02B-Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Getting to know your data with Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pandas is the Python Data Analysis Library. \n",
    "\n",
    "Pandas is an extremely versatile tool for manipulating datasets.   \n",
    "\n",
    "It also produces high quality plots with matplotlib, and integrates nicely with other libraries that expect NumPy arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The most important tool provided by Pandas is the **data frame.**\n",
    "\n",
    "A data frame is a table in which each row and column is given a label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pandas DataFrames are documented at:\n",
    "\n",
    "http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import pandas_datareader.data as web\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns\n",
    "\n",
    "from datetime import datetime\n",
    "\n",
    "#pd.__version__\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Fetching, storing and retrieving your data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "For demonstration purposes, we'll use a library built-in to Pandas that fetches data from standard online sources, such as Yahoo! Finance.\n",
    "\n",
    "More information on what types of data you can fetch is at:\n",
    "http://pandas.pydata.org/pandas-docs/stable/remote_data.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2015-01-02 to 2015-12-31\n",
      "Data columns (total 6 columns):\n",
      "Open         252 non-null float64\n",
      "High         252 non-null float64\n",
      "Low          252 non-null float64\n",
      "Close        252 non-null float64\n",
      "Adj Close    252 non-null float64\n",
      "Volume       252 non-null int64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 13.8 KB\n"
     ]
    }
   ],
   "source": [
    "stocks = 'YELP'\n",
    "data_source = 'yahoo'\n",
    "start = datetime(2015,1,1)\n",
    "end = datetime(2015,12,31)\n",
    "\n",
    "yahoo_stocks = web.DataReader(stocks, data_source, start, end)\n",
    "\n",
    "yahoo_stocks.head()\n",
    "yahoo_stocks.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reading data from a .csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,Open,High,Low,Close,Adj Close,Volume\n",
      "2015-01-02,55.459998999999996,55.599998,54.240002000000004,55.150002,55.150002,1664500\n",
      "2015-01-05,54.540001000000004,54.950001,52.330002,52.529999,52.529999,2023000\n",
      "2015-01-06,52.549999,53.93,50.75,52.439999,52.439999,3762800\n",
      "2015-01-07,53.32,53.75,51.759997999999996,52.209998999999996,52.209998999999996,1548200\n",
      "2015-01-08,52.59,54.139998999999996,51.759997999999996,53.830002,53.830002,2015300\n",
      "2015-01-09,55.959998999999996,56.990002000000004,54.720001,56.07,56.07,6222600\n",
      "2015-01-12,56.0,56.060001,53.43,54.02,54.02,2405100\n",
      "2015-01-13,54.470001,54.799999,52.52,53.18,53.18,1952100\n",
      "2015-01-14,52.799999,53.68,51.459998999999996,52.200001,52.200001,1854600\n",
      "2015-01-15,53.0,53.610001000000004,50.029999,50.119999,50.119999,2647800\n",
      "2015-01-16,50.18,51.490002000000004,50.029999,51.389998999999996,51.389998999999996,2183300\n",
      "2015-01-20,51.650002,51.779999,50.689999,51.41,51.41,1227600\n",
      "2015-01-21,51.200001,53.5,51.200001,53.41,53.41,3248100\n",
      "2015-01-22,53.869999,55.279999,53.119999,54.799999,54.799999,2295400\n",
      "2015-01-23,54.66,55.639998999999996,54.299999,55.189999,55.189999,1629000\n",
      "2015-01-26,55.119999,55.790001000000004,54.830002,55.41,55.41,1450300\n",
      "2015-01-27,56.060001,56.16,54.57,55.630001,55.630001,2410400\n",
      "2015-01-28,56.150002,56.150002,52.919998,53.0,53.0,2013100\n",
      "2015-01-29,52.849998,53.310001,51.41,52.93,52.93,1844100\n",
      "2015-01-30,52.59,53.419998,52.049999,52.470001,52.470001,1875400\n",
      "2015-02-02,52.939999,53.5,51.209998999999996,53.470001,53.470001,2105500\n",
      "2015-02-03,53.830002,55.93,53.41,55.779999,55.779999,2876400\n",
      "2015-02-04,55.529999,57.07,55.25,56.740002000000004,56.740002000000004,2498600\n",
      "2015-02-05,57.599998,57.700001,56.080002,57.470001,57.470001,4657300\n",
      "2015-02-06,47.700001,48.169998,44.860001000000004,45.110001000000004,45.110001000000004,25137400\n",
      "2015-02-09,44.91,45.040001000000004,42.099998,42.169998,42.169998,13079300\n",
      "2015-02-10,43.830002,45.549999,43.310001,44.66,44.66,11267700\n",
      "2015-02-11,45.389998999999996,46.43,44.810001,46.18,46.18,6359400\n",
      "2015-02-12,46.450001,47.84,45.950001,47.630001,47.630001,4375000\n",
      "2015-02-13,48.509997999999996,49.049999,47.220001,47.529999,47.529999,4713100\n",
      "2015-02-17,47.439999,48.619999,47.029999,48.189999,48.189999,2390100\n",
      "2015-02-18,47.939999,48.689999,47.200001,47.540001000000004,47.540001000000004,2529500\n",
      "2015-02-19,47.16,47.790001000000004,46.869999,47.34,47.34,1642200\n",
      "2015-02-20,47.400002,47.919998,47.099998,47.790001000000004,47.790001000000004,1688500\n",
      "2015-02-23,47.549999,47.740002000000004,46.529999,47.349998,47.349998,2086000\n",
      "2015-02-24,46.98,47.73,46.619999,47.290001000000004,47.290001000000004,1506500\n",
      "2015-02-25,46.939999,47.450001,46.5,47.150002,47.150002,1924600\n",
      "2015-02-26,48.630001,48.810001,47.560001,47.75,47.75,3059400\n",
      "2015-02-27,48.32,48.439999,47.049999,48.0,48.0,2118400\n",
      "2015-03-02,48.02,48.459998999999996,47.189999,47.860001000000004,47.860001000000004,1933700\n",
      "2015-03-03,47.75,48.98,47.330002,48.580002,48.580002,2352100\n",
      "2015-03-04,48.68,48.869999,47.310001,47.790001000000004,47.790001000000004,2218800\n",
      "2015-03-05,47.689999,48.700001,47.41,47.939999,47.939999,1696900\n",
      "2015-03-06,47.75,48.580002,46.919998,47.049999,47.049999,1994800\n",
      "2015-03-09,46.959998999999996,46.959998999999996,45.34,45.82,45.82,2554800\n",
      "2015-03-10,45.040001000000004,45.900002,44.25,45.23,45.23,2361400\n",
      "2015-03-11,45.080002,46.73,44.73,45.73,45.73,2227300\n",
      "2015-03-12,45.900002,46.82,45.540001000000004,46.799999,46.799999,1657400\n",
      "2015-03-13,46.759997999999996,47.5,46.130001,46.450001,46.450001,2259400\n",
      "2015-03-16,46.349998,46.75,45.599998,46.709998999999996,46.709998999999996,1607800\n",
      "2015-03-17,46.599998,47.59,46.310001,47.220001,47.220001,1723000\n",
      "2015-03-18,47.040001000000004,47.57,46.599998,46.82,46.82,2514800\n",
      "2015-03-19,46.669998,47.240002000000004,44.34,45.18,45.18,9280900\n",
      "2015-03-20,45.32,46.400002,44.860001000000004,44.939999,44.939999,4240400\n",
      "2015-03-23,44.860001000000004,47.150002,44.740002000000004,47.029999,47.029999,3658100\n",
      "2015-03-24,46.970001,47.360001000000004,46.529999,47.040001000000004,47.040001000000004,2266600\n",
      "2015-03-25,47.040001000000004,47.240002000000004,45.73,45.759997999999996,45.759997999999996,2524700\n",
      "2015-03-26,45.650002,46.599998,45.310001,45.709998999999996,45.709998999999996,1692000\n",
      "2015-03-27,45.82,47.349998,45.759997999999996,47.16,47.16,1885200\n",
      "2015-03-30,47.099998,48.240002000000004,46.709998999999996,47.389998999999996,47.389998999999996,3333800\n",
      "2015-03-31,47.029999,47.919998,46.790001000000004,47.349998,47.349998,1922500\n",
      "2015-04-01,47.25,47.369999,45.09,45.5,45.5,3670000\n",
      "2015-04-02,45.400002,47.5,45.32,47.139998999999996,47.139998999999996,2594600\n",
      "2015-04-06,46.25,47.82,46.080002,47.310001,47.310001,1560500\n",
      "2015-04-07,47.279999,48.169998,46.959998999999996,46.990002000000004,46.990002000000004,1434800\n",
      "2015-04-08,46.82,47.779999,46.369999,47.43,47.43,1856500\n",
      "2015-04-09,47.400002,48.099998,46.779999,47.0,47.0,1184400\n",
      "2015-04-10,47.049999,47.720001,46.549999,47.650002,47.650002,1407400\n",
      "2015-04-13,47.720001,48.34,47.27,47.400002,47.400002,1485900\n",
      "2015-04-14,47.360001000000004,47.950001,46.709998999999996,47.599998,47.599998,1692500\n",
      "2015-04-15,47.619999,50.200001,47.580002,49.57,49.57,5395300\n",
      "2015-04-16,49.330002,50.0,48.529999,49.369999,49.369999,2694500\n",
      "2015-04-17,48.91,49.009997999999996,47.939999,48.299999,48.299999,2068500\n",
      "2015-04-20,48.450001,48.490002000000004,47.830002,48.25,48.25,1112200\n",
      "2015-04-21,48.419998,49.75,48.150002,49.310001,49.310001,1686900\n",
      "2015-04-22,49.959998999999996,51.150002,49.790001000000004,50.450001,50.450001,2499300\n",
      "2015-04-23,50.509997999999996,50.75,49.700001,50.32,50.32,1457100\n",
      "2015-04-24,50.830002,51.220001,50.27,50.610001000000004,50.610001000000004,1433500\n",
      "2015-04-27,50.98,52.509997999999996,50.880001,51.02,51.02,2388300\n",
      "2015-04-28,51.299999,52.25,51.060001,51.220001,51.220001,2433700\n",
      "2015-04-29,51.0,51.73,50.380001,51.279999,51.279999,6480400\n",
      "2015-04-30,41.25,42.290001000000004,38.75,39.389998999999996,39.389998999999996,25307400\n",
      "2015-05-01,39.310001,39.889998999999996,38.540001000000004,39.759997999999996,39.759997999999996,5876800\n",
      "2015-05-04,39.689999,39.709998999999996,38.68,39.610001000000004,39.610001000000004,5171100\n",
      "2015-05-05,39.540001000000004,39.990002000000004,38.689999,38.880001,38.880001,2500500\n",
      "2015-05-06,38.799999,39.09,37.91,38.220001,38.220001,2689200\n",
      "2015-05-07,38.220001,48.73,38.220001,47.009997999999996,47.009997999999996,33831600\n",
      "2015-05-08,47.25,50.990002000000004,47.200001,49.93,49.93,24155600\n",
      "2015-05-11,49.360001000000004,50.290001000000004,47.880001,48.619999,48.619999,10430300\n",
      "2015-05-12,48.220001,49.860001000000004,48.150002,48.830002,48.830002,7587400\n",
      "2015-05-13,48.830002,49.349998,47.25,47.84,47.84,5577100\n",
      "2015-05-14,47.849998,48.470001,47.259997999999996,47.349998,47.349998,3539400\n",
      "2015-05-15,47.360001000000004,47.57,46.68,46.889998999999996,46.889998999999996,3567700\n",
      "2015-05-18,45.700001,46.619999,45.5,46.560001,46.560001,2890700\n",
      "2015-05-19,46.52,48.560001,46.0,46.459998999999996,46.459998999999996,4677600\n",
      "2015-05-20,46.549999,46.93,45.75,46.43,46.43,1605000\n",
      "2015-05-21,46.099998,46.880001,45.849998,46.139998999999996,46.139998999999996,1929800\n",
      "2015-05-22,46.0,46.830002,46.0,46.48,46.48,1373800\n",
      "2015-05-26,46.130001,46.810001,45.360001000000004,45.549999,45.549999,2142300\n",
      "2015-05-27,45.849998,46.68,44.830002,45.52,45.52,2544100\n",
      "2015-05-28,45.099998,47.919998,44.849998,47.75,47.75,4838900\n",
      "2015-05-29,47.16,48.689999,46.75,47.91,47.91,3600400\n",
      "2015-06-01,47.709998999999996,48.27,46.869999,47.41,47.41,2296200\n",
      "2015-06-02,47.450001,48.900002,47.310001,48.580002,48.580002,2060700\n",
      "2015-06-03,48.66,48.799999,47.279999,47.560001,47.560001,2200200\n",
      "2015-06-04,47.330002,47.860001000000004,46.790001000000004,47.150002,47.150002,3160400\n",
      "2015-06-05,47.529999,48.5,47.099998,48.220001,48.220001,2715200\n",
      "2015-06-08,47.900002,48.040001000000004,45.619999,45.669998,45.669998,3707000\n",
      "2015-06-09,45.970001,46.27,45.23,45.439999,45.439999,2635900\n",
      "2015-06-10,45.810001,45.810001,44.400002,44.459998999999996,44.459998999999996,3719400\n",
      "2015-06-11,44.610001000000004,44.860001000000004,43.41,43.540001000000004,43.540001000000004,5014700\n",
      "2015-06-12,43.380001,44.299999,43.25,44.040001000000004,44.040001000000004,3401900\n",
      "2015-06-15,43.490002000000004,44.389998999999996,43.490002000000004,44.0,44.0,2341400\n",
      "2015-06-16,43.939999,46.220001,43.830002,44.700001,44.700001,5072500\n",
      "2015-06-17,44.939999,45.189999,44.18,44.599998,44.599998,1711700\n",
      "2015-06-18,44.509997999999996,45.830002,44.360001000000004,45.439999,45.439999,2045600\n",
      "2015-06-19,45.060001,45.459998999999996,44.75,45.119999,45.119999,2302200\n",
      "2015-06-22,45.349998,46.02,44.830002,45.279999,45.279999,2115400\n",
      "2015-06-23,45.509997999999996,46.400002,45.080002,46.02,46.02,1833600\n",
      "2015-06-24,46.0,46.25,45.189999,45.25,45.25,1427000\n",
      "2015-06-25,45.540001000000004,45.849998,44.84,44.880001,44.880001,1368800\n",
      "2015-06-26,44.959998999999996,45.240002000000004,44.209998999999996,44.509997999999996,44.509997999999996,2859400\n",
      "2015-06-29,43.669998,44.32,42.32,42.470001,42.470001,2402100\n",
      "2015-06-30,42.970001,43.349998,42.540001000000004,43.029999,43.029999,1834600\n",
      "2015-07-01,43.360001000000004,43.490002000000004,42.049999,42.439999,42.439999,1696000\n",
      "2015-07-02,42.369999,42.369999,36.099998,38.18,38.18,13264600\n",
      "2015-07-06,37.709998999999996,38.34,36.650002,37.490002000000004,37.490002000000004,6536900\n",
