{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Content under Creative Commons Attribution license CC-BY 4.0, code under BSD 3-Clause License © 2017 L.A. Barba, N.C. Clementi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Seeing stats in a new light\n",
    "\n",
    "Welcome to the second lesson in \"Take off with stats,\" Module 2 of our series in _Engineering Computations_. In the previous lesson, [Cheers! Stats with Beers](http://go.gwu.edu/engcomp2lesson1), we did some exploratory data analysis with a data set of canned craft beers in the US [1]. We'll continue using that same data set here, but with a new focus on _visualizing statistics_.\n",
    "\n",
    "In her lecture [\"Looking at Data\"](https://youtu.be/QYDuAo9r1xE), Prof. Kristin Sainani says that you should always plot your data. Immediately, several things can come to light: are there outliers in your data? (Outliers are data points that look abnormally far from other values in the sample.) Are there data points that don't make sense? (Errors in data entry can be spotted this way.) And especially, you want to get a _visual_ representation of how data are distributed in your sample.\n",
    "\n",
    "In this lesson, we'll play around with different ways of visualizing data. We have so many ways to play! Have a look at the gallery of [The Data Viz Project](http://datavizproject.com) by _ferdio_ (a data viz agency in Copenhagen). Aren't those gorgeous? Wouldn't you like to be able to make some pretty pics like that? Python can help!\n",
    "\n",
    "Let's begin. We'll import our favorite Python libraries, and set some font parameters for our plots to look nicer. Then we'll load our data set for craft beers and begin!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy\n",
    "import pandas\n",
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "\n",
    "#Import rcParams to set font styles\n",
    "from matplotlib import rcParams\n",
    "\n",
    "#Set font style and size \n",
    "rcParams['font.family'] = 'serif'\n",
    "rcParams['font.size'] = 16"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the data\n",
    "\n",
    "Like in the previous lesson, we will load the data from a `.csv` file. You may have the file in your working directory if you downloaded it when working through the previous lesson. In that case, you could load it like this:\n",
    "\n",
    "```Python\n",
    "beers = pandas.read_csv(\"beers.csv\")\n",
    "```\n",
    "\n",
    "If you downloaded the full set of lesson files from our public repository, you can find the file in the `/data` folder, and you can load it with the full path:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Load the beers data set using pandas, and assign it to a dataframe\n",
    "beers = pandas.read_csv(\"../data/beers.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Note:\n",
    "\n",
    "If you don't have the data file locally, download it by adding a code cell, and executing the following code in it:\n",
    "\n",
    "```Python\n",
    "from urllib.request import urlretrieve\n",
    "URL = 'http://go.gwu.edu/engcomp2data1?accessType=DOWNLOAD'\n",
    "urlretrieve(URL, 'beers.csv')\n",
    "```\n",
    "The data file will be downloaded to your working directory, and you will load it like described above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK. Let's have a look at the first few rows of the `pandas` dataframe we just created from the file, and confirm that it's a dataframe using the `type()` function. We only display the first 10 rows to save some space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(beers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>abv</th>\n",
       "      <th>ibu</th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>style</th>\n",
       "      <th>brewery_id</th>\n",
       "      <th>ounces</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.050</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1436</td>\n",
       "      <td>Pub Beer</td>\n",
       "      <td>American Pale Lager</td>\n",
       "      <td>408</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.066</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2265</td>\n",
       "      <td>Devil's Cup</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2264</td>\n",
       "      <td>Rise of the Phoenix</td>\n",
       "      <td>American IPA</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.090</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2263</td>\n",
       "      <td>Sinister</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.075</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2262</td>\n",
       "      <td>Sex and Candy</td>\n",
       "      <td>American