timmy / 使用 Streamlit 建立 YAML 員工資料管理系統

employees: - Age: 34 City: New York Name: Alice Occupation: Engineer Salary: 70000 - Age: 27 City: Los Angeles Name: Bob Occupation: Doctor Salary: 120000 - Age: 22 City: Chicago Name: Charlie Occupation: Artist Salary: 50000

import streamlit as st import pandas as pd import yaml from yaml.loader import SafeLoader # Read YAML file def read_yaml(file_path): with open(file_path, 'r') as file: data = yaml.load(file, Loader=SafeLoader) return data # Write to YAML file def write_yaml(file_path, data): with open(file_path, 'w') as file: yaml.dump(data, file) # Read data yaml_file = 'data.yaml' data = read_yaml(yaml_file) # Convert data to DataFrame df = pd.DataFrame(data['employees']) # Display DataFrame st.write('## Employee Data Table') st.write(df) # Allow user to edit data st.write('## Edit Employee Data') edited_df = st.data_editor(df) # When user submits edits, write data back to YAML file if st.button('Save Changes'): data['employees'] = edited_df.to_dict(orient='records') write_yaml(yaml_file, data) st.success('Changes saved successfully!') # Hide certain columns columns_to_hide = ['Salary'] df_visible = df.drop(columns=columns_to_hide) # Display DataFrame with hidden columns st.write('## Employee Data Table (Filtered)') st.write(df_visible) # Interaction: Slider age_filter = st.slider('Select age range', 20, 30, (20, 30)) # Filter DataFrame filtered_df = df_visible[(df_visible['Age'] >= age_filter[0]) & (df_visible['Age'] <= age_filter[1])] # Display filtered DataFrame st.write('## Filtered Employee Data Table') st.write(filtered_df)