05-merge-pivot-slides.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "(c) 2016 - present. Enplus Advisors, Inc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.460286Z",
     "start_time": "2019-04-28T22:05:16.630625Z"
    },
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import Image\n",
    "\n",
    "pd.set_option('precision', 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Programming with Data:<br>Advanced Python and Pandas\n",
    "\n",
    "# Merge, Join, & Combine"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Types of Joins\n",
    "\n",
    "* Inner\n",
    "* Left\n",
    "* Right\n",
    "* Full"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Inner Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.471824Z",
     "start_time": "2019-04-28T22:05:17.462109Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 2,
     "metadata": {
      "image/png": {
       "height": 190,
       "width": 329
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='assets/inner-join.png', retina=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "**TODO:** Show with a markdown table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Left/Right Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.478485Z",
     "start_time": "2019-04-28T22:05:17.474256Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "image/png": {
       "height": 190,
       "width": 329
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='assets/left-join.png', retina=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Full Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.484777Z",
     "start_time": "2019-04-28T22:05:17.480589Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "image/png": {
       "height": 190,
       "width": 329
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='assets/full-join.png', retina=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## The Data\n",
    "\n",
    "<img src=\"assets/stock-trading-1600x1200.jpg\" width=700></img>\n",
    "\n",
    "Photo by Chris Liverani on Unsplash"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Going to use stock data because I used to work as a quant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Somewhat simple data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.506112Z",
     "start_time": "2019-04-28T22:05:17.486652Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>101.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>35.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open\n",
       "0   AAPL  426.23\n",
       "1   MSFT   42.30\n",
       "2    IBM  101.65\n",
       "3   YHOO   35.53\n",
       "4   GOOG  200.41"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({\n",
    "    'ticker': ['AAPL', 'MSFT', 'IBM', 'YHOO', 'GOOG'],\n",
    "    'open': [426.23, 42.30, 101.65, 35.53, 200.41]\n",
    "})\n",
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "`df1` has ticker and `open` price (the price of the stock when the NYSE first opens at 0930)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### More somewhat simple data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Tickers and close prices. Additional ticker for `NFLX`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.520001Z",
     "start_time": "2019-04-28T22:05:17.507619Z"
    },
    "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>ticker</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NFLX</td>\n",
       "      <td>91.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker   close\n",
       "0   AAPL  427.53\n",
       "1   GOOG  210.96\n",
       "2   NFLX   91.86"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\n",
    "    'ticker': ['AAPL', 'GOOG', 'NFLX'],\n",
    "    'close': [427.53, 210.96, 91.86]\n",
    "}, columns=['ticker', 'close'])\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Coding an inner join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "An inner join gives us the intersection of the keys."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.539425Z",
     "start_time": "2019-04-28T22:05:17.522215Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open   close\n",
       "0   AAPL  426.23  427.53\n",
       "1   GOOG  200.41  210.96"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1m2 = pd.merge(df1, df2, on='ticker')\n",
    "df1m2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Verifying the inner join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We drop everything except tickers that are present **both** data frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.546596Z",
     "start_time": "2019-04-28T22:05:17.541871Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AAPL', 'GOOG'}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_tickers = set(df1.ticker) & set(df2.ticker)\n",
    "common_tickers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.551480Z",
     "start_time": "2019-04-28T22:05:17.548943Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "assert set(df1m2.ticker) == common_tickers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Aside: 99% of the time, use `pd.merge`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Most flexible way to join two data frames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "\n",
    "* `pd.concat` is more general - useful to join a collection (e.g. `list`) of data frames\n",
    "* `pd.DataFrame.join` works in more specific circumstances"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Left Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Include all keys from the `left` data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.563296Z",
     "start_time": "2019-04-28T22:05:17.552988Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>101.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>35.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open   close\n",
       "0   AAPL  426.23  427.53\n",
       "1   MSFT   42.30     NaN\n",
       "2    IBM  101.65     NaN\n",
       "3   YHOO   35.53     NaN\n",
       "4   GOOG  200.41  210.96"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1m2_left = pd.merge(df1, df2, on='ticker', how='left')\n",
    "df1m2_left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.567055Z",
     "start_time": "2019-04-28T22:05:17.564554Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "assert set(df1.ticker) == set(df1m2_left.ticker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Filling missing levels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.580407Z",
     "start_time": "2019-04-28T22:05:17.568356Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>101.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>35.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open   close\n",
       "0   AAPL  426.23  427.53\n",
       "1   MSFT   42.30     NaN\n",
       "2    IBM  101.65     NaN\n",
       "3   YHOO   35.53     NaN\n",
       "4   GOOG  200.41  210.96"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1m2_left"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice that `pandas` fills missing levels from `df2` with `NaN`. Comparable to `SQL`\n",
    "where values would be `NULL`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Right Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Include all keys from the `right` data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.592876Z",
     "start_time": "2019-04-28T22:05:17.582057Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NFLX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open   close\n",
       "0   AAPL  426.23  427.53\n",
       "1   GOOG  200.41  210.96\n",
       "2   NFLX     NaN   91.86"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, on='ticker', how='right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Same missingness handling as `left` join."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Outer/Full Join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.606884Z",
     "start_time": "2019-04-28T22:05:17.594128Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>427.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IBM</td>\n",
       "      <td>101.65</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHOO</td>\n",
       "      <td>35.53</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GOOG</td>\n",
       "      <td>200.41</td>\n",
