03-Iterators-Groupby.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Iterators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics\n",
    "\n",
    "- Stream larger-than-memory data through a pipeline\n",
    "- Composable thanks to the iterator protocol"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My favorite \"feature\" of pandas is that it's written in Python.\n",
    "Python has great language-level features for handling streams of data\n",
    "that may not fit in memory.\n",
    "This can be a useful pre-processing step to reading the data into a DataFrame or\n",
    "NumPy array.\n",
    "You can get quite far using just the builtin data structures as David Beazley proves in [this PyData keynote](https://www.youtube.com/watch?v=lyDLAutA88s)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import gzip\n",
    "from pathlib import Path\n",
    "from itertools import islice, takewhile\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import dask.dataframe as dd\n",
    "from toolz import partition_all, partitionby\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.max_rows = 10\n",
    "sns.set(context='talk')\n",
    "plt.style.use(\"default\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Beer Reviews Dataset\n",
    "\n",
    "- A review is a list of lines\n",
    "- Each review line is formated like `meta/field: value`\n",
    "- Reviews are separated by blank lines (i.e. the line is just `'\\n'`)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stanford has a [dataset on beer reviews](https://snap.stanford.edu/data/web-BeerAdvocate.html). The raw file is too large for me to include, but I split off a couple subsets for us to work with.\n",
    "\n",
    "Pandas can't read this file natively, but we have Python!\n",
    "We'll use Python to parse the raw file and tranform it into a tabular format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "beer/name: Sausa Weizen\n",
      "beer/beerId: 47986\n",
      "beer/brewerId: 10325\n",
      "beer/ABV: 5.00\n",
      "beer/style: Hefeweizen\n",
      "review/appearance: 2.5\n",
      "review/aroma: 2\n",
      "review/palate: 1.5\n",
      "review/taste: 1.5\n",
      "review/overall: 1.5\n",
      "review/time: 1234817823\n",
      "review/profileName: stcules\n",
      "review/text: A lot of foam. But a lot.\tIn the smell some banana, and then lactic and tart. Not a good start.\tQuite dark orange in color, with a lively carbonation (now visible, under the foam).\tAgain tending to lactic sourness.\tSame for the taste. With some yeast and banana.\t\t\n",
      "\n",
      "beer/name: Red Moon\n",
      "beer/beerId: 48213\n",
      "beer/brewerId: 10325\n",
      "beer/ABV: 6.20\n",
      "beer/style: English Strong Ale\n",
      "review/appearance: 3\n",
      "review/aroma: 2.5\n",
      "review/palate: 3\n",
      "review/taste: 3\n",
      "review/overall: 3\n",
      "review/time: 1235915097\n",
      "review/profileName: stcules\n",
      "review/text: Dark red color, light beige foam, average.\tIn the smell malt and caramel, not really light.\tAgain malt and caramel in the taste, not bad in the end.\tMaybe a note of honey in teh back, and a light fruitiness.\tAverage body.\tIn the aftertaste a light bitterness, with the malt and red fruit.\tNothing exceptional, but not bad, drinkable beer.\t\t\n",
      "\n",
      "beer/name: Black Horse Black Beer\n",
      "beer/beerId: 48215\n",
      "beer/brewerId: 10325\n",
      "beer/ABV: 6.50\n",
      "beer/style: Foreign / Export Stout\n",
      "review/appearance: 3\n",
      "review/aroma: 2.5\n",
      "review/palate: 3\n",
      "review/taste: 3\n",
      "review/overall: 3\n",
      "review/time: 1235916604\n",
      "review/profileName: stcules\n",
      "review/text: Almost totally black. Beige foam, quite compact, not bad.\tLight smell, just a bit of roas\n"
     ]
    }
   ],
   "source": [
    "with gzip.open(\"data/beer-raw-small.txt.gz\", \"r\") as f:\n",
    "    print(f.read(1500).decode('utf-8'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full compressed raw dataset is about 500MB, so reading it all into memory might not be pleasent (we're working with a small subset that would fit in memory, but pretend it didn't).\n",
    "Fortunately, Python's iterator protocol and generators make dealing with large streams of data pleasent."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Developing a solution\n",
    "\n",
    "Let's build a solution together. I'll provide some guidance as we go along."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'beer/name: Sausa Weizen\\n'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get a handle to the data\n",
    "f = gzip.open(\"data/beer-raw-small.txt.gz\", \"rt\")\n",
    "f.readline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parsing Tasks\n",
    "\n",
    "1. split the raw text stream into individual reviews\n",
    "2. transform each individual review into a data container\n",
    "3. combine a chunk of transformed individual reviews into a collection\n",
    "4. store the chunk to disk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 1**: Split the text stream\n",
    "\n",
    "We'll use `toolz.partitionby`. It takes an iterator like `f`, and splits it according to `func`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('beer/name: Sausa Weizen\\n',\n",
       " 'beer/beerId: 47986\\n',\n",
       " 'beer/brewerId: 10325\\n',\n",
       " 'beer/ABV: 5.00\\n',\n",
       " 'beer/style: Hefeweizen\\n',\n",
       " 'review/appearance: 2.5\\n',\n",
       " 'review/aroma: 2\\n',\n",
       " 'review/palate: 1.5\\n',\n",
       " 'review/taste: 1.5\\n',\n",
       " 'review/overall: 1.5\\n',\n",
       " 'review/time: 1234817823\\n',\n",
       " 'review/profileName: stcules\\n',\n",
       " 'review/text: A lot of foam. But a lot.\\tIn the smell some banana, and then lactic and tart. Not a good start.\\tQuite dark orange in color, with a lively carbonation (now visible, under the foam).\\tAgain tending to lactic sourness.\\tSame for the taste. With some yeast and banana.\\t\\t\\n')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.seek(0)  # Make the cell idempotent\n",
    "split = partitionby(lambda x: x == '\\n', f)\n",
    "a, b = next(split), next(split)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('\\n',)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we've gone from\n",
    "\n",
    "```python\n",
    "[\n",
    "    \"beer/name: Susa Weizen\\n\",\n",
    "    ...\n",
    "    \"review/text: ...\\n\",\n",
    "    \"\\n\",\n",
    "    \"beer/name: Beer 2\\n\",\n",
    "    \"...\",\n",
    "    \"review/text: ...\\n\",\n",
    "    \"\\n\",\n",
    "]\n",
    "```\n",
    "\n",
    "To\n",
    "\n",
    "```python\n",
    "[\n",
    "    (\n",
    "        \"beer/name Susa Weizen\\n\",\n",
    "        ...\n",
    "        \"review/text: ...\\n\"\n",
    "    ),\n",
    "    (\"\\n\",),\n",
    "    (\n",
    "        \"beer/name: Beer 2\\n\",\n",
    "        ...\n",
    "        \"review/text: ...\\n\"\n",
    "    ),\n",
    "    (\"\\n\",),\n",
    "    ...\n",
    "]\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So we can clean up those newlines with a generator expression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<generator object <genexpr> at 0x10e045d58>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.seek(0)\n",
    "reviews = (x for x in partitionby(lambda x: x == \"\\n\", f)\n",
    "           if x != (\"\\n\",))\n",
    "reviews"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Step 2**: Parse each review\n",
    "\n",
    "Let's grab out the first review, and turn it into something a bit nicer than a tuple of strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('beer/name: Sausa Weizen\\n',\n",
       " 'beer/beerId: 47986\\n',\n",
       " 'beer/brewerId: 10325\\n',\n",
       " 'beer/ABV: 5.00\\n',\n",
       " 'beer/style: Hefeweizen\\n',\n",
       " 'review/appearance: 2.5\\n',\n",
       " 'review/aroma: 2\\n',\n",
       " 'review/palate: 1.5\\n',\n",
       " 'review/taste: 1.5\\n',\n",
       " 'review/overall: 1.5\\n',\n",
       " 'review/time: 1234817823\\n',\n",
       " 'review/profileName: stcules\\n',\n",
       " 'review/text: A lot of foam. But a lot.\\tIn the smell some banana, and then lactic and tart. Not a good start.\\tQuite dark orange in color, with a lively carbonation (now visible, under the foam).\\tAgain tending to lactic sourness.\\tSame for the taste. With some yeast and banana.\\t\\t\\n')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.seek(0);  # make the cell idempotent\n",
    "review = next(partitionby(lambda x: x == '\\n', f))\n",
    "review"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Format Review\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Format Review</h1>\n",
    "</div>\n",
    "<p>Write a function `format_review` that converts an item like `review` into a dict</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It will have one entry per line, where the are the stuff to the left of the colon and the values are the stuff to the right.\n",
    "For example, the first line would be\n",
    "\n",
    "`'beer/name: Sausa Weizen\\n',` => `'beer/name': 'Sausa Weizen'`\n",
    "\n",
    "Make sure to clean up the line endings too.\n",
    "\n",
    "- Hint: Check out the [python string methods](https://docs.python.org/3/library/stdtypes.html#string-methods)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can check your function against `expected` by evaluating the next cell.\n",
    "If you get a failure, adjust your `format_review` until it passes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F\n",
