Pollutants and Performance

Explore the relationship between environmental factors and human health

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

plt.rcParams['figure.figsize'] = (5, 8)
plt.rcParams['font.size'] = 12

health_clean = pd.read_csv('data/CitieSHealth_BCN_DATA_PanelStudy_20220414_Clean.csv')
health_clean.head()

	no2	pm25	black carbon	performance	occurrence_mental	wellbeing	stress	sleep quality	gender	diet	alcohol	drug_use	education	access_greenbluespaces	smoke	age
0	28.54	8.16	0.50	51.24	2	8	5	8	Female	Yes	No	No	university	Yes	No	37
1	44.51	13.38	1.51	56.01	10	9	8	9	Female	Yes	Yes	No	university	Yes	Yes	28
2	33.81	16.53	1.17	58.18	14	3	5	2	Female	Yes	No	No	university	Yes	No	29
3	37.86	15.67	1.21	71.48	12	9	6	2	Female	No	Yes	No	university	Yes	No	33
4	37.82	18.45	1.56	46.05	9	3	5	10	Female	Yes	Yes	No	baccalaureate	No	No	45

sns.pairplot(health_clean,
             x_vars=['no2','pm25', 'black carbon'],   # env factors
             y_vars=['performance', 'occurrence_mental', 'wellbeing', 'stress', 'sleep quality'],
            hue='gender')  # wellbeing measurements
plt.savefig('outputs/pollutants_vs_health')

p=sns.jointplot(data = health_clean, x = 'no2', y = 'performance',kind = 'kde', hue='gender')
p.refline(x=health_clean['no2'].mean(), y=health_clean['performance'].mean(), color='r')
p.fig.suptitle("NO2 Levels vs. Performance in the Stroop Test")
p.fig.subplots_adjust(top=0.93)

plt.savefig('outputs/no2_vs_performance')

g=sns.jointplot(data = health_clean, x = 'pm25', y = 'performance', kind = 'kde', hue='gender')
g.refline(x=health_clean['pm25'].mean(), y=health_clean['performance'].mean(), color='r')
g.fig.suptitle("PM2.5 Levels vs. Performance in the Stroop Test")
g.fig.subplots_adjust(top=0.95)

plt.savefig('outputs/pm25_vs_performance')

h=sns.jointplot(data = health_clean, x = 'black carbon', y = 'performance', kind = 'kde', hue='gender')
h.refline(x=health_clean['black carbon'].mean(), y=health_clean['performance'].mean(), color='r')
h.fig.suptitle("Black Carbon Levels vs. Performance in the Stroop Test")
h.fig.subplots_adjust(top=0.95)

plt.savefig('outputs/blackcarbon_vs_performance')