distributions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10eaa3590>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rn2Oi9j322GP47W9/iy1btmDevHm47777UFNTk69S05KofZ2dnViyZAnC4TCC\nwSDeffddXHfddTmtT+pu3Jw5c7Br1y4sXrw4tkz/xRdfxOTJk3H77bejvr4eS5cuhRACjz76qHRj\npsnaJ7tE7dM0DX/7298QCASwY8cOAMD3v/99zJo1K89Vpy7Z72/GjBmoq6uLjQtXVVXlu+Qx+Vf+\n+7z99tsxd+5cLFq0CCaTCXPnzsW0adNyWh+XxxMRSUiu/6cREREAhjcRkZQY3kREEmJ4ExFJiOFN\nRCQhhjcRkYQY3kREEvo/m+jJztro+eQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ea07dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "values = numpy.random.beta(0.1, 8, 3000)\n",
    "sns.distplot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c20fc10>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IcRmkyC/TUFmxciH16Rc+AUoONeCVfViEiBj9KvKysjJWrVr1ldtfeeUVFi5cyKpVq1i1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\nIYSIcFLkQggR4aTIhRAiwkmRCyFEhPv/kFHHAEh34y0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c07f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "values = scipy.stats.gompertz.rvs(3, size=1000)\n",
    "sns.distplot(values)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "_parse_args() got an unexpected keyword argument 'size'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-de628a3af062>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor_codes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mscipy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpdf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python2.7/site-packages/scipy/stats/_distn_infrastructure.pyc\u001b[0m in \u001b[0;36mpdf\u001b[0;34m(self, x, *args, **kwds)\u001b[0m\n\u001b[1;32m   1643\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1644\u001b[0m         \"\"\"\n\u001b[0;32m-> 1645\u001b[0;31m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_parse_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1646\u001b[0m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1647\u001b[0m         \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: _parse_args() got an unexpected keyword argument 'size'"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set(color_codes=True)\n",
    "values = scipy.stats.beta.pdf(2, 8, size=100)\n",
    "sns.distplot(values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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