Skip to content

Instantly share code, notes, and snippets.

@highsmallxu

highsmallxu/read_multiple_files.ipynb

Created July 10, 2022 14:52
Show Gist options

  • Select an option

    Learn more about clone URLs
  • Save highsmallxu/cff797b7082fd2c080291c5f1581e768 to your computer and use it in GitHub Desktop.

Select an option

Learn more about clone URLs
Save highsmallxu/cff797b7082fd2c080291c5f1581e768 to your computer and use it in GitHub Desktop.
Download ZIP
Display the source blob
Display the rendered blob
Raw
read_multiple_files.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import dask.dataframe as dd\n",
"import datatable as dt\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 49.6 s, sys: 28.7 s, total: 1min 18s\n",
"Wall time: 1min 27s\n"
]
}
],
"source": [
"%%time\n",
"files = [\"1.csv\",\"2.csv\",\"3.csv\",\"4.csv\",\"5.csv\"]\n",
"combined = []\n",
"for f in files:\n",
" combined.append(pd.read_csv(f))\n",
"combined_df = pd.concat(combined,ignore_index=True)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 3.26 s, sys: 12.2 s, total: 15.5 s\n",
"Wall time: 20.5 s\n"
]
}
],
"source": [
"%%time\n",
"df = dd.read_csv(files)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1min 55s, sys: 1min 12s, total: 3min 8s\n",
"Wall time: 2min 20s\n"
]
}
],
"source": [
"%%time\n",
"df = dt.iread(files)\n",
"df = dt.rbind(df)\n",
"df = df.to_pandas()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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('s')\n",
"ax.set_title('Speed of reading multiple csv files')\n",
"lib = ['pandas', 'dask', 'datatable']\n",
"perf = [87,21,140]\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
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment