spektom/distributions.ipynb

## distributions.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats, integrate\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set(color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(sum(map(ord, \"distributions\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f56045949d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.random.beta(1, 10, 5000)\n",
    "sns.distplot(x);"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 1,
	"metadata": {},
	"outputs": [],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 2,
	"metadata": {},
	"outputs": [],
	"source": [
	"import numpy as np\n",
	"import pandas as pd\n",
	"from scipy import stats, integrate\n",
	"import matplotlib.pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 3,
	"metadata": {},
	"outputs": [],
	"source": [
	"import seaborn as sns\n",
	"sns.set(color_codes=True)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {},
	"outputs": [],
	"source": [
	"np.random.seed(sum(map(ord, \"distributions\")))"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 11,
	"metadata": {},
	"outputs": [
	{
	"data": {
	"image/png": 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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f56045949d0>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"x = np.random.beta(1, 10, 5000)\n",
	"sns.distplot(x);"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 2",
	"language": "python",
	"name": "python2"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 2
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython2",
	"version": "2.7.14"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 2
	}