02B-Pandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Essential Tools: 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": {},
   "source": [
    "Use of Pandas is a data science __best practice.__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "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\n",
    "\n",
    "Get in the habit: whenever you load data, place it into a dataframe as your first step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "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 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 utility library that fetches data from standard online sources, such as Yahoo! Finance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 High        Low       Open      Close   Volume  Adj Close\n",
       "Date                                                                      \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002\n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999\n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999\n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999\n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "This is a typical example of a dataframe.  \n",
    "\n",
    "Notice how each row has a label and each column has a label."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "A dataframe is an object that has __many__ methods associated with it, to do all sorts of useful things.\n",
    "\n",
    "Here is a simple method: ``.info()``"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": true,
    "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",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   High       252 non-null    float64\n",
      " 1   Low        252 non-null    float64\n",
      " 2   Open       252 non-null    float64\n",
      " 3   Close      252 non-null    float64\n",
      " 4   Volume     252 non-null    int64  \n",
      " 5   Adj Close  252 non-null    float64\n",
      "dtypes: float64(5), int64(1)\n",
      "memory usage: 13.8 KB\n"
     ]
    }
   ],
   "source": [
    "yahoo_stocks.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Reading to/from a ``.csv`` file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Continuing to explore methods, let's write the dataframe out to a ``.csv`` file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "yahoo_stocks.to_csv('yahoo_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date,High,Low,Open,Close,Volume,Adj Close\r\n",
      "2015-01-02,55.599998474121094,54.2400016784668,55.459999084472656,55.150001525878906,1664500,55.150001525878906\r\n",
      "2015-01-05,54.95000076293945,52.33000183105469,54.540000915527344,52.529998779296875,2023000,52.529998779296875\r\n",
      "2015-01-06,53.93000030517578,50.75,52.54999923706055,52.439998626708984,3762800,52.439998626708984\r\n",
      "2015-01-07,53.75,51.7599983215332,53.31999969482422,52.209999084472656,1548200,52.209999084472656\r\n",
      "2015-01-08,54.13999938964844,51.7599983215332,52.59000015258789,53.83000183105469,2015300,53.83000183105469\r\n",
      "2015-01-09,56.9900016784668,54.720001220703125,55.959999084472656,56.06999969482422,6224200,56.06999969482422\r\n",
      "2015-01-12,56.060001373291016,53.43000030517578,56.0,54.02000045776367,2407700,54.02000045776367\r\n",
      "2015-01-13,54.79999923706055,52.52000045776367,54.470001220703125,53.18000030517578,1958400,53.18000030517578\r\n",
      "2015-01-14,53.68000030517578,51.459999084472656,52.79999923706055,52.20000076293945,1854600,52.20000076293945\r\n"
     ]
    }
   ],
   "source": [
    "!head yahoo_data.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "And of course we can likewise read a ``.csv`` file into a dataframe.   This is probably the most common way you will get data into Pandas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date       High        Low       Open      Close   Volume  Adj Close\n",
       "0  2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002\n",
       "1  2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999\n",
       "2  2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999\n",
       "3  2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999\n",
       "4  2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('yahoo_data.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Working with data columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "In general, we'll usually organize things so that rows in the dataframe are __items__ and columns are __features__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Date', 'High', 'Low', 'Open', 'Close', 'Volume', 'Adj Close'], dtype='object')"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pandas allows you to use standard python __indexing__ to refer to columns (eg features) in your dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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",
       "         ...    \n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "251    28.100000\n",
       "Name: Open, Length: 252, dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Open']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pandas also allows you to use a syntax like an object attribute to refer to a column.\n",
    "\n",
    "But note that the column name cannot include a space in this case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "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",
       "         ...    \n",
       "247    28.270000\n",
       "248    28.120001\n",
       "249    27.950001\n",
       "250    28.580000\n",
       "251    28.100000\n",
