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| import streamlit as st | |
| import pandas as pd | |
| def clicked(button): | |
| st.session_state.clicked[button] = True | |
| def checkbox_clicked(button): | |
| st.session_state.checkbox_menu[button] = st.session_state.checkbox_menu[button] == False | |
| def additional_clicked_fun(button): | |
| st.session_state.refreshed[button] += 1 | |
| def describe_dataframe(df): | |
| # Initialize a list to hold descriptions for each column | |
| column_descriptions = [] | |
| for column in df.columns: | |
| # Basic column data | |
| col_type = df[column].dtype | |
| num_nulls = df[column].isnull().sum() | |
| null_info = "has some missing values" if num_nulls > 0 else "has no missing values" | |
| # Detailed stats for numeric columns | |
| if pd.api.types.is_numeric_dtype(df[column]): | |
| max_value = df[column].max() | |
| min_value = df[column].min() | |
| mean_value = df[column].mean() | |
| column_descriptions.append(f"{column} (numeric) - type: {col_type}, {null_info}, max: {max_value}, min: {min_value}, mean: {mean_value:.2f}") | |
| # Add more conditions for other data types (e.g., categorical, datetime) as needed | |
| else: | |
| column_descriptions.append(f"{column} - type: {col_type}, {null_info}") | |
| # Combine all column descriptions into a single string | |
| detailed_description = "; ".join(column_descriptions) | |
| overall_description = f"The dataset has {len(df)} rows and {len(df.columns)} columns. Column details: {detailed_description}." | |
| return overall_description | |
| def to_show(df, show_selected, rows_to_show): | |
| switch_dic = { | |
| 'First few rows': df.head(rows_to_show), 'Last few rows': df.tail(rows_to_show), 'Random':df.sample(rows_to_show) | |
| } | |
| st.write(f'There are {len(df)} rows and {len(df.columns)} columns.') | |
| # columns = [col for col in df.columns] | |
| # st.write('Column Names') | |
| # st.write(columns) | |
| return switch_dic[show_selected] | |