       "      <td>210.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NFLX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>91.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open   close\n",
       "0   AAPL  426.23  427.53\n",
       "1   MSFT   42.30     NaN\n",
       "2    IBM  101.65     NaN\n",
       "3   YHOO   35.53     NaN\n",
       "4   GOOG  200.41  210.96\n",
       "5   NFLX     NaN   91.86"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1m2_full = pd.merge(df1, df2, on='ticker', how='outer')\n",
    "df1m2_full"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.613140Z",
     "start_time": "2019-04-28T22:05:17.609391Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "assert set(df1.ticker) | set(df2.ticker) == set(df1m2_full.ticker)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Concatenation/Binding\n",
    "\n",
    "* Add rows\\* (`append`)\n",
    "* Add columns (`pd.concat`)\n",
    "* Add rows and columns (`pd.concat` or `append`)\n",
    "\n",
    "\\* `append` will also add columns!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T21:57:49.236364Z",
     "start_time": "2019-04-28T21:57:49.233873Z"
    },
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Data for Concatenation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "We're going to make this data explicitly **daily** so we're going to \n",
    "add a `date` column. Take the first two records only so the data\n",
    "fits on the slide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.629651Z",
     "start_time": "2019-04-28T22:05:17.614830Z"
    },
    "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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date\n",
       "0   AAPL  426.23 2018-01-04\n",
       "1   MSFT   42.30 2018-01-04"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = df1.assign(date=pd.datetime(2018, 1, 4))\\\n",
    "    .iloc[:2, ] # first 2 rows only\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.646471Z",
     "start_time": "2019-04-28T22:05:17.631213Z"
    },
    "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",
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>436.23</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>52.30</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date\n",
       "0   AAPL  436.23 2018-01-05\n",
       "1   MSFT   52.30 2018-01-05"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = df3.assign(\n",
    "    date=pd.datetime(2018, 1, 5),\n",
    "    open=lambda x: x.open + 10\n",
    ")\n",
    "df4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Adding rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.656406Z",
     "start_time": "2019-04-28T22:05:17.647763Z"
    },
    "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",
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       "\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>436.23</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>52.30</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date\n",
       "0   AAPL  426.23 2018-01-04\n",
       "1   MSFT   42.30 2018-01-04\n",
       "0   AAPL  436.23 2018-01-05\n",
       "1   MSFT   52.30 2018-01-05"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.append(df4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Notice how the index is repeated and duplicated for the default `pd.RangeIndex`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### No dups please"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "To check for duplicated index values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.662685Z",
     "start_time": "2019-04-28T22:05:17.657752Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Indexes have overlapping values: Int64Index([0, 1], dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    df3.append(df4, verify_integrity=True)\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Ignore the index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.673287Z",
     "start_time": "2019-04-28T22:05:17.664189Z"
    },
    "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",
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       "\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>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>2018-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>436.23</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>52.30</td>\n",
       "      <td>2018-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date\n",
       "0   AAPL  426.23 2018-01-04\n",
       "1   MSFT   42.30 2018-01-04\n",
       "2   AAPL  436.23 2018-01-05\n",
       "3   MSFT   52.30 2018-01-05"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.append(df4, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "We usually don't need to validate the index when we pass `ignore_index`\n",
    "because we're creating a new index!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rows and Columns with `append`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.683924Z",
     "start_time": "2019-04-28T22:05:17.674714Z"
    },
    "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",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>ticker</th>\n",
       "      <th>open</th>\n",
       "      <th>date</th>\n",
       "      <th>close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AAPL</td>\n",
       "      <td>426.23</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>435.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
       "      <td>42.30</td>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>51.30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date   close\n",
       "0   AAPL  426.23 2018-01-04  435.23\n",
       "1   MSFT   42.30 2018-01-04   51.30"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3a = df3.assign(close=lambda x: (x.open + 9))\n",
    "df3a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### We've been warned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.699635Z",
     "start_time": "2019-04-28T22:05:17.685577Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/daniel/anaconda3/envs/pydata/lib/python3.7/site-packages/pandas/core/frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "  sort=sort)\n"
     ]
    },
    {
     "data": {
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       "      <td>51.30</td>\n",
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      ],
      "text/plain": [
       "    close       date    open ticker\n",
       "0  435.23 2018-01-04  426.23   AAPL\n",
       "1   51.30 2018-01-04   42.30   MSFT\n",
       "2     NaN 2018-01-05  436.23   AAPL\n",
       "3     NaN 2018-01-05   52.30   MSFT"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3a.append(df4, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Probably the most common `append`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.717702Z",
     "start_time": "2019-04-28T22:05:17.702662Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "      <td>435.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MSFT</td>\n",
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       "      <th>2</th>\n",
       "      <td>AAPL</td>\n",
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MSFT</td>\n",
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       "      <td>2018-01-05</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ticker    open       date   close\n",
       "0   AAPL  426.23 2018-01-04  435.23\n",
       "1   MSFT   42.30 2018-01-04   51.30\n",
       "2   AAPL  436.23 2018-01-05     NaN\n",
       "3   MSFT   52.30 2018-01-05     NaN"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3a.append(df4, ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Anytime you're repeating code, put it in a function and stay `DRY` (Don't repeat yourself)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-04-28T22:05:17.722872Z",
     "start_time": "2019-04-28T22:05:17.719681Z"
    },
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def pwd_append(x: pd.DataFrame, y: pd.DataFrame, **kwargs) -> pd.DataFrame:\n",
    "    defaults = {\n",
    "        'ignore_index': True,\n",
    "        'sort': True,\n",
    "        'verify_integrity': False \n",
    "    }\n",
    "    defaults.update(kwargs)\n",
    "    return x.append(y, **defaults)"
   ]
  }
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