      "2015-07-07,37.310001,37.360001000000004,35.099998,36.299999,36.299999,6272400\n",
      "2015-07-08,35.639998999999996,36.25,35.139998999999996,35.400002,35.400002,2428400\n",
      "2015-07-09,35.919998,36.060001,34.630001,34.75,34.75,2778900\n",
      "2015-07-10,35.099998,35.43,34.650002,34.73,34.73,2223300\n",
      "2015-07-13,34.84,35.700001,34.650002,35.43,35.43,4069800\n",
      "2015-07-14,35.43,36.040001000000004,34.73,35.990002000000004,35.990002000000004,2972400\n",
      "2015-07-15,35.799999,36.080002,34.849998,35.0,35.0,2763000\n",
      "2015-07-16,35.25,35.549999,34.549999,35.110001000000004,35.110001000000004,2186800\n",
      "2015-07-17,35.459998999999996,35.900002,34.669998,34.959998999999996,34.959998999999996,3014200\n",
      "2015-07-20,33.939999,35.349998,33.349998,34.549999,34.549999,4215400\n",
      "2015-07-21,34.549999,35.650002,34.5,35.57,35.57,2824500\n",
      "2015-07-22,34.950001,35.290001000000004,33.889998999999996,34.439999,34.439999,2852400\n",
      "2015-07-23,34.439999,35.450001,34.34,35.040001000000004,35.040001000000004,2188300\n",
      "2015-07-24,35.290001000000004,35.299999,34.0,34.560001,34.560001,2094600\n",
      "2015-07-27,33.98,34.32,33.529999,33.73,33.73,3470300\n",
      "2015-07-28,33.060001,33.799999,32.360001000000004,33.509997999999996,33.509997999999996,8529200\n",
      "2015-07-29,24.940001000000002,25.5,23.66,25.059998999999998,25.059998999999998,34598900\n",
      "2015-07-30,24.91,26.440001000000002,24.809998999999998,26.030001000000002,26.030001000000002,7878800\n",
      "2015-07-31,25.690001000000002,27.48,25.690001000000002,26.4,26.4,7889400\n",
      "2015-08-03,26.6,27.700001,25.559998999999998,25.92,25.92,6636900\n",
      "2015-08-04,25.879998999999998,26.32,25.4,26.18,26.18,3176900\n",
      "2015-08-05,26.290001,27.07,26.049999,26.129998999999998,26.129998999999998,2830200\n",
      "2015-08-06,26.02,26.23,25.040001,25.379998999999998,25.379998999999998,2389000\n",
      "2015-08-07,25.280001000000002,25.75,24.99,25.35,25.35,2167200\n",
      "2015-08-10,25.6,26.459999,25.530001000000002,26.0,26.0,2139100\n",
      "2015-08-11,25.65,25.969998999999998,24.91,25.35,25.35,2356000\n",
      "2015-08-12,24.93,25.629998999999998,24.57,25.469998999999998,25.469998999999998,2341800\n",
      "2015-08-13,25.52,25.75,24.51,24.540001,24.540001,2263600\n",
      "2015-08-14,25.26,26.139999,24.91,25.77,25.77,4242700\n",
      "2015-08-17,25.73,27.040001,25.5,26.639999,26.639999,4077300\n",
      "2015-08-18,26.610001,26.66,25.01,25.41,25.41,4037700\n",
      "2015-08-19,25.129998999999998,25.68,24.709999,25.25,25.25,2257700\n",
      "2015-08-20,24.719998999999998,25.040001,24.0,24.01,24.01,2930700\n",
      "2015-08-21,23.709999,23.719998999999998,22.67,23.09,23.09,5473700\n",
      "2015-08-24,21.66,24.23,20.5,22.98,22.98,4882000\n",
      "2015-08-25,23.870001000000002,24.15,22.719998999999998,22.75,22.75,2860500\n",
      "2015-08-26,23.34,23.51,22.51,23.379998999999998,23.379998999999998,3354500\n",
      "2015-08-27,24.040001,24.530001000000002,23.16,23.98,23.98,4050900\n",
      "2015-08-28,23.639999,24.33,23.639999,23.959999,23.959999,2625100\n",
      "2015-08-31,23.91,24.58,23.700001,24.280001000000002,24.280001000000002,2257200\n",
      "2015-09-01,23.59,24.389999,23.16,23.4,23.4,3158300\n",
      "2015-09-02,23.700001,24.290001,23.24,24.280001000000002,24.280001000000002,2358900\n",
      "2015-09-03,24.299999,24.48,23.879998999999998,24.17,24.17,1614900\n",
      "2015-09-04,23.700001,24.66,23.6,24.209999,24.209999,1894400\n",
      "2015-09-08,24.610001,24.610001,23.940001000000002,23.98,23.98,3224400\n",
      "2015-09-09,24.08,25.33,24.07,24.68,24.68,2660400\n",
      "2015-09-10,24.73,25.5,24.6,25.120001000000002,25.120001000000002,1786900\n",
      "2015-09-11,25.0,25.370001000000002,24.629998999999998,24.709999,24.709999,1702900\n",
      "2015-09-14,24.77,24.940001000000002,24.17,24.469998999999998,24.469998999999998,1678700\n",
      "2015-09-15,24.41,25.200001,24.299999,24.940001000000002,24.940001000000002,1316500\n",
      "2015-09-16,24.809998999999998,25.93,24.73,25.290001,25.290001,3299800\n",
      "2015-09-17,25.120001000000002,25.68,24.9,25.129998999999998,25.129998999999998,2011400\n",
      "2015-09-18,24.74,25.440001000000002,24.549999,24.75,24.75,2556500\n",
      "2015-09-21,24.860001,24.959999,24.1,24.23,24.23,2279600\n",
      "2015-09-22,23.92,24.49,23.209999,23.35,23.35,2394800\n",
      "2015-09-23,23.5,23.5,22.93,23.1,23.1,1655100\n",
      "2015-09-24,22.469998999999998,22.67,22.0,22.58,22.58,2778600\n",
      "2015-09-25,22.68,22.99,21.959999,22.17,22.17,1701300\n",
      "2015-09-28,22.15,22.32,21.370001000000002,21.379998999999998,21.379998999999998,1974700\n",
      "2015-09-29,21.67,21.969998999999998,21.0,21.139999,21.139999,2474400\n",
      "2015-09-30,21.49,22.110001,21.219998999999998,21.66,21.66,1933600\n",
      "2015-10-01,21.65,21.870001000000002,20.75,20.870001000000002,20.870001000000002,2769100\n",
      "2015-10-02,20.75,22.48,20.6,22.379998999999998,22.379998999999998,2248900\n",
      "2015-10-05,22.379998999999998,22.709999,22.059998999999998,22.68,22.68,1809300\n",
      "2015-10-06,22.719998999999998,23.190001000000002,22.51,22.68,22.68,1704300\n",
      "2015-10-07,22.879998999999998,23.530001000000002,22.459999,23.5,23.5,1527900\n",
      "2015-10-08,23.459999,23.870001000000002,22.879998999999998,23.610001,23.610001,1299600\n",
      "2015-10-09,23.610001,25.02,23.35,24.879998999999998,24.879998999999998,3320600\n",
      "2015-10-12,24.690001000000002,24.82,22.219998999999998,22.74,22.74,4755000\n",
      "2015-10-13,22.809998999999998,23.299999,22.469998999999998,22.49,22.49,1696300\n",
      "2015-10-14,22.48,22.879998999999998,21.870001000000002,21.98,21.98,2223100\n",
      "2015-10-15,22.200001,22.84,22.059998999999998,22.610001,22.610001,2083700\n",
      "2015-10-16,22.709999,22.799999,22.040001,22.65,22.65,1720600\n",
      "2015-10-19,22.51,22.84,22.309998999999998,22.6,22.6,1402900\n",
      "2015-10-20,22.58,23.120001000000002,22.129998999999998,22.52,22.52,1663800\n",
      "2015-10-21,22.52,22.75,22.190001000000002,22.389999,22.389999,1340800\n",
      "2015-10-22,22.51,23.0,22.07,22.52,22.52,2341200\n",
      "2015-10-23,22.799999,22.889999,22.110001,22.559998999999998,22.559998999999998,3188700\n",
      "2015-10-26,22.6,24.799999,22.32,24.43,24.43,6890700\n",
      "2015-10-27,24.299999,24.299999,22.01,22.9,22.9,6616200\n",
      "2015-10-28,21.639999,22.809998999999998,21.43,22.07,22.07,9403700\n",
      "2015-10-29,23.26,24.200001,22.41,22.950001,22.950001,9628800\n",
      "2015-10-30,23.110001,23.15,22.0,22.25,22.25,5010000\n",
      "2015-11-02,22.26,23.85,22.209999,23.799999,23.799999,6459600\n",
      "2015-11-03,23.67,24.450001,23.6,24.120001000000002,24.120001000000002,2482100\n",
      "2015-11-04,24.030001000000002,24.6,23.879998999999998,24.42,24.42,1698500\n",
      "2015-11-05,24.5,25.559998999999998,24.32,25.049999,25.049999,2794300\n",
      "2015-11-06,25.049999,25.530001000000002,24.5,25.5,25.5,2095400\n",
      "2015-11-09,25.34,25.65,24.629998999999998,24.959999,24.959999,1567200\n",
      "2015-11-10,24.85,25.360001,24.59,25.16,25.16,1267900\n",
      "2015-11-11,25.1,25.309998999999998,24.540001,24.98,24.98,1364400\n",
      "2015-11-12,25.18,27.219998999999998,24.9,25.9,25.9,5213300\n",
      "2015-11-13,26.719998999999998,27.49,26.120001000000002,27.1,27.1,4976000\n",
      "2015-11-16,27.0,27.59,26.469998999999998,27.440001000000002,27.440001000000002,3066700\n",
      "2015-11-17,27.34,27.610001,26.860001,27.540001,27.540001,2018000\n",
      "2015-11-18,27.540001,28.83,27.309998999999998,28.23,28.23,3091600\n",
      "2015-11-19,28.190001000000002,28.690001000000002,27.91,28.059998999999998,28.059998999999998,1487500\n",
      "2015-11-20,28.1,31.25,28.049999,31.209999,31.209999,6697500\n",
      "2015-11-23,30.58,30.809998999999998,29.15,29.860001,29.860001,4029900\n",
      "2015-11-24,29.459999,30.629998999999998,29.450001,30.01,30.01,2584500\n",
      "2015-11-25,29.790001,30.540001,29.709999,30.51,30.51,1287100\n",
      "2015-11-27,30.5,30.6,29.610001,30.18,30.18,1058900\n",
      "2015-11-30,30.110001,30.719998999999998,29.77,30.129998999999998,30.129998999999998,2015600\n",
      "2015-12-01,30.110001,30.459999,29.799999,30.309998999999998,30.309998999999998,1886000\n",
      "2015-12-02,30.299999,32.470001,30.290001,31.389999,31.389999,4650300\n",
      "2015-12-03,31.389999,32.240002000000004,30.48,30.629998999999998,30.629998999999998,2698900\n",
      "2015-12-04,30.530001000000002,30.860001,29.32,30.450001,30.450001,2313800\n",
      "2015-12-07,30.379998999999998,30.639999,29.629998999999998,30.040001,30.040001,1362300\n",
      "2015-12-08,29.809998999999998,31.379998999999998,29.5,30.92,30.92,1830200\n",
      "2015-12-09,30.98,31.139999,29.26,30.0,30.0,2238500\n",
      "2015-12-10,30.110001,31.299999,29.99,30.83,30.83,1251800\n",
      "2015-12-11,30.690001000000002,30.75,29.6,29.65,29.65,1415000\n",
      "2015-12-14,29.6,29.889999,28.85,29.58,29.58,2328600\n",
      "2015-12-15,29.68,30.0,26.459999,26.870001000000002,26.870001000000002,5759200\n",
      "2015-12-16,26.889999,28.24,26.26,28.030001000000002,28.030001000000002,2992100\n",
      "2015-12-17,28.139999,28.32,27.190001000000002,27.42,27.42,1483900\n",
      "2015-12-18,27.309998999999998,27.91,26.9,27.17,27.17,1299800\n",
      "2015-12-21,27.17,27.360001,26.030001000000002,26.25,26.25,1947600\n",
      "2015-12-22,26.25,28.700001,26.15,27.93,27.93,2952700\n",
      "2015-12-23,27.950001,28.42,27.440001000000002,28.15,28.15,1001000\n",
      "2015-12-24,28.27,28.59,27.9,28.4,28.4,587400\n",
      "2015-12-28,28.120001000000002,28.379998999999998,27.77,27.879998999999998,27.879998999999998,1004500\n",
      "2015-12-29,27.950001,28.540001,27.74,28.48,28.48,1103900\n",
      "2015-12-30,28.58,28.780001000000002,28.17,28.25,28.25,1068000\n",
      "2015-12-31,28.1,28.969998999999998,28.02,28.799999,28.799999,1301500\n",
      "\n"
     ]
    }
   ],
   "source": [
    "yahoo_stocks.to_csv('yahoo_data.csv')\n",
    "print(open('yahoo_data.csv').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2015-01-09</td>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6222600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2015-01-12</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>53.430000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>2405100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-01-13</td>\n",
       "      <td>54.470001</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>52.520000</td>\n",
       "      <td>53.180000</td>\n",
       "      <td>53.180000</td>\n",
       "      <td>1952100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2015-01-14</td>\n",
       "      <td>52.799999</td>\n",
       "      <td>53.680000</td>\n",
       "      <td>51.459999</td>\n",
       "      <td>52.200001</td>\n",
       "      <td>52.200001</td>\n",
       "      <td>1854600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-01-15</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.610001</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>50.119999</td>\n",
       "      <td>50.119999</td>\n",
       "      <td>2647800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-01-16</td>\n",
       "      <td>50.180000</td>\n",
       "      <td>51.490002</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>2183300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-01-20</td>\n",
       "      <td>51.650002</td>\n",
       "      <td>51.779999</td>\n",
       "      <td>50.689999</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>1227600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-01-21</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>3248100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-01-22</td>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-01-23</td>\n",
       "      <td>54.660000</td>\n",
       "      <td>55.639999</td>\n",
       "      <td>54.299999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>1629000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-01-26</td>\n",
       "      <td>55.119999</td>\n",
       "      <td>55.790001</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1450300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015-01-27</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>56.160000</td>\n",
       "      <td>54.570000</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>2410400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-01-28</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>52.919998</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2013100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-01-29</td>\n",
       "      <td>52.849998</td>\n",
       "      <td>53.310001</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>52.930000</td>\n",
       "      <td>52.930000</td>\n",
       "      <td>1844100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-01-30</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.419998</td>\n",
       "      <td>52.049999</td>\n",
       "      <td>52.470001</td>\n",
       "      <td>52.470001</td>\n",
       "      <td>1875400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-02-02</td>\n",
       "      <td>52.939999</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.209999</td>\n",
       "      <td>53.470001</td>\n",
       "      <td>53.470001</td>\n",
       "      <td>2105500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-02-03</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>55.930000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>2876400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-02-04</td>\n",
       "      <td>55.529999</td>\n",
       "      <td>57.070000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>2498600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-02-05</td>\n",
       "      <td>57.599998</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>56.080002</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>4657300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-02-06</td>\n",
       "      <td>47.700001</td>\n",
       "      <td>48.169998</td>\n",
       "      <td>44.860001</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>25137400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-02-09</td>\n",
       "      <td>44.910000</td>\n",
       "      <td>45.040001</td>\n",
       "      <td>42.099998</td>\n",
       "      <td>42.169998</td>\n",
       "      <td>42.169998</td>\n",
       "      <td>13079300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-02-10</td>\n",
       "      <td>43.830002</td>\n",
       "      <td>45.549999</td>\n",
       "      <td>43.310001</td>\n",
       "      <td>44.660000</td>\n",
       "      <td>44.660000</td>\n",
       "      <td>11267700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-02-11</td>\n",
       "      <td>45.389999</td>\n",
       "      <td>46.430000</td>\n",
       "      <td>44.810001</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>6359400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-02-12</td>\n",
       "      <td>46.450001</td>\n",
       "      <td>47.840000</td>\n",
       "      <td>45.950001</td>\n",
       "      <td>47.630001</td>\n",
       "      <td>47.630001</td>\n",
       "      <td>4375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-02-13</td>\n",
       "      <td>48.509998</td>\n",
       "      <td>49.049999</td>\n",
       "      <td>47.220001</td>\n",
       "      <td>47.529999</td>\n",
       "      <td>47.529999</td>\n",
       "      <td>4713100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>222</th>\n",
       "      <td>2015-11-18</td>\n",
       "      <td>27.540001</td>\n",
       "      <td>28.830000</td>\n",
       "      <td>27.309999</td>\n",
       "      <td>28.230000</td>\n",
       "      <td>28.230000</td>\n",
       "      <td>3091600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>2015-11-19</td>\n",
       "      <td>28.190001</td>\n",
       "      <td>28.690001</td>\n",
       "      <td>27.910000</td>\n",
       "      <td>28.059999</td>\n",
       "      <td>28.059999</td>\n",
       "      <td>1487500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>2015-11-20</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>28.049999</td>\n",
       "      <td>31.209999</td>\n",
       "      <td>31.209999</td>\n",
       "      <td>6697500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2015-11-23</td>\n",
       "      <td>30.580000</td>\n",
       "      <td>30.809999</td>\n",
       "      <td>29.150000</td>\n",
       "      <td>29.860001</td>\n",
       "      <td>29.860001</td>\n",
       "      <td>4029900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>2015-11-24</td>\n",
       "      <td>29.459999</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>29.450001</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>2584500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>2015-11-25</td>\n",
       "      <td>29.790001</td>\n",
       "      <td>30.540001</td>\n",
       "      <td>29.709999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>1287100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>2015-11-27</td>\n",
       "      <td>30.500000</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>29.610001</td>\n",
       "      <td>30.180000</td>\n",
       "      <td>30.180000</td>\n",
       "      <td>1058900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>2015-11-30</td>\n",
       "      <td>30.110001</td>\n",
       "      <td>30.719999</td>\n",
       "      <td>29.770000</td>\n",
       "      <td>30.129999</td>\n",
       "      <td>30.129999</td>\n",
       "      <td>2015600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>30.110001</td>\n",
       "      <td>30.459999</td>\n",
       "      <td>29.799999</td>\n",
       "      <td>30.309999</td>\n",
       "      <td>30.309999</td>\n",
       "      <td>1886000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>2015-12-02</td>\n",
       "      <td>30.299999</td>\n",
       "      <td>32.470001</td>\n",
       "      <td>30.290001</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>4650300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>2015-12-03</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>32.240002</td>\n",
       "      <td>30.480000</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>2698900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>2015-12-04</td>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.860001</td>\n",
       "      <td>29.320000</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>2313800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>2015-12-07</td>\n",
       "      <td>30.379999</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>29.629999</td>\n",
       "      <td>30.040001</td>\n",
       "      <td>30.040001</td>\n",
       "      <td>1362300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>2015-12-08</td>\n",
       "      <td>29.809999</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>30.920000</td>\n",
       "      <td>30.920000</td>\n",
       "      <td>1830200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>2015-12-09</td>\n",
       "      <td>30.980000</td>\n",
       "      <td>31.139999</td>\n",
       "      <td>29.260000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2238500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>2015-12-10</td>\n",
       "      <td>30.110001</td>\n",
       "      <td>31.299999</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>30.830000</td>\n",
       "      <td>30.830000</td>\n",
       "      <td>1251800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>2015-12-11</td>\n",
       "      <td>30.690001</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>1415000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>2015-12-14</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>29.889999</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>2328600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>2015-12-15</td>\n",
       "      <td>29.680000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>26.459999</td>\n",
       "      <td>26.870001</td>\n",
       "      <td>26.870001</td>\n",
       "      <td>5759200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>2015-12-16</td>\n",
       "      <td>26.889999</td>\n",
       "      <td>28.240000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>28.030001</td>\n",
       "      <td>28.030001</td>\n",
       "      <td>2992100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2015-12-17</td>\n",
       "      <td>28.139999</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>27.190001</td>\n",
       "      <td>27.420000</td>\n",
       "      <td>27.420000</td>\n",
       "      <td>1483900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2015-12-18</td>\n",
       "      <td>27.309999</td>\n",
       "      <td>27.910000</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>27.170000</td>\n",
       "      <td>27.170000</td>\n",
       "      <td>1299800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2015-12-21</td>\n",
       "      <td>27.170000</td>\n",
       "      <td>27.360001</td>\n",
       "      <td>26.030001</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>1947600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2015-12-22</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>28.700001</td>\n",
       "      <td>26.150000</td>\n",
       "      <td>27.930000</td>\n",
       "      <td>27.930000</td>\n",
       "      <td>2952700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>2015-12-23</td>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.420000</td>\n",
       "      <td>27.440001</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>1001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>2015-12-24</td>\n",
       "      <td>28.270000</td>\n",
       "      <td>28.590000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>587400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>2015-12-28</td>\n",
       "      <td>28.120001</td>\n",
       "      <td>28.379999</td>\n",
       "      <td>27.770000</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>1004500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>2015-12-29</td>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.540001</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>1103900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>2015-12-30</td>\n",
       "      <td>28.580000</td>\n",
       "      <td>28.780001</td>\n",
       "      <td>28.170000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>1068000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>28.969999</td>\n",
       "      <td>28.020000</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>1301500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date       Open       High        Low      Close  Adj Close  \\\n",
       "0    2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002   \n",
       "1    2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999   \n",
       "2    2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999   \n",
       "3    2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999   \n",
       "4    2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002   \n",
       "5    2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000   \n",
       "6    2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000   \n",
       "7    2015-01-13  54.470001  54.799999  52.520000  53.180000  53.180000   \n",
       "8    2015-01-14  52.799999  53.680000  51.459999  52.200001  52.200001   \n",
       "9    2015-01-15  53.000000  53.610001  50.029999  50.119999  50.119999   \n",
       "10   2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999   \n",
       "11   2015-01-20  51.650002  51.779999  50.689999  51.410000  51.410000   \n",
       "12   2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000   \n",
       "13   2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999   \n",
       "14   2015-01-23  54.660000  55.639999  54.299999  55.189999  55.189999   \n",
       "15   2015-01-26  55.119999  55.790001  54.830002  55.410000  55.410000   \n",
       "16   2015-01-27  56.060001  56.160000  54.570000  55.630001  55.630001   \n",
       "17   2015-01-28  56.150002  56.150002  52.919998  53.000000  53.000000   \n",
       "18   2015-01-29  52.849998  53.310001  51.410000  52.930000  52.930000   \n",
       "19   2015-01-30  52.590000  53.419998  52.049999  52.470001  52.470001   \n",
       "20   2015-02-02  52.939999  53.500000  51.209999  53.470001  53.470001   \n",
       "21   2015-02-03  53.830002  55.930000  53.410000  55.779999  55.779999   \n",
       "22   2015-02-04  55.529999  57.070000  55.250000  56.740002  56.740002   \n",
       "23   2015-02-05  57.599998  57.700001  56.080002  57.470001  57.470001   \n",
       "24   2015-02-06  47.700001  48.169998  44.860001  45.110001  45.110001   \n",
       "25   2015-02-09  44.910000  45.040001  42.099998  42.169998  42.169998   \n",
       "26   2015-02-10  43.830002  45.549999  43.310001  44.660000  44.660000   \n",
       "27   2015-02-11  45.389999  46.430000  44.810001  46.180000  46.180000   \n",
       "28   2015-02-12  46.450001  47.840000  45.950001  47.630001  47.630001   \n",
       "29   2015-02-13  48.509998  49.049999  47.220001  47.529999  47.529999   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "222  2015-11-18  27.540001  28.830000  27.309999  28.230000  28.230000   \n",
       "223  2015-11-19  28.190001  28.690001  27.910000  28.059999  28.059999   \n",
