Cov Probiotika

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from pandas.api.types import CategoricalDtype

df = pd.read_excel('data_in.xlsx')

# Erase empty hospitalisation_days values and deseased values

sel = df.hospitalisation_days.isnull()
df_sel = df[df["hospitalisation_days"]!=sel]

df_sel2 = df_sel[df_sel['Deceased']!="yes"]
df_sel2 = df_sel2.dropna(subset = "hospitalisation_days", axis = 0)
df_sel2.columns



    
    

# age group differentiation into three categories
df_sel2["age_group"] = 0
 
df_sel2['age_group'] = pd.cut(df_sel2["age"], bins = 3, right = True, labels = ["<53", "53 - 70", '>70'])

print(df_sel2.groupby(["age_group"])["age"].agg(["min", "max", "count"]))

sns.swarmplot(data=df_sel2, x="hospitalisation_days", y="age_group", hue='bmi_group', alpha = 1, marker = 'o')

print(df_sel2[['age_group', 'suplemented', 'hospitalisation_days', 'bmi_group']].head())

pivot_table = pd.pivot_table(df_sel2, values=['hospitalisation_days'], index=['age_group', 'suplemented', 'bmi_group'], columns=[], aggfunc=np.mean)
print(pivot_table)

import seaborn as sns

sns.boxplot(data=df_sel2, x="age_group", y="hospitalisation_days", hue="bmi_group", palette="Set3")
sns.swarmplot(data=df_sel2, x="age_group", y="hospitalisation_days", hue="suplemented", dodge=True)

sns.boxplot(data=df_sel2, y="hospitalisation_days", x="suplemented", hue='age_group')

application = pd.read_excel("data.xlsx", header=1)
application

import seaborn as sns
import matplotlib.pyplot as plt

meanprops = {"marker": "o", "markerfacecolor": "white", "markeredgecolor": "black"}

sns.boxplot(data=application[["Day_7", "Day_14", "Day_21"]], width=0.5, showmeans=True, meanprops=meanprops, medianprops={'visible': False})
plt.ylabel("Score")
plt.title("Visual score of skin defects") # Changed plt.name to plt.title
plt.show()

sns.swarmplot(data=application[["Start", "Day_7", "Day_14", "Day_21"]])
plt.ylabel("Score")
plt.title("Visual score of skin defects") # Changed plt.name to plt.title
plt.show()