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@Zsailer
Created August 29, 2019 18:01
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test
matplotlib
numpy
altair
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"2+2"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0\n",
"1\n",
"2\n",
"3\n",
"4\n",
"5\n",
"6\n",
"7\n",
"8\n",
"9\n"
]
}
],
"source": [
"for i in range(10):\n",
" print(i)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is plot of:\n",
"\n",
"$$\n",
"y = sin(x)\n",
"$$"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11921ae90>]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(0,10, 1000)\n",
"y = np.sin(x)\n",
"\n",
"plt.plot(x,y)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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