      "======================================================================\n",
      "FAIL: test_format_review (__main__.TestFormat)\n",
      "----------------------------------------------------------------------\n",
      "Traceback (most recent call last):\n",
      "  File \"<ipython-input-10-cd431c002d6d>\", line 34, in test_format_review\n",
      "    self.assertEqual(result, expected)\n",
      "AssertionError: Ellipsis != {'beer/ABV': '5.00', 'beer/beerId': '4798[553 chars]823'}\n",
      "\n",
      "----------------------------------------------------------------------\n",
      "Ran 1 test in 0.002s\n",
      "\n",
      "FAILED (failures=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<unittest.runner.TextTestResult run=1 errors=0 failures=1>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import unittest\n",
    "from typing import List, Dict\n",
    "\n",
    "f.seek(0);  # make the cell idempotent\n",
    "review = next(partitionby(lambda x: x == '\\n', f))\n",
    "\n",
    "\n",
    "def format_review(review: List[str]) -> Dict[str, str]:\n",
    "    \"\"\"Your code goes below\"\"\"\n",
    "    formatted = ...\n",
    "    return formatted\n",
    "    \n",
    "\n",
    "class TestFormat(unittest.TestCase):\n",
    "    maxDiff = None\n",
    "\n",
    "    def test_format_review(self):\n",
    "        result = format_review(review)\n",
    "        expected = {\n",
    "            'beer/ABV': '5.00',\n",
    "            'beer/beerId': '47986',\n",
    "            'beer/brewerId': '10325',\n",
    "            'beer/name': 'Sausa Weizen',\n",
    "            'beer/style': 'Hefeweizen',\n",
    "            'review/appearance': '2.5',\n",
    "            'review/aroma': '2',\n",
    "            'review/overall': '1.5',\n",
    "            'review/palate': '1.5',\n",
    "            'review/profileName': 'stcules',\n",
    "            'review/taste': '1.5',\n",
    "            'review/text': 'A lot of foam. But a lot.\\tIn the smell some banana, and then lactic and tart. Not a good start.\\tQuite dark orange in color, with a lively carbonation (now visible, under the foam).\\tAgain tending to lactic sourness.\\tSame for the taste. With some yeast and banana.\\t\\t',\n",
    "            'review/time': '1234817823'\n",
    "        }\n",
    "        self.assertEqual(result, expected)\n",
    "\n",
    "suite = unittest.TestLoader().loadTestsFromModule(TestFormat())\n",
    "unittest.TextTestRunner().run(suite)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load solutions/groupby_format_review.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that optional argument to split, which controls the number of splits made; If a review text had contained a literal `': '`, we'd be in trouble since it'd get split again.\n",
    "\n",
    "Make sure you executed the above solution cell twice (first to load, second to execute) as we'll be using that `format_review` function down below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To a DataFrame\n",
    "\n",
    "Assuming we've processed many reviews into a list, we'll then build up a DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>abv</th>\n",
       "      <th>beer_id</th>\n",
       "      <th>brewer_id</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>text</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>47986</td>\n",
       "      <td>10325</td>\n",
       "      <td>Sausa Weizen</td>\n",
       "      <td>Hefeweizen</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>stcules</td>\n",
       "      <td>1.5</td>\n",
       "      <td>A lot of foam. But a lot.\\tIn the smell some b...</td>\n",
       "      <td>2009-02-16 20:57:03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   abv beer_id brewer_id     beer_name  beer_style  review_appearance  \\\n",
       "0  5.0   47986     10325  Sausa Weizen  Hefeweizen                2.5   \n",
       "\n",
       "   review_aroma  review_overall  review_palate profile_name  review_taste  \\\n",
       "0           2.0             1.5            1.5      stcules           1.5   \n",
       "\n",
       "                                                text                time  \n",
       "0  A lot of foam. But a lot.\\tIn the smell some b... 2009-02-16 20:57:03  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = [format_review(review)]  # imagine a list of many reviews\n",
    "\n",
    "col_names = {\n",
    "    'beer/ABV': 'abv',\n",
    "    'beer/beerId': 'beer_id',\n",
    "    'beer/brewerId': 'brewer_id',\n",
    "    'beer/name': 'beer_name',\n",
    "    'beer/style': 'beer_style',\n",
    "    'review/appearance': 'review_appearance',\n",
    "    'review/aroma': 'review_aroma',\n",
    "    'review/overall': 'review_overall',\n",
    "    'review/palate': 'review_palate',\n",
    "    'review/profileName': 'profile_name',\n",
    "    'review/taste': 'review_taste',\n",
    "    'review/text': 'text',\n",
    "    'review/time': 'time'\n",
    "}\n",
    "df = pd.DataFrame(r)\n",
    "numeric = ['abv', 'review_appearance', 'review_aroma',\n",
    "           'review_overall', 'review_palate', 'review_taste']\n",
    "df = (df.rename(columns=col_names)\n",
    "        .replace('', np.nan))\n",
    "df[numeric] = df[numeric].astype(float)\n",
    "df['time'] = pd.to_datetime(df.time.astype(int), unit='s')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, writing that as a function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def as_dataframe(reviews):\n",
    "    df = pd.DataFrame(list(reviews))\n",
    "\n",
    "    col_names = {\n",
    "        'beer/ABV': 'abv',\n",
    "        'beer/beerId': 'beer_id',\n",
    "        'beer/brewerId': 'brewer_id',\n",
    "        'beer/name': 'beer_name',\n",
    "        'beer/style': 'beer_style',\n",
    "        'review/appearance': 'review_appearance',\n",
    "        'review/aroma': 'review_aroma',\n",
    "        'review/overall': 'review_overall',\n",
    "        'review/palate': 'review_palate',\n",
    "        'review/profileName': 'profile_name',\n",
    "        'review/taste': 'review_taste',\n",
    "        'review/text': 'text',\n",
    "        'review/time': 'time'\n",
    "    }\n",
    "    order = ['brewer_id', 'beer_id', 'beer_name',\n",
    "             'beer_style', 'abv',\n",
    "             'profile_name', 'time',\n",
    "             'review_appearance', 'review_aroma',\n",
    "             'review_palate', 'review_taste',\n",
    "             'review_overall',\n",
    "             'text']\n",
    "    df = df.rename(columns=col_names)[order]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Full pipeline\n",
    "\n",
    "1. `file -> review_lines : List[str]`\n",
    "2. `review_lines -> reviews : Dict[str, str]`\n",
    "3. `reviews -> DataFrames`\n",
    "4. `DataFrames -> CSV`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full pipeline would look something like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\r",
      "1\r",
      "2\r",
      "3\r",
      "4\r",
      "5\r",
      "6\r",
      "7\r"
     ]
    }
   ],
   "source": [
    "BATCH_SIZE = 100  # Number of reviews to process per chunk\n",
    "                  # Intentionally small for demonstration    \n",
    "p = Path(\"data/beer-raw-small.txt.gz\")\n",
    "\n",
    "with gzip.open(p, \"rt\") as f:\n",
    "    \n",
    "    review_lines_and_newlines = partitionby(lambda x: x == '\\n', f)\n",
    "    # so filter out the newlines\n",
    "    review_lines = (x for x in review_lines_and_newlines if x != (\"\\n\",))\n",
    "    \n",
    "    # generator expression to go from List[str] -> Dict[str, str]\n",
    "    reviews = (format_review(x) for x in review_lines)\n",
    "    \n",
    "    # `reviews` yields one dict per review.\n",
    "    # Won't fit in memory, so do `BATCH_SIZE` per chunk\n",
    "    chunks = partition_all(BATCH_SIZE, reviews)\n",
    "    dfs = (as_dataframe(chunk) for chunk in chunks)\n",
    "\n",
    "    p.parent.joinpath(\"beer\").mkdir(exist_ok=True)\n",
    "\n",
    "    # the first time we read from disk\n",
    "    for i, df in enumerate(dfs):\n",
    "        df.to_csv(\"data/beer/chunk_%s.csv.gz\" % i, index=False,\n",
    "                  compression=\"gzip\")\n",
    "        print(i, end='\\r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "This runs comfortably in memory. At any given time, we only have `BATCH_SIZE` reviews in memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Brief Aside on [Dask](http://dask.pydata.org/en/latest/)\n",
    "\n",
    "> Dask is a flexible parallel computing library for analytic computing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The original dataset is in random order, but I wanted to select an interesting subset for us to work on: all the reviews by the top 100 reviewers.\n",
    "\n",
    "You might know enough pandas now to figure out the top-100 reviewers by count.\n",
    "Do a `value_counts` , select the top 100, then select the index.\n",
    "\n",
    "```python\n",
    "top_reviewers = df.profile_name.value_counts().nlargest(100).index\n",
    "```\n",
    "\n",
    "\n",
    "Recall that `value_counts` will be a `Series` where the index is each unique `profile_name` and the values is the count of reviews for that profile.\n",
    "We use `nlargets(100)` to get the 100 largest values, and `.index` to get the actual profile names. \n",
    "\n",
    "With that we could do an `isin` like\n",
    "\n",
    "```python\n",
    "subset = df[df.profile_name.isin(top_reviewers)\n",
    "```\n",
    "\n",
    "To get the subset of reviews that came from the 100 most active reviewers.\n",
    "\n",
    "But that assumes we have a `df` containing the full dataset in memory.\n",
