Update Preprocessing1.py

Preprocessing1.py

@@ -1,145 +1,128 @@
-import streamlit as st
-import pandas as pd
-import numpy as np
-import io
-import matplotlib.pyplot as plt
-from sklearn.preprocessing import LabelEncoder
-import seaborn as sns
-import base64
-
-
-def preview_data():
-    if "data" in st.session_state:
-        data = st.session_state["data"]
-
-        st.write("### Dataset Preview Options:")
-
-        preview_option = st.radio(
-            "Select how to preview the dataset:",
-            options=["Head", "Tail", "Custom Number of Rows"],
-            index=0
-        )
-
-        if preview_option == "Head":
-            st.write("### First 5 Rows of the Dataset:")
-            st.dataframe(data.head())
-        elif preview_option == "Tail":
-            st.write("### Last 5 Rows of the Dataset:")
-            st.dataframe(data.tail())
-        elif preview_option == "Custom Number of Rows":
-            number = st.slider(
-                "Select Number of Rows to Display:", 1, len(data))
-            st.write(f"### First {number} Rows of the Dataset:")
-            st.dataframe(data.head(number))
-
-        # Show entire data
-        if st.checkbox("Show all data"):
-            st.write(data)
-
-        # Show column names
-        if st.checkbox("Show Column Names"):
-            st.write(data.columns)
-
-        # Show dataset dimensions (rows and columns)
-        if st.checkbox("Show Dimensions"):
-            st.write(data.shape)
-
-    else:
-        st.warning("Please upload a dataset to view options.")
-
-
-def data_cleaning():
-    if "data" in st.session_state:
-        data = st.session_state["data"]
-
-        st.subheader("Data Cleaning")
-
-        col_option = st.selectbox("Choose your option", [
-                                  "Check all numeric features are numeric?", "Show unique values of categorical features"])
-
-        # Check and convert numeric columns
-        if col_option == "Check all numeric features are numeric?":
-            st.write("Converting all numeric columns to numeric types...")
-            numeric_columns = list(
-                data.select_dtypes(include=np.number).columns)
-            for col in numeric_columns:
-                data[col] = pd.to_numeric(data[col], errors='coerce')
-
-            st.success("Done!")
-
-        # Show unique values for categorical features
-        elif col_option == "Show unique values of categorical features":
-            st.write("Unique values for categorical features:")
-            for column in data.columns:
-                # check for categorical features (strings)
-                if data[column].dtype == object:
-                    st.write(f"{column}: {data[column].unique()}")
-            st.write("====================================")
-
-    else:
-        st.warning("Please upload a dataset to perform data cleaning.")
-
-
-def modify_column_names():
-    st.title("Modify Column Names")
-
-    # Ensure data exists in the session
-    if "data" in st.session_state:
-        df = st.session_state["data"]
-
-        # Ensure modified_columns is initialized in session state
-        if "modified_columns" not in st.session_state:
-            st.session_state.modified_columns = list(df.columns)
-
-        st.write('### *Current Column Names*')
-        st.table(df.columns)
-
-        st.write('### *Modify Column Names*')
-        with st.expander("Modify Column Names", expanded=True):
-            # Use the modified columns from session state
-            before_col = st.session_state.modified_columns
-            before_col_df = pd.DataFrame(before_col, columns=['Column Name'])
-            st.table(before_col_df)
-
-            col3, col4, col5, col6 = st.columns(4)
-            changes_made = False  # Flag to track if any change is made
-
-            if st.button('Convert to Uppercase'):
-                st.session_state.modified_columns = [
-                    col.upper() for col in before_col]
-                changes_made = True
-            if st.button('Convert to Lowercase'):
-                st.session_state.modified_columns = [
-                    col.lower() for col in before_col]
-                changes_made = True
-            if st.button('Replace Spaces with Underscore'):
-                st.session_state.modified_columns = [
-                    col.replace(" ", "_") for col in before_col]
-                changes_made = True
-            if st.button('Capitalize First Letters'):
-                st.session_state.modified_columns = [
-                    col.title() for col in before_col]
-                changes_made = True
-
-            # Apply the changes only if a change was made
-            if changes_made:
-                df.columns = st.session_state.modified_columns
-                st.success("Changes applied successfully.")
-                st.table(pd.DataFrame(
-                    df.columns, columns=['Modified Columns']))
-
-        st.write("### *Modify a Specific Column Name*")
-        column_select = st.selectbox(
-            'Select column to modify', options=st.session_state.modified_columns)
-        new_column_name = st.text_input('Enter new column name')
-        if st.button('Update Column Name'):
-            if column_select and new_column_name:
-                st.session_state.modified_columns = [
-                    new_column_name if col == column_select else col for col in st.session_state.modified_columns]
-                df.columns = st.session_state.modified_columns
-                st.success("Column name updated.")