       "224  2015-11-20  28.100000  31.250000  28.049999  31.209999  31.209999   \n",
       "225  2015-11-23  30.580000  30.809999  29.150000  29.860001  29.860001   \n",
       "226  2015-11-24  29.459999  30.629999  29.450001  30.010000  30.010000   \n",
       "227  2015-11-25  29.790001  30.540001  29.709999  30.510000  30.510000   \n",
       "228  2015-11-27  30.500000  30.600000  29.610001  30.180000  30.180000   \n",
       "229  2015-11-30  30.110001  30.719999  29.770000  30.129999  30.129999   \n",
       "230  2015-12-01  30.110001  30.459999  29.799999  30.309999  30.309999   \n",
       "231  2015-12-02  30.299999  32.470001  30.290001  31.389999  31.389999   \n",
       "232  2015-12-03  31.389999  32.240002  30.480000  30.629999  30.629999   \n",
       "233  2015-12-04  30.530001  30.860001  29.320000  30.450001  30.450001   \n",
       "234  2015-12-07  30.379999  30.639999  29.629999  30.040001  30.040001   \n",
       "235  2015-12-08  29.809999  31.379999  29.500000  30.920000  30.920000   \n",
       "236  2015-12-09  30.980000  31.139999  29.260000  30.000000  30.000000   \n",
       "237  2015-12-10  30.110001  31.299999  29.990000  30.830000  30.830000   \n",
       "238  2015-12-11  30.690001  30.750000  29.600000  29.650000  29.650000   \n",
       "239  2015-12-14  29.600000  29.889999  28.850000  29.580000  29.580000   \n",
       "240  2015-12-15  29.680000  30.000000  26.459999  26.870001  26.870001   \n",
       "241  2015-12-16  26.889999  28.240000  26.260000  28.030001  28.030001   \n",
       "242  2015-12-17  28.139999  28.320000  27.190001  27.420000  27.420000   \n",
       "243  2015-12-18  27.309999  27.910000  26.900000  27.170000  27.170000   \n",
       "244  2015-12-21  27.170000  27.360001  26.030001  26.250000  26.250000   \n",
       "245  2015-12-22  26.250000  28.700001  26.150000  27.930000  27.930000   \n",
       "246  2015-12-23  27.950001  28.420000  27.440001  28.150000  28.150000   \n",
       "247  2015-12-24  28.270000  28.590000  27.900000  28.400000  28.400000   \n",
       "248  2015-12-28  28.120001  28.379999  27.770000  27.879999  27.879999   \n",
       "249  2015-12-29  27.950001  28.540001  27.740000  28.480000  28.480000   \n",
       "250  2015-12-30  28.580000  28.780001  28.170000  28.250000  28.250000   \n",
       "251  2015-12-31  28.100000  28.969999  28.020000  28.799999  28.799999   \n",
       "\n",
       "       Volume  \n",
       "0     1664500  \n",
       "1     2023000  \n",
       "2     3762800  \n",
       "3     1548200  \n",
       "4     2015300  \n",
       "5     6222600  \n",
       "6     2405100  \n",
       "7     1952100  \n",
       "8     1854600  \n",
       "9     2647800  \n",
       "10    2183300  \n",
       "11    1227600  \n",
       "12    3248100  \n",
       "13    2295400  \n",
       "14    1629000  \n",
       "15    1450300  \n",
       "16    2410400  \n",
       "17    2013100  \n",
       "18    1844100  \n",
       "19    1875400  \n",
       "20    2105500  \n",
       "21    2876400  \n",
       "22    2498600  \n",
       "23    4657300  \n",
       "24   25137400  \n",
       "25   13079300  \n",
       "26   11267700  \n",
       "27    6359400  \n",
       "28    4375000  \n",
       "29    4713100  \n",
       "..        ...  \n",
       "222   3091600  \n",
       "223   1487500  \n",
       "224   6697500  \n",
       "225   4029900  \n",
       "226   2584500  \n",
       "227   1287100  \n",
       "228   1058900  \n",
       "229   2015600  \n",
       "230   1886000  \n",
       "231   4650300  \n",
       "232   2698900  \n",
       "233   2313800  \n",
       "234   1362300  \n",
       "235   1830200  \n",
       "236   2238500  \n",
       "237   1251800  \n",
       "238   1415000  \n",
       "239   2328600  \n",
       "240   5759200  \n",
       "241   2992100  \n",
       "242   1483900  \n",
       "243   1299800  \n",
       "244   1947600  \n",
       "245   2952700  \n",
       "246   1001000  \n",
       "247    587400  \n",
       "248   1004500  \n",
       "249   1103900  \n",
       "250   1068000  \n",
       "251   1301500  \n",
       "\n",
       "[252 rows x 7 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('yahoo_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The number of rows in the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "252"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Working with data columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The columns or \"features\" in your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['IBM', 'MSFT', 'ORCL', 'TSLA', 'YELP'], dtype='object')"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Selecting a single column from your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      55.459999\n",
       "1      54.540001\n",
       "2      52.549999\n",
       "3      53.320000\n",
       "4      52.590000\n",
       "5      55.959999\n",
       "6      56.000000\n",
       "7      54.470001\n",
       "8      52.799999\n",
       "9      53.000000\n",
       "10     50.180000\n",
       "11     51.650002\n",
       "12     51.200001\n",
       "13     53.869999\n",
       "14     54.660000\n",
       "15     55.119999\n",
       "16     56.060001\n",
       "17     56.150002\n",
       "18     52.849998\n",
       "19     52.590000\n",
       "20     52.939999\n",
       "21     53.830002\n",
       "22     55.529999\n",
       "23     57.599998\n",
       "24     47.700001\n",
       "25     44.910000\n",
       "26     43.830002\n",
       "27     45.389999\n",
       "28     46.450001\n",
       "29     48.509998\n",
       "         ...    \n",
       "222    27.540001\n",
       "223    28.190001\n",
       "224    28.100000\n",
       "225    30.580000\n",
       "226    29.459999\n",
       "227    29.790001\n",
       "228    30.500000\n",
       "229    30.110001\n",
       "230    30.110001\n",
       "231    30.299999\n",
       "232    31.389999\n",
       "233    30.530001\n",
       "234    30.379999\n",
       "235    29.809999\n",
       "236    30.980000\n",
       "237    30.110001\n",
       "238    30.690001\n",
       "239    29.600000\n",
       "240    29.680000\n",
       "241    26.889999\n",
       "242    28.139999\n",
       "243    27.309999\n",
       "244    27.170000\n",
       "245    26.250000\n",
       "246    27.950001\n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "251    28.100000\n",
       "Name: Open, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Another way of selecting a single column from your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      55.459999\n",
       "1      54.540001\n",
       "2      52.549999\n",
       "3      53.320000\n",
       "4      52.590000\n",
       "5      55.959999\n",
       "6      56.000000\n",
       "7      54.470001\n",
       "8      52.799999\n",
       "9      53.000000\n",
       "10     50.180000\n",
       "11     51.650002\n",
       "12     51.200001\n",
       "13     53.869999\n",
       "14     54.660000\n",
       "15     55.119999\n",
       "16     56.060001\n",
       "17     56.150002\n",
       "18     52.849998\n",
       "19     52.590000\n",
       "20     52.939999\n",
       "21     53.830002\n",
       "22     55.529999\n",
       "23     57.599998\n",
       "24     47.700001\n",
       "25     44.910000\n",
       "26     43.830002\n",
       "27     45.389999\n",
       "28     46.450001\n",
       "29     48.509998\n",
       "         ...    \n",
       "222    27.540001\n",
       "223    28.190001\n",
       "224    28.100000\n",
       "225    30.580000\n",
       "226    29.459999\n",
       "227    29.790001\n",
       "228    30.500000\n",
       "229    30.110001\n",
       "230    30.110001\n",
       "231    30.299999\n",
       "232    31.389999\n",
       "233    30.530001\n",
       "234    30.379999\n",
       "235    29.809999\n",
       "236    30.980000\n",
       "237    30.110001\n",
       "238    30.690001\n",
       "239    29.600000\n",
       "240    29.680000\n",
       "241    26.889999\n",
       "242    28.139999\n",
       "243    27.309999\n",
       "244    27.170000\n",
       "245    26.250000\n",
       "246    27.950001\n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "251    28.100000\n",
       "Name: Open, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Open"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Open      Close\n",
       "0  55.459999  55.150002\n",
       "1  54.540001  52.529999\n",
       "2  52.549999  52.439999\n",
       "3  53.320000  52.209999\n",
       "4  52.590000  53.830002"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Open','Close']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-01-02\n",
       "1    2015-01-05\n",
       "2    2015-01-06\n",
       "3    2015-01-07\n",
       "4    2015-01-08\n",
       "5    2015-01-09\n",
       "6    2015-01-12\n",
       "7    2015-01-13\n",
       "8    2015-01-14\n",
       "9    2015-01-15\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "242    2015-12-17\n",
       "243    2015-12-18\n",
       "244    2015-12-21\n",
       "245    2015-12-22\n",
       "246    2015-12-23\n",
       "247    2015-12-24\n",
       "248    2015-12-28\n",
       "249    2015-12-29\n",
       "250    2015-12-30\n",
       "251    2015-12-31\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Changing the column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 252 entries, 0 to 251\n",
      "Data columns (total 7 columns):\n",
      "date         252 non-null object\n",
      "open         252 non-null float64\n",
      "high         252 non-null float64\n",
      "low          252 non-null float64\n",
      "close        252 non-null float64\n",
      "adj_close    252 non-null float64\n",
      "volume       252 non-null int64\n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 13.9+ KB\n"
     ]
    }
   ],
   "source": [
    "new_column_names = [x.lower().replace(' ','_') for x in df.columns]\n",
    "df.columns = new_column_names\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now **all** columns can be accessed using the **dot** notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    55.150002\n",
       "1    52.529999\n",
       "2    52.439999\n",
       "3    52.209999\n",
       "4    53.830002\n",
       "Name: adj_close, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.adj_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Data Frame methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "A DataFrame object has many useful methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         3.728766e+01\n",
       "high         3.805464e+01\n",
       "low          3.656373e+01\n",
       "close        3.729917e+01\n",
       "adj_close    3.729917e+01\n",
       "volume       3.490978e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         1.128093e+01\n",
       "high         1.138111e+01\n",
       "low          1.113097e+01\n",
       "close        1.125233e+01\n",
       "adj_close    1.125233e+01\n",
       "volume       4.145025e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         3.796500e+01\n",
       "high         3.871500e+01\n",
       "low          3.637500e+01\n",
       "close        3.783500e+01\n",
       "adj_close    3.783500e+01\n",
       "volume       2.354050e+06\n",
       "dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.287658698412692"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.open.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.054642952380952"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.high.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1159356d8>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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42HVKcz7exOJJLBYFVVEG7Ws6EaA/GcY19xAdCejoNLefU7ueSDBJJJjzezBM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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115935ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.adj_close.plot(label='adj')\n",
    "df.low.plot(label='close')\n",
    "plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117cc12e8>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115930f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.adj_close.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Bulk Operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Methods like **sum( )** and **std( )** work on entire columns. \n",
    "\n",
    "We can run our own functions across all values in a column (or row) using **apply( )**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 252 entries, 0 to 251\n",
      "Data columns (total 7 columns):\n",
      "Date         252 non-null object\n",
      "Open         252 non-null float64\n",
      "High         252 non-null float64\n",
      "Low          252 non-null float64\n",
      "Close        252 non-null float64\n",
      "Adj Close    252 non-null float64\n",
      "Volume       252 non-null int64\n",
      "dtypes: float64(5), int64(1), object(1)\n",
      "memory usage: 13.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015-01-02\n",
       "1    2015-01-05\n",
       "2    2015-01-06\n",
       "3    2015-01-07\n",
       "4    2015-01-08\n",
       "Name: Date, dtype: object"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The **values** property of the column returns a list of values for the column. Inspecting the first value reveals that these are strings with a particular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-01-02'"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_date = df.date.values[0]\n",
    "first_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 2, 0, 0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(first_date, \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2015-01-02\n",
       "1   2015-01-05\n",
       "2   2015-01-06\n",
       "3   2015-01-07\n",
       "4   2015-01-08\n",
       "Name: date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d\"))\n",
    "df.date.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Each row in a DataFrame is associated with an index, which is a label that uniquely identifies a row."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The row indices so far have been auto-generated by pandas, and are simply integers starting from 0. \n",
    "\n",
    "From now on we will use dates instead of integers for indices -- the benefits of this will show later. \n",
    "\n",
    "Overwriting the index is as easy as assigning to the **`index`** property of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date       open       high        low      close  adj_close  \\\n",
       "date                                                                           \n",
       "2015-01-02 2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002   \n",
       "2015-01-05 2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999   \n",
       "2015-01-06 2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999   \n",
       "2015-01-07 2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999   \n",
       "2015-01-08 2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002   \n",
       "\n",
       "             volume  \n",
       "date                 \n",
       "2015-01-02  1664500  \n",
       "2015-01-05  2023000  \n",
       "2015-01-06  3762800  \n",
       "2015-01-07  1548200  \n",
       "2015-01-08  2015300  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index = df.date\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now that we have made an index based on date, we can drop the original `date` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 252 entries, 2015-01-02 to 2015-12-31\n",
      "Data columns (total 6 columns):\n",
      "open         252 non-null float64\n",
      "high         252 non-null float64\n",
      "low          252 non-null float64\n",
      "close        252 non-null float64\n",
      "adj_close    252 non-null float64\n",
      "volume       252 non-null int64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 13.8 KB\n"
     ]
    }
   ],
   "source": [
    "df = df.drop(['date'],axis=1)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Accessing rows of the DataFrame\n",
    "\n",
    "So far we've seen how to access a column of the DataFrame.  To access a row we use a different notation.\n",
    "\n",
    "To access a row by its index value, use the **`.loc()`** method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         5.466000e+01\n",
       "high         5.564000e+01\n",
       "low          5.430000e+01\n",
       "close        5.519000e+01\n",
       "adj_close    5.519000e+01\n",
       "volume       1.629000e+06\n",
       "Name: 2015-01-23 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[datetime(2015,1,23,0,0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To access a row by its sequence number (ie, like an array index), use **`.iloc()`** ('Integer Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "open         5.546000e+01\n",
       "high         5.560000e+01\n",
       "low          5.424000e+01\n",
       "close        5.515000e+01\n",
       "adj_close    5.515000e+01\n",
       "volume       1.664500e+06\n",
       "Name: 2015-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To iterate over the rows, use **`.iterrows()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The total number of positive-gain days is 126.\n"
     ]
    }
   ],
   "source": [
    "num_positive_days = 0\n",
    "for idx, row in df.iterrows():\n",
    "    if row.close > row.open:\n",
    "        num_positive_days += 1\n",
    "        \n",
    "print(\"The total number of positive-gain days is {}.\".format(num_positive_days))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Filtering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "It is very easy to select interesting rows from the data.  \n",
    "\n",
    "All these operations below return a new DataFrame, which itself can be treated the same way as all DataFrames we have seen so far."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2015-01-02     True\n",
       "2015-01-05    False\n",
       "2015-01-06    False\n",
       "2015-01-07    False\n",
       "2015-01-08    False\n",
       "Name: high, dtype: bool"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_high = df.high > 55\n",
    "tmp_high.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Summing a Boolean array is the same as counting the number of **`True`** values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(tmp_high)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now, let's select only the rows of **`df1`** that correspond to **`tmp_high`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6222600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>53.430000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>2405100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>54.660000</td>\n",
       "      <td>55.639999</td>\n",
       "      <td>54.299999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>1629000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>55.119999</td>\n",
       "      <td>55.790001</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1450300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>56.060001</td>\n",
       "      <td>56.160000</td>\n",
       "      <td>54.570000</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>2410400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>56.150002</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>52.919998</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2013100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>53.830002</td>\n",
       "      <td>55.930000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>2876400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>55.529999</td>\n",
       "      <td>57.070000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>2498600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>57.599998</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>56.080002</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>4657300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume\n",
       "date                                                                      \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500\n",
       "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6222600\n",
       "2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000  2405100\n",
       "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400\n",
       "2015-01-23  54.660000  55.639999  54.299999  55.189999  55.189999  1629000\n",
       "2015-01-26  55.119999  55.790001  54.830002  55.410000  55.410000  1450300\n",
       "2015-01-27  56.060001  56.160000  54.570000  55.630001  55.630001  2410400\n",
       "2015-01-28  56.150002  56.150002  52.919998  53.000000  53.000000  2013100\n",
       "2015-02-03  53.830002  55.930000  53.410000  55.779999  55.779999  2876400\n",
       "2015-02-04  55.529999  57.070000  55.250000  56.740002  56.740002  2498600\n",
       "2015-02-05  57.599998  57.700001  56.080002  57.470001  57.470001  4657300"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[tmp_high]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Putting it all together, we have the following commonly-used patterns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6222600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>50.180000</td>\n",
       "      <td>51.490002</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>2183300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>3248100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume\n",
       "date                                                                      \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300\n",
       "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6222600\n",
       "2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999  2183300\n",
       "2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000  3248100\n",
       "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_days = df[df.close > df.open]\n",
    "positive_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-07</th>\n",
       "      <td>38.220001</td>\n",
       "      <td>48.73</td>\n",
       "      <td>38.220001</td>\n",
       "      <td>47.009998</td>\n",
       "      <td>47.009998</td>\n",
       "      <td>33831600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open   high        low      close  adj_close    volume\n",
       "date                                                                   \n",
       "2015-05-07  38.220001  48.73  38.220001  47.009998  47.009998  33831600"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "very_positive_days = df[df.close-df.open > 4]\n",
    "very_positive_days.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Creating new columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To create a new column, simply assign values to it.  Think of the columns as a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "           profit  \n",
       "date               \n",
       "2015-01-02  False  \n",
       "2015-01-05  False  \n",
       "2015-01-06  False  \n",
       "2015-01-07  False  \n",
       "2015-01-08   True  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['profit'] = (df.open < df.close)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "      <th>gain</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "           profit         gain  \n",
       "date                            \n",
       "2015-01-02  False   small_gain  \n",
       "2015-01-05  False  medium_gain  \n",
       "2015-01-06  False   small_gain  \n",
       "2015-01-07  False  medium_gain  \n",
       "2015-01-08   True     negative  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for idx, row in df.iterrows():\n",
    "    if row.close > row.open:\n",
    "        df.loc[idx,'gain']='negative'\n",
    "    elif (row.open - row.close) < 1:\n",
    "        df.loc[idx,'gain']='small_gain'\n",
    "    elif (row.open - row.close) < 6:\n",
    "        df.loc[idx,'gain']='medium_gain'\n",
    "    else:\n",
    "        df.loc[idx,'gain']='large_gain'\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here is another, more \"functional\", way to accomplish the same thing.\n",
    "\n",
    "Define a function that classifies rows, and **`apply`** it to each row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def namerow(row):\n",
    "    if row.close > row.open:\n",
    "        return 'negative'\n",
    "    elif (row.open - row.close) < 1:\n",
    "        return 'small_gain'\n",
    "    elif (row.open - row.close) < 6:\n",
    "        return 'medium_gain'\n",
    "    else:\n",
    "        return 'large_gain'\n",
    "\n",
    "df['test_column'] = df.apply(namerow, axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>adj_close</th>\n",
       "      <th>volume</th>\n",
       "      <th>profit</th>\n",
       "      <th>gain</th>\n",
       "      <th>test_column</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "      <td>small_gain</td>\n",
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       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
       "\n",
       "           profit         gain  test_column  \n",
       "date                                         \n",
       "2015-01-02  False   small_gain   small_gain  \n",
       "2015-01-05  False  medium_gain  medium_gain  \n",
       "2015-01-06  False   small_gain   small_gain  \n",
       "2015-01-07  False  medium_gain  medium_gain  \n",
       "2015-01-08   True     negative     negative  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "OK, point made, let's get rid of that extraneous `test_column`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>open</th>\n",
