Update pages/2_Data_CLeaning_and_Preprocessing.py

pages/2_Data_CLeaning_and_Preprocessing.py

@@ -4,7 +4,101 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 from io import StringIO
 
+# Page Title
+st.markdown("<h1 style='text-align:center; color:white;'>Data Cleaning and Processing</h1>", unsafe_allow_html=True)
 
+# Access dataset from session state
+df = st.session_state.get("dataset")
+
+# Exclude 'ProductID' from the dataset
+if df is not None:
+    df = df.drop(columns=['ProductID'], errors='ignore')  # Exclude 'ProductID' if it exists
+
+    st.subheader("Dataset Preview:")
+    st.write(df.head())
+
+    st.subheader("Info of the Dataset:")
+    # Redirect the output of df.info() to a string buffer
+    buffer = StringIO()
+    df.info(buf=buffer)
+    
+    # Display the content in Streamlit
+    st.write(buffer.getvalue())
+
+    st.subheader("Dataset Description:")
+    st.write(df.describe())
+
+    st.subheader("Shape of the Dataset:")
+    st.write(df.shape)
+
+    # Visualize Numeric Data (Histograms and Boxplots in subplots)
+    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
+    if len(numeric_columns) > 0:
+        st.subheader("Histograms for Numeric Columns:")
+        # Create a multidimensional subplot (grid) for all histograms
+        num_plots = len(numeric_columns)
+        rows = (num_plots + 1) // 2  # To create a 2-column grid layout for histograms
+        fig, axs = plt.subplots(rows, 2, figsize=(12, 12))
+        axs = axs.flatten()  # Flatten the 2D array of axes to iterate over
+        
+        color_palettes_hist = ['Set1', 'Set2', 'Set3', 'Paired', 'Pastel1']  # Different color palettes for histograms
+        for i, col in enumerate(numeric_columns):
+            palette = sns.color_palette(color_palettes_hist[i % len(color_palettes_hist)])  # Ensure different palette for each plot
+            sns.histplot(df[col], bins=30, kde=True, color=palette[0], ax=axs[i])  # Apply the color palette
+            axs[i].set_title(f'Histogram of {col}')
+        st.pyplot(fig)
+        plt.clf()
+
+        st.subheader("Boxplots for Numeric Columns:")
+        # Create a multidimensional subplot (grid) for all boxplots
+        fig, axs = plt.subplots(rows, 2, figsize=(12, 12))
+        axs = axs.flatten()  # Flatten the 2D array of axes to iterate over
+        
+        color_palettes_box = ['coolwarm', 'Blues', 'viridis', 'cubehelix', 'crest']  # Different color palettes for boxplots
+        for i, col in enumerate(numeric_columns):
+            palette = sns.color_palette(color_palettes_box[i % len(color_palettes_box)])  # Ensure different palette for each plot
+            sns.boxplot(x=df[col], ax=axs[i], palette=palette)
+            axs[i].set_title(f'Boxplot of {col}')
+        st.pyplot(fig)
+        plt.clf()
+    else:
+        st.warning("No numeric columns available for visualization.")
+
+    # Visualize Categorical Data
+    categorical_columns = df.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(df[selected_cat_col].value_counts())
+
+        plt.figure(figsize=(12, 6))
+        sns.countplot(x=selected_cat_col, data=df, palette='coolwarm')  # Unique palette for categorical data
+        plt.title(f'Bar Plot of {selected_cat_col}')
+        st.pyplot(plt)
+        plt.clf()
+    else:
+        st.warning("No categorical columns available for visualization.")
+
+    st.subheader("Cleaned Dataset:")
+    cleaned_data = df.drop_duplicates()
+    st.write(cleaned_data)
+
+    # Store cleaned data in session state for use in next page
+    st.session_state.cleaned_data = cleaned_data  # Store cleaned data in session state
+
+    # Convert cleaned data to CSV and provide a download button
+    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)
@@ -49,108 +143,12 @@ st.markdown(
         }}
     </style>
     
