Upload 5 files

.gitignore

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+__pycache__/
+*.py[cod]
+*$py.class
+.env
+.venv
+env/
+venv/
+ENV/
+.streamlit/
+.idea/
+.ipynb_checkpoints/ 

README.md

@@ -1,13 +1,25 @@
----
-title: DataAnalyticsPortal
-emoji: 😻
-colorFrom: purple
-colorTo: red
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
-short_description: An app to analyze your csv or xlsx file
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Data Analytics Portal
+
+A simple and interactive web application built with Streamlit for data analysis and visualization.
+
+## Features
+
+- Upload CSV or Excel files
+- View data summary statistics
+- Explore top and bottom rows
+- Check data types
+- Value counting with visualizations (bar chart, line chart, pie chart)
+
+## How to Use
+
+1. Upload a CSV or Excel file using the file uploader
+2. Explore the basic information about your dataset
+3. Navigate through the tabs to view different aspects of your data
+4. Use the Value Count expander to visualize frequency distributions
+
+## Technologies Used
+
+- Python
+- Streamlit
+- Pandas
+- Plotly Express 

Spaces.yml

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+title: Data Analytics Portal
+emoji: 📊
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: 1.29.0
+app_file: app.py
+pinned: false 

app.py

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+import pandas as pd 
+import plotly.express as px
+import streamlit as st
+
+st.set_page_config(
+    page_title='Data Analytics Portal',
+    page_icon='📊'
+)
+
+st.title(':blue[📊 Data Analytics Portal]')
+st.subheader(':grey[ An easy way to Analyse Data!!]', divider='green')
+
+file = st.file_uploader('Drop csv or excel file', type=['csv', 'xlsx'])
+if file:
+    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='🚨')
+
+    st.subheader(':blue[Basic information of the dataset]', divider='green')
+    
+    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(':blue[Column Values To Count]', divider='green')
+    
+    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)
+else:
+    st.warning('Please upload a CSV or Excel file to get started', icon='⚠️')