    "My laptop can't load the entire dataset though (recall that we're working with a subset today).\n",
    "\n",
    "It wouldn't be *that* hard to write a custom solution in python or pandas using chunking like we did up above.\n",
    "We'd split our task into parts\n",
    "\n",
    "- read a chunk\n",
    "- compute `chunk.profile_name.value_counts()`\n",
    "- store that intermediate `value_counts` in a global container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figures/map-reduce-count.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we've processed each chunk, our final steps are to\n",
    "\n",
    "- merge each `value_counts` chunk by summing\n",
    "- filter to the top 100\n",
    "\n",
    "This pattern of processing chunks independently (map) and combining the results into a smaller output (reduce) is common, and doing it manually gets old.\n",
    "Dask can help out here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Collections, Tasks, Schedulers\n",
    "\n",
    "![](http://dask.pydata.org/en/latest/_images/collections-schedulers.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask has several components, so it can be hard to succinctly describe the library.\n",
    "Right now, we'll view it as providing \"big dataframes\" (one of its \"collections\")."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import dask.dataframe as dd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><strong>Dask DataFrame Structure:</strong></div>\n",
       "<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>brewer_id</th>\n",
       "      <th>beer_id</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>abv</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>time</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npartitions=8</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <td>int64</td>\n",
       "      <td>int64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>object</td>\n",
       "      <td>object</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>float64</td>\n",
       "      <td>object</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "<div>Dask Name: from-delayed, 24 tasks</div>"
      ],
      "text/plain": [
       "Dask DataFrame Structure:\n",
       "              brewer_id beer_id beer_name beer_style      abv profile_name    time review_appearance review_aroma review_palate review_taste review_overall    text\n",
       "npartitions=8                                                                                                                                                      \n",
       "                  int64   int64    object     object  float64       object  object           float64      float64       float64      float64        float64  object\n",
       "                    ...     ...       ...        ...      ...          ...     ...               ...          ...           ...          ...            ...     ...\n",
       "...                 ...     ...       ...        ...      ...          ...     ...               ...          ...           ...          ...            ...     ...\n",
       "                    ...     ...       ...        ...      ...          ...     ...               ...          ...           ...          ...            ...     ...\n",
       "                    ...     ...       ...        ...      ...          ...     ...               ...          ...           ...          ...            ...     ...\n",
       "Dask Name: from-delayed, 24 tasks"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = dd.read_csv(\"data/beer/chunk*.csv.gz\", compression=\"gzip\", blocksize=None,\n",
    "                 parse_dates=['time'])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That API should look familiar to you, now that you're experienced pandas users.\n",
    "We swap out `pd` for `dd`, and stuff mostly just works.\n",
    "Occasionally, you'll have a `dask`-specific thing like `blocksize` (number of bytes per smaller dataframe) that don't apply to pandas, which assumes things fit in memory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our \"big dataframe\", we can do normal pandas operations like `.value_counts`: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dask Series Structure:\n",
       "npartitions=1\n",
       "    int64\n",
       "      ...\n",
       "Name: profile_name, dtype: int64\n",
       "Dask Name: value-counts-agg, 41 tasks"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews_per_person = df.profile_name.value_counts()\n",
    "reviews_per_person"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a `dask.Series`, the dask analog to a `dask.dataframe`.\n",
    "One important point: we haven't actually done any real work yet.\n",
    "The operations on a `dask.dataframe` is really just a pandas-like API for\n",
    "manipulating the directed acylic graph (DAG) of operations, and a bit of metadata about those operations.\n",
    "We can visualize that DAG with the `.visualize` method. I've done it ahead of\n",
    "time since it uses `graphviz`, which can be a pain to install on every system."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "reviews_per_person.visualize(rankdir=\"LR\")\n",
    "```\n",
    "\n",
    "![](figures/reviews_per_person.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get the 100 most active reviewers. There's a couple ways to do this.\n",
    "\n",
    "1. Sort `reviews_per_person`, then take the the last 100\n",
    "2. Scan `reviews_per_person`, keeps the 100 largest you've seen\n",
    "\n",
    "For large datasets, 2 is *much* easier / faster. It's implemented as `.nlargest` on pandas and dask Series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Dask Index Structure:\n",
       "npartitions=1\n",
       "    object\n",
       "       ...\n",
       "dtype: object\n",
       "Dask Name: series-nlargest-agg, 44 tasks"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_reviewers = reviews_per_person.nlargest(100).index\n",
    "top_reviewers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At this point, we still just have a dask object (a DAG of operations, to be computed later). To actually get a concrete value, you hand the DAG off to a *scheduler*, using `compute`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Beerandraiderfan', 'Halcyondays', 'ccrida', 'RedDiamond',\n",
       "       'barleywinefiend', 'Reidrover', 'msubulldog25', 'flexabull',\n",
       "       'BeerAdvocate', 'Tilley4', 'oberon', 'russpowell', 'JohnGalt1',\n",
       "       'zoso1967', 'stcules', 'Proteus93', 'augustgarage', 'Knapp85',\n",
       "       'jeff1973', 'BuckeyeNation', 'Slatetank', 'Spikester', 'merlin48',\n",
       "       'TheGordianKnot', 'harrymel', 'philbe311', 'ElGordo', 'Deuane',\n",
       "       'beer2day', 'DeanMoriarty', 'snaotheus', 'onix1agr', 'Wetpaperbag',\n",
       "       'mcallister', 'ckeegan04', 'djeucalyptus', 'IronLover', 'climax',\n",
       "       'largadeer', 'StaveHooks', 'SurlyDuff', 'womencantsail', 'corby112',\n",
       "       'SpdKilz', 'czechsaaz', 'd0ggnate', 'dedrinker', 'cswhitehorse',\n",
       "       'ClockworkOrange', 'chumba526', 'Cresant', 'Seanibus', 'woemad',\n",
       "       'ColForbinBC', 'PDXHops', 'CrellMoset', 'Phelps', 'therica', 'RoyalT',\n",
       "       'tempest', 'elgiacomo', 'Scotchboy', 'brentk56', 'maximum12',\n",
       "       'HopDerek', 'kevinmkirk', 'MuenchenerKindl', 'Kegatron', 'mdagnew',\n",
       "       'dyan', 'mtrentm', 'woodychandler', 'mgbickel', 'Arbitrator',\n",
       "       'mikereaser', 'mikesgroove', 'cburian', 'Monkeyknife', 'Mora2000',\n",
       "       'Goliath', 'indiapaleale', 'Thorpe429', 'bump8628', 'GbVDave', 'Klym',\n",
       "       'MrVonzipper', 'GarrettMD', 'northyorksammy', 'notchucknorris',\n",
       "       'BeerSox', 'oline73', 'brewandbbq', 'BeerZack', 'FosterJM', 'meatyard',\n",
       "       'wcudwight', 'organicbrewer', 'JayQue', 'BEERchitect', 'beerman207'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_reviewers.compute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dask.dataframe` uses the threaded scheduler, so the actual computation is done in parallel. You could also use the `multiprocessing` scheduler (if you have operations that hold the GIL), or the `distributed` scheduler if you have a cluster handy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `top_reviewers` as a boolean mask, just like in regular pandas.\n",
    "\n",
    "```python\n",
    ">>> df[df.profile_name.isin(top_reviewers.compute())].to_parquet(\n",
    "    \"data/subset.parq\", compression='gzip'\n",
    ")\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Back to pandas\n",
    "\n",
    "I've provided the reviews by the top 100 reviewers.\n",
    "We'll use it for talking about groupby."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_parquet(\"data/subset.parq\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 76014 entries, 0 to 76013\n",
      "Data columns (total 13 columns):\n",
      "abv                  68126 non-null float64\n",
      "beer_id              76014 non-null int64\n",
      "brewer_id            76014 non-null int64\n",
      "beer_name            76014 non-null object\n",
      "beer_style           76014 non-null object\n",
      "review_appearance    76014 non-null float64\n",
      "review_aroma         76014 non-null float64\n",
      "review_overall       76014 non-null float64\n",
      "review_palate        76014 non-null float64\n",
      "profile_name         76014 non-null object\n",
      "review_taste         76014 non-null float64\n",
      "text                 76014 non-null object\n",
      "time                 76014 non-null datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(6), int64(2), object(4)\n",