       "Name: Open, Length: 252, dtype: float64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Open"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "You can select a list of columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Open','Close']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Putting things together -- make sure this syntax is clear to you:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Date.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Changing column names is as simple as assigning to the ``.columns`` property.\n",
    "\n",
    "Let's adjust column names so that none of them include spaces:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "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",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   date       252 non-null    object \n",
      " 1   high       252 non-null    float64\n",
      " 2   low        252 non-null    float64\n",
      " 3   open       252 non-null    float64\n",
      " 4   close      252 non-null    float64\n",
      " 5   volume     252 non-null    int64  \n",
      " 6   adj_close  252 non-null    float64\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": [
    "(Be sure you understand the __list comprehension__ used above -- it's a common and important way to process a list in python.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now **all** columns can be accessed using the **dot** notation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.adj_close.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## A sampling of DataFrame methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "A dataframe object has many useful methods.\n",
    "\n",
    "Familiarize yourself with dataframe methods -- they are very useful.\n",
    "\n",
    "These should be self-explanatory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         3.805464e+01\n",
       "low          3.656373e+01\n",
       "open         3.728766e+01\n",
       "close        3.729917e+01\n",
       "volume       3.492407e+06\n",
       "adj_close    3.729917e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         1.138111e+01\n",
       "low          1.113097e+01\n",
       "open         1.128093e+01\n",
       "close        1.125233e+01\n",
       "volume       4.146378e+06\n",
       "adj_close    1.125233e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         3.871500e+01\n",
       "low          3.637500e+01\n",
       "open         3.796500e+01\n",
       "close        3.783500e+01\n",
       "volume       2.354050e+06\n",
       "adj_close    3.783500e+01\n",
       "dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['high', 'low', 'open', 'close', 'volume', 'adj_close']].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37.28765870654394"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.open.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "38.05464292707897"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.high.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting methods\n",
    "\n",
    "Pandas has an extensive library of plotting functions, and they are very easy to use.\n",
    "\n",
    "These are your \"first look\" functions.\n",
    "\n",
    "(Later you will use specialized graphics packages for more sophisticated visualizations.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.high.plot(label='High')\n",
    "df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.adj_close.hist()\n",
    "plt.xlabel('Adjusted Closing Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.title('YELP');"
   ]
  },
  {
   "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": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "As an example, let's go back to this plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.high.plot(label='High')\n",
    "df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "It's __almost__ perfect.  The only problem is the $x$-axis: it should show time.\n",
    "\n",
    "To fix this, we need to make the dataframe __index__ -- that is, the __row labels__ -- into dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We have a problem however: the \"dates\" in our data are only __strings__.   We need Pandas to understand that they are actually dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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": 76,
     "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": 77,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2015-01-02'"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_date = df.date.values[0]\n",
    "first_date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To convert these strings to actual dates we'll use the ``datetime`` standard python package:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2015, 1, 2, 0, 0)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datetime.strptime(first_date, \"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "And to do this for each string in the ``date`` column we will use ``.apply()``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = df.copy()\n",
    "new_df.date = df.date.apply(lambda d: datetime.strptime(d, \"%Y-%m-%d\"))\n",
    "new_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",
    "Fixing this is as easy as assigning to the `index` property of the DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>2015-01-06</td>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>2015-01-07</td>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>2015-01-08</td>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 date       high        low       open      close   volume  \\\n",
       "date                                                                         \n",
       "2015-01-02 2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500   \n",
       "2015-01-05 2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000   \n",
       "2015-01-06 2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800   \n",
       "2015-01-07 2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200   \n",
