boarpig/Day of friday.ipynb

## Day of friday.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x6e9aba90>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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f9HP71VJNoA3oiIitEbEfeIBkozUSUVvrfFAOcyPhVcD96fD91PFNgYcpHxx8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x6e9185f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from datetime import datetime, timedelta\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "day = datetime(2017, 5, 12)\n",
    "week = timedelta(days=7)\n",
    "\n",
    "weekdays = [0]*32\n",
    "\n",
    "\n",
    "for i in range(100000):\n",
    "    day = day + week\n",
    "    weekdays[day.day] += 1\n",
    "\n",
    "plt.plot(weekdays[1:])"
   ]
  }
 ],
 "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 22,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"[<matplotlib.lines.Line2D at 0x6e9aba90>]"
	]
	},
	"execution_count": 22,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
	"image/png": 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4ISJagVZJV5S1NGZmDabqNYGI2BER69PhPcBLwIeBA8C4dLTxwPZ0eDbwQER0RcQWoANo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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x6e9185f0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"%matplotlib inline\n",
	"\n",
	"from datetime import datetime, timedelta\n",
	"import matplotlib.pyplot as plt\n",
	"\n",
	"day = datetime(2017, 5, 12)\n",
	"week = timedelta(days=7)\n",
	"\n",
	"weekdays = [0]*32\n",
	"\n",
	"\n",
	"for i in range(100000):\n",
	" day = day + week\n",
	" weekdays[day.day] += 1\n",
	"\n",
	"plt.plot(weekdays[1:])"
	]
	}
	],
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