| 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 handle_categorical_values(): |
| if "data" in st.session_state: |
| data = st.session_state["data"] |
|
|
| st.subheader("Handle Categorical Values") |
|
|
| categorical_cols_features = list(data.select_dtypes(include="object").columns) |
|
|
| one_hot_enc = st.multiselect("Select nominal categorical columns", categorical_cols_features) |
|
|
| if one_hot_enc: |
| for column in one_hot_enc: |
| if data[column].dtype == 'object': |
| data = pd.get_dummies(data, columns=[column]) |
| st.session_state["data"] = data |
| st.write("### Data after One-Hot Encoding:") |
| st.write(data.head()) |
|
|
| label_encoder = LabelEncoder() |
| label_enc = st.multiselect("Select ordinal categorical columns", categorical_cols_features) |
|
|
| if label_enc: |
| for column in label_enc: |
| if data[column].dtype == 'object': |
| data[column] = label_encoder.fit_transform(data[column]) |
| st.session_state["data"] = data |
| st.write("### Data after Label Encoding:") |
| st.write(data.head()) |
|
|
| else: |
| st.warning("Please upload a dataset to handle categorical values.") |
|
|
|
|
| def handle_missing_values(): |
| st.title("Handle Missing Values") |
|
|
| if "data" in st.session_state: |
| data = st.session_state["data"].copy() |
|
|
| action = st.selectbox( |
| "Select Action", ["Drop", "Dropna", "Fill missing val"]) |
|
|
| column = st.selectbox("Select Column", data.columns) |
|
|
| st.write("### Before:") |
| st.dataframe(data) |
|
|
| modified_data = data.copy() |
|
|
| if action == "Drop": |
| modified_data.drop(columns=[column], inplace=True) |
| elif action == "Dropna": |
| modified_data.dropna(subset=[column], inplace=True) |
| elif action == "Fill missing val": |
| fill_method = st.selectbox( |
| "Select fill method", ["Mean", "Mode", "Median"]) |
|
|
| if fill_method == "Mean": |
| fill_value = data[column].mean() |
| elif fill_method == "Mode": |
| fill_value = data[column].mode()[0] |
| elif fill_method == "Median": |
| fill_value = data[column].median() |
|
|
| modified_data[column].fillna(fill_value, inplace=True) |
|
|
| st.write("### After (Preview):") |
| st.dataframe(modified_data) |
|
|
| if st.button("OK"): |
| st.session_state["data"] = modified_data |
| st.success("Done! The action has been applied.") |
| st.write("### After:") |
| st.dataframe(modified_data) |
|
|
| else: |
| st.warning("Please upload a dataset first.") |
|
|
| def handle_duplicates(): |
| st.title("Handle Duplicates") |
|
|
| if "data" in st.session_state: |
| data = st.session_state["data"].copy() |
|
|
| action = st.selectbox( |
| "Select Action", ["Drop Duplicates", "Drop Duplicates in Column", "Keep First", "Keep Last"]) |
|
|
| if action in ["Drop Duplicates in Column", "Keep First", "Keep Last"]: |
| column = st.selectbox("Select Column", data.columns) |
| else: |
| column = None |
|
|
| st.write("### Before:") |
| st.dataframe(data) |
|
|
| modified_data = data.copy() |
|
|
| if action == "Drop Duplicates": |
| modified_data.drop_duplicates(inplace=True) |
| elif action == "Drop Duplicates in Column": |
| modified_data.drop_duplicates(subset=[column], inplace=True) |
| elif action == "Keep First": |
| modified_data.drop_duplicates( |
| subset=[column], keep="first", inplace=True) |
| elif action == "Keep Last": |
| modified_data.drop_duplicates( |
| subset=[column], keep="last", inplace=True) |
|
|
| st.write("### After (Preview):") |
| st.dataframe(modified_data) |
|
|
| if st.button("OK"): |
| st.session_state["data"] = modified_data |
| st.success("Done! The action has been applied.") |
| st.write("### After:") |
| st.dataframe(modified_data) |
|
|
| else: |
| st.warning("Please upload a dataset first.") |
|
|
|
|
| def handle_outliers(): |
| st.title("Handle Outliers") |
|
|
| if "data" in st.session_state: |
| data = st.session_state["data"].copy() |
|
|
| column = st.selectbox("Select Column", data.select_dtypes( |
| include=[np.number]).columns) |
|
|
| action = st.selectbox( |
| "Select Action", |
| ["Remove Outliers (IQR)", "Set Bounds Manually", |
| "Replace Outliers"] |
| ) |
|
|
| st.write("### Before:") |
| st.dataframe(data) |
|
|
| modified_data = data.copy() |
|
|
| if action == "Remove Outliers (IQR)": |
| Q1 = data[column].quantile(0.25) |
| Q3 = data[column].quantile(0.75) |
| IQR = Q3 - Q1 |
| lower_bound = Q1 - 1.5 * IQR |
| upper_bound = Q3 + 1.5 * IQR |
|
|
| |
| modified_data = modified_data[( |
| modified_data[column] >= lower_bound) & (modified_data[column] <= upper_bound)] |
|
|
| elif action == "Set Bounds Manually": |
| |
| lower_bound = st.number_input( |
| f"Set lower bound for {column}", value=float(data[column].min())) |
| upper_bound = st.number_input( |
| f"Set upper bound for {column}", value=float(data[column].max())) |
|
|
| modified_data = modified_data[( |
| modified_data[column] >= lower_bound) & (modified_data[column] <= upper_bound)] |
|
|
| elif action == "Replace Outliers": |
|
|
| Q1 = data[column].quantile(0.25) |
| Q3 = data[column].quantile(0.75) |
| IQR = Q3 - Q1 |
| lower_bound = Q1 - 1.5 * IQR |
| upper_bound = Q3 + 1.5 * IQR |
|
|
| replace_method = st.radio( |
| "Select Replacement Method", |
| ["Mean", "Median"] |
| ) |
|
|
| if replace_method == "Mean": |
| replacement_value = data[column].mean() |
| else: |
| replacement_value = data[column].median() |
|
|
| |
| modified_data[column] = modified_data[column].apply( |
| lambda x: replacement_value if x < lower_bound or x > upper_bound else x |
| ) |
|
|
| |
| st.write("### After (Preview):") |
| st.dataframe(modified_data) |
|
|
| if st.button("OK"): |
| st.session_state["data"] = modified_data |
| st.success("Done! The action has been applied.") |
| st.write("### After:") |
| st.dataframe(modified_data) |
|
|
| else: |
| st.warning("Please upload a dataset first.") |
|
|
