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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import pandas as pd\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([500, 1000, 5000, 10000, 25000, 50000, 75000, 100000])\n",
"y_tmp = np.array([34933, 18122, 3916, 2125, 940, 533, 366, 269])\n",
"y = y_tmp/720 # Dividing by 60 (results in minutes) x 12 (number of threads)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Convert to pandas dataframe\n",
"d = {'x' : x, 'y' :y}\n",
"data = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# plot using lineplot\n",
"sns.set(style='darkgrid')\n",
"plot = sns.lineplot(x='x', y='y', data=data)\n",
"plot.set_xlabel(' value')\n",
"plot.set_ylabel('Time (minutes)')\n",
"plt.savefig('movies_time.png', dpi=300)\n"
]
}
],
"metadata": {
"interpreter": {
"hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
},
"kernelspec": {
"display_name": "Python 3.9.7 64-bit",
"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": "3.9.7"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}