       "2015-01-08 2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300   \n",
       "\n",
       "            adj_close  \n",
       "date                   \n",
       "2015-01-02  55.150002  \n",
       "2015-01-05  52.529999  \n",
       "2015-01-06  52.439999  \n",
       "2015-01-07  52.209999  \n",
       "2015-01-08  53.830002  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.index = new_df.date\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now that we have made an index based on a real date, we can drop the original `date` column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close\n",
       "date                                                                      \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002\n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999\n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999\n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999\n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df = new_df.drop(['date'],axis=1)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Now we can see that Pandas handles these dates quite nicely:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "new_df.high.plot(label='High')\n",
    "new_df.low.plot(label='Low')\n",
    "plt.title('YELP Stock Price')\n",
    "plt.ylabel('Dollars')\n",
    "plt.legend(loc='best');"
   ]
  },
  {
   "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": 83,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         5.564000e+01\n",
       "low          5.430000e+01\n",
       "open         5.466000e+01\n",
       "close        5.519000e+01\n",
       "volume       1.636400e+06\n",
       "adj_close    5.519000e+01\n",
       "Name: 2015-01-23 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.loc[datetime(2015,1,23,0,0)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To access a row by its sequence number (ie, like an array index), use **`.iloc()`** ('Integer Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "high         5.560000e+01\n",
       "low          5.424000e+01\n",
       "open         5.546000e+01\n",
       "close        5.515000e+01\n",
       "volume       1.664500e+06\n",
       "adj_close    5.515000e+01\n",
       "Name: 2015-01-02 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.iloc[0,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To iterate over the rows, use **`.iterrows()`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "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 easy to select interesting rows from the data.  \n",
    "\n",
    "All the 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": 86,
   "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": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmp_high = new_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": 87,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 87,
     "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": 88,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6224200</td>\n",
       "      <td>56.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-12</th>\n",
       "      <td>56.060001</td>\n",
       "      <td>53.430000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>54.020000</td>\n",
       "      <td>2407700</td>\n",
       "      <td>54.020000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>53.869999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "      <td>54.799999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-23</th>\n",
       "      <td>55.639999</td>\n",
       "      <td>54.299999</td>\n",
       "      <td>54.660000</td>\n",
       "      <td>55.189999</td>\n",
       "      <td>1636400</td>\n",
       "      <td>55.189999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-26</th>\n",
       "      <td>55.790001</td>\n",
       "      <td>54.830002</td>\n",
       "      <td>55.119999</td>\n",
       "      <td>55.410000</td>\n",
       "      <td>1450300</td>\n",
       "      <td>55.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-27</th>\n",
       "      <td>56.160000</td>\n",
       "      <td>54.570000</td>\n",
       "      <td>56.060001</td>\n",
       "      <td>55.630001</td>\n",
       "      <td>2410400</td>\n",
       "      <td>55.630001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-28</th>\n",
       "      <td>56.150002</td>\n",
       "      <td>52.919998</td>\n",
       "      <td>56.150002</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>2013100</td>\n",
       "      <td>53.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-03</th>\n",
       "      <td>55.930000</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>55.779999</td>\n",
       "      <td>2885400</td>\n",
       "      <td>55.779999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-04</th>\n",
       "      <td>57.070000</td>\n",
       "      <td>55.250000</td>\n",
       "      <td>55.529999</td>\n",
       "      <td>56.740002</td>\n",
       "      <td>2498600</td>\n",
       "      <td>56.740002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-05</th>\n",
       "      <td>57.700001</td>\n",
       "      <td>56.080002</td>\n",
       "      <td>57.599998</td>\n",
       "      <td>57.470001</td>\n",
       "      <td>4657300</td>\n",
       "      <td>57.470001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close\n",
       "date                                                                      \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002\n",
       "2015-01-09  56.990002  54.720001  55.959999  56.070000  6224200  56.070000\n",
       "2015-01-12  56.060001  53.430000  56.000000  54.020000  2407700  54.020000\n",
       "2015-01-22  55.279999  53.119999  53.869999  54.799999  2295400  54.799999\n",
