Update pages/2_Data_CLeaning_and_Preprocessing.py

pages/2_Data_CLeaning_and_Preprocessing.py

@@ -3,9 +3,15 @@ import pandas as pd
 import os
 from io import StringIO
 import sys
+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+import plotly.express as px
+from plotly.subplots import make_subplots
+from io import StringIO
 
 # Page Title
-st.markdown("<h1 style='text-align:center; color:#008080;'>Data Cleaning and Processing</h1>", unsafe_allow_html=True)
+st.markdown("<h1 style='text-align:center; color:wh;'>Data Cleaning and Processing</h1>", unsafe_allow_html=True)
 
 # Access dataset from session state
 df = st.session_state.get("dataset")
@@ -27,13 +33,70 @@ if df is not None:
 
     st.subheader("Shape of the Dataset:")
     st.write(df.shape)
+
+    # Visualize Numeric Data
+    numeric_columns = data.select_dtypes(include=['float64', 'int64']).columns
+    if len(numeric_columns) > 0:
+        st.subheader("Histograms for Numeric Columns:")
+        fig = make_subplots(rows=len(numeric_columns), cols=1, subplot_titles=numeric_columns)
+        for i, col in enumerate(numeric_columns):
+            hist = px.histogram(data, x=col, nbins=30, title=f'Histogram of {col}')
+            fig.add_trace(hist.data[0], row=i + 1, col=1)
+
+        fig.update_layout(height=500 * len(numeric_columns), title_text="Histograms for Numeric Columns")
+        st.plotly_chart(fig)
+
+        st.subheader("Boxplots for Numeric Columns:")
+        fig = make_subplots(rows=len(numeric_columns), cols=1, subplot_titles=numeric_columns)
+        for i, col in enumerate(numeric_columns):
+            boxplot = px.box(data, y=col, title=f'Boxplot of {col}')
+            fig.add_trace(boxplot.data[0], row=i + 1, col=1)
+
+        fig.update_layout(height=500 * len(numeric_columns), title_text="Boxplots for Numeric Columns")
+        st.plotly_chart(fig)
+    else:
+        st.warning("No numeric columns available for visualization.")
+
+    # Visualize Categorical Data
+    categorical_columns = data.select_dtypes(include=['object', 'category']).columns
+    if len(categorical_columns) > 0:
+        st.subheader("Bar Plots for Categorical Columns:")
+        selected_cat_col = st.selectbox("Select a Categorical Column", categorical_columns)
+
+        st.write(f"Value Counts for '{selected_cat_col}':")
+        st.write(data[selected_cat_col].value_counts())
+
+        fig = px.bar(data, x=selected_cat_col, title=f'Bar Plot of {selected_cat_col}', color=selected_cat_col)
+        st.plotly_chart(fig)
+    else:
+        st.warning("No categorical columns available for visualization.")
+
+    # Correlation Matrix for Numeric Columns
+    if len(numeric_columns) > 1:
+        st.subheader("Correlation Matrix:")
+        corr_matrix = data[numeric_columns].corr()
+        fig = px.imshow(corr_matrix, title="Correlation Matrix", color_continuous_scale='coolwarm')
+        st.plotly_chart(fig)
+
+    st.subheader("Cleaned Dataset:")
+    cleaned_data = data.drop_duplicates()
+    st.write(cleaned_data)
+
+    cleaned_csv = cleaned_data.to_csv(index=False).encode('utf-8')
+    st.download_button(
+        label="Download Cleaned Dataset",
+        data=cleaned_csv,
+        file_name="cleaned_dataset.csv",
+        mime="text/csv"
+    )
+
 else:
     st.warning("No dataset found. Please upload a dataset on the Home page.")
 
 
 # Define the URL of the background image (use your own image URL)
 # Apply custom CSS for the background image and overlay
-background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/675fab3a2d0851e23d23cad3/FlisTFfpU7flDCWj_KttH.jpeg"
+background_image_url = "https://cdn-uploads.huggingface.co/production/uploads/67441c51a784a9d15cb12871/6EvD_NR-zVMVJI5okpx8c.jpeg"
 
 st.markdown(
     f"""