      "memory usage: 8.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aside: Namespaces\n",
    "\n",
    "Pandas has been expanding its use of namespaces (or accessors) on `DataFrame` to group together related methods. This also limits the number of methods direclty attached to `DataFrame` itself, which can be overwhelming.\n",
    "\n",
    "Currently, we have these namespaces:\n",
    "\n",
    "- `.str`: defined on `Series` and `Index`es containing strings (object dtype)\n",
    "- `.dt`: defined on `Series` with `datetime` or `timedelta` dtype\n",
    "- `.cat`: defined on `Series` and `Indexes` with `category` dtype\n",
    "- `.plot`: defined on `Series` and `DataFrames`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Reviews by Hour\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Reviews by Hour</h1>\n",
    "</div>\n",
    "\n",
    "<p>Make a barplot of the count of reviews by hour of the day.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Hint: Use the `.dt` namespace to get the `hour` component of a `datetime`\n",
    "- Hint: We've seen `Series.value_counts` for getting the count of each value\n",
    "- Hint: Use `.sort_index` to make sure the data is ordered by hour, not count\n",
    "- Hint: Use the [`.plot`](http://pandas.pydata.org/pandas-docs/stable/api.html#plotting) namespace to get a `bar` chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TTEyMLrzwQs2cOTP4L2k7wYEDB5SVlaXk5GTFxMToiiuu0Lp16+p8/9qey2am\nBx98UJ07d1ZMTIxSU1P1xRdfhFzvr3/9q9LT09WxY0d5PB7l5+eH1WN5ebnuu+8+9e3bV61atVJi\nYqJuueUWFRYWhtzjQw89pJ49e6pVq1Zq166dUlNTtWbNmpB7PNkvf/lLeTwe/fGPfwy53q233nrK\n83LIkCFh9bdlyxYNGzZMsbGxatWqlS677DLt3Lkz5JrV7Tsej0ePPfZYSPUOHjyou+++W126dFFM\nTIx69eql5557rsb+6lJz7969uvXWW5WYmKiWLVtqyJAhp31+1+VYffToUWVmZqpDhw5q3bq1Ro0a\ndcoHyQZTb968efrxj38sr9crj8ejkpKSkPv77rvvdM8996hHjx5q2bKlkpKSdO+99/q/8DnUHn/5\ny1/qwgsvVExMjDp16qThw4fX+FVGoSLQ/Msbb7yhyZMna8aMGdq4caP69++v9PR0FRcXh1Tv0KFD\n6t+/v+bMmVMv/eXm5iozM1OffPKJcnJyVF5errS0NB06dCikel26dNHs2bO1fv16rV+/Xtddd52G\nDx+uzZs3h93runXrNG/ePPXr1y/sWr1799aePXv8y0cffRRyrf379+vKK69UZGSk3n//fX3++ed6\n/PHH1a5du5Brrlu3LqC/nJwcSdJPf/rTkOo98sgjys7O1pw5c7RlyxY98sgjevTRR/XMM8+E3OPt\nt9+unJwc/fnPf1ZBQYHS0tKUmpqq3bt31+n+tT2XH330UT399NPKzs7WmjVr1KpVK6Wnp+vo0aMh\n1Tt06JCuvPJKzZ49u24bWEvNw4cPa+PGjZo+fbo2btyov/71r9q6dauGDRsWUj1J+sEPfqA5c+ao\noKBAH330kc4//3ylpaXpf//3f0OuWeWtt97SmjVrav0em7rUGzJkSMDz8/XXXw+53ldffaWrrrpK\nPXv21KpVq/Tpp59q+vTpatGiRcg1T+xtz549eumll+TxeDRq1KiQ6k2ePFnLli3Tq6++qi1btigr\nK0t333233nnnnZB6NDONGDFC27dv19tvv61NmzYpOTlZqampNR5763KsnjRpkpYsWaJFixYpNzdX\nhYWFGjlyZMj1Dh8+rCFDhujXv/51jdtZ13qFhYUqLCzUH/7wBxUUFGjBggVatmyZxo8fH3JNSRow\nYIDmz5+vLVu2aPny5TIzpaWlqaKiotae6yzIL8I+Z11++eWWmZnp/39FRYUlJibarFmzwq4t6ZRv\nDg9XcXGxSbLc3Nx6q9muXTt74YUXwqpx4MABu+iiiywnJ8euueYamzhxYsi1ZsyYYf379w+rnxPd\nd999dtVVV9VbvepMnDjRLrzk9uIsAAANtElEQVTwQqusrAzp/hkZGTZu3LiAdSNHjrQxY8aEVO/w\n4cMWERFhS5cuDVh/6aWX2m9+85ug6538XK6srLSEhAR77LHH/OtKSkosOjraXn/99aDrnWjHjh0m\nyTZt2hRWj9VZu3atSbJ//vOf9VKvtLTUJNnf/va3sHr85ptv7LzzzrPPPvvMkpOT7cknnwy53tix\nY2348OF1un9d6o0ePdp+/vOfh1SvpponGz58uF133XUh1+vdu7f97ne/C1gXzHP95Jpbt241SfbZ\nZ5/511VUVFinTp3s+eefr1PNk4/VJSUlFhkZaYsWLfKP2bJli0myvLy8oOud6MMPPzRJtn///jr1\nVlu9Km+++aZFRUVZeXl5vdX8+9//bpLsyy+/rHOvtWGGRtKxY8e0YcMGpaam+tc1a9ZMqampysvL\nO4ud1axq+q99+/Zh16qoqNDChQt16NChsL9aIjMzUxkZGQE/y3B88cUXSkxM1AUXXKAxY8acdnq7\nNu+8844GDhyon/70p4qLi9Mll1yi559/vl76lL5/Hr366qsaN25cnb/h/WRXXHGFVqxYoW3btkmS\n/v73v+ujjz7S0KFDQ6p3/PhxVVRUnPJXdExMTFizXVV27NihoqKigN93bGysBg0a1Gj3Hen7/cfj\n8YT8BbYnOnbsmObNm6fY2Fj1798/5DqVlZW6+eabNWXKFPXu3TvsviRp1apViouLU48ePXTnnXfq\n22+/Dbm3d999Vz/4wQ+Unp6uuLg4DRo0KOy30k+0d+9evfvuu6edCajNFVdcoXfeeUe7d++WmenD\nDz/Utm3blJaWFlK9srIySQrYf5o1a6bo6Og67z8nH6s3bNig8vLygH2mZ8+eSkpKqtM+U5/H/rrW\nKy0tldfrrfMn/dZW89ChQ5o/f766deumrl27BtlxzQg0kvbt26eKiopTvvgyPj5eRUVFZ6mrmlVW\nViorK0tXXnml+vTpE3KdgoICtW7dWtHR0ZowYYIWL16sXr16hVxv4cKF2rhxo2bNmhVyjRMNGjTI\nP92ZnZ2tHTt26Ec/+pEOHDgQUr3t27crOztbF110kZYvX64JEybo3nvv1SuvvFIv/b711lsqKSnR\nrbfeGnKN+++/Xz/72c/Us2dPRUZG6pJLLlFWVpbGjBkTUr02bdooJSVFM2fOVGFhoSoqKvTqq68q\nLy9Pe/bsCbnPKlX7hyv7jvT9+Qv33XefbrrpprC+UXjp0qVq3bq1WrRooSeffFI5OTnq2LFjyPUe\neeQRNW/eXPfee2/INU40ZMgQvfLKK1qxYoUeeeQR5ebmaujQoSFN8RcXF+vgwYOaPXu2hgwZog8+\n+EA33HCDRo4cqdzc3Hrp9+WXX1abNm1qfOulLp555hn16tVLXbp0UVRUlIYMGaK5c+fq6quvDqle\nz549lZycrGnTpmn//v06duyYHnnkEX3zzTd12n+qO1YXFRUpKirqlDBdl32mvo79wdTbt2+fZs6c\nqTvuuCPsms8++6xat26t1q1ba9myZcrJyVFUVFTY21GlYb9YwXFmFvJf2g0pMzNTn332Wdh/Yffo\n0UP5+fkqKSnRX/7yF40dO1a5ubkhhZpdu3Zp4sSJ+uCDD077nnowTpyV6NevnwYNGqTk5GS9+eab\nIf0VV1lZqYEDB+rhhx+WJF1yySXavHmzsrOzdcstt4Td74svvqihQ4fWeu7D6bz55pv67//+b732\n2mvq3bu38vPzlZWVpcTERI0dOzakmn/+8581btw4nXfeeYqIiNCll16qm266SRs3bgy5z9qYmZo1\na3x/L5WXl+vGG2+UmSk7OzusWtdee63y8/O1b98+Pf/887rxxhu1Zs0axcXFBV1rw4YNeuqpp7Rx\n48Z6O+b87Gc/8/+7b9++6tevny688EKtWrVKgwcPDqpWZWWlJGn48OGaNGmSJOniiy/Wxx9/rOee\ne07XXHNN2P2+9NJLGjNmTFjHj2eeeUaffPKJ3nnnHSUnJ2v16tXKzMxUYmJiSLPGkZGR+stf/qLx\n48erffv2ioiIUGpqap1nTIM5Vtfl9aa+jv11refz+ZSRkaFevXqprl/7eLqaY8aM0b/9279pz549\n+sMf/qAbb7xR//M//1NvrxmcQ2NmZWVlFhERccr7sbfccosNGzYs7Pqqx3NoMjMzrUuXLrZ9+/Z6\nqXeiwYMH2x133BHSfRcvXmySLCIiwr9IMo/HYxEREXb8+PF66XHgwIF2//33h3TfpKQkGz9+fMC6\nZ5991hITE8Pu6+uvv7ZmzZrZW2+9FVadLl262Jw5cwLWzZw503r06BFWXTOzgwcPWmFhoZmZ3Xjj\njfaTn/wk6BonP5e/+uqras9zufrqq+3ee+8Nut6J6vscmmPHjtmIESOsX79+tm/fvrDrnax79+72\n8MMPh1TzySef9O8rJ+4/zZo1s+Tk5HrrsWPHjvbcc88FXa+srMyaN29uM2fODBg3depUu+KKK2qt\nV1uPq1evNkmWn59fp1rV1Tt8+LBFRkaecr7Y+PHjLT09PeweS0pKrLi42My+P+fyrrvuOm2tmo7V\nK1asqPY8l6SkJHviiSeCrneiYM6hqa2ez+ezlJQUGzx4sB05cqTWenXtsUpZWZm1bNnSXnvttTrV\nrovG9yfUWRAVFaUBAwZoxYoV/nWVlZVasWJF2OeU1Bcz0913363Fixdr5cqV6tatW70/RmVlpf89\n42ANHjxYBQUFys/P9y8DBw7UmDFjlJ+fr4iIiLD7O3jwoL766it17tw5pPtfeeWVp1xKuG3bNiUn\nJ4fd2/z58xUXF6eMjIyw6hw+fPiUmY2IiAj/X8jhaNWqlTp37qz9+/dr+fLlGj58eNg1u3XrpoSE\nhIB9x+fzac2aNY1m35H+f2bmiy++0N/+9jd16NCh3h8jnP3n5ptv1qeffhqw/yQmJmrKlClavnx5\nvfT3zTff6Ntvvw1p/4mKitJll13WYPvPiy++qAEDBoR1DlJ5ebnKy8sbbP+JjY1Vp06d9MUXX2j9\n+vU17j+1HasHDBigyMjIgH1m27Zt2rlzZ7X7TH0f++tSz+fzKS0tTVFRUXrnnXdqnUEJpUczk5mF\nvM9Uh7ec/mXy5Mm65ZZbNHDgQF1++eX64x//qEOHDum2224Lqd7Bgwf15Zdf+v+/Y8cO5efnq337\n9kpKSgq6XmZmpl577TW9/fbbatOmjf+91tjYWMXExARd79e//rWGDh2qrl276sCBA3rttde0atWq\nkA+ebdq0OeX90latWqlDhw4hv9f7n//5n7r++uuVnJyswsJCzZgxQxEREbrppptCqjdp0iRdccUV\nevjhh3XjjTdq7dq1mjdvnubNmxdSvSqVlZWaP3++xo4dW+eT5mpy/fXX6/e//72SkpLUu3dvbdq0\nSU888YTGjRsXcs2qSyR79OihL7/8UlOmTFGPHj3q/Nyu7bmclZWl//qv/9JFF12kbt26afr06UpM\nTNSIESNCqvfdd99p586d/s+JqXoRTUhIUEJCQtA1ExMT9e///u/auHGjli5dqoqKCv/+0759+2rf\nwz9dvQ4dOuj3v/+9hg0bps6dO2vfvn2aO3eudu/efdrL9Wvb7pNDVmRkpBISEtSjR4+g67Vv316/\n/e1vNWrUKCUkJOirr77S1KlT1b17d6Wnp4fU35QpUzR69GhdffXVuvbaa7Vs2TItWbJEq1atCnmb\npe9fPBctWqTHH3+8xjp1rXfNNddoypQpiomJUXJysnJzc/XKK6/oiSeeCLnmokWL1KlTJyUlJamg\noEATJ07UiBEjajzRuLZjdWxsrMaPH6/Jkyerffv28nq9uueee5SSkqIf/vCHQdeTvj8vp6ioyL8d\nBQUFatOmjZKSkk45Mbe2egcOHFBaWpoOHz6sV199VT6fTz6fT5LUqVOnav84ra3m9u3b9cYbbygt\nLU2dOnXSN998o9mzZysmJkY/+clPavzdBK3e5nrOAc8884wlJSVZVFSUXX755fbJJ5+EXKtq6u/k\nZezYsSHVq66WJJs/f35I9caNG2fJyckWFRVlnTp1ssGDB9sHH3wQUq2ahHvZ9ujRo61z584WFRVl\n5513no0ePTrsS/yWLFliffr0sejoaOvZs6fNmzcvrHpmZsuXLzdJtnXr1rBr+Xw+mzhxoiUlJVmL\nFi3sggsusN/85jdWVlYWcs033njDLrjgAouKirKEhATLzMy0kpKSOt+/tudyZWWlTZ8+3eLj4y06\nOtoGDx582p9FbfXmz59f7e0zZswIqWbVW1fVLR9++GHQ9Y4cOWI33HCDJSYmWlRUlHXu3NmGDRtm\na9euDevneLLaLts+Xb3Dhw9bWlqaderUySIjIy05Odl+8YtfWFFRUVj9vfjii9a9e3dr0aKF9e/f\nv9a3WOtS809/+pPFxMTU6TlZW709e/bYrbfeaomJidaiRQvr0aOHPf7446f9GIXaaj711FPWpUsX\ni4yMtKSkJHvggQdOuz/W5Vh95MgRu+uuu6xdu3bWsmVLu+GGG2zPnj0h15sxY0adXx9qq1fTz0OS\n7dixI6Qed+/ebUOHDrW4uDiLjIy0Ll262H/8x3/YP/7xjxp/jqHw/KsZAAAAZ3EODQAAcB6BBgAA\nOI9AAwAAnEegAQAAziPQAAAA5xFoAACA8wg0AADAeQQaAADgPAINAABwHoEGAAA4j0ADAACc939u\n/VMLlIif6AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c10484940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/groupby_03.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Pale Ales\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Pale Ales</h1>\n",
    "</div>\n",
    "<p>\n",
    "Make a variable `pale_ales` that filters `df` to just rows where `beer_style` contains the string `'pale ale'` (ignoring case)\n",
    "</p>\n",
    "- Hint: Use the `df.beer_style.str` namespace and find a method for checking whether a string contains another string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "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>abv</th>\n",
       "      <th>beer_id</th>\n",
       "      <th>brewer_id</th>\n",
       "      <th>beer_name</th>\n",
       "      <th>beer_style</th>\n",
       "      <th>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>profile_name</th>\n",
       "      <th>review_taste</th>\n",
       "      <th>text</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.3</td>\n",
       "      <td>93</td>\n",
       "      <td>33</td>\n",
       "      <td>Steel Rail Extra Pale Ale</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Todd</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Presentation: Pint size, on tap from the Easts...</td>\n",
       "      <td>1996-08-22 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.5</td>\n",
       "      <td>324</td>\n",
       "      <td>100</td>\n",
       "      <td>McNeill's Champ Ale</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Todd</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Presentation: 12 oz. bottle. Another story on ...</td>\n",
       "      <td>1998-02-17 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.0</td>\n",
       "      <td>264</td>\n",
       "      <td>6994</td>\n",
       "      <td>Alexander Keith's India Pale Ale</td>\n",
       "      <td>English India Pale Ale (IPA)</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Todd</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Presentation: 12oz brown twisty, with a red st...</td>\n",
       "      <td>1998-04-16 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>7.6</td>\n",
       "      <td>411</td>\n",
       "      <td>112</td>\n",
       "      <td>Pranqster</td>\n",
       "      <td>Belgian Strong Pale Ale</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Todd</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Presentation: 12oz fat twisty with a black&amp;whi...</td>\n",
       "      <td>1998-06-01 00:00:01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>5.5</td>\n",
       "      <td>6900</td>\n",
       "      <td>495</td>\n",
       "      <td>Nor'Wester Oregon Pale Ale</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Jason</td>\n",
       "      <td>4.5</td>\n",
       "      <td>Copper colour on the light side with a decent ...</td>\n",
       "      <td>1998-08-16 09:35:25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    abv  beer_id  brewer_id                         beer_name  \\\n",
       "0   5.3       93         33         Steel Rail Extra Pale Ale   \n",
       "5   5.5      324        100               McNeill's Champ Ale   \n",
       "11  5.0      264       6994  Alexander Keith's India Pale Ale   \n",
       "17  7.6      411        112                         Pranqster   \n",
       "30  5.5     6900        495        Nor'Wester Oregon Pale Ale   \n",
       "\n",
       "                      beer_style  review_appearance  review_aroma  \\\n",
       "0        American Pale Ale (APA)                3.5           3.5   \n",
       "5        American Pale Ale (APA)                4.0           3.5   \n",
       "11  English India Pale Ale (IPA)                2.5           2.5   \n",
       "17       Belgian Strong Pale Ale                4.0           4.0   \n",
       "30       American Pale Ale (APA)                4.0           4.5   \n",
       "\n",
       "    review_overall  review_palate profile_name  review_taste  \\\n",
       "0              4.0            4.0         Todd           4.0   \n",
       "5              4.0            3.0         Todd           3.5   \n",
       "11             4.0            3.0         Todd           2.5   \n",
       "17             5.0            5.0         Todd           5.0   \n",
       "30             4.0            4.0        Jason           4.5   \n",
       "\n",
       "                                                 text                time  \n",
       "0   Presentation: Pint size, on tap from the Easts... 1996-08-22 00:00:01  \n",
       "5   Presentation: 12 oz. bottle. Another story on ... 1998-02-17 00:00:01  \n",
       "11  Presentation: 12oz brown twisty, with a red st... 1998-04-16 00:00:01  \n",
       "17  Presentation: 12oz fat twisty with a black&whi... 1998-06-01 00:00:01  \n",
       "30  Copper colour on the light side with a decent ... 1998-08-16 09:35:25  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/groupby_04.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Groupby operations come up in a lot of contexts.\n",
    "At its root, groupby about doing an operation on many subsets of the data, each of which shares something in common.\n",
    "The components of a groupby operation are:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Components of a groupby\n",
    "\n",
    "1. **split** a table into groups\n",
    "2. **apply** a function to each group\n",
    "3. **combine** the results into a single DataFrame or Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In pandas the `split` step looks like\n",
    "\n",
    "```python\n",
    "df.groupby( grouper )\n",
    "```\n",
    "\n",
    "`grouper` can be many things\n",
    "\n",
    "- Series (or string indicating a column in `df`)\n",
    "- function (to be applied on the index)\n",
    "- dict : groups by *values*\n",
    "- `levels=[ names of levels in a MultiIndex ]`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split\n",
    "\n",
    "Break a table into smaller logical tables according to some rule"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x1081908d0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gr = df.groupby(\"beer_name\")\n",
    "gr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We haven't really done any actual work yet, but pandas knows what it needs to know to break the larger `df` into many smaller pieces, one for each distinct `beer_name`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Apply & Combine\n",
    "\n",
    "To finish the groupby, we apply a method to the groupby object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "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>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>review_taste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer_name</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>\"50\" Golden Anniversary Lager</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Alt\"ered State</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Double Wrecked Wreck\" IPA</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Marnie\" Gras Carnival Ale</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Old Yeltsin\" Imperial Stout</th>\n",
       "      <td>4.176471</td>\n",
       "      <td>4.088235</td>\n",
       "      <td>3.676471</td>\n",
       "      <td>3.882353</td>\n",
       "      <td>3.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Öko Ur-Weizen</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Öko Urtrunk</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ölsch</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Über Pils</th>\n",
       "      <td>4.088235</td>\n",
       "      <td>4.029412</td>\n",
       "      <td>4.235294</td>\n",
       "      <td>3.882353</td>\n",
       "      <td>4.147059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Über Weisse</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12018 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               review_appearance  review_aroma  \\\n",
       "beer_name                                                        \n",
       "\"50\" Golden Anniversary Lager           5.000000      3.500000   \n",
       "\"Alt\"ered State                         4.000000      3.750000   \n",
       "\"Double Wrecked Wreck\" IPA              3.250000      3.750000   \n",
       "\"Marnie\" Gras Carnival Ale              3.666667      4.333333   \n",
       "\"Old Yeltsin\" Imperial Stout            4.176471      4.088235   \n",
       "...                                          ...           ...   \n",
       "Öko Ur-Weizen                           3.750000      4.000000   \n",
       "Öko Urtrunk                             4.000000      4.000000   \n",
       "Ölsch                                   4.000000      4.000000   \n",
       "Über Pils                               4.088235      4.029412   \n",
       "Über Weisse                             4.000000      4.500000   \n",
       "\n",
       "                               review_overall  review_palate  review_taste  \n",
       "beer_name                                                                   \n",
       "\"50\" Golden Anniversary Lager        4.000000       3.500000      3.500000  \n",
       "\"Alt\"ered State                      3.500000       4.000000      4.000000  \n",
       "\"Double Wrecked Wreck\" IPA           4.000000       4.000000      3.750000  \n",
       "\"Marnie\" Gras Carnival Ale           3.833333       4.000000      4.000000  \n",
       "\"Old Yeltsin\" Imperial Stout         3.676471       3.882353      3.970588  \n",
       "...                                       ...            ...           ...  \n",
       "Öko Ur-Weizen                        3.500000       3.250000      3.500000  \n",
       "Öko Urtrunk                          4.500000       4.000000      4.000000  \n",
       "Ölsch                                4.500000       4.000000      4.500000  \n",
       "Über Pils                            4.235294       3.882353      4.147059  \n",
       "Über Weisse                          4.000000       4.000000      4.000000  \n",
       "\n",
       "[12018 rows x 5 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_cols = ['review_appearance', 'review_aroma', 'review_overall',\n",
    "               'review_palate', 'review_taste']\n",
    "\n",
    "df.groupby('beer_name')[review_cols].agg('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, the function we applied was `'mean'`.\n",
    "Pandas has implemented cythonized versions of certain common methods like mean, sum, etc.\n",
    "You can also pass in regular functions like `np.mean`.\n",
    "\n",
    "In terms of split, apply, combine, split was `df.groupby('beer_name')`. \n",
    "We apply the `mean` function by passing in `'mean'`.\n",
    "Finally, by using the `.agg` method (for aggregate) we tell pandas to combine the results with one output row per group.\n",
    "\n",
    "You can also pass in regular functions like `np.mean`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>review_taste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer_name</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>\"50\" Golden Anniversary Lager</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Alt\"ered State</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Double Wrecked Wreck\" IPA</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Marnie\" Gras Carnival Ale</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Old Yeltsin\" Imperial Stout</th>\n",
       "      <td>4.176471</td>\n",
       "      <td>4.088235</td>\n",
       "      <td>3.676471</td>\n",
       "      <td>3.882353</td>\n",
