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
from plotly.subplots import make_subplots

st.set_page_config(layout="wide")
st.title("Advanced Plotly Visualizations Showcase")

# Generate sample data
np.random.seed(42)
dates = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')
values = np.random.normal(0, 1, size=len(dates)).cumsum()
df = pd.DataFrame({
    'date': dates,
    'value': values,
    'category': np.random.choice(['A', 'B', 'C'], size=len(dates)),
    'size': np.random.randint(10, 100, size=len(dates))
})

# 1. 3D Surface Plot
st.header("1. 3D Surface Plot")
X = np.linspace(-5, 5, 100)
Y = np.linspace(-5, 5, 100)
X, Y = np.meshgrid(X, Y)
Z = np.sin(np.sqrt(X**2 + Y**2))

fig_surface = go.Figure(data=[go.Surface(z=Z, x=X, y=Y)])
fig_surface.update_layout(
    title='3D Surface Plot of sin(sqrt(x² + y²))',
    autosize=False,
    width=800,
    height=600,
    scene=dict(
        camera=dict(
            up=dict(x=0, y=0, z=1),
            center=dict(x=0, y=0, z=0),
            eye=dict(x=1.5, y=1.5, z=1.5)
        )
    )
)
st.plotly_chart(fig_surface)

# 2. Animated Bubble Chart
st.header("2. Animated Bubble Chart")
bubble_df = px.data.gapminder()
fig_bubble = px.scatter(
    bubble_df,
    x="gdpPercap",
    y="lifeExp",
    animation_frame="year",
    animation_group="country",
    size="pop",
    color="continent",
    hover_name="country",
    log_x=True,
    size_max=55,
    range_x=[100, 100000],
    range_y=[25, 90],
    title="GDP per Capita vs Life Expectancy (1952-2007)"
)
st.plotly_chart(fig_bubble)

# 3. Sunburst Chart
st.header("3. Sunburst Chart")
fig_sunburst = px.sunburst(
    px.data.tips(),
    path=['day', 'time', 'sex'],
    values='total_bill',
    title="Restaurant Tips Analysis"
)
st.plotly_chart(fig_sunburst)

# 4. Candlestick Chart with Range Slider
st.header("4. Candlestick Chart with Range Slider")
df_stocks = pd.DataFrame({
    'Date': pd.date_range(start='2023-01-01', end='2023-12-31', freq='D'),
    'Open': np.random.normal(100, 2, 365).cumsum(),
    'High': None,
    'Low': None,
    'Close': None
})
df_stocks['High'] = df_stocks['Open'] + np.random.uniform(0, 5, 365)
df_stocks['Low'] = df_stocks['Open'] - np.random.uniform(0, 5, 365)
df_stocks['Close'] = df_stocks['Open'] + np.random.normal(0, 2, 365)

fig_candlestick = go.Figure(data=[go.Candlestick(
    x=df_stocks['Date'],
    open=df_stocks['Open'],
    high=df_stocks['High'],
    low=df_stocks['Low'],
    close=df_stocks['Close']
)])
fig_candlestick.update_layout(
    title='Stock Price Movement',
    yaxis_title='Stock Price',
    xaxis_title='Date',
    xaxis_rangeslider_visible=True
)
st.plotly_chart(fig_candlestick)

# 5. Polar Plot
st.header("5. Polar Plot")
theta = np.linspace(0, 2*np.pi, 100)
r = 1 + np.sin(4*theta)
fig_polar = go.Figure(go.Scatterpolar(
    r=r,
    theta=theta,
    mode='lines',
    line_color='purple',
    fill='toself'
))
fig_polar.update_layout(
    title="Mathematical Rose Pattern",
    showlegend=False
)
st.plotly_chart(fig_polar)

# 6. Statistical Distribution Plot
st.header("6. Statistical Distribution Plot")
dist_data = np.random.normal(0, 1, 1000)
fig_dist = go.Figure()
fig_dist.add_trace(go.Histogram(
    x=dist_data,
    histnorm='probability density',
    name='Histogram'
))
fig_dist.add_trace(go.Scatter(
    x=np.linspace(-4, 4, 100),
    y=1/(np.sqrt(2*np.pi)) * np.exp(-np.linspace(-4, 4, 100)**2/2),
    mode='lines',
    name='Normal Distribution PDF'
))
fig_dist.update_layout(
    title='Normal Distribution with Theoretical PDF',
    xaxis_title='Value',
    yaxis_title='Density'
)
st.plotly_chart(fig_dist)

# 7. 3D Scatter Plot with Categories
st.header("7. 3D Scatter Plot with Categories")
n_points = 1000
df_3d = pd.DataFrame({
    'x': np.random.normal(0, 1, n_points),
    'y': np.random.normal(0, 1, n_points),
    'z': np.random.normal(0, 1, n_points),
    'category': np.random.choice(['A', 'B', 'C'], size=n_points),
    'size': np.random.uniform(5, 20, n_points)
})

fig_3d = px.scatter_3d(
    df_3d,
    x='x',
    y='y',
    z='z',
    color='category',
    size='size',
    title='3D Scatter Plot with Categories'
)
st.plotly_chart(fig_3d)

# 8. Heatmap with Annotations
st.header("8. Heatmap with Annotations")
matrix = np.random.randn(10, 10)
fig_heatmap = px.imshow(
    matrix,
    labels=dict(x="X Axis", y="Y Axis", color="Value"),
    title="Correlation Heatmap"
)
fig_heatmap.update_traces(text=np.round(matrix, 2), texttemplate="%{text}")
st.plotly_chart(fig_heatmap)