# 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)
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.query("clinic == 'clinic 1'")
clinic_2 = yearly.query("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
# 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())
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', 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