       "      <td>3.970588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               review_appearance  review_aroma  \\\n",
       "beer_name                                                        \n",
       "\"50\" Golden Anniversary Lager           5.000000      3.500000   \n",
       "\"Alt\"ered State                         4.000000      3.750000   \n",
       "\"Double Wrecked Wreck\" IPA              3.250000      3.750000   \n",
       "\"Marnie\" Gras Carnival Ale              3.666667      4.333333   \n",
       "\"Old Yeltsin\" Imperial Stout            4.176471      4.088235   \n",
       "\n",
       "                               review_overall  review_palate  review_taste  \n",
       "beer_name                                                                   \n",
       "\"50\" Golden Anniversary Lager        4.000000       3.500000      3.500000  \n",
       "\"Alt\"ered State                      3.500000       4.000000      4.000000  \n",
       "\"Double Wrecked Wreck\" IPA           4.000000       4.000000      3.750000  \n",
       "\"Marnie\" Gras Carnival Ale           3.833333       4.000000      4.000000  \n",
       "\"Old Yeltsin\" Imperial Stout         3.676471       3.882353      3.970588  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('beer_name')[review_cols].agg(np.mean).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, [certain methods](http://pandas.pydata.org/pandas-docs/stable/api.html#id35) have been attached to `Groupby` objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "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>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>review_taste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>beer_name</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>\"50\" Golden Anniversary Lager</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Alt\"ered State</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Double Wrecked Wreck\" IPA</th>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Marnie\" Gras Carnival Ale</th>\n",
       "      <td>3.666667</td>\n",
       "      <td>4.333333</td>\n",
       "      <td>3.833333</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>\"Old Yeltsin\" Imperial Stout</th>\n",
       "      <td>4.176471</td>\n",
       "      <td>4.088235</td>\n",
       "      <td>3.676471</td>\n",
       "      <td>3.882353</td>\n",
       "      <td>3.970588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Öko Ur-Weizen</th>\n",
       "      <td>3.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>3.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Öko Urtrunk</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ölsch</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Über Pils</th>\n",
       "      <td>4.088235</td>\n",
       "      <td>4.029412</td>\n",
       "      <td>4.235294</td>\n",
       "      <td>3.882353</td>\n",
       "      <td>4.147059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Über Weisse</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12018 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               review_appearance  review_aroma  \\\n",
       "beer_name                                                        \n",
       "\"50\" Golden Anniversary Lager           5.000000      3.500000   \n",
       "\"Alt\"ered State                         4.000000      3.750000   \n",
       "\"Double Wrecked Wreck\" IPA              3.250000      3.750000   \n",
       "\"Marnie\" Gras Carnival Ale              3.666667      4.333333   \n",
       "\"Old Yeltsin\" Imperial Stout            4.176471      4.088235   \n",
       "...                                          ...           ...   \n",
       "Öko Ur-Weizen                           3.750000      4.000000   \n",
       "Öko Urtrunk                             4.000000      4.000000   \n",
       "Ölsch                                   4.000000      4.000000   \n",
       "Über Pils                               4.088235      4.029412   \n",
       "Über Weisse                             4.000000      4.500000   \n",
       "\n",
       "                               review_overall  review_palate  review_taste  \n",
       "beer_name                                                                   \n",
       "\"50\" Golden Anniversary Lager        4.000000       3.500000      3.500000  \n",
       "\"Alt\"ered State                      3.500000       4.000000      4.000000  \n",
       "\"Double Wrecked Wreck\" IPA           4.000000       4.000000      3.750000  \n",
       "\"Marnie\" Gras Carnival Ale           3.833333       4.000000      4.000000  \n",
       "\"Old Yeltsin\" Imperial Stout         3.676471       3.882353      3.970588  \n",
       "...                                       ...            ...           ...  \n",
       "Öko Ur-Weizen                        3.500000       3.250000      3.500000  \n",
       "Öko Urtrunk                          4.500000       4.000000      4.000000  \n",
       "Ölsch                                4.500000       4.000000      4.500000  \n",
       "Über Pils                            4.235294       3.882353      4.147059  \n",
       "Über Weisse                          4.000000       4.000000      4.000000  \n",
       "\n",
       "[12018 rows x 5 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('beer_name')[review_cols].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Highest Variance\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Highest Variance</h1>\n",
    "</div>\n",
    "\n",
    "<p>Find the `beer_style`s with the greatest variance in `abv`.</p>\n",
    "\n",
    "- hint: `.var` calculates the varaince and is available on `GroupBy` objects like `gr.abv`.\n",
    "- hint: use `.sort_values` to sort a Series by the values (it took us a while to come up with that name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_style\n",
       "American Double / Imperial Stout    13.696583\n",
       "American Strong Ale                 10.459941\n",
       "Braggot                              6.856215\n",
       "American Double / Imperial IPA       4.450049\n",
       "Old Ale                              4.136272\n",
       "                                      ...    \n",
       "Bière de Champagne / Bière Brut      0.055477\n",
       "Roggenbier                           0.055074\n",
       "Japanese Rice Lager                  0.028082\n",
       "Gose                                 0.005333\n",
       "American Black Ale                   0.000000\n",
       "Name: abv, Length: 103, dtype: float64"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%load solutions/groupby_abv.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `.agg` output shape\n",
    "\n",
    "The output shape is determined by the grouper, data, and aggregation\n",
    "\n",
    "- Grouper: Controls the output index\n",
    "    * single grouper -> Index\n",
    "    * array-like grouper -> MultiIndex\n",
    "- Subject (Groupee): Controls the output data values\n",
    "    * single column -> Series (or DataFrame if multiple aggregations)\n",
    "    * multiple columns -> DataFrame\n",
    "- Aggregation: Controls the output columns\n",
    "    * single aggfunc -> Index in the colums\n",
    "    * multiple aggfuncs -> MultiIndex in the columns (Or 1-D Index if groupee is 1-D)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "We'll go into MultiIndexes in a bit, but for know, think of them as regular Indexes with multiple levels (columns)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_style\n",
       "Altbier                       3.844017\n",
       "American Adjunct Lager        2.708166\n",
       "American Amber / Red Ale      3.741607\n",
       "American Amber / Red Lager    3.455166\n",
       "American Barleywine           4.005499\n",
       "                                ...   \n",
       "Vienna Lager                  3.697218\n",
       "Weizenbock                    4.040816\n",
       "Wheatwine                     4.013514\n",
       "Winter Warmer                 3.792023\n",
       "Witbier                       3.886922\n",
       "Name: review_overall, Length: 103, dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# single grouper, single groupee, single aggregation\n",
    "df.groupby('beer_style').review_overall.agg('mean')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">review_appearance</th>\n",
       "      <th colspan=\"5\" halign=\"left\">review_aroma</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"5\" halign=\"left\">review_palate</th>\n",
       "      <th colspan=\"5\" halign=\"left\">review_taste</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
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       "      <th>...</th>\n",
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       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>brewer_id</th>\n",
       "      <th>beer_name</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",
       "      <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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>Eisbrau Czech</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambrinus</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.060660</td>\n",
       "      <td>2</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gambrinus Pale</th>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pilsner Urquell</th>\n",
       "      <td>3.746575</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.507741</td>\n",
       "      <td>73</td>\n",
       "      <td>3.383562</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.770644</td>\n",
       "      <td>73</td>\n",
       "      <td>...</td>\n",
