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chore: restructure examples
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examples/Tutorial.ipynb

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "4d39d0b5-5fe4-4bb9-8e55-83f5deb7f471",
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"metadata": {},
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"outputs": [],
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"source": [
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"from vega_datasets import data\n",
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"import persist_ext as PR"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "1e0ad7ad-0c9a-4a4f-9a6b-6109ebb0e259",
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"metadata": {
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"__GENERATED_DATAFRAMES__": "ᯡࠣ䅬Ԁ朤壠ᜣ琢〹夤゠⹰〮⁁䁻қ䚾ኊ㇠നСࠩ瀮晼Ƭ穅5愠៤⠠ ",
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"__has_persist_output": true,
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"trrack_graph": "ᯡ࠽䈌ʀ匦㢠⹰恆øĀ᣻ϐҀ୮䁃Ḣನ˭㓲䐩曀̠晃⠰ᖴ掚偶嘃ҴE䨲ࡔⳙѫ䥵埄櫣䯏㽡懅∠桠ø⨊ẰӁĠפწW౐㮨⃎濹۠Ǝᒅ\"㣙အ娠嵣ᆳ䘼梢摖悤⢌ば™ᠲ°亭⁅‶ᥐʦ{భ䊬∡⌤䍴淤ჹ䑨應᫠ඎ⣂ޗ䉂Þ䎞獠ೢ䶣で㕤䄀䷂搰චᏠĂ娕磣㇛ᬰ煷㟃䊾ᅅប恱⁉ᠤ㉂Ḥ抁䵧䒀䴰⡆佤ӻ䂹℔㨻ʢ⠦᫫ㇻቐ⩴☻ᇃࠨᛑ屒اߣࢄ┦䏀ᣬ㬳䈁砢Ⴄ✓〠⮰∵᷼央Ⴧ掴≺䩬桉ᵕ᱒ "
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},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "96c32320017445e283e79f1ad93b995a",
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"version_major": 2,
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"version_minor": 1
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},
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"text/plain": [
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"PersistWidget(data_values=[{'__id_column': '1', 'Name': 'chevrolet chevelle malibu', 'Miles_per_Gallon': 18.0,…"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"cars_df = data.cars()\n",
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"\n",
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"PR.PersistTable(cars_df)"
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]
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}
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],
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"metadata": {
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"__persist_keys_record": [
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"__GENERATED_DATAFRAMES__",
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"__persist_nb_uuid__",
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"trrack_graph"
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],
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"__persist_nb_uuid__": "71bbd9f0-dd59-46c9-9e0f-7194b6f50588",
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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examples/f.txt examples/_old/f.txt

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