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       "  <tbody>\n",
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       "      <td>55.150002</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.540001</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>52.549999</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.320000</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6222600</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>56.000000</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>53.430000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>2405100</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-13</th>\n",
       "      <td>54.470001</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>52.520000</td>\n",
       "      <td>53.180000</td>\n",
       "      <td>53.180000</td>\n",
       "      <td>1952100</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-14</th>\n",
       "      <td>52.799999</td>\n",
       "      <td>53.680000</td>\n",
       "      <td>51.459999</td>\n",
       "      <td>52.200001</td>\n",
       "      <td>52.200001</td>\n",
       "      <td>1854600</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-15</th>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.610001</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>50.119999</td>\n",
       "      <td>50.119999</td>\n",
       "      <td>2647800</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>50.180000</td>\n",
       "      <td>51.490002</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>2183300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-20</th>\n",
       "      <td>51.650002</td>\n",
       "      <td>51.779999</td>\n",
       "      <td>50.689999</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>1227600</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>3248100</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>53.869999</td>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>54.660000</td>\n",
       "      <td>55.639999</td>\n",
       "      <td>54.299999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>1629000</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>55.119999</td>\n",
       "      <td>55.790001</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1450300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>56.060001</td>\n",
       "      <td>56.160000</td>\n",
       "      <td>54.570000</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>2410400</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>56.150002</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>52.919998</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2013100</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-29</th>\n",
       "      <td>52.849998</td>\n",
       "      <td>53.310001</td>\n",
       "      <td>51.410000</td>\n",
       "      <td>52.930000</td>\n",
       "      <td>52.930000</td>\n",
       "      <td>1844100</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-30</th>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.419998</td>\n",
       "      <td>52.049999</td>\n",
       "      <td>52.470001</td>\n",
       "      <td>52.470001</td>\n",
       "      <td>1875400</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-02</th>\n",
       "      <td>52.939999</td>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.209999</td>\n",
       "      <td>53.470001</td>\n",
       "      <td>53.470001</td>\n",
       "      <td>2105500</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>53.830002</td>\n",
       "      <td>55.930000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>2876400</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>55.529999</td>\n",
       "      <td>57.070000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>2498600</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>57.599998</td>\n",
       "      <td>57.700001</td>\n",
       "      <td>56.080002</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>4657300</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-06</th>\n",
       "      <td>47.700001</td>\n",
       "      <td>48.169998</td>\n",
       "      <td>44.860001</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>45.110001</td>\n",
       "      <td>25137400</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-09</th>\n",
       "      <td>44.910000</td>\n",
       "      <td>45.040001</td>\n",
       "      <td>42.099998</td>\n",
       "      <td>42.169998</td>\n",
       "      <td>42.169998</td>\n",
       "      <td>13079300</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-10</th>\n",
       "      <td>43.830002</td>\n",
       "      <td>45.549999</td>\n",
       "      <td>43.310001</td>\n",
       "      <td>44.660000</td>\n",
       "      <td>44.660000</td>\n",
       "      <td>11267700</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-11</th>\n",
       "      <td>45.389999</td>\n",
       "      <td>46.430000</td>\n",
       "      <td>44.810001</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>46.180000</td>\n",
       "      <td>6359400</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-12</th>\n",
       "      <td>46.450001</td>\n",
       "      <td>47.840000</td>\n",
       "      <td>45.950001</td>\n",
       "      <td>47.630001</td>\n",
       "      <td>47.630001</td>\n",
       "      <td>4375000</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-13</th>\n",
       "      <td>48.509998</td>\n",
       "      <td>49.049999</td>\n",
       "      <td>47.220001</td>\n",
       "      <td>47.529999</td>\n",
       "      <td>47.529999</td>\n",
       "      <td>4713100</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-18</th>\n",
       "      <td>27.540001</td>\n",
       "      <td>28.830000</td>\n",
       "      <td>27.309999</td>\n",
       "      <td>28.230000</td>\n",
       "      <td>28.230000</td>\n",
       "      <td>3091600</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-19</th>\n",
       "      <td>28.190001</td>\n",
       "      <td>28.690001</td>\n",
       "      <td>27.910000</td>\n",
       "      <td>28.059999</td>\n",
       "      <td>28.059999</td>\n",
       "      <td>1487500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-20</th>\n",
       "      <td>28.100000</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>28.049999</td>\n",
       "      <td>31.209999</td>\n",
       "      <td>31.209999</td>\n",
       "      <td>6697500</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-23</th>\n",
       "      <td>30.580000</td>\n",
       "      <td>30.809999</td>\n",
       "      <td>29.150000</td>\n",
       "      <td>29.860001</td>\n",
       "      <td>29.860001</td>\n",
       "      <td>4029900</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-24</th>\n",
       "      <td>29.459999</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>29.450001</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>30.010000</td>\n",
       "      <td>2584500</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-25</th>\n",
       "      <td>29.790001</td>\n",
       "      <td>30.540001</td>\n",
       "      <td>29.709999</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>30.510000</td>\n",
       "      <td>1287100</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-27</th>\n",
       "      <td>30.500000</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>29.610001</td>\n",
       "      <td>30.180000</td>\n",
       "      <td>30.180000</td>\n",
       "      <td>1058900</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-30</th>\n",
       "      <td>30.110001</td>\n",
       "      <td>30.719999</td>\n",
       "      <td>29.770000</td>\n",
       "      <td>30.129999</td>\n",
       "      <td>30.129999</td>\n",
       "      <td>2015600</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>30.110001</td>\n",
       "      <td>30.459999</td>\n",
       "      <td>29.799999</td>\n",
       "      <td>30.309999</td>\n",
       "      <td>30.309999</td>\n",
       "      <td>1886000</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-02</th>\n",
       "      <td>30.299999</td>\n",
       "      <td>32.470001</td>\n",
       "      <td>30.290001</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>31.389999</td>\n",
       "      <td>4650300</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-03</th>\n",
       "      <td>31.389999</td>\n",
       "      <td>32.240002</td>\n",
       "      <td>30.480000</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>30.629999</td>\n",
       "      <td>2698900</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-04</th>\n",
       "      <td>30.530001</td>\n",
       "      <td>30.860001</td>\n",
       "      <td>29.320000</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>30.450001</td>\n",
       "      <td>2313800</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-07</th>\n",
       "      <td>30.379999</td>\n",
       "      <td>30.639999</td>\n",
       "      <td>29.629999</td>\n",
       "      <td>30.040001</td>\n",
       "      <td>30.040001</td>\n",
       "      <td>1362300</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-08</th>\n",
       "      <td>29.809999</td>\n",
       "      <td>31.379999</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>30.920000</td>\n",
       "      <td>30.920000</td>\n",
       "      <td>1830200</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-09</th>\n",
       "      <td>30.980000</td>\n",
       "      <td>31.139999</td>\n",
       "      <td>29.260000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>2238500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-10</th>\n",
       "      <td>30.110001</td>\n",
       "      <td>31.299999</td>\n",
       "      <td>29.990000</td>\n",
       "      <td>30.830000</td>\n",
       "      <td>30.830000</td>\n",
       "      <td>1251800</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-11</th>\n",
       "      <td>30.690001</td>\n",
       "      <td>30.750000</td>\n",
       "      <td>29.600000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>1415000</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-14</th>\n",
       "      <td>29.600000</td>\n",
       "      <td>29.889999</td>\n",
       "      <td>28.850000</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>29.580000</td>\n",
       "      <td>2328600</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-15</th>\n",
       "      <td>29.680000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>26.459999</td>\n",
       "      <td>26.870001</td>\n",
       "      <td>26.870001</td>\n",
       "      <td>5759200</td>\n",
       "      <td>False</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-16</th>\n",
       "      <td>26.889999</td>\n",
       "      <td>28.240000</td>\n",
       "      <td>26.260000</td>\n",
       "      <td>28.030001</td>\n",
       "      <td>28.030001</td>\n",
       "      <td>2992100</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-17</th>\n",
       "      <td>28.139999</td>\n",
       "      <td>28.320000</td>\n",
       "      <td>27.190001</td>\n",
       "      <td>27.420000</td>\n",
       "      <td>27.420000</td>\n",
       "      <td>1483900</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-18</th>\n",
       "      <td>27.309999</td>\n",
       "      <td>27.910000</td>\n",
       "      <td>26.900000</td>\n",
       "      <td>27.170000</td>\n",
       "      <td>27.170000</td>\n",
       "      <td>1299800</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-21</th>\n",
       "      <td>27.170000</td>\n",
       "      <td>27.360001</td>\n",
       "      <td>26.030001</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>26.250000</td>\n",
       "      <td>1947600</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-22</th>\n",
       "      <td>26.250000</td>\n",
       "      <td>28.700001</td>\n",
       "      <td>26.150000</td>\n",
       "      <td>27.930000</td>\n",
       "      <td>27.930000</td>\n",
       "      <td>2952700</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-23</th>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.420000</td>\n",
       "      <td>27.440001</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>28.150000</td>\n",
       "      <td>1001000</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-24</th>\n",
       "      <td>28.270000</td>\n",
       "      <td>28.590000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>587400</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>28.120001</td>\n",
       "      <td>28.379999</td>\n",
       "      <td>27.770000</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>1004500</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.540001</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>1103900</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>28.580000</td>\n",
       "      <td>28.780001</td>\n",
       "      <td>28.170000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>1068000</td>\n",
       "      <td>False</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>28.100000</td>\n",
       "      <td>28.969999</td>\n",
       "      <td>28.020000</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>1301500</td>\n",
       "      <td>True</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close  adj_close    volume  \\\n",
       "date                                                                          \n",
       "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002   1664500   \n",
       "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999   2023000   \n",
       "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999   3762800   \n",
       "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999   1548200   \n",
       "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002   2015300   \n",
       "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000   6222600   \n",
       "2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000   2405100   \n",
       "2015-01-13  54.470001  54.799999  52.520000  53.180000  53.180000   1952100   \n",
       "2015-01-14  52.799999  53.680000  51.459999  52.200001  52.200001   1854600   \n",
       "2015-01-15  53.000000  53.610001  50.029999  50.119999  50.119999   2647800   \n",
       "2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999   2183300   \n",
       "2015-01-20  51.650002  51.779999  50.689999  51.410000  51.410000   1227600   \n",
       "2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000   3248100   \n",
       "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999   2295400   \n",
       "2015-01-23  54.660000  55.639999  54.299999  55.189999  55.189999   1629000   \n",
       "2015-01-26  55.119999  55.790001  54.830002  55.410000  55.410000   1450300   \n",
       "2015-01-27  56.060001  56.160000  54.570000  55.630001  55.630001   2410400   \n",
       "2015-01-28  56.150002  56.150002  52.919998  53.000000  53.000000   2013100   \n",
       "2015-01-29  52.849998  53.310001  51.410000  52.930000  52.930000   1844100   \n",
       "2015-01-30  52.590000  53.419998  52.049999  52.470001  52.470001   1875400   \n",
       "2015-02-02  52.939999  53.500000  51.209999  53.470001  53.470001   2105500   \n",
       "2015-02-03  53.830002  55.930000  53.410000  55.779999  55.779999   2876400   \n",
       "2015-02-04  55.529999  57.070000  55.250000  56.740002  56.740002   2498600   \n",
       "2015-02-05  57.599998  57.700001  56.080002  57.470001  57.470001   4657300   \n",
       "2015-02-06  47.700001  48.169998  44.860001  45.110001  45.110001  25137400   \n",
       "2015-02-09  44.910000  45.040001  42.099998  42.169998  42.169998  13079300   \n",
       "2015-02-10  43.830002  45.549999  43.310001  44.660000  44.660000  11267700   \n",
       "2015-02-11  45.389999  46.430000  44.810001  46.180000  46.180000   6359400   \n",
       "2015-02-12  46.450001  47.840000  45.950001  47.630001  47.630001   4375000   \n",
       "2015-02-13  48.509998  49.049999  47.220001  47.529999  47.529999   4713100   \n",
       "...               ...        ...        ...        ...        ...       ...   \n",
       "2015-11-18  27.540001  28.830000  27.309999  28.230000  28.230000   3091600   \n",
       "2015-11-19  28.190001  28.690001  27.910000  28.059999  28.059999   1487500   \n",
       "2015-11-20  28.100000  31.250000  28.049999  31.209999  31.209999   6697500   \n",
       "2015-11-23  30.580000  30.809999  29.150000  29.860001  29.860001   4029900   \n",
       "2015-11-24  29.459999  30.629999  29.450001  30.010000  30.010000   2584500   \n",
       "2015-11-25  29.790001  30.540001  29.709999  30.510000  30.510000   1287100   \n",
       "2015-11-27  30.500000  30.600000  29.610001  30.180000  30.180000   1058900   \n",
       "2015-11-30  30.110001  30.719999  29.770000  30.129999  30.129999   2015600   \n",
       "2015-12-01  30.110001  30.459999  29.799999  30.309999  30.309999   1886000   \n",
       "2015-12-02  30.299999  32.470001  30.290001  31.389999  31.389999   4650300   \n",
       "2015-12-03  31.389999  32.240002  30.480000  30.629999  30.629999   2698900   \n",
       "2015-12-04  30.530001  30.860001  29.320000  30.450001  30.450001   2313800   \n",
       "2015-12-07  30.379999  30.639999  29.629999  30.040001  30.040001   1362300   \n",
       "2015-12-08  29.809999  31.379999  29.500000  30.920000  30.920000   1830200   \n",
       "2015-12-09  30.980000  31.139999  29.260000  30.000000  30.000000   2238500   \n",
       "2015-12-10  30.110001  31.299999  29.990000  30.830000  30.830000   1251800   \n",
       "2015-12-11  30.690001  30.750000  29.600000  29.650000  29.650000   1415000   \n",
       "2015-12-14  29.600000  29.889999  28.850000  29.580000  29.580000   2328600   \n",
       "2015-12-15  29.680000  30.000000  26.459999  26.870001  26.870001   5759200   \n",
       "2015-12-16  26.889999  28.240000  26.260000  28.030001  28.030001   2992100   \n",
       "2015-12-17  28.139999  28.320000  27.190001  27.420000  27.420000   1483900   \n",
       "2015-12-18  27.309999  27.910000  26.900000  27.170000  27.170000   1299800   \n",
       "2015-12-21  27.170000  27.360001  26.030001  26.250000  26.250000   1947600   \n",
       "2015-12-22  26.250000  28.700001  26.150000  27.930000  27.930000   2952700   \n",
       "2015-12-23  27.950001  28.420000  27.440001  28.150000  28.150000   1001000   \n",
       "2015-12-24  28.270000  28.590000  27.900000  28.400000  28.400000    587400   \n",
       "2015-12-28  28.120001  28.379999  27.770000  27.879999  27.879999   1004500   \n",
       "2015-12-29  27.950001  28.540001  27.740000  28.480000  28.480000   1103900   \n",
       "2015-12-30  28.580000  28.780001  28.170000  28.250000  28.250000   1068000   \n",
       "2015-12-31  28.100000  28.969999  28.020000  28.799999  28.799999   1301500   \n",
       "\n",
       "           profit         gain  \n",
       "date                            \n",
       "2015-01-02  False   small_gain  \n",
       "2015-01-05  False  medium_gain  \n",
       "2015-01-06  False   small_gain  \n",
       "2015-01-07  False  medium_gain  \n",
       "2015-01-08   True     negative  \n",
       "2015-01-09   True     negative  \n",
       "2015-01-12  False  medium_gain  \n",
       "2015-01-13  False  medium_gain  \n",
       "2015-01-14  False   small_gain  \n",
       "2015-01-15  False  medium_gain  \n",
       "2015-01-16   True     negative  \n",
       "2015-01-20  False   small_gain  \n",
       "2015-01-21   True     negative  \n",
       "2015-01-22   True     negative  \n",
       "2015-01-23   True     negative  \n",
       "2015-01-26   True     negative  \n",
       "2015-01-27  False   small_gain  \n",
       "2015-01-28  False  medium_gain  \n",
       "2015-01-29   True     negative  \n",
       "2015-01-30  False   small_gain  \n",
       "2015-02-02   True     negative  \n",
       "2015-02-03   True     negative  \n",
       "2015-02-04   True     negative  \n",
       "2015-02-05  False   small_gain  \n",
       "2015-02-06  False  medium_gain  \n",
       "2015-02-09  False  medium_gain  \n",
       "2015-02-10   True     negative  \n",
       "2015-02-11   True     negative  \n",
       "2015-02-12   True     negative  \n",
       "2015-02-13  False   small_gain  \n",
       "...           ...          ...  \n",
       "2015-11-18   True     negative  \n",
       "2015-11-19  False   small_gain  \n",
       "2015-11-20   True     negative  \n",
       "2015-11-23  False   small_gain  \n",
       "2015-11-24   True     negative  \n",
       "2015-11-25   True     negative  \n",
       "2015-11-27  False   small_gain  \n",
       "2015-11-30   True     negative  \n",
       "2015-12-01   True     negative  \n",
       "2015-12-02   True     negative  \n",
       "2015-12-03  False   small_gain  \n",
       "2015-12-04  False   small_gain  \n",
       "2015-12-07  False   small_gain  \n",
       "2015-12-08   True     negative  \n",
       "2015-12-09  False   small_gain  \n",
       "2015-12-10   True     negative  \n",
       "2015-12-11  False  medium_gain  \n",
       "2015-12-14  False   small_gain  \n",
       "2015-12-15  False  medium_gain  \n",
       "2015-12-16   True     negative  \n",
       "2015-12-17  False   small_gain  \n",
       "2015-12-18  False   small_gain  \n",
       "2015-12-21  False   small_gain  \n",
       "2015-12-22   True     negative  \n",
       "2015-12-23   True     negative  \n",
       "2015-12-24   True     negative  \n",
       "2015-12-28  False   small_gain  \n",
       "2015-12-29   True     negative  \n",
       "2015-12-30  False   small_gain  \n",
       "2015-12-31   True     negative  \n",
       "\n",
       "[252 rows x 8 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop('test_column', axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Grouping"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "An **extremely** powerful DataFrame method is **`groupby()`**. \n",
    "\n",
    "This is entirely analagous to **`GROUP BY`** in SQL.\n",
    "\n",
    "It will group the rows of a DataFrame by the values in one (or more) columns, and let you iterate through each group."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Here we will look at the average gain among the  categories of gains (negative, small, medium and large) we defined above and stored in column `gain`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "gain_groups = df.groupby('gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Essentially, **`gain_groups`** behaves like a dictionary\n",
    "* whose keys are the unique values found in the `gain` column, and \n",
    "* whose values are DataFrames that contain only the rows having the corresponding unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "medium_gain\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-05  54.540001  54.950001  52.330002  52.529999  52.529999  2023000   \n",
      "2015-01-07  53.320000  53.750000  51.759998  52.209999  52.209999  1548200   \n",
      "2015-01-12  56.000000  56.060001  53.430000  54.020000  54.020000  2405100   \n",
      "2015-01-13  54.470001  54.799999  52.520000  53.180000  53.180000  1952100   \n",
      "2015-01-15  53.000000  53.610001  50.029999  50.119999  50.119999  2647800   \n",
      "\n",
      "           profit         gain  test_column  \n",
      "date                                         \n",
      "2015-01-05  False  medium_gain  medium_gain  \n",
      "2015-01-07  False  medium_gain  medium_gain  \n",
      "2015-01-12  False  medium_gain  medium_gain  \n",
      "2015-01-13  False  medium_gain  medium_gain  \n",
      "2015-01-15  False  medium_gain  medium_gain  \n",
      "=============================\n",
      "negative\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-08  52.590000  54.139999  51.759998  53.830002  53.830002  2015300   \n",
      "2015-01-09  55.959999  56.990002  54.720001  56.070000  56.070000  6222600   \n",
      "2015-01-16  50.180000  51.490002  50.029999  51.389999  51.389999  2183300   \n",
      "2015-01-21  51.200001  53.500000  51.200001  53.410000  53.410000  3248100   \n",
      "2015-01-22  53.869999  55.279999  53.119999  54.799999  54.799999  2295400   \n",
      "\n",
      "           profit      gain test_column  \n",
      "date                                     \n",
      "2015-01-08   True  negative    negative  \n",
      "2015-01-09   True  negative    negative  \n",
      "2015-01-16   True  negative    negative  \n",
      "2015-01-21   True  negative    negative  \n",
      "2015-01-22   True  negative    negative  \n",
      "=============================\n",
      "small_gain\n",
      "                 open       high        low      close  adj_close   volume  \\\n",
      "date                                                                         \n",
      "2015-01-02  55.459999  55.599998  54.240002  55.150002  55.150002  1664500   \n",
      "2015-01-06  52.549999  53.930000  50.750000  52.439999  52.439999  3762800   \n",
      "2015-01-14  52.799999  53.680000  51.459999  52.200001  52.200001  1854600   \n",
      "2015-01-20  51.650002  51.779999  50.689999  51.410000  51.410000  1227600   \n",
      "2015-01-27  56.060001  56.160000  54.570000  55.630001  55.630001  2410400   \n",
      "\n",
      "           profit        gain test_column  \n",
      "date                                       \n",
      "2015-01-02  False  small_gain  small_gain  \n",
      "2015-01-06  False  small_gain  small_gain  \n",
      "2015-01-14  False  small_gain  small_gain  \n",
      "2015-01-20  False  small_gain  small_gain  \n",
      "2015-01-27  False  small_gain  small_gain  \n",
      "=============================\n"
     ]
    }
   ],
   "source": [
    "for gain, gain_data in gain_groups:\n",
    "    print(gain)\n",
    "    print(gain_data.head())\n",
    "    print('=============================')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average closing value for the medium_gain group is 41.008888629629645\n",
      "The average closing value for the negative group is 37.21357140476189\n",
      "The average closing value for the small_gain group is 36.39636363636364\n"
     ]
    }
   ],
   "source": [
    "for gain, gain_data in df.groupby(\"gain\"):\n",
    "    print('The average closing value for the {} group is {}'.format(gain,\n",
    "                                                           gain_data.close.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Other Pandas Classes\n",
    "\n",
    "A DataFrame is essentially an annotated 2-D array.\n",
    "\n",
    "Pandas also has annotated versions of 1-D and 3-D arrays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "A 1-D array in Pandas is called a **`Series`**.\n",
    "\n",
    "A 3-D array in Pandas is called a **`Panel`**.\n",
    "\n",
    "To use these, read the documentation!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Comparing multiple stocks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As a last task, we will use the experience we obtained so far -- and learn some new things -- in order to compare the performance of different stocks we obtained from Yahoo finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>YELP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>160.440002</td>\n",
       "      <td>46.450001</td>\n",
       "      <td>44.970001</td>\n",
       "      <td>222.410004</td>\n",
       "      <td>54.730000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-30</th>\n",
       "      <td>160.050003</td>\n",
       "      <td>47.020000</td>\n",
       "      <td>45.340000</td>\n",
       "      <td>222.229996</td>\n",
       "      <td>54.240002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-29</th>\n",
       "      <td>160.509995</td>\n",
       "      <td>47.450001</td>\n",
       "      <td>45.610001</td>\n",
       "      <td>225.710007</td>\n",
       "      <td>53.009998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-26</th>\n",
       "      <td>162.339996</td>\n",
       "      <td>47.880001</td>\n",
       "      <td>46.099998</td>\n",
       "      <td>227.820007</td>\n",
       "      <td>52.939999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-24</th>\n",
       "      <td>161.820007</td>\n",
       "      <td>48.139999</td>\n",
       "      <td>46.230000</td>\n",
       "      <td>222.259995</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   IBM       MSFT       ORCL        TSLA       YELP\n",
       "Date                                                               \n",
       "2014-12-31  160.440002  46.450001  44.970001  222.410004  54.730000\n",
       "2014-12-30  160.050003  47.020000  45.340000  222.229996  54.240002\n",
       "2014-12-29  160.509995  47.450001  45.610001  225.710007  53.009998\n",
       "2014-12-26  162.339996  47.880001  46.099998  227.820007  52.939999\n",
       "2014-12-24  161.820007  48.139999  46.230000  222.259995  53.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks = ['ORCL', 'TSLA', 'IBM','YELP', 'MSFT']\n",
    "attr = 'Close'\n",
    "df = web.DataReader(stocks, \n",
    "                    data_source,                               \n",
    "                    start=datetime(2014, 1, 1), \n",
    "                    end=datetime(2014, 12, 31))[attr]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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9D215AqVCyb0zbyXIJ5CG1iYKqgsoLSzjYEUu5S2VzEmYyZnp7ZsOAbLrALpn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DYCaaACvAjtLviYWZYBavN2qQEnX5Kn5aQi3baVqcGf4ERiZDvPJtd7ka0rYLFYDptYPE/v1pdS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\nHXPT3z6BThN9luhgvaCBD+R9L3pylauVr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U8cglykj6Id9y7oWnxgZkOQleh7SHGa2Epy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\nB6Z4q0lywCraAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117eb5eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.ORCL.plot(label = 'oracle')\n",
    "df.TSLA.plot(label = 'tesla')\n",
    "df.IBM.plot(label = 'ibm')\n",
    "df.MSFT.plot(label = 'msft')\n",
    "df.YELP.plot(label = 'yelp')\n",
    "_ = plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Next, we will calculate returns over a period of length $T$, defined as:\n",
    "\n",
    "$$r(t) = \\frac{f(t)-f(t-T)}{f(t)} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The returns can be computed with a simple DataFrame method **`pct_change()`**.  Note that for the first $T$ timesteps, this value is not defined (of course):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>YELP</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-11-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-17</th>\n",
       "      <td>0.023186</td>\n",
       "      <td>0.064801</td>\n",
       "      <td>-0.084723</td>\n",
       "      <td>0.141945</td>\n",
       "      <td>0.059748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-14</th>\n",
       "      <td>0.025679</td>\n",
       "      <td>0.054445</td>\n",
       "      <td>-0.099250</td>\n",
       "      <td>0.164019</td>\n",
       "      <td>0.102876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-13</th>\n",
       "      <td>0.014205</td>\n",
       "      <td>0.045522</td>\n",
       "      <td>-0.107213</td>\n",
       "      <td>0.115148</td>\n",
       "      <td>0.096020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-12</th>\n",
       "      <td>-0.002587</td>\n",
       "      <td>0.018797</td>\n",
       "      <td>-0.129284</td>\n",
       "      <td>0.093407</td>\n",
       "      <td>0.144314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-11</th>\n",
       "      <td>0.009146</td>\n",
       "      <td>0.015164</td>\n",
       "      <td>-0.124594</td>\n",
       "      <td>0.129668</td>\n",
       "      <td>0.175660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 IBM      MSFT      ORCL      TSLA      YELP\n",
       "Date                                                        \n",
       "2014-11-24       NaN       NaN       NaN       NaN       NaN\n",
       "2014-11-21       NaN       NaN       NaN       NaN       NaN\n",
       "2014-11-20       NaN       NaN       NaN       NaN       NaN\n",
       "2014-11-19       NaN       NaN       NaN       NaN       NaN\n",
       "2014-11-18       NaN       NaN       NaN       NaN       NaN\n",
       "2014-11-17  0.023186  0.064801 -0.084723  0.141945  0.059748\n",
       "2014-11-14  0.025679  0.054445 -0.099250  0.164019  0.102876\n",
       "2014-11-13  0.014205  0.045522 -0.107213  0.115148  0.096020\n",
       "2014-11-12 -0.002587  0.018797 -0.129284  0.093407  0.144314\n",
       "2014-11-11  0.009146  0.015164 -0.124594  0.129668  0.175660"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = df.pct_change(30)\n",
    "rets.iloc[25:35]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now we'll plot the timeseries of the returns of the different stocks.\n",
    "\n",
    "Notice that the `NaN` values are gracefully dropped by the plotting function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9OLfddhMDAwOccMJJfO5zl404fqT3sx1NjQBmD5TOjQ8yb/mFUF36E39uMKOF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IAAAg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180cad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.ORCL.plot(label = 'oracle')\n",
    "rets.TSLA.plot(label = 'tesla')\n",
    "rets.IBM.plot(label = 'ibm')\n",
    "rets.MSFT.plot(label = 'msft')\n",
    "rets.YELP.plot(label = 'yelp')\n",
    "_ = plt.legend(loc='best')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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X8Y9xkLq6GjZvPpHRb+aREu3PyJHHcRuNGDHieGfV1kTOyQS36/hf/7Wajo5/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\nCIIgCILS8f9C02iV+7A/KgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1180e9358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(rets.TSLA, rets.YELP)\n",
    "plt.xlabel('TESLA 30-day returns')\n",
    "_ = plt.ylabel('YELP 30-day returns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "There appears to be some (fairly strong) correlation between the movement of TSLA and YELP stocks.  Let's measure this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "The correlation coefficient between variables $X$ and $Y$ is defined as follows:\n",
    "\n",
    "$$\\text{Corr}(X,Y) = \\frac{E\\left[(X-\\mu_X)(Y-\\mu_Y)\\right]}{\\sigma_X\\sigma_Y}$$\n",
    "\n",
    "Pandas provides a DataFrame method to compute the correlation coefficient of all pairs of columns: **`corr()`**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IBM</th>\n",
       "      <th>MSFT</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>YELP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.321583</td>\n",
       "      <td>0.042213</td>\n",
       "      <td>0.208735</td>\n",
       "      <td>0.103837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.321583</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.130515</td>\n",
       "      <td>0.492674</td>\n",
       "      <td>0.282827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ORCL</th>\n",
       "      <td>0.042213</td>\n",
       "      <td>0.130515</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.032724</td>\n",
       "      <td>-0.065211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.208735</td>\n",
       "      <td>0.492674</td>\n",
       "      <td>0.032724</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.800936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YELP</th>\n",
       "      <td>0.103837</td>\n",
       "      <td>0.282827</td>\n",
       "      <td>-0.065211</td>\n",
       "      <td>0.800936</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           IBM      MSFT      ORCL      TSLA      YELP\n",
       "IBM   1.000000  0.321583  0.042213  0.208735  0.103837\n",
       "MSFT  0.321583  1.000000  0.130515  0.492674  0.282827\n",
       "ORCL  0.042213  0.130515  1.000000  0.032724 -0.065211\n",
       "TSLA  0.208735  0.492674  0.032724  1.000000  0.800936\n",
       "YELP  0.103837  0.282827 -0.065211  0.800936  1.000000"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "It takes a bit of time to examine that table and draw conclusions.  \n",
    "\n",
    "To speed that process up it helps to visualize the table.\n",
    "\n",
    "We will learn more about visualization later, but for now this is a simple example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+PUxyipH/m70UHx9vPhnRj8Khd34ghgYVQO/jzdk/71zKITYxifibyZQsXNC5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\nIhLiFKxFRERC3P8DG+VIu/W1te4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1182b8c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = sns.heatmap(rets.corr(), annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Finally, it is important to know that the plotting performed by Pandas is just a layer on top of `matplotlib` (i.e., the `plt` package).  \n",
    "\n",
    "So Panda's plots can (and should) be replaced or improved by using additional functions from `matplotlib`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "For example, suppose we want to know both the returns as well as the standard deviation of the returns of a stock (i.e., its risk).  \n",
    "\n",
    "Here is visualization of the result of such an analysis, and we construct the plot using only functions from `matplotlib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PIuAFEfHliNgcEX8TES+etJE3U/J79EPAbuBBYDvVbNtvFR/xxDSpk8xcmXt/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\nkqSiDBeSJKkow4UkSSrKcCFJkor6/5nzeEAGj1jjAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118245748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = plt.scatter(rets.mean(), rets.std())\n",
    "# plt.xlabel('Expected returns')\n",
    "# plt.ylabel('Standard Deviation (Risk)')\n",
    "# for label, x, y in zip(rets.columns, rets.mean(), rets.std()):\n",
    "#    plt.annotate(\n",
    "#        label, \n",
    "#        xy = (x, y), xytext = (20, -20),\n",
    "#        textcoords = 'offset points', ha = 'right', va = 'bottom',\n",
    "#        bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
    "#        arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To understand what these functions are doing, (especially the `annotate` function), you will need to consult the online documentation for matplotlib.  Just use Google to find it."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}