import pandas as pd
from pyodide.http import open_url
data=pd.read_csv(open_url("https://raw.githubusercontent.com/knavee12345/knavee12345/main/Certifications/depressed.csv"))
data=data.fillna(0)
depressed_data=data.query("(mental_health=='Depressed')")
Top 5 Depressed Ages:
j=0
for i in depressed_data.age.value_counts()[:5].index.values:
print(f"{i}-{round(depressed_data.age.value_counts()[:5].values[j]/len(depressed_data)*100)}%")
j+=1
Relationship Status
j=0
for i in depressed_data.relationship.value_counts().index.values:
print(f"{i}-{round(depressed_data.relationship.value_counts().values[j]/len(depressed_data)*100)}%")
j+=1
Depressed people's Children Count:
j=0
for i in depressed_data.number_children.value_counts()[:5].index.values:
print(f"{i}-{round(depressed_data.number_children.value_counts()[:5].values[j]/len(depressed_data)*100)}%")
j+=1
Educational Status:
j=0
for i in depressed_data.education_level.value_counts()[:5].index.values:
print(f"{i}-{round(depressed_data.education_level.value_counts()[:5].values[j]/len(depressed_data)*100)}%")
j+=1
j=0
for i in depressed_data.sex.value_counts().index.values:
i="Male" if i=="M" else "Female"
print(f"{i}-{round(depressed_data.sex.value_counts()[:5].values[j]/len(depressed_data)*100)}%")
j+=1
pyscript.write("dreport","Detailed Report")