-    <div class="content-container">
-        <div class="stMarkdown">
-            <!-- Replace this with your app's content -->
-            <p>
-                # Page Title
-                st.markdown("<h1 style='text-align:center; color:white;'>Data Cleaning and Processing</h1>", unsafe_allow_html=True)
-                
-                # Access dataset from session state
-                df = st.session_state.get("dataset")
-                
-                # Exclude 'ProductID' from the dataset
-                if df is not None:
-                    df = df.drop(columns=['ProductID'], errors='ignore')  # Exclude 'ProductID' if it exists
-                
-                    st.subheader("Dataset Preview:")
-                    st.write(df.head())
-                
-                    st.subheader("Info of the Dataset:")
-                    # Redirect the output of df.info() to a string buffer
-                    buffer = StringIO()
-                    df.info(buf=buffer)
-                    
-                    # Display the content in Streamlit
-                    st.write(buffer.getvalue())
-                
-                    st.subheader("Dataset Description:")
-                    st.write(df.describe())
-                
-                    st.subheader("Shape of the Dataset:")
-                    st.write(df.shape)
-                
-                    # Visualize Numeric Data (Histograms and Boxplots in subplots)
-                    numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns
-                    if len(numeric_columns) > 0:
-                        st.subheader("Histograms for Numeric Columns:")
-                        # Create a multidimensional subplot (grid) for all histograms
-                        num_plots = len(numeric_columns)
-                        rows = (num_plots + 1) // 2  # To create a 2-column grid layout for histograms
-                        fig, axs = plt.subplots(rows, 2, figsize=(12, 12))
-                        axs = axs.flatten()  # Flatten the 2D array of axes to iterate over
-                        
-                        color_palettes_hist = ['Set1', 'Set2', 'Set3', 'Paired', 'Pastel1']  # Different color palettes for histograms
-                        for i, col in enumerate(numeric_columns):
-                            palette = sns.color_palette(color_palettes_hist[i % len(color_palettes_hist)])  # Ensure different palette for each plot
-                            sns.histplot(df[col], bins=30, kde=True, color=palette[0], ax=axs[i])  # Apply the color palette
-                            axs[i].set_title(f'Histogram of {col}')
-                        st.pyplot(fig)
-                        plt.clf()
-                
-                        st.subheader("Boxplots for Numeric Columns:")
-                        # Create a multidimensional subplot (grid) for all boxplots
-                        fig, axs = plt.subplots(rows, 2, figsize=(12, 12))
-                        axs = axs.flatten()  # Flatten the 2D array of axes to iterate over
-                        
-                        color_palettes_box = ['coolwarm', 'Blues', 'viridis', 'cubehelix', 'crest']  # Different color palettes for boxplots
-                        for i, col in enumerate(numeric_columns):
-                            palette = sns.color_palette(color_palettes_box[i % len(color_palettes_box)])  # Ensure different palette for each plot
-                            sns.boxplot(x=df[col], ax=axs[i], palette=palette)
-                            axs[i].set_title(f'Boxplot of {col}')
-                        st.pyplot(fig)
-                        plt.clf()
-                    else:
-                        st.warning("No numeric columns available for visualization.")
-                
-                    # Visualize Categorical Data
-                    categorical_columns = df.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(df[selected_cat_col].value_counts())
-                
-                        plt.figure(figsize=(12, 6))
-                        sns.countplot(x=selected_cat_col, data=df, palette='coolwarm')  # Unique palette for categorical data
-                        plt.title(f'Bar Plot of {selected_cat_col}')
-                        st.pyplot(plt)
-                        plt.clf()
-                    else:
-                        st.warning("No categorical columns available for visualization.")
-                
-                    st.subheader("Cleaned Dataset:")
-                    cleaned_data = df.drop_duplicates()
-                    st.write(cleaned_data)
-                
-                    # Store cleaned data in session state for use in next page
-                    st.session_state.cleaned_data = cleaned_data  # Store cleaned data in session state
-                
-                    # Convert cleaned data to CSV and provide a download button
-                    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.")
-            </p>
-        </div>
-    </div>
+        # <div class="content-container">
+        #     <div class="stMarkdown">
+        #         <!-- Replace this with your app's content -->
+        #         <p>Insert your app's content here. All elements will be aligned to the center and limited to 70% width.</p>
+        #     </div>
+        # </div>
     """, 
     unsafe_allow_html=True
 )