       "2015-01-23  55.639999  54.299999  54.660000  55.189999  1636400  55.189999\n",
       "2015-01-26  55.790001  54.830002  55.119999  55.410000  1450300  55.410000\n",
       "2015-01-27  56.160000  54.570000  56.060001  55.630001  2410400  55.630001\n",
       "2015-01-28  56.150002  52.919998  56.150002  53.000000  2013100  53.000000\n",
       "2015-02-03  55.930000  53.410000  53.830002  55.779999  2885400  55.779999\n",
       "2015-02-04  57.070000  55.250000  55.529999  56.740002  2498600  56.740002\n",
       "2015-02-05  57.700001  56.080002  57.599998  57.470001  4657300  57.470001"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_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": 89,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-09</th>\n",
       "      <td>56.990002</td>\n",
       "      <td>54.720001</td>\n",
       "      <td>55.959999</td>\n",
       "      <td>56.070000</td>\n",
       "      <td>6224200</td>\n",
       "      <td>56.070000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-16</th>\n",
       "      <td>51.490002</td>\n",
       "      <td>50.029999</td>\n",
       "      <td>50.180000</td>\n",
       "      <td>51.389999</td>\n",
       "      <td>2183300</td>\n",
       "      <td>51.389999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-21</th>\n",
       "      <td>53.500000</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>51.200001</td>\n",
       "      <td>53.410000</td>\n",
       "      <td>3248100</td>\n",
       "      <td>53.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-22</th>\n",
       "      <td>55.279999</td>\n",
       "      <td>53.119999</td>\n",
       "      <td>53.869999</td>\n",
       "      <td>54.799999</td>\n",
       "      <td>2295400</td>\n",
       "      <td>54.799999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close\n",
       "date                                                                      \n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002\n",
       "2015-01-09  56.990002  54.720001  55.959999  56.070000  6224200  56.070000\n",
       "2015-01-16  51.490002  50.029999  50.180000  51.389999  2183300  51.389999\n",
       "2015-01-21  53.500000  51.200001  51.200001  53.410000  3248100  53.410000\n",
       "2015-01-22  55.279999  53.119999  53.869999  54.799999  2295400  54.799999"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_days = new_df[new_df.close > new_df.open]\n",
    "positive_days.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>48.73</td>\n",
       "      <td>38.220001</td>\n",
       "      <td>38.220001</td>\n",
       "      <td>47.009998</td>\n",
       "      <td>33831600</td>\n",
       "      <td>47.009998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             high        low       open      close    volume  adj_close\n",
       "date                                                                   \n",
       "2015-05-07  48.73  38.220001  38.220001  47.009998  33831600  47.009998"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "very_positive_days = new_df[(new_df.close - new_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": 91,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002   \n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999   \n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999   \n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999   \n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002   \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": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df['profit'] = (new_df.open < new_df.close)\n",
    "new_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Let's give each row a ``gain`` value as a categorical variable:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002   \n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999   \n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999   \n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999   \n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002   \n",
       "\n",
       "            profit         gain  \n",
       "date                             \n",
       "2015-01-02   False     negative  \n",
       "2015-01-05   False     negative  \n",
       "2015-01-06   False     negative  \n",
       "2015-01-07   False     negative  \n",
       "2015-01-08    True  medium_gain  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for idx, row in new_df.iterrows():\n",
    "    if row.open > row.close:\n",
    "        new_df.loc[idx,'gain']='negative'\n",
    "    elif (row.close - row.open) < 1:\n",
    "        new_df.loc[idx,'gain']='small_gain'\n",
    "    elif (row.close - row.open) < 6:\n",
    "        new_df.loc[idx,'gain']='medium_gain'\n",
    "    else:\n",
    "        new_df.loc[idx,'gain']='large_gain'\n",
    "new_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": 93,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "def namerow(row):\n",
    "    if row.open > row.close:\n",
    "        return 'negative'\n",
    "    elif (row.close - row.open) < 1:\n",
    "        return 'small_gain'\n",
    "    elif (row.close - row.open) < 6:\n",
    "        return 'medium_gain'\n",
    "    else:\n",
    "        return 'large_gain'\n",
    "\n",
    "new_df['test_column'] = new_df.apply(namerow, axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_gain</td>\n",
       "      <td>medium_gain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002   \n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999   \n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999   \n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999   \n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002   \n",
       "\n",
       "            profit         gain  test_column  \n",
       "date                                          \n",
