Code Editor for app.py

import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import numpy as np
import pandas as pd

st.title("Plotly Charts Demo")

# Create sample data
np.random.seed(42)
dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
values = np.random.normal(loc=100, scale=15, size=len(dates))
df = pd.DataFrame({
    'date': dates,
    'value': values
})

# Add some categorical data
categories = ['A', 'B', 'C', 'D']
df['category'] = np.random.choice(categories, size=len(df))

# Sidebar for chart selection
chart_type = st.sidebar.selectbox(
    "Select Chart Type",
    ["Line Plot", "Scatter Plot", "Bar Chart", "Box Plot", "Histogram"]
)

# Display different charts based on selection
if chart_type == "Line Plot":
    st.subheader("Line Plot")
    fig = px.line(df, x='date', y='value',
                  title='Time Series Data')
    st.plotly_chart(fig, use_container_width=True)

elif chart_type == "Scatter Plot":
    st.subheader("Scatter Plot")
    fig = px.scatter(df, x='date', y='value', color='category',
                     title='Scatter Plot with Categories')
    st.plotly_chart(fig, use_container_width=True)

elif chart_type == "Bar Chart":
    st.subheader("Bar Chart")
    monthly_avg = df.groupby([df['date'].dt.month, 'category'])['value'].mean().reset_index()
    monthly_avg['month'] = monthly_avg['date'].apply(lambda x: pd.datetime(2023, x, 1).strftime('%B'))
    fig = px.bar(monthly_avg, x='month', y='value', color='category',
                 title='Monthly Average by Category',
                 barmode='group')
    st.plotly_chart(fig, use_container_width=True)

elif chart_type == "Box Plot":
    st.subheader("Box Plot")
    fig = px.box(df, x='category', y='value',
                 title='Value Distribution by Category')
    st.plotly_chart(fig, use_container_width=True)

else:  # Histogram
    st.subheader("Histogram")
    fig = px.histogram(df, x='value', color='category',
                      title='Value Distribution',
                      marginal='box')
    st.plotly_chart(fig, use_container_width=True)

# Add interactive features
st.sidebar.markdown("---")
st.sidebar.subheader("Chart Customization")

if st.sidebar.checkbox("Show Raw Data"):
    st.subheader("Raw Data")
    st.dataframe(df)

# Add 3D Scatter plot as a bonus
if st.sidebar.checkbox("Show 3D Scatter Plot"):
    st.subheader("3D Scatter Plot")
    df['extra_dim'] = np.random.normal(loc=50, scale=10, size=len(df))
    fig = px.scatter_3d(df, x='date', y='value', z='extra_dim',
                        color='category',
                        title='3D Scatter Plot')
    st.plotly_chart(fig, use_container_width=True)