{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2014 hourly data from http://www.nordpoolspot.com/historical-market-data/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.set(style=\"white\", palette=\"muted\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = np.array([[float(x.strip() or 0) for x in line.split(\",\")] for line in open(\"wind.csv\")])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = data[:, 1] == 0\n",
    "mask.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8758, 2)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data[~mask]\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fraction = data[:, 0] / data[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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U80qdSCuZaF/3uflCXh3HOnT1zJyGT06qbdf+HagQALCVCPA6LM1A9zb4DPRQKKxoJFbX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\npe8PoPmQVouWzwI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NqZKt6dL3rjpdDrDtmj/AM2XJkPwRWuBAs+u7IylfxKPxl+d09cyc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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b83f160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(1, 1, figsize=(7, 7), sharex=True)\n",
    "sns.despine(left=True)\n",
    "\n",
    "# Plot a simple histogram with binsize determined automatically\n",
    "# sns.distplot(fraction, kde=False, color=\"b\", ax=axes[0, 0])\n",
    "\n",
    "# Plot a kernel density estimate and rug plot\n",
    "# sns.distplot(fraction, hist=False, rug=True, color=\"r\", ax=axes[0, 1])\n",
    "\n",
    "# Plot a filled kernel density estimate\n",
    "# sns.distplot(fraction, hist=False, color=\"g\", kde_kws={\"shade\": True}, ax=axes[1, 0])\n",
    "\n",
    "# Plot a historgram and kernel density estimate\n",
    "# sns.distplot(fraction, color=\"m\", ax=axes[1, 1])\n",
    "sns.distplot(fraction, color=\"m\")\n",
    "# sns.distplot(fraction, color=\"m\")\n",
    "\n",
    "plt.setp(axes, yticks=[])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
