Upload app.py

app.py

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+import pandas as pd 
+import plotly.express as px
+import streamlit as st   
+
+# Configuring the Streamlit App:
+st.set_page_config(
+    page_title='MAK Analytics Portal',
+    page_icon='📊'
+)
+
+# Adding Titles and Subtitles:
+st.title(':rainbow[Data Analytics Portal]')
+st.subheader(':gray[Explore Data with ease.]',divider='rainbow')
+
+# File Upload Feature:
+file = st.file_uploader('Drop csv,tsv or excel file', type=['csv', 'xlsx','tsv'])
+if file:
+    if file.name.endswith('csv'):
+        data = pd.read_csv(file)
+    elif file.name.endswith('tsv'):
+        data=pd.read_tsv(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))
+    
+    # Column Value Counts:
+    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:
+            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)
+        
+    # Grouping Data for Deeper Insights:
+    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(
+                ans=(operation_col, operation)
+            ).reset_index()
+
+            st.dataframe(result)
+
+            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='ans')
+                st.plotly_chart(fig)