Update pages/2_Data_CLeaning_and_Preprocessing.py

pages/2_Data_CLeaning_and_Preprocessing.py

@@ -1,4 +1,147 @@
-# Access the dataframe stored in session state
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+# Page Title
+st.title("Exploratory Data Analysis (EDA) App")
+
+st.markdown("""
+### Perform EDA and Clean Data
+Upload a CSV file to begin. This app will provide basic insights into the dataset, 
+highlight missing values, and visualize numeric and categorical columns.
+---
+""")
+
+# File Upload Section
+st.header("Upload Dataset")
+
+uploaded_file = st.file_uploader("Upload CSV", type=["csv"])
+
+# Check if file is uploaded
+if uploaded_file is not None:
+    if uploaded_file.size > 0:
+        try:
+            # Read the CSV file
+            data = pd.read_csv(uploaded_file)
+            st.session_state['df'] = data  # Store the data for use in other pages
+            st.success("Dataset uploaded successfully!")
+            
+            # Show Data Preview
+            st.write("### Preview of Dataset")
+            st.dataframe(data.head())
+
+            # Overview Section
+            st.write("### Dataset Overview")
+            st.write(data.describe())
+
+            # Missing Values
+            st.write("### Missing Values")
+            st.write(data.isnull().sum())
+
+            # Duplicate Rows
+            st.write("### Duplicate Rows")
+            st.write(f"Number of duplicate rows: {data.duplicated().sum()}")
+
+            # Visualize Numeric Data
+            numeric_columns = data.select_dtypes(include=['float64', 'int64']).columns
+            if len(numeric_columns) > 0:
+                st.write("### Histograms for Numeric Columns")
+                for col in numeric_columns:
+                    fig, ax = plt.subplots()
+                    sns.histplot(data[col], kde=True, ax=ax)
+                    ax.set_title(f'Histogram of {col}')
+                    st.pyplot(fig)
+
+                st.write("### Boxplots for Numeric Columns")
+                for col in numeric_columns:
+                    fig, ax = plt.subplots()
+                    sns.boxplot(x=data[col], ax=ax)
+                    ax.set_title(f'Boxplot of {col}')
+                    st.pyplot(fig)
+            else:
+                st.write("No numeric columns available for visualization.")
+
+            # Visualize Categorical Data
+            categorical_columns = data.select_dtypes(include=['object', 'category']).columns
+            if len(categorical_columns) > 0:
+                st.write("### 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, ax = plt.subplots()
+                sns.countplot(x=selected_cat_col, data=data, ax=ax)
+                ax.set_title(f'Bar Plot of {selected_cat_col}')
+                st.pyplot(fig)
+            else:
+                st.write("No categorical columns available for visualization.")
+
+            # Correlation Matrix
+            if len(numeric_columns) > 1:
+                st.write("### Correlation Matrix")
+                corr_matrix = data[numeric_columns].corr()
+                st.write(corr_matrix)
+
+                fig, ax = plt.subplots(figsize=(10, 8))
+                sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', ax=ax)
+                st.pyplot(fig)
+
+            # Check the columns before renaming
+            st.write("### Dataset Columns:")
+            st.write(data.columns)
+
+            # Renaming columns if they exist
+            if 'ProductCategory' in data.columns and 'ProductBrand' in data.columns and 'ProductPrice' in data.columns:
+                data = data.rename(columns={'ProductCategory': 'Category', 'ProductBrand': 'Brand', 'ProductPrice': 'Price'})
+                st.success("Columns renamed successfully!")
+            else:
+                st.warning("Columns 'ProductCategory', 'ProductBrand', or 'ProductPrice' not found in the dataset.")
+
+            # Now check if 'Category' exists and plot
+            if 'Category' in data.columns:
+                st.write("### Bar Plot for Category")
+                fig, ax = plt.subplots()
+                sns.countplot(x='Category', data=data, palette='viridis', ax=ax)
+                st.pyplot(fig)
+            else:
+                st.warning("'Category' column not found for plotting.")
+            
+            # Binning of age column
+            bins = [0, 18, 35, 50, 65, 100]
+            labels = ['Child', 'Young Adult', 'Adult', 'Middle Aged', 'Senior']
+            data['age_bins'] = pd.cut(data['CustomerAge'], bins=bins, labels=labels, right=False)
+
+            # Data Cleaning Section
+            st.write("### Cleaned Dataset")
+            cleaned_data = data.drop_duplicates()
+            st.dataframe(cleaned_data)
+
+            # Save cleaned data to CSV and provide download option
+            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"
+            )
+
+            # Store the cleaned dataframe in session state for use in other pages
+            st.session_state['df'] = cleaned_data
+
+        except pd.errors.EmptyDataError:
+            st.error("The uploaded CSV file is empty. Please upload a valid file.")
+        except pd.errors.ParserError:
+            st.error("The file is not properly formatted as a CSV. Please check the data.")
+        except Exception as e:
+            st.error(f"An unexpected error occurred: {e}")
+    else:
+        st.error("The uploaded file is empty.")
+else:
+    st.info("Upload a CSV file to get started.")
+
+# Session State Access on Other Pages
 if 'df' in st.session_state:
     data = st.session_state['df']
     st.write("Dataset available for further analysis.")