(x="date", y="proportion_deaths", label ="After handwashing", ax = ax)
# 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()
print(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([0.05, 0.95])
confidence_interval
# The data Semmelweis collected points to that:
doctors_should_wash_their_hands = True
Hidden output