eda_app.py

import streamlit as st

import pandas as pd

import matplotlib.pyplot as plt
import matplotlib
matplotlib.use("Agg")
import seaborn as sns
import plotly.express as px


@st.cache_data
def load_data(data):
    df=pd.read_csv(data)
    return df


def run_eda_app():
    st.subheader("Exploratory Data Analysis")
    #df=pd.read_csv("data/diabetes_data_upload.csv")
    df=load_data("data/diabetes_data_upload.csv")
    df_encoded=load_data("data/diabetes_data_upload_clean.csv")
    freq_df=load_data("data/freqdist_of_age_data.csv")

    submenu=st.sidebar.selectbox("Submenu",["Descriptive","Plots"])
    
    if submenu =="Descriptive":
        st.dataframe(df)

        with st.expander("Data Types", icon="😊"):
            st.dataframe(df.dtypes)

        with st.expander("Descriptive Summary", icon="🚨"):
            st.dataframe(df_encoded.describe())

        with st.expander("Class Distribution", icon="🔥"):
            st.dataframe(df["class"].value_counts())

        with st.expander("Gender Distribution", icon="👻"):
            st.dataframe(df["Gender"].value_counts())

    elif submenu=="Plots":
        st.subheader("Plots")
        # Layouts
        col1, col2= st.columns([2,1])
        with col1:
            with st.expander("Dist Plot of Gender"):
                # #Using Seaborn
                # fig=plt.figure()
                # sns.countplot(df["Gender"])
                # st.pyplot(fig)

                gen_df=df["Gender"].value_counts().to_frame()
                gen_df=gen_df.reset_index()
                gen_df.columns=["Gender Type", "Counts"]
                #st.dataframe(gen_df)

                pl=px.pie(gen_df, names="Gender Type", values="Counts") 
                st.plotly_chart(pl, use_container_width=True)

            with st.expander("Dist Plot of Class"):
                fig=plt.figure()
                sns.countplot(df["class"])
                st.pyplot(fig)
                
        with col2:  
            with st.expander("Gender Distribution"):
                 st.dataframe(gen_df)

            with st.expander("Class Distribution"):
                st.dataframe(df["class"].value_counts())

        with st.expander("Frequency Dist of Age"):
            st.dataframe(freq_df)
            p2=px.bar(freq_df, x="Age", y="count")
            st.plotly_chart(p2, use_container_width=True)

        with st.expander("Outlier Detection Plot"):
            fig=plt.figure()
            sns.boxplot(df["Age"])
            st.pyplot(fig)
            st.info("There are some outliers in the Age Feature", icon="ℹ️")
            
            p3=px.box(df, x="Age", color="Gender")
            st.plotly_chart(p3, use_container_width=True)

        with st.expander("Correlation Plot"):
            corr_matrix=df_encoded.corr()
            fig=plt.figure(figsize=(20,10))
            sns.heatmap(corr_matrix, annot=True)
            st.pyplot(fig)
            p4=px.imshow(corr_matrix)
            st.plotly_chart(p4, use_container_width=True)