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workshop/pandas-shoes/performance-test.ipynb
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workshop/pandas-shoes/performance-test.ipynb
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{
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"cells": [
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Test de performance Dask\n",
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"\n",
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"Test d'un tri sur un `DateFrame` de 10 000 lignes et 34 colonnes."
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],
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"id": "b623744e3d523007"
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},
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2024-06-30T14:02:48.704914Z",
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"start_time": "2024-06-30T14:02:45.257624Z"
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}
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},
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"source": [
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"from contexttimer import Timer\n",
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"from dask import dataframe as dd\n",
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"\n",
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"\n",
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"loops: int = 10\n",
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"df: dd.DataFrame = dd.read_csv(\"data/womens-shoes.csv.xz\", low_memory=False, dtype={\"asins\": \"object\", \"prices.offer\": \"object\", \"upc\": \"object\", \"weight\": \"object\"})\n",
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"\n",
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"# Dask Dataframe (±4800ms)\n",
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"with Timer() as timer:\n",
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" for i in range(loops):\n",
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" df2 = df.sort_values(\"prices.amountMin\", ascending=bool(i % 2)).compute()\n",
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"print(f\"Dask Dataframe: {timer.elapsed:.4f} seconds\")\n"
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],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Dask Dataframe: 3.4439 seconds\n"
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]
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}
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],
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"execution_count": 8
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Test de performance Pandas\n",
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"\n",
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"Test d'un tri sur un `DateFrame` de 10 000 lignes et 34 colonnes :\n",
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"\n",
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"- Avec des types `numpy`\n",
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"- Avec des types `pyarrow`"
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],
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"id": "2d1c8a87edd35ebd"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-30T15:55:33.485090Z",
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"start_time": "2024-06-30T15:55:32.684991Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from contexttimer import Timer\n",
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"import pandas as pd\n",
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"\n",
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"loops: int = 10\n",
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"pf = pd.read_csv(\"data/womens-shoes.csv.xz\")\n",
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"af = pd.read_csv(\"data/womens-shoes.csv.xz\", engine=\"pyarrow\")\n",
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"\n",
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"# Pandas Dataframe (±35ms)\n",
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"with Timer() as timer:\n",
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" for i in range(loops):\n",
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" df2 = pf.sort_values(\"prices.amountMin\", ascending=bool(i % 2))\n",
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"print(f\"Pandas Dataframe: {timer.elapsed:.4f} seconds\")\n",
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"\n",
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"# Pandas PyArrow Dataframe (±29ms)\n",
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"with Timer() as timer:\n",
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" for i in range(loops):\n",
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" df2 = af.sort_values(\"prices.amountMin\", ascending=bool(i % 2))\n",
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"print(f\"Pandas PyArrow Dataframe: {timer.elapsed:.4f} seconds\")\n",
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"\n",
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"\n"
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],
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"id": "53cce38938154ec2",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Pandas Dataframe: 0.0489 seconds\n",
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"Pandas PyArrow Dataframe: 0.0401 seconds\n"
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]
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}
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],
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"execution_count": 11
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Test de performance Polars\n",
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"\n",
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"Test d'un tri sur un `DateFrame` de 10 000 lignes et 34 colonnes "
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],
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"id": "c9fb11f939e169b7"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-30T15:55:16.739324Z",
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"start_time": "2024-06-30T15:55:16.185126Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import polars as pl\n",
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"from contexttimer import Timer\n",
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"\n",
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"\n",
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"loops: int = 10\n",
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"pf = pl.read_csv(\"data/womens-shoes.csv\", infer_schema_length=2 ** 30)\n",
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"\n",
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"# Polars Dataframe (±9ms)\n",
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"with Timer() as timer:\n",
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" for i in range(loops):\n",
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" df2 = pf.sort(\"prices.amountMin\", descending=bool(i % 2))\n",
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"print(f\"Polars Dataframe: {timer.elapsed:.4f} seconds\")\n",
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"\n"
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],
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"id": "58d61771c0970c52",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Polars Dataframe: 0.0092 seconds\n"
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]
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}
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],
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"execution_count": 9
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"source": [
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"# Test de performance DuckDB\n",
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"\n",
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"Test d'un tri sur un `DateFrame` de 10 000 lignes et 34 colonnes "
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],
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"id": "7a152865db550d98"
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2024-06-30T15:54:26.130463Z",
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"start_time": "2024-06-30T15:54:24.404916Z"
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}
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},
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"cell_type": "code",
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"source": [
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"import pandas as pd\n",
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"import duckdb as duck\n",
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"from contexttimer import Timer\n",
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"\n",
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"\n",
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"loops: int = 10\n",
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"df = pd.read_csv(\"data/womens-shoes.csv\")\n",
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"\n",
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"# DuckDB Dataframe (±1000ms)\n",
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"with Timer() as timer:\n",
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" for i in range(loops):\n",
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" asc = \"ASC\" if i % 2 == 0 else \"DESC\"\n",
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" df2 = duck.query(f\"\"\"SELECT * FROM df ORDER BY \"prices.amountMin\" {asc}\"\"\").to_df()\n",
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"print(f\"DuckDB on Pandas Dataframe: {timer.elapsed:.4f} seconds\")\n"
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],
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"id": "1f92c50608a41220",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"DuckDB on Pandas Dataframe: 1.2534 seconds\n"
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]
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}
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],
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"execution_count": 2
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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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": 2
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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": "ipython2",
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"version": "2.7.6"
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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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