app.py

import streamlit as st
import pandas as pd
from datetime import date
import altair as alt

today = date.today()
global df 


#streamlit config
st.set_page_config(page_title="Explore Data", page_icon="🔬", layout="wide")

# ---- HIDE STREAMLIT STYLE ----
hide_st_style = """<style>footer {visibility: hidden;}</style>"""
st.markdown(hide_st_style, unsafe_allow_html=True)
st.markdown("<h1 style='text-align: center; color: yellowgreen; padding:10px;'> Explore Data 🔬</h1>", unsafe_allow_html=True)
st.write("<hr><br>", unsafe_allow_html=True)

#setup file upload
file_upload = st.file_uploader(label="Upload data. (1GB Max)", type=['csv', 'xlsx',])

#df not defined fix

if file_upload is not None:
   
    print(f"\n{file_upload}")
    print("\n------------File uploaded!------------")
    try:
        df = pd.read_csv(file_upload)
    except Exception as e:
        print(e)
        df = pd.read_excel(file_upload)
try:

    if file_upload is not None:
        file_name = file_upload.name
        st.markdown("<br><br>",unsafe_allow_html=True)
        st.markdown(f"<h2 style='text-align:center;'>Data Preview: <span style='color:yellowgreen;'>{file_name}</span></h2>", unsafe_allow_html=True)
        

        
        st.subheader("Head")
        st.table(df.head(5))
        st.subheader("Tail")
        st.table(df.tail(5))
        #Dataset MetaData
        st.write("<hr><br>", unsafe_allow_html=True)
        st.subheader("Dataset MetaData")
        dataset_meta_data = {'Headers':[len(df.columns)],'Rows':[f'{df.shape[0]:,}'],'Total Data Points':[f'{sum(df.count()):,}'], 'DF Dimensions':[f'{df.ndim:,}'],\
            'Dataset Size (MB)':[f'{file_upload.size/1000000:,}']}
        df_meta_data = pd.DataFrame(data=dataset_meta_data)
        st.table(df_meta_data)
        
        
        #Header display and count
        st.write("<hr><br>", unsafe_allow_html=True)
        st.subheader(f'\nHeaders: {len(df.columns)}\n')
        st.table(df.count())
        
        
        #dypes
        st.write("<hr><br>", unsafe_allow_html=True)
        st.subheader("Header Data Types")
        st.table(df.dtypes.astype(str))


        #data summary
        st.write("<hr><br>", unsafe_allow_html=True)
        st.subheader(f'\nDataset Summary\n')
        st.table(df.describe())

        
        #missing values
        st.write("<hr><br>", unsafe_allow_html=True)
        st.subheader(f'\nMissing Values\n')
        st.table(df.isna().sum())

        #duplicate rows
        st.write("<hr><br>", unsafe_allow_html=True)

        st.subheader(f"Duplicate Rows")
        st.table(df[df.duplicated()])
        st.write(len(df.duplicated))
    
        
        #visualize    
        #control_field = st.multiselect("Select A Field for a summary, values must be numerical", df.columns)
        #st.table(df[control_field].describe())
        #st.bar_chart(df[target_field].value_counts())
        

except Exception as e:
    print(e)