File size: 2,232 Bytes
c044378 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import streamlit as st
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
# Sample data (replace this with your actual data)
df = pd.read_csv('Time-Wasters on Social Media.csv')
# Set up Streamlit layout
st.title("Country-wise Addiction Levels by Demographics")
# Country Selection Dropdown
countries = df['Location'].unique()
selected_country = st.selectbox("Select Country", countries)
# Gender Selection
genders = df['Gender'].unique()
selected_gender = st.selectbox("Select Gender", genders)
# Age Slider (assuming 'Age' column is in the dataset)
min_age, max_age = int(df['Age'].min()), int(df['Age'].max())
selected_age_range = st.slider("Select Age Range", min_age, max_age, (min_age, max_age))
# Platform Selection
platforms = df['Platform'].unique()
selected_platform = st.selectbox("Select Platform", platforms)
# Filter Data based on user selections
filtered_data = df[
(df['Location'] == selected_country) &
(df['Gender'] == selected_gender) &
(df['Age'] >= selected_age_range[0]) &
(df['Age'] <= selected_age_range[1]) &
(df['Platform'] == selected_platform)
]
# Show filtered data as a table (optional, for debugging purposes)
# st.write(filtered_data)
# Calculate average addiction level for the filtered data
avg_addiction_level = filtered_data['Addiction Level'].mean()
# Display the result
st.subheader(f"Average Addiction Level in {selected_country} for {selected_gender} users aged {selected_age_range[0]} to {selected_age_range[1]} on {selected_platform}:")
st.write(f"*{avg_addiction_level:.2f}*")
# Visualize the Addiction Level (you can use any type of plot, here we use a bar chart)
st.subheader("Addiction Level Distribution")
plt.figure(figsize=(10, 6))
sns.histplot(data=filtered_data, x='Addiction Level', kde=True, bins=10, color='teal')
plt.title(f"Addiction Level Distribution in {selected_country} for {selected_gender} users")
plt.xlabel('Addiction Level')
plt.ylabel('Frequency')
# Display the plot in Streamlit
st.pyplot(plt)
# Show a breakdown of the most common watch reasons for the filtered data
watch_reason_counts = filtered_data['Watch Reason'].value_counts()
st.subheader("Most Common Watch Reasons")
st.write(watch_reason_counts) |