       "      <td>3.513699</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.676863</td>\n",
       "      <td>73</td>\n",
       "      <td>3.547945</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.812935</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Primus</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <td>...</td>\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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">24926</th>\n",
       "      <th>Mongozo (Banana)</th>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.408248</td>\n",
       "      <td>4</td>\n",
       "      <td>4.625000</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.645497</td>\n",
       "      <td>4</td>\n",
       "      <td>3.875000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mongozo (Palmnut)</th>\n",
       "      <td>3.894737</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.591114</td>\n",
       "      <td>19</td>\n",
       "      <td>3.868421</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.466729</td>\n",
       "      <td>19</td>\n",
       "      <td>...</td>\n",
       "      <td>3.868421</td>\n",
       "      <td>2.5</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.435957</td>\n",
       "      <td>19</td>\n",
       "      <td>3.763158</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.510131</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24964</th>\n",
       "      <th>Haffenreffer Private Stock</th>\n",
       "      <td>3.071429</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.786796</td>\n",
       "      <td>7</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3.142857</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.029332</td>\n",
       "      <td>7</td>\n",
       "      <td>3.285714</td>\n",
       "      <td>1.5</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.859125</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26715</th>\n",
       "      <th>Gageleer</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>3.5</td>\n",
       "      <td>3.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27039</th>\n",
       "      <th>Heady Topper</th>\n",
       "      <td>4.333333</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>3</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.577350</td>\n",
       "      <td>3</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>4.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.288675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13237 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     review_appearance                      \\\n",
       "                                                  mean  min  max       std   \n",
       "brewer_id beer_name                                                          \n",
       "1         Eisbrau Czech                       3.000000  3.0  3.0       NaN   \n",
       "          Gambrinus                           4.000000  3.5  4.5  0.707107   \n",
       "          Gambrinus Pale                      3.500000  3.5  3.5       NaN   \n",
       "          Pilsner Urquell                     3.746575  2.0  5.0  0.507741   \n",
       "          Primus                              3.000000  3.0  3.0       NaN   \n",
       "...                                                ...  ...  ...       ...   \n",
       "24926     Mongozo (Banana)                    4.500000  4.0  5.0  0.408248   \n",
       "          Mongozo (Palmnut)                   3.894737  2.5  5.0  0.591114   \n",
       "24964     Haffenreffer Private Stock          3.071429  2.0  4.0  0.786796   \n",
       "26715     Gageleer                            5.000000  5.0  5.0       NaN   \n",
       "27039     Heady Topper                        4.333333  4.0  4.5  0.288675   \n",
       "\n",
       "                                           review_aroma                      \\\n",
       "                                     count         mean  min  max       std   \n",
       "brewer_id beer_name                                                           \n",
       "1         Eisbrau Czech                  1     3.000000  3.0  3.0       NaN   \n",
       "          Gambrinus                      2     4.000000  4.0  4.0  0.000000   \n",
       "          Gambrinus Pale                 1     3.500000  3.5  3.5       NaN   \n",
       "          Pilsner Urquell               73     3.383562  1.0  5.0  0.770644   \n",
       "          Primus                         1     3.500000  3.5  3.5       NaN   \n",
       "...                                    ...          ...  ...  ...       ...   \n",
       "24926     Mongozo (Banana)               4     4.625000  4.5  5.0  0.250000   \n",
       "          Mongozo (Palmnut)             19     3.868421  3.0  5.0  0.466729   \n",
       "24964     Haffenreffer Private Stock     7     3.000000  2.0  3.5  0.577350   \n",
       "26715     Gageleer                       1     3.500000  3.5  3.5       NaN   \n",
       "27039     Heady Topper                   3     4.500000  4.0  5.0  0.500000   \n",
       "\n",
       "                                            ...  review_palate            \\\n",
       "                                     count  ...           mean  min  max   \n",
       "brewer_id beer_name                         ...                            \n",
       "1         Eisbrau Czech                  1  ...       3.500000  3.5  3.5   \n",
       "          Gambrinus                      2  ...       3.750000  3.0  4.5   \n",
       "          Gambrinus Pale                 1  ...       3.500000  3.5  3.5   \n",
       "          Pilsner Urquell               73  ...       3.513699  1.0  5.0   \n",
       "          Primus                         1  ...       3.000000  3.0  3.0   \n",
       "...                                    ...  ...            ...  ...  ...   \n",
       "24926     Mongozo (Banana)               4  ...       3.250000  2.5  4.0   \n",
       "          Mongozo (Palmnut)             19  ...       3.868421  2.5  4.5   \n",
       "24964     Haffenreffer Private Stock     7  ...       3.142857  1.5  4.0   \n",
       "26715     Gageleer                       1  ...       3.500000  3.5  3.5   \n",
       "27039     Heady Topper                   3  ...       4.666667  4.0  5.0   \n",
       "\n",
       "                                                     review_taste            \\\n",
       "                                           std count         mean  min  max   \n",
       "brewer_id beer_name                                                           \n",
       "1         Eisbrau Czech                    NaN     1     3.500000  3.5  3.5   \n",
       "          Gambrinus                   1.060660     2     4.000000  4.0  4.0   \n",
       "          Gambrinus Pale                   NaN     1     3.500000  3.5  3.5   \n",
       "          Pilsner Urquell             0.676863    73     3.547945  1.0  4.5   \n",
       "          Primus                           NaN     1     3.000000  3.0  3.0   \n",
       "...                                        ...   ...          ...  ...  ...   \n",
       "24926     Mongozo (Banana)            0.645497     4     3.875000  3.5  4.0   \n",
       "          Mongozo (Palmnut)           0.435957    19     3.763158  2.5  5.0   \n",
       "24964     Haffenreffer Private Stock  1.029332     7     3.285714  1.5  4.0   \n",
       "26715     Gageleer                         NaN     1     3.500000  3.5  3.5   \n",
       "27039     Heady Topper                0.577350     3     4.666667  4.5  5.0   \n",
       "\n",
       "                                                      \n",
       "                                           std count  \n",
       "brewer_id beer_name                                   \n",
       "1         Eisbrau Czech                    NaN     1  \n",
       "          Gambrinus                   0.000000     2  \n",
       "          Gambrinus Pale                   NaN     1  \n",
       "          Pilsner Urquell             0.812935    73  \n",
       "          Primus                           NaN     1  \n",
       "...                                        ...   ...  \n",
       "24926     Mongozo (Banana)            0.250000     4  \n",
       "          Mongozo (Palmnut)           0.510131    19  \n",
       "24964     Haffenreffer Private Stock  0.859125     7  \n",
       "26715     Gageleer                         NaN     1  \n",
       "27039     Heady Topper                0.288675     3  \n",
       "\n",
       "[13237 rows x 25 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# multiple groupers, multiple groupee, single aggregation\n",
    "df.groupby(['brewer_id', 'beer_name'])[review_cols].agg(['mean', 'min', 'max', 'std', 'count'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Rating by length\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Rating by length</h1>\n",
    "</div>\n",
    "\n",
    "<p>Plot the relationship between review length (number of characters) and average `reveiw_overall`.</p>\n",
    "\n",
    "- Hint: use `.plot(style='k.')`\n",
    "- We've grouped by columns so far, you can also group by any series with the same length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c111595f8>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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sWbnGxkbU1dUZeipdtmwZvv76a5SVlallNm3aZNpGIBBQfXpUVFQYev3UmuFaeQZVnJQp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x107293ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/groupby_00.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Reviews by Length\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Reviews by Length</h1>\n",
