content/first-post.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First post!\n",
    "\n",
    "This will be the first post in our new Pelican blog! You can add any code or markdown cells to this Jupyter notebook, and they will be rendered on your blog."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "random.randint(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 2.,  1.,  1.,  0.,  2.,  0.,  1.,  0.,  0.,  2.]),\n",
       " array([  1. ,   1.9,   2.8,   3.7,   4.6,   5.5,   6.4,   7.3,   8.2,\n",
       "          9.1,  10. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEACAYAAABI5zaHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nl6SGjLskNWTcJakh4y5JDf0vZB9FlJ1A1boAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x104d3c898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "plt.hist([10,5,7,10,1,1,2,3,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}