Update pages/2_Data_CLeaning_and_Preprocessing.py

pages/2_Data_CLeaning_and_Preprocessing.py

@@ -1,146 +1,4 @@
-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)
-            
-            # df.head()
-            # Data Cleaning Section
-            st.write("### Cleaned Dataset")
-            cleaned_data = data.drop_duplicates()
-            st.dataframe(cleaned_data)
-
-            # Download 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"
-            )
-
-        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
+# Access the dataframe stored in session state
 if 'df' in st.session_state:
     data = st.session_state['df']
     st.write("Dataset available for further analysis.")