    "</div>\n",
    "\n",
    "<p>Find the relationship between review length (number of **words** and average `reveiw_overall`.)</p>\n",
    "\n",
    "- Hint: You can pass a [regular expression](https://docs.python.org/3/howto/regex.html#matching-characters) to any of the `.str` methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1c1b3fb5f8>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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EwZY+5OO6sLAwZnlq8m+88UZLxymfN7Vx4XQ6MXXqVN1nVus4Ps+E9BBsn8OJ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\n8n8nBOF7/mdlLQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1c1b4487b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/groupby_00b.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Rating by number of Reviews\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Rating by number of Reviews</h1>\n",
    "</div>\n",
    "\n",
    "<p>Find the relationship between the number of reviews for a beer and the average `review_overall`.</p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JmOsUi8XA4/HA5/MRDoeXnPnE5dixYxAKhXjzzTczOi5bt8KtZVkLslWjc/Xq1YTJ5nw+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vGfcblc2LdvHwBALpfj0qVLuHr1KjQaDXQ63bKLFBMSum/fPnR1dUGhUGDHjh0ZV5+XG8bJRvq2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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10819cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/groupby_01.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform\n",
    "\n",
    "A *transform* is a function whose output is the same shape as the input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Recall that a groupby has three steps: split, apply, combine.\n",
    "So far, all of the functions we've applied have been *aggregations*: the rule for \"combine\" is one row per group.\n",
    "\n",
    "You can use `Groupby.transform` when you have an operation that should be done *groupwise*, but the result should be the same shape.\n",
    "For example, suppose we wanted to de-mean each reviewer's scores by their average score. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define demean(v: array) -> array\n",
    "def demean(v):\n",
    "    return v - v.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just calling `demean` on the entire Series will noramilze by the *global* average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.245731\n",
       "1        0.245731\n",
       "2        0.745731\n",
       "3        0.245731\n",
       "4        0.245731\n",
       "           ...   \n",
       "76009    0.245731\n",
       "76010    0.745731\n",
       "76011    1.245731\n",
       "76012    0.745731\n",
       "76013   -0.254269\n",
       "Name: review_overall, Length: 76014, dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "demean(df.review_overall)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's de-mean each individual's reviews by their own average.\n",
    "This could be useful if, for example, you were building a recommendation system.\n",
    "A rating of 4 from someone's whose average is 2 is in some sense more meaningful that a 4 from someone who always gives 4s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "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>review_appearance</th>\n",
       "      <th>review_aroma</th>\n",
       "      <th>review_overall</th>\n",
       "      <th>review_palate</th>\n",
       "      <th>review_taste</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.122437</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>0.231604</td>\n",
       "      <td>0.389023</td>\n",
       "      <td>0.278649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.622437</td>\n",
       "      <td>0.503016</td>\n",
       "      <td>0.231604</td>\n",
       "      <td>0.889023</td>\n",
       "      <td>0.778649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.755879</td>\n",
       "      <td>0.357983</td>\n",
       "      <td>0.710705</td>\n",
       "      <td>0.068379</td>\n",
       "      <td>0.758663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.622437</td>\n",
       "      <td>-0.996984</td>\n",
       "      <td>0.231604</td>\n",
       "      <td>-0.110977</td>\n",
       "      <td>-0.221351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.122437</td>\n",
       "      <td>0.003016</td>\n",
       "      <td>0.231604</td>\n",
       "      <td>-0.110977</td>\n",
       "      <td>0.278649</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_appearance  review_aroma  review_overall  review_palate  \\\n",
       "0          -0.122437      0.003016        0.231604       0.389023   \n",
       "1          -0.622437      0.503016        0.231604       0.889023   \n",
       "2           0.755879      0.357983        0.710705       0.068379   \n",
       "3          -0.622437     -0.996984        0.231604      -0.110977   \n",
       "4          -0.122437      0.003016        0.231604      -0.110977   \n",
       "\n",
       "   review_taste  \n",
       "0      0.278649  \n",
       "1      0.778649  \n",
       "2      0.758663  \n",
       "3     -0.221351  \n",
       "4      0.278649  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "normalized = df.groupby(\"profile_name\")[review_cols].transform(demean)\n",
    "normalized.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We used `.transform` because the desired output was the same shape as the input.\n",
    "Just like `.agg` informs pandas that you want `1 input group → 1 output row`, the `.transform` method informs pandas that you want `1 input row → 1 output row`.\n",
    "\n",
    "`.transform` operates on each column independently."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-success\" data-title=\"Personal Trend?\">\n",
    "  <h1><i class=\"fa fa-tasks\" aria-hidden=\"true\"></i> Exercise: Personal Trend?</h1>\n",
    "</div>\n",
    "\n",
    "<p>Do reviewer's `review_overall` trend over a person's time reviewing?</p>\n",
    "\n",
    "Hint: Need an indictor that tracks which review this is for that person. That is, we need a cumulative count of reviews per person.\n",
    "\n",
    "Implement `cumcount` to match the example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ellipsis"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cumcount(s):\n",
    "    \"\"\"Returns an array with counting up to the length of 's'\n",
    "    \n",
    "    Examples\n",
    "    --------\n",
    "    >>> cumcount([1, 2, 2, 1, 2])\n",
    "    array([0, 1, 2, 4])\n",
    "    \"\"\"\n",
    "    return ...\n",
    "\n",
    "cumcount([1, 2, 2, 1, 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now make a variable `order` that has which review it was for that person.\n",
    "For example, if the raw reviews were like\n",
    "\n",
    "<table>\n",
    "  <thead>\n",
    "      <th>Reviewer</th>\n",
    "      <th>Review Overall</th>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>3</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>3</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Bob</td>\n",
    "          <td>2</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>4</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Bob</td>\n",
    "          <td>5</td>\n",
    "      </tr>\n",
    "  </tbody>\n",
    "</table>\n",
    "\n",
    "The `order` table would be\n",
    "\n",
    "\n",
    "<table>\n",
    "  <thead>\n",
    "      <th>Reviewer</th>\n",
    "      <th>Order</th>\n",
    "  </thead>\n",
    "  <tbody>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>0</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>1</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Bob</td>\n",
    "          <td>0</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Alice</td>\n",
    "          <td>2</td>\n",
    "      </tr>\n",
    "      <tr>\n",
    "          <td>Bob</td>\n",
    "          <td>2</td>\n",
    "      </tr>\n",
    "  </tbody>\n",
    "\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "order = df.groupby(\"profile_name\").review_overall.transform(...)\n",
    "order"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, what do we do with `order`? Hint: It's the same shape as `df` and we\n",
    "want to compute the average `review_overall` for all people with `order=0`,\n",
    "and all people with `order=1`, and `order=2`..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10e1a6780>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MrvVFaC3pCqrQ0KKgGBjKdv34LqBdPxaNitiiQkSWp+UG6t614VQ7qlBlytNlpY/F709a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\nvuJ3fLc7B0+P7Yz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HsBkaYkOZTOudmV0/ANAiThpcxEcbH89gtHfs6k4JmH1LTyQ1ipJs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\nGJMsLlQYY4wxJln/BZS0xVl20FlBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e154240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%load solutions/groupby_02.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## General `.apply`\n",
    "\n",
    "We've seen `.agg` for outputting 1 row per group, and `.transform` for outputting 1 row per input row.\n",
    "\n",
    "The final kind of function application is `.apply`.\n",
    "This can do pretty much whatever you want.\n",
    "We'll see an example in a later notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary\n",
    "\n",
    "- We used Python's iterator protocol to transform the raw data to a table\n",
    "- We saw how Dask could handle larger-than-memory data with a familiar API\n",
    "- We used groupby to analyze data by subsets"
   ]
  }
 ],
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  "kernelspec": {
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