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streamlit_app.py
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streamlit_app.py
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import pandas as pd
import streamlit as st
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
def services_piechart():
#plotly.express pie chart
# Calculate the count of each unique value in the column
value_counts = df['Primary streaming service'].value_counts()
# Create a Pie chart using Plotly Express
fig = px.pie(names=value_counts.index, values=value_counts.values, title='Primary Streaming Services')
fig.update_layout(
title=dict( font=dict(size=24)), autosize=True)
st.plotly_chart(fig, use_container_width=True)
def music_effects_piechart():
# Calculate the count of each unique value in the column
value_counts = df['Music effects'].value_counts()
# Create a Pie chart using Plotly Express
fig = px.pie(names=value_counts.index, values=value_counts.values, title='Outcome of listening to music')
fig.update_layout(
title=dict( font=dict(size=24)), autosize=True)
st.plotly_chart(fig, use_container_width=True)
def mh_issues_boxplot():
df_long = pd.melt(df, id_vars=['Age'], value_vars=['Anxiety', 'Depression', 'Insomnia', 'OCD'],
var_name='Condition', value_name='Level')
fig = px.box(df_long, x='Condition', y='Level', title='Distribution of Conditions by Level')
fig.update_layout(autosize=True, title=dict(font=dict(size=28)) )
st.plotly_chart(fig)
def effects_by_issue():
fig = make_subplots(rows=2, cols=2, start_cell="bottom-left", shared_xaxes=True,
subplot_titles=['Depression', 'Anxiety','Insomnia', 'OCD'])
filtered_df = df[(df['Depression'] > 7)]
# Count the occurrences of each level of improvement for the filtered data
improvement_counts = filtered_df['Music effects'].value_counts()
# Create a bar chart
fig.add_trace(go.Bar(x=improvement_counts.index,y=improvement_counts.values),
row=1, col=1)
filtered_df = df[(df['Anxiety'] > 7)]
# Count the occurrences of each level of improvement for the filtered data
improvement_counts = filtered_df['Music effects'].value_counts()
fig.add_trace(go.Bar( x=improvement_counts.index,y=improvement_counts.values),
row=1, col=2)
filtered_df = df[(df['Insomnia'] > 7)]
# Count the occurrences of each level of improvement for the filtered data
improvement_counts = filtered_df['Music effects'].value_counts()
fig.add_trace(go.Bar( x=improvement_counts.index,y=improvement_counts.values),
row=2, col=1)
filtered_df = df[(df['OCD'] > 7)]
# Count the occurrences of each level of improvement for the filtered data
improvement_counts = filtered_df['Music effects'].value_counts()
fig.add_trace(go.Bar( x=improvement_counts.index,y=improvement_counts.values),
row=2, col=2)
fig.update_traces(showlegend=False)
fig.update_layout(height=600, width=800,
title_text="Music Effects for different Mental Health Issues", title=dict(font=dict(size=28)))
# Update y-axis range for both rows
for row in [1, 2]:
for col in [1, 2]:
fig.update_yaxes(range=[0, 200], row=row, col=col)
st.plotly_chart(fig)
def services_piechart_by_age(age_filter):
if age_filter == '10-20':
age_data = df[(df['Age'] >= 10) & (df['Age'] <= 20)]
if age_filter == '21-30':
age_data = df[(df['Age'] >= 21) & (df['Age'] <= 30)]
if age_filter == '31-40':
age_data = df[(df['Age'] >= 31) & (df['Age'] <= 40)]
if age_filter == '41-50':
age_data = df[(df['Age'] >= 41) & (df['Age'] <= 50)]
value_counts = age_data['Primary streaming service'].value_counts()
fig = px.pie(names=value_counts.index, values=value_counts.values, title='Primary Streaming Services')
fig.update_layout(title=dict( font=dict(size=28)))
st.plotly_chart(fig)
def age_cond_barchart(cond_filter):
avg_condition = df.groupby('Age')[cond_filter].mean().reset_index()
fig = px.bar(avg_condition, x='Age', y=cond_filter, labels={'Age': 'Age', cond_filter: 'Average ' + cond_filter},
title="Mental Health Issue by Age")
fig.update_layout(autosize=True, title=dict(font=dict(size=28)) )
st.plotly_chart(fig)
def music_info():
st.subheader("Music Information")
col1, col2 = st.columns(2)
with col1:
music_effects_piechart()
with col2:
services_piechart()
def mh_info():
st.subheader("Mental Health Information")
mh_issues_boxplot()
effects_by_issue()
def interactive_info():
st.subheader("Interactive Information")
age_filter = st.selectbox(label='Choose an age group to view preferences',
options=('10-20', '21-30', '31-40', '41-50'), label_visibility='collapsed')
services_piechart_by_age(age_filter)
cond_filter = st.radio(label='Choose which mental health issue you want to view',
options=('Depression', 'Anxiety', 'Insomnia', 'OCD'), label_visibility='collapsed')
age_cond_barchart(cond_filter)
@st.cache_data
def get_data():
df = pd.read_csv('data/mxmh_survey_results.csv')
df = df[df['Age'] <= 50]
return df
# start of main page layout
st.set_page_config(page_title = 'Music and Mental Health Dashboard',
layout='wide',
initial_sidebar_state="expanded"
)
st.title("Music & Mental Health Dashboard")
df = get_data()
with st.sidebar:
st.subheader("Menu")
selected = st.selectbox(' ',
['Home',
'Music Information',
'Mental Health Information',
'Interactive Information']
)
if selected == 'Home':
st.subheader('Data file')
st.write(df)
elif selected == 'Music Information':
music_info()
elif selected == 'Mental Health Information':
mh_info()
elif selected == 'Interactive Information':
interactive_info()