TenTenDistrib.py

import pandas as pd
import streamlit as st

# Load the XLSX file using pandas
data = pd.read_excel('your_file.xlsx')

# Perform EDA on the data
# ...

# Function to create the top ten ordered distribution
def create_top_ten_distribution(data, column):
    # Count the unique values and blanks in the specified column
    value_counts = data[column].value_counts(dropna=False)
    
    # Create a DataFrame with the top ten values and their counts
    top_ten_df = pd.DataFrame({'Value': value_counts.index, 'Count': value_counts.values})
    top_ten_df['Rank'] = range(1, len(top_ten_df) + 1)
    
    return top_ten_df.head(10)

# Function to display the filtered dataframe based on rank
def display_filtered_dataframe(data, column, rank):
    # Get the value corresponding to the specified rank
    value = top_ten_df[top_ten_df['Rank'] == rank]['Value'].values[0]
    
    # Filter the dataframe based on the value
    filtered_df = data[data[column] == value]
    
    return filtered_df

# Streamlit app
def main():
    st.title('Top Ten Distribution and Filtered Dataframe')
    
    # Specify the column for creating the top ten distribution
    column = 'your_column_name'
    
    # Create the top ten ordered distribution
    top_ten_df = create_top_ten_distribution(data, column)
    
    # Display the top ten distribution as a markdown table
    st.markdown('### Top Ten Distribution')
    st.markdown(top_ten_df.to_markdown(index=False))
    
    # Get the user input for the rank
    rank = st.number_input('Enter the rank to filter the dataframe', min_value=1, max_value=10, value=1, step=1)
    
    # Display the filtered dataframe based on the rank
    filtered_df = display_filtered_dataframe(data, column, rank)
    st.markdown(f'### Filtered Dataframe (Rank: {rank})')
    st.dataframe(filtered_df)

if __name__ == '__main__':
    main()