app.py

import io
import numpy as np
import pandas as pd
import streamlit as st
import ydata_profiling
from streamlit_pandas_profiling import st_profile_report


# Web App Title
st.markdown('''
# **The EDA App**

This is the **EDA App** created in Streamlit using the **pandas-profiling** library.

---
''')

# Upload CSV data
with st.sidebar.header('1. Upload your CSV data'):
    uploaded_file = st.sidebar.file_uploader("Upload your input CSV file", type=["csv"])
    st.sidebar.markdown("""
[Example CSV input file](https://raw.githubusercontent.com/dataprofessor/data/master/delaney_solubility_with_descriptors.csv)
""")

# Pandas Profiling Report
if uploaded_file is not None:
    @st.cache
    def load_csv():
        csv = pd.read_csv(uploaded_file,sep=";",encoding = 'unicode_escape')
        return csv
    df = load_csv()

    # For showing dataframe info
    buffer = io.StringIO()
    df.info(verbose=True,buf=buffer)
    s = buffer.getvalue() 
    with st.expander("See dataset info"):
        st.text(s)

    pr = ProfileReport(df, explorative=True)
    st.header('**Input DataFrame**')
    st.write(df)
    st.write('---')
    st.header('**Pandas Profiling Report**')
    st_profile_report(pr)
else:
    st.info('Awaiting for CSV file to be uploaded.')
    if st.button('Press to use Example Dataset'):
        # Example data
        @st.cache
        def load_data():
            a = pd.DataFrame(
                np.random.rand(100, 5),
                columns=['a', 'b', 'c', 'd', 'e']
            )
            return a
        df = load_data()
        # For showing dataframe info
        buffer = io.StringIO()
        df.info(verbose=True,buf=buffer)
        s = buffer.getvalue() 
        with st.expander("See dataset info"):
            st.text(s)

        pr = ProfileReport(df, explorative=True)
        st.header('**Input DataFrame**')
        st.write(df)
        st.write('---')
        st.header('**Pandas Profiling Report**')
        st_profile_report(pr)
st.markdown('''
    **Credit:** App built in **Python** + **Streamlit** adapted by [Roberto](https://github.com/Rchatru). Original idea ([Data Professor](http://youtube.com/dataprofessor))
''')