{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "vals = np.random.normal(0, 0.5, 10000)\n",
    "\n",
    "plt.hist(vals, 50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0009360172270793777"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(vals, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6377966146892147"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(vals, 90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.41850731901570576"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(vals, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Activity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Experiment with different parameters when creating the test data. What effect does it have on the percentiles?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}