| 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.") | |