       "2015-01-02   False     negative     negative  \n",
       "2015-01-05   False     negative     negative  \n",
       "2015-01-06   False     negative     negative  \n",
       "2015-01-07   False     negative     negative  \n",
       "2015-01-08    True  medium_gain  medium_gain  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_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": 95,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>adj_close</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>55.599998</td>\n",
       "      <td>54.240002</td>\n",
       "      <td>55.459999</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>1664500</td>\n",
       "      <td>55.150002</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>54.950001</td>\n",
       "      <td>52.330002</td>\n",
       "      <td>54.540001</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>2023000</td>\n",
       "      <td>52.529999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>53.930000</td>\n",
       "      <td>50.750000</td>\n",
       "      <td>52.549999</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>3762800</td>\n",
       "      <td>52.439999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>53.750000</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>53.320000</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>1548200</td>\n",
       "      <td>52.209999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>54.139999</td>\n",
       "      <td>51.759998</td>\n",
       "      <td>52.590000</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>2015300</td>\n",
       "      <td>53.830002</td>\n",
       "      <td>True</td>\n",
       "      <td>medium_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>2015-12-24</th>\n",
       "      <td>28.590000</td>\n",
       "      <td>27.900000</td>\n",
       "      <td>28.270000</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>587400</td>\n",
       "      <td>28.400000</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-28</th>\n",
       "      <td>28.379999</td>\n",
       "      <td>27.770000</td>\n",
       "      <td>28.120001</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>1004500</td>\n",
       "      <td>27.879999</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-29</th>\n",
       "      <td>28.540001</td>\n",
       "      <td>27.740000</td>\n",
       "      <td>27.950001</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>1103900</td>\n",
       "      <td>28.480000</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-30</th>\n",
       "      <td>28.780001</td>\n",
       "      <td>28.170000</td>\n",
       "      <td>28.580000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>1068000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>False</td>\n",
       "      <td>negative</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31</th>\n",
       "      <td>28.969999</td>\n",
       "      <td>28.020000</td>\n",
       "      <td>28.100000</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>1301500</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>True</td>\n",
       "      <td>small_gain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 high        low       open      close   volume  adj_close  \\\n",
       "date                                                                         \n",
       "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002   \n",
       "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999   \n",
       "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999   \n",
       "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999   \n",
       "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002   \n",
       "...               ...        ...        ...        ...      ...        ...   \n",
       "2015-12-24  28.590000  27.900000  28.270000  28.400000   587400  28.400000   \n",
       "2015-12-28  28.379999  27.770000  28.120001  27.879999  1004500  27.879999   \n",
       "2015-12-29  28.540001  27.740000  27.950001  28.480000  1103900  28.480000   \n",
       "2015-12-30  28.780001  28.170000  28.580000  28.250000  1068000  28.250000   \n",
       "2015-12-31  28.969999  28.020000  28.100000  28.799999  1301500  28.799999   \n",
       "\n",
       "            profit         gain  \n",
       "date                             \n",
       "2015-01-02   False     negative  \n",
       "2015-01-05   False     negative  \n",
       "2015-01-06   False     negative  \n",
       "2015-01-07   False     negative  \n",
       "2015-01-08    True  medium_gain  \n",
       "...            ...          ...  \n",
       "2015-12-24    True   small_gain  \n",
       "2015-12-28   False     negative  \n",
       "2015-12-29    True   small_gain  \n",
       "2015-12-30   False     negative  \n",
       "2015-12-31    True   small_gain  \n",
       "\n",
       "[252 rows x 8 columns]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_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": 96,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "gain_groups = new_df.groupby('gain')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Essentially, **`gain_groups`** behaves like a dictionary:\n",
    "* the keys are the unique values found in the `gain` column, and \n",
    "* the values are DataFrames that contain only the rows having the corresponding unique values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "large_gain\n",
      "             high        low       open      close    volume  adj_close  \\\n",
      "date                                                                      \n",
      "2015-05-07  48.73  38.220001  38.220001  47.009998  33831600  47.009998   \n",
      "\n",
      "            profit        gain test_column  \n",
