Code Editor for app.py

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

st.set_page_config(page_title="Data Explorer", layout="wide")

@st.cache_data
def load_data(url):
    """Load and cache data from URL"""
    try:
        df = pd.read_csv(url)
        return df, None
    except Exception as e:
        return None, str(e)

def determine_column_type(series):
    """Determine if a column is numeric or categorical"""
    if pd.api.types.is_numeric_dtype(series):
        return 'numeric'
    else:
        return 'categorical'

def create_visualization(df, var1, var2=None):
    """Create appropriate visualization based on variable types"""
    if var2 is None:
        # Single variable visualization
        var1_type = determine_column_type(df[var1])
        
        if var1_type == 'numeric':
            # Histogram for numeric variables
            fig = px.histogram(df, x=var1, title=f'Distribution of {var1}')
            fig.update_layout(showlegend=False)
        else:
            # Bar chart for categorical variables
            value_counts = df[var1].value_counts()
            fig = px.bar(x=value_counts.index, y=value_counts.values, 
                        title=f'Count of {var1}')
            fig.update_xaxes(title=var1)
            fig.update_yaxes(title='Count')
            
    else:
        # Two variable visualization
        var1_type = determine_column_type(df[var1])
        var2_type = determine_column_type(df[var2])
        
        if var1_type == 'numeric' and var2_type == 'numeric':
            # Scatter plot for two numeric variables
            fig = px.scatter(df, x=var1, y=var2, 
                           title=f'{var1} vs {var2}')
        elif var1_type == 'categorical' and var2_type == 'numeric':
            # Box plot for categorical vs numeric
            fig = px.box(df, x=var1, y=var2, 
                        title=f'{var2} by {var1}')
        elif var1_type == 'numeric' and var2_type == 'categorical':
            # Box plot for numeric vs categorical (swap axes)
            fig = px.box(df, x=var2, y=var1, 
                        title=f'{var1} by {var2}')
        else:
            # Stacked bar chart for two categorical variables
            crosstab = pd.crosstab(df[var1], df[var2])
            fig = px.bar(crosstab, title=f'{var1} vs {var2}')
            fig.update_xaxes(title=var1)
            fig.update_yaxes(title='Count')
    
    return fig

# Main app
st.title("📊 Data Explorer")
st.markdown("Load CSV data from a URL and explore it with interactive visualizations")

# URL input
url = st.text_input(
    "Enter CSV URL:", 
    value="https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv",
    help="Enter the URL of a CSV file to load and explore"
)

if url:
    # Load data
    with st.spinner("Loading data..."):
        df, error = load_data(url)
    
    if error:
        st.error(f"Error loading data: {error}")
    elif df is not None:
        st.success(f"Data loaded successfully! Shape: {df.shape}")
        
        # Display basic info about the dataset
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Rows", df.shape[0])
        with col2:
            st.metric("Columns", df.shape[1])
        with col3:
            st.metric("Missing Values", df.isnull().sum().sum())
        
        # Controls
        st.markdown("---")
        col1, col2 = st.columns(2)
        
        with col1:
            num_rows = st.slider(
                "Number of rows to display:", 
                min_value=1, 
                max_value=len(df), 
                value=min(100, len(df))
            )
        
        with col2:
            shuffle = st.checkbox("Randomly shuffle DataFrame")
        
        # Process data based on controls
        display_df = df.copy()
        if shuffle:
            display_df = display_df.sample(frac=1).reset_index(drop=True)
        display_df = display_df.head(num_rows)
        
        # Display DataFrame
        st.markdown("### 📋 Data Preview")
        st.dataframe(display_df, use_container_width=True)
        
        # Variable selection for visualization
        st.markdown("---")
        st.markdown("### 📈 Data Visualization")
        
        # Get column names
        columns = list(df.columns)
        
        col1, col2 = st.columns(2)
        with col1:
            var1 = st.selectbox("Select first variable:", columns)
        with col2:
            var2 = st.selectbox("Select second variable (optional):", [None] + columns)
        
        if var1:
            # Create and display visualization
            try:
                fig = create_visualization(df, var1, var2)
                st.plotly_chart(fig, use_container_width=True)
                
                # Show some statistics
                st.markdown("### 📊 Variable Statistics")
                if var2 is None:
                    # Single variable stats
                    if determine_column_type(df[var1]) == 'numeric':
                        stats = df[var1].describe()
                        st.write(f"**{var1} Statistics:**")
                        st.write(stats)
                    else:
                        value_counts = df[var1].value_counts()
                        st.write(f"**{var1} Value Counts:**")
                        st.write(value_counts)
                else:
                    # Two variable stats
                    col1, col2 = st.columns(2)
                    with col1:
                        if determine_column_type(df[var1]) == 'numeric':
                            st.write(f"**{var1} Statistics:**")
                            st.write(df[var1].describe())
                        else:
                            st.write(f"**{var1} Value Counts:**")
                            st.write(df[var1].value_counts())
                    
                    with col2:
                        if determine_column_type(df[var2]) == 'numeric':
                            st.write(f"**{var2} Statistics:**")
                            st.write(df[var2].describe())
                        else:
                            st.write(f"**{var2} Value Counts:**")
                            st.write(df[var2].value_counts())
                    
                    # Correlation if both numeric
                    if (determine_column_type(df[var1]) == 'numeric' and 
                        determine_column_type(df[var2]) == 'numeric'):
                        correlation = df[var1].corr(df[var2])
                        st.metric("Correlation", f"{correlation:.3f}")
                        
            except Exception as e:
                st.error(f"Error creating visualization: {e}")
        
        # Data info section
        with st.expander("📋 Dataset Information"):
            st.write("**Column Information:**")
            info_df = pd.DataFrame({
                'Column': df.columns,
                'Type': [str(dtype) for dtype in df.dtypes],
                'Non-Null Count': [df[col].count() for col in df.columns],
                'Null Count': [df[col].isnull().sum() for col in df.columns]
            })
            st.dataframe(info_df, use_container_width=True)
            
            st.write("**Sample Data:**")
            st.write(df.head())