Interactive_Analytics_Data_Automation/app.py at main · pradipece/Interactive_Analytics_Data_Automation · GitHub

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# Interactive Data Analytics Portal with Streamlit

# import libraries
import pandas as pd
import plotly.express as px
import streamlit as st


# Configuration the streamlit app (using the st.set_page_config)
st.set_page_config(
    page_title='Console flare Analytics Portal',
    page_icon='📊'
)

# title add (st.title) and Sub-title add (st.subheader) and (divider : use for the line colorful)
st.title(':rainbow[Data Analytics Portal]')
st.subheader(':gray[Explore Data with ease.]', divider='rainbow')

# File upload feature (st.file_uploader)
file = st.file_uploader('Drop csv or excel file', type=['csv', 'xlsx'])
if (file != None):
    if (file.name.endswith('csv')):
        data = pd.read_csv(file)
    else:
        data = pd.read_excel(file)

    st.dataframe(data)
    st.info('File is successfully Uploaded', icon='🚨')

    #Exploring basic information
    st.subheader(':rainbow[Basic information of the dataset]', divider='rainbow')
    tab1, tab2, tab3, tab4 = st.tabs(['Summary', 'Top and Bottom Rows', 'Data Types', 'Columns'])

    with tab1:
        st.write(f'There are {data.shape[0]} rows in dataset and  {data.shape[1]} columns in the dataset')
        st.subheader(':gray[Statistical summary of the dataset]')
        st.dataframe(data.describe())
    with tab2:
        st.subheader(':gray[Top Rows]')
        toprows = st.slider('Number of rows you want', 1, data.shape[0], key='topslider')
        st.dataframe(data.head(toprows))
        st.subheader(':gray[Bottom Rows]')
        bottomrows = st.slider('Number of rows you want', 1, data.shape[0], key='bottomslider')
        st.dataframe(data.tail(bottomrows))
    with tab3:
        st.subheader(':grey[Data types of column]')
        st.dataframe(data.dtypes)
    with tab4:
        st.subheader('Column Names in Dataset')
        st.write(list(data.columns))

    st.subheader(':rainbow[Column Values To Count]', divider='rainbow')
    with st.expander('Value Count'):
        col1, col2 = st.columns(2)
        with col1:
            column = st.selectbox('Choose Column name', options=list(data.columns))
        with col2:
            toprows = st.number_input('Top rows', min_value=1, step=1)

        count = st.button('Count')
        if (count == True):
            result = data[column].value_counts().reset_index().head(toprows)
            st.dataframe(result)
            st.subheader('Visualization', divider='gray')
            fig = px.bar(data_frame=result, x=column, y='count', text='count', template='plotly_white')
            st.plotly_chart(fig)
            fig = px.line(data_frame=result, x=column, y='count', text='count', template='plotly_white')
            st.plotly_chart(fig)
            fig = px.pie(data_frame=result, names=column, values='count')
            st.plotly_chart(fig)
    #Column value counts
    st.subheader(':rainbow[Groupby : Simplify your data analysis]', divider='rainbow')
    st.write('The groupby lets you summarize data by specific categories and groups')
    with st.expander('Group By your columns'):
        col1, col2, col3 = st.columns(3)
        with col1:
            groupby_cols = st.multiselect('Choose your column to groupby', options=list(data.columns))
        with col2:
            operation_col = st.selectbox('Choose column for operation', options=list(data.columns))
        with col3:
            operation = st.selectbox('Choose operation', options=['sum', 'max', 'min', 'mean', 'median', 'count'])

        if (groupby_cols):
            result = data.groupby(groupby_cols).agg(
                newcol=(operation_col, operation)
            ).reset_index()

            st.dataframe(result)
            #Grouping data for deeper insights
            st.subheader(':gray[Data Visualization]', divider='gray')
            graphs = st.selectbox('Choose your graphs', options=['line', 'bar', 'scatter', 'pie', 'sunburst'])
            if (graphs == 'line'):
                x_axis = st.selectbox('Choose X axis', options=list(result.columns))
                y_axis = st.selectbox('Choose Y axis', options=list(result.columns))
                color = st.selectbox('Color Information', options=[None] + list(result.columns))
                fig = px.line(data_frame=result, x=x_axis, y=y_axis, color=color, markers='o')
                st.plotly_chart(fig)
            elif (graphs == 'bar'):
                x_axis = st.selectbox('Choose X axis', options=list(result.columns))
                y_axis = st.selectbox('Choose Y axis', options=list(result.columns))
                color = st.selectbox('Color Information', options=[None] + list(result.columns))
                facet_col = st.selectbox('Column Information', options=[None] + list(result.columns))
                fig = px.bar(data_frame=result, x=x_axis, y=y_axis, color=color, facet_col=facet_col, barmode='group')
                st.plotly_chart(fig)
            elif (graphs == 'scatter'):
                x_axis = st.selectbox('Choose X axis', options=list(result.columns))
                y_axis = st.selectbox('Choose Y axis', options=list(result.columns))
                color = st.selectbox('Color Information', options=[None] + list(result.columns))
                size = st.selectbox('Size Column', options=[None] + list(result.columns))
                fig = px.scatter(data_frame=result, x=x_axis, y=y_axis, color=color, size=size)
                st.plotly_chart(fig)
            elif (graphs == 'pie'):
                values = st.selectbox('Choose Numerical Values', options=list(result.columns))
                names = st.selectbox('Choose labels', options=list(result.columns))
                fig = px.pie(data_frame=result, values=values, names=names)
                st.plotly_chart(fig)
            elif (graphs == 'sunburst'):
                path = st.multiselect('Choose your Path', options=list(result.columns))
                fig = px.sunburst(data_frame=result, path=path, values='newcol')
                st.plotly_chart(fig)