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highsmallxu/read_single_file.ipynb

Created July 10, 2022 14:07
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read_single_file.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Fontconfig warning: ignoring UTF-8: not a valid region tag\n"
]
}
],
"source": [
"import pandas as pd\n",
"import dask.dataframe as dd\n",
"import datatable as dt\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 53.5 ms, sys: 15 ms, total: 68.5 ms\n",
"Wall time: 67.1 ms\n"
]
},
{
"data": {
"text/plain": [
"pandas.core.frame.DataFrame"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"df = pd.read_csv(\"data.csv\")\n",
"type(df)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 8.53 ms, sys: 4.39 ms, total: 12.9 ms\n",
"Wall time: 11.3 ms\n"
]
},
{
"data": {
"text/plain": [
"dask.dataframe.core.DataFrame"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"df = dd.read_csv(\"data.csv\")\n",
"type(df)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 289 ms, sys: 26 ms, total: 315 ms\n",
"Wall time: 39.7 ms\n"
]
},
{
"data": {
"text/plain": [
"datatable.Frame"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"df = dt.fread(\"data.csv\")\n",
"type(df)"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure()\n",
"ax = fig.add_axes([0,0,1,1])\n",
"ax.set_ylabel('ms')\n",
"ax.set_title('Speed of reading single csv file (4.7MB)')\n",
"lib = ['pandas', 'dask', 'datatable']\n",
"perf = [67.1,11.3,39.7]\n",
"ax.bar(lib,perf)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.9.0 64-bit ('3.9.0')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.0"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "285b4027c56aef32f9cffa7b798ac9ff266d7923f973d093da0977b0f49ab1ea"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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