-                st.table(pd.DataFrame(
-                    df.columns, columns=['Modified Columns']))
-
-    else:
-        st.warning("Please upload a dataset first.")
+import streamlit as st
+import pandas as pd
+import numpy as np
+import io
+import matplotlib.pyplot as plt
+from sklearn.preprocessing import LabelEncoder
+import seaborn as sns
+import base64
+
+
+def preview_data():
+    if "data" in st.session_state:
+        data = st.session_state["data"]
+
+        st.write("### Dataset Preview Options:")
+
+        preview_option = st.radio(
+            "Select how to preview the dataset:",
+            options=["Head", "Tail", "Custom Number of Rows"],
+            index=0
+        )
+
+        if preview_option == "Head":
+            st.write("### First 5 Rows of the Dataset:")
+            st.dataframe(data.head())
+        elif preview_option == "Tail":
+            st.write("### Last 5 Rows of the Dataset:")
+            st.dataframe(data.tail())
+        elif preview_option == "Custom Number of Rows":
+            number = st.slider("Select Number of Rows to Display:", 1, len(data))
+            st.write(f"### First {number} Rows of the Dataset:")
+            st.dataframe(data.head(number))
+
+        if st.checkbox("Show all data"):
+            st.write(data)
+
+        if st.checkbox("Show Column Names"):
+            st.write(data.columns)
+
+        if st.checkbox("Show Dimensions"):
+            st.write(data.shape)
+
+    else:
+        st.warning("Please upload a dataset to view options.")
+
+
+
+def data_cleaning():
+    if "data" in st.session_state:
+        data = st.session_state["data"]
+
+        st.subheader("Data Cleaning")
+
+        col_option = st.selectbox("Choose your option", [
+            "Check all numeric features are numeric?", "Show unique values of categorical features"])
+
+        if col_option == "Check all numeric features are numeric?":
+            st.write("Converting all numeric columns to numeric types...")
+            numeric_columns = list(data.select_dtypes(include=np.number).columns)
+            for col in numeric_columns:
+                data[col] = pd.to_numeric(data[col], errors='coerce')
+
+            st.session_state["data"] = data
+            st.success("Done!")
+
+        elif col_option == "Show unique values of categorical features":
+            st.write("Unique values for categorical features:")
+            for column in data.columns:
+                if data[column].dtype == object:
+                    st.write(f"{column}: {data[column].unique()}")
+
+    else:
+        st.warning("Please upload a dataset to perform data cleaning.")
+
+
+def modify_column_names():
+    st.title("Modify Column Names")
+
+    if "data" in st.session_state:
+        df = st.session_state["data"]
+
+        if "modified_columns" not in st.session_state:
+            st.session_state.modified_columns = list(df.columns)
+
+        st.write('### *Current Column Names*')
+        st.table(df.columns)
+
+        st.write('### *Modify Column Names*')
+        with st.expander("Modify Column Names", expanded=True):
+            before_col = st.session_state.modified_columns
+            before_col_df = pd.DataFrame(before_col, columns=['Column Name'])
+            st.table(before_col_df)
+
+            col3, col4, col5, col6 = st.columns(4)
+            changes_made = False
+
+            if st.button('Convert to Uppercase'):
+                st.session_state.modified_columns = [col.upper() for col in before_col]
+                changes_made = True
+            if st.button('Convert to Lowercase'):
+                st.session_state.modified_columns = [col.lower() for col in before_col]
+                changes_made = True
+            if st.button('Replace Spaces with Underscore'):
+                st.session_state.modified_columns = [col.replace(" ", "_") for col in before_col]
+                changes_made = True
+            if st.button('Capitalize First Letters'):
+                st.session_state.modified_columns = [col.title() for col in before_col]
+                changes_made = True
+
+            if changes_made:
+                df.columns = st.session_state.modified_columns
+                st.session_state["data"] = df
+                st.success("Changes applied successfully.")
+                st.table(pd.DataFrame(df.columns, columns=['Modified Columns']))
+
+        st.write("### *Modify a Specific Column Name*")
+        column_select = st.selectbox('Select column to modify', options=st.session_state.modified_columns)
+        new_column_name = st.text_input('Enter new column name')
+        if st.button('Update Column Name'):
+            if column_select and new_column_name:
+                st.session_state.modified_columns = [new_column_name if col == column_select else col for col in st.session_state.modified_columns]
+                df.columns = st.session_state.modified_columns
+                st.session_state["data"] = df
+                st.success("Column name updated.")
+                st.table(pd.DataFrame(df.columns, columns=['Modified Columns']))
+
+    else:
+        st.warning("Please upload a dataset first.")