      "date                                        \n",
      "2015-05-07    True  large_gain  large_gain  \n",
      "=============================\n",
      "medium_gain\n",
      "                 high        low       open      close   volume  adj_close  \\\n",
      "date                                                                         \n",
      "2015-01-08  54.139999  51.759998  52.590000  53.830002  2015300  53.830002   \n",
      "2015-01-16  51.490002  50.029999  50.180000  51.389999  2183300  51.389999   \n",
      "2015-01-21  53.500000  51.200001  51.200001  53.410000  3248100  53.410000   \n",
      "2015-02-03  55.930000  53.410000  53.830002  55.779999  2885400  55.779999   \n",
      "2015-02-04  57.070000  55.250000  55.529999  56.740002  2498600  56.740002   \n",
      "\n",
      "            profit         gain  test_column  \n",
      "date                                          \n",
      "2015-01-08    True  medium_gain  medium_gain  \n",
      "2015-01-16    True  medium_gain  medium_gain  \n",
      "2015-01-21    True  medium_gain  medium_gain  \n",
      "2015-02-03    True  medium_gain  medium_gain  \n",
      "2015-02-04    True  medium_gain  medium_gain  \n",
      "=============================\n",
      "negative\n",
      "                 high        low       open      close   volume  adj_close  \\\n",
      "date                                                                         \n",
      "2015-01-02  55.599998  54.240002  55.459999  55.150002  1664500  55.150002   \n",
      "2015-01-05  54.950001  52.330002  54.540001  52.529999  2023000  52.529999   \n",
      "2015-01-06  53.930000  50.750000  52.549999  52.439999  3762800  52.439999   \n",
      "2015-01-07  53.750000  51.759998  53.320000  52.209999  1548200  52.209999   \n",
      "2015-01-12  56.060001  53.430000  56.000000  54.020000  2407700  54.020000   \n",
      "\n",
      "            profit      gain test_column  \n",
      "date                                      \n",
      "2015-01-02   False  negative    negative  \n",
      "2015-01-05   False  negative    negative  \n",
      "2015-01-06   False  negative    negative  \n",
      "2015-01-07   False  negative    negative  \n",
      "2015-01-12   False  negative    negative  \n",
      "=============================\n",
      "small_gain\n",
      "                 high        low       open      close   volume  adj_close  \\\n",
      "date                                                                         \n",
      "2015-01-09  56.990002  54.720001  55.959999  56.070000  6224200  56.070000   \n",
      "2015-01-22  55.279999  53.119999  53.869999  54.799999  2295400  54.799999   \n",
      "2015-01-23  55.639999  54.299999  54.660000  55.189999  1636400  55.189999   \n",
      "2015-01-26  55.790001  54.830002  55.119999  55.410000  1450300  55.410000   \n",
      "2015-01-29  53.310001  51.410000  52.849998  52.930000  1844100  52.930000   \n",
      "\n",
      "            profit        gain test_column  \n",
      "date                                        \n",
      "2015-01-09    True  small_gain  small_gain  \n",
      "2015-01-22    True  small_gain  small_gain  \n",
      "2015-01-23    True  small_gain  small_gain  \n",
      "2015-01-26    True  small_gain  small_gain  \n",
      "2015-01-29    True  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": 98,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average closing value for the large_gain group is 47.0099983215332\n",
      "The average closing value for the medium_gain group is 39.72307696709266\n",
      "The average closing value for the negative group is 37.38476184057811\n",
      "The average closing value for the small_gain group is 36.45555554014264\n"
     ]
    }
   ],
   "source": [
    "for gain, gain_data in new_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 created using a ``MultiIndex``.\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": 99,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</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>2014-01-02</th>\n",
       "      <td>37.840000</td>\n",
       "      <td>30.020000</td>\n",
       "      <td>185.529999</td>\n",
       "      <td>67.919998</td>\n",
       "      <td>37.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>37.619999</td>\n",
       "      <td>29.912001</td>\n",
       "      <td>186.639999</td>\n",
       "      <td>67.660004</td>\n",
       "      <td>36.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>37.470001</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>186.000000</td>\n",
       "      <td>71.720001</td>\n",
       "      <td>36.130001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>37.849998</td>\n",
       "      <td>29.872000</td>\n",
       "      <td>189.710007</td>\n",
       "      <td>72.660004</td>\n",
       "      <td>36.410000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>37.720001</td>\n",
       "      <td>30.256001</td>\n",
       "      <td>187.970001</td>\n",
       "      <td>78.419998</td>\n",
       "      <td>35.759998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols          ORCL       TSLA         IBM       YELP       MSFT\n",
       "Date                                                              \n",
       "2014-01-02  37.840000  30.020000  185.529999  67.919998  37.160000\n",
       "2014-01-03  37.619999  29.912001  186.639999  67.660004  36.910000\n",
       "2014-01-06  37.470001  29.400000  186.000000  71.720001  36.130001\n",
       "2014-01-07  37.849998  29.872000  189.710007  72.660004  36.410000\n",
       "2014-01-08  37.720001  30.256001  187.970001  78.419998  35.759998"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stocks = ['ORCL', 'TSLA', 'IBM','YELP', 'MSFT']\n",
    "attr = 'Close'\n",
