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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
# Remove outliers
modified_data = modified_data[(
modified_data[column] >= lower_bound) & (modified_data[column] <= upper_bound)]
elif action == "Set Bounds Manually":
# User inputs for bounds
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()
# Replace outliers
modified_data[column] = modified_data[column].apply(
lambda x: replacement_value if x < lower_bound or x > upper_bound else x
)
# After Visualization
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.")
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