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{
"cells": [
{
"cell_type": "code",
"execution_count": 27,
"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": 28,
"metadata": {},
"outputs": [],
"source": [
"x = np.array([5, 10, 20, 30, 40, 50, 60, 70])\n",
"y_tmp = np.array([124524, 40162, 12673, 5561, 2796, 1365, 774, 449])\n",
"y = y_tmp/720"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Convert to pandas dataframe\n",
"d = {'x' : x, 'y' :y}\n",
"data = pd.DataFrame(d)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"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('MIN_ACTORS value')\n",
"plot.set_ylabel('Time (minutes)')\n",
"plt.savefig('actors_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
}