| 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.") |
|
|
