pages/2_Simple_EDA.py

import streamlit as st
import pandas as pd
import io

st.markdown("""
    <div style="text-align: center; margin-bottom: 20px;">
        <h2 style="color: #c71585; font-size: 36px;">Simple EDA: Understanding Your Data🔍</h1>
        <h3 style="color: #4F4F4F; font-size: 20px;">
            This helps us understand the quality of the data and see how the data looks.
        </h3>
    </div>
""", unsafe_allow_html=True)

if "df" in st.session_state and st.session_state.df is not None:
    df = st.session_state.df

    # Dataset Preview
    st.markdown("<h3 style='color: #2a52be;'>Dataset Preview📌</h3>", unsafe_allow_html=True)
    st.dataframe(df.head())
    
    # Shape of the Data
    st.markdown("<h3 style='color: #843f5b;'>Dataset Shape</h3>", unsafe_allow_html=True)
    st.write(f"🔹 The dataset contains **{df.shape[0]} rows** and **{df.shape[1]} columns**.")
    
    # Column Names & Data Types
    st.markdown("<h3 style='color: #e25822;'>Column Names & Data Types</h3>", unsafe_allow_html=True)
    st.write(df.dtypes)

    # 📝 Dataset Information (Equivalent to df.info())
    st.markdown("<h3 style='color: #9400d3;'>Dataset Information📝</h3>", unsafe_allow_html=True)
    
    buffer = io.StringIO()
    df.info(buf=buffer)
    info_str = buffer.getvalue()
    st.text(info_str)

    st.markdown(f"<pre style='background-color: #f8f8f8; padding: 10px; border-radius: 5px; font-size: 14px; font-family: monospace;'>{info_str}</pre>", unsafe_allow_html=True)


    # Numerical and categorical Columns
    st.markdown("<h3 style='color: #9400d3;'>Numerical and Categorical Columns</h3>", unsafe_allow_html=True)
    
    numerical_cols = df.select_dtypes(include=['int64', 'float64']).columns.tolist()
    categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
    
    st.write(f"🔹 **Numerical Columns ({len(numerical_cols)}):** {', '.join(numerical_cols) if numerical_cols else 'None'}")
    st.write(f"🔹 **Categorical Columns ({len(categorical_cols)}):** {', '.join(categorical_cols) if categorical_cols else 'None'}")

    # Unique Values in Categorical Columns
    st.markdown("<h3 style='color: #e25822;'>Unique Values in Categorical Columns</h3>", unsafe_allow_html=True)
    
    if categorical_cols:
        for col in categorical_cols:
            unique_count = df[col].nunique()
            st.write(f"**{col}:** {unique_count} unique values")
    else:
        st.info("No categorical columns detectedℹ️.")
    
    # Value Counts in Categorical Columns
    st.markdown("<h3 style='color: #9400d3;'>Value Counts in Categorical Columns</h3>", unsafe_allow_html=True)
    
    if categorical_cols:
        for col in categorical_cols:
            st.write(f"🔹 **{col} Value Distribution:**")
            st.write(df[col].value_counts().head(10))  # Show top 10 categories
    else:
        st.info("No categorical columns detectedℹ️.")


    # Summary Statistics
    st.markdown("<h3 style='color: #843f5b;'>Summary Statistics for Numerical Columns</h3>", unsafe_allow_html=True)
    
    st.write("🔹 **Basic statistical insights into the dataset:**")
    st.write(df.describe())

    st.markdown("<h3 style='color: #2a52be;'>Summary Statistics for Categorical Columns</h3>", unsafe_allow_html=True)
    
    if categorical_cols:
        st.write(df[categorical_cols].describe(include='object'))
    else:
        st.info("No categorical columns detectedℹ️.")
    
    # Checking for Missing Values
    st.markdown("<h3 style='color: #9400d3;'>Missing Values in the Dataset⚠️</h3>", unsafe_allow_html=True)
    missing_values = df.isnull().sum()
    
    if missing_values.sum() == 0:
        st.success("No missing values found!")
    else:
        st.warning(f"Found missing values in the dataset.")
        st.write("🔹 **Columns with Missing Values:**")
        st.write(missing_values[missing_values > 0])

    # Checking for Duplicate Records
    st.markdown("<h3 style='color: #2a52be;'>Duplicate Records</h3>", unsafe_allow_html=True)
    duplicate_count = df.duplicated().sum()
    
    if duplicate_count == 0:
        st.success("No duplicate records found!")
    else:
        st.warning(f"Found {duplicate_count} duplicate rows in the dataset.")
        st.write("🔹 **Example Duplicate Rows:**")
        st.dataframe(df[df.duplicated()].head())

    # 📊 Outlier Detection
    st.markdown("<h3 style='color: #e25822;'>Outlier Detection</h3>", unsafe_allow_html=True)
    
    if numerical_cols:
        outlier_info = {}
        
        for col in numerical_cols:
            Q1 = df[col].quantile(0.25)
            Q3 = df[col].quantile(0.75)
            IQR = Q3 - Q1
            lower_bound = Q1 - 1.5 * IQR
            upper_bound = Q3 + 1.5 * IQR
            outliers = ((df[col] < lower_bound) | (df[col] > upper_bound)).sum()
            
            if outliers > 0:
                outlier_info[col] = outliers
    
        if outlier_info:
            st.warning("Outliers detected:")
            for col, count in outlier_info.items():
                st.write(f"🔹 **{col}:** {count} outliers")
        else:
            st.success("No significant outliers detected!")
    else:
        st.info("No numerical columns detectedℹ️.")


else:
    st.warning("No dataset found! Please upload a dataset first⚠️.")