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| import streamlit as st | |
| import pandas as pd | |
| import json | |
| def render_dataset_preview(dataset, dataset_type): | |
| """ | |
| Renders a preview of the dataset with pagination options. | |
| Args: | |
| dataset: The dataset to preview (pandas DataFrame) | |
| dataset_type: The type of dataset (csv, json, etc.) | |
| """ | |
| if dataset is None: | |
| st.warning("No dataset to preview.") | |
| return | |
| st.markdown(f"<h3>Dataset Preview: {st.session_state.dataset_name}</h3>", unsafe_allow_html=True) | |
| # Show basic info | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Rows", f"{dataset.shape[0]:,}") | |
| with col2: | |
| st.metric("Columns", f"{dataset.shape[1]:,}") | |
| with col3: | |
| st.metric("Type", dataset_type.upper()) | |
| # Preview options | |
| col1, col2 = st.columns([1, 3]) | |
| with col1: | |
| num_rows = st.number_input("Rows to display", min_value=5, max_value=100, value=10, step=5) | |
| with col2: | |
| preview_mode = st.radio("Preview mode", ["Head", "Tail", "Sample"], horizontal=True) | |
| # Display dataset preview | |
| st.markdown("<div class='dataset-preview'>", unsafe_allow_html=True) | |
| if preview_mode == "Head": | |
| st.dataframe(dataset.head(num_rows), use_container_width=True) | |
| elif preview_mode == "Tail": | |
| st.dataframe(dataset.tail(num_rows), use_container_width=True) | |
| else: # Sample | |
| st.dataframe(dataset.sample(min(num_rows, len(dataset))), use_container_width=True) | |
| st.markdown("</div>", unsafe_allow_html=True) | |
| # Show dataset schema | |
| with st.expander("Dataset Schema"): | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.markdown("**Column Types**") | |
| type_df = pd.DataFrame({ | |
| 'Column': dataset.dtypes.index, | |
| 'Type': dataset.dtypes.values.astype(str) | |
| }) | |
| st.dataframe(type_df, use_container_width=True) | |
| with col2: | |
| st.markdown("**Missing Values**") | |
| missing_df = pd.DataFrame({ | |
| 'Column': dataset.columns, | |
| 'Missing': dataset.isna().sum().values, | |
| 'Percentage': dataset.isna().sum().values / len(dataset) * 100 | |
| }) | |
| st.dataframe(missing_df.style.format({ | |
| 'Percentage': '{:.2f}%' | |
| }), use_container_width=True) | |
| # Raw data | |
| with st.expander("Raw Data (First 5 records)"): | |
| if dataset_type == 'csv': | |
| st.code(dataset.head(5).to_csv(index=False), language="text") | |
| else: # json or jsonl | |
| st.code(dataset.head(5).to_json(orient='records', indent=2), language="json") | |