    "stock_df = web.DataReader(stocks, \n",
    "                    data_source,                               \n",
    "                    start=datetime(2014, 1, 1), \n",
    "                    end=datetime(2014, 12, 31))[attr]\n",
    "stock_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stock_df.plot();"
   ]
  },
  {
   "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": 101,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</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>2014-02-07</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>2014-02-10</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>2014-02-11</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>2014-02-12</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>2014-02-13</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>2014-02-14</th>\n",
       "      <td>0.003700</td>\n",
       "      <td>0.320653</td>\n",
       "      <td>-0.009918</td>\n",
       "      <td>0.321849</td>\n",
       "      <td>0.012379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-18</th>\n",
       "      <td>0.009304</td>\n",
       "      <td>0.361995</td>\n",
       "      <td>-0.018485</td>\n",
       "      <td>0.355897</td>\n",
       "      <td>0.013817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-19</th>\n",
       "      <td>0.010675</td>\n",
       "      <td>0.317279</td>\n",
       "      <td>-0.016398</td>\n",
       "      <td>0.254880</td>\n",
       "      <td>0.038195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-20</th>\n",
       "      <td>0.011096</td>\n",
       "      <td>0.405798</td>\n",
       "      <td>-0.028728</td>\n",
       "      <td>0.257501</td>\n",
       "      <td>0.036803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-21</th>\n",
       "      <td>0.010074</td>\n",
       "      <td>0.385510</td>\n",
       "      <td>-0.027558</td>\n",
       "      <td>0.170875</td>\n",
       "      <td>0.062081</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols         ORCL      TSLA       IBM      YELP      MSFT\n",
       "Date                                                        \n",
       "2014-02-07       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-10       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-11       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-12       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-13       NaN       NaN       NaN       NaN       NaN\n",
       "2014-02-14  0.003700  0.320653 -0.009918  0.321849  0.012379\n",
       "2014-02-18  0.009304  0.361995 -0.018485  0.355897  0.013817\n",
       "2014-02-19  0.010675  0.317279 -0.016398  0.254880  0.038195\n",
       "2014-02-20  0.011096  0.405798 -0.028728  0.257501  0.036803\n",
       "2014-02-21  0.010074  0.385510 -0.027558  0.170875  0.062081"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rets = stock_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": 102,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rets.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "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": 104,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Symbols</th>\n",
       "      <th>ORCL</th>\n",
       "      <th>TSLA</th>\n",
       "      <th>IBM</th>\n",
       "      <th>YELP</th>\n",
       "      <th>MSFT</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Symbols</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>ORCL</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000981</td>\n",
       "      <td>0.025742</td>\n",
       "      <td>-0.084176</td>\n",
       "      <td>0.114090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TSLA</th>\n",
       "      <td>0.000981</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.196504</td>\n",
       "      <td>0.768509</td>\n",
       "      <td>0.415746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IBM</th>\n",
       "      <td>0.025742</td>\n",
       "      <td>0.196504</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.104776</td>\n",
       "      <td>0.340422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YELP</th>\n",
       "      <td>-0.084176</td>\n",
       "      <td>0.768509</td>\n",
       "      <td>0.104776</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.262595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSFT</th>\n",
       "      <td>0.114090</td>\n",
       "      <td>0.415746</td>\n",
       "      <td>0.340422</td>\n",
       "      <td>0.262595</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Symbols      ORCL      TSLA       IBM      YELP      MSFT\n",
       "Symbols                                                  \n",
       "ORCL     1.000000  0.000981  0.025742 -0.084176  0.114090\n",
       "TSLA     0.000981  1.000000  0.196504  0.768509  0.415746\n",
       "IBM      0.025742  0.196504  1.000000  0.104776  0.340422\n",
       "YELP    -0.084176  0.768509  0.104776  1.000000  0.262595\n",
       "MSFT     0.114090  0.415746  0.340422  0.262595  1.000000"
      ]
     },
     "execution_count": 104,
     "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": 105,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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 often 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": 106,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plt.scatter(rets.mean(), rets.std());\n",
    "plt.xlabel('Expected returns')\n",
    "plt.ylabel('Standard Deviation (Risk)')\n",
    "plt.xlim([-.05,.1])\n",
    "plt.ylim([0,.3])\n",
    "for label, x, y in zip(rets.columns, rets.mean(), rets.std()):\n",
    "    plt.annotate(\n",
    "        label, \n",
    "        xy = (x, y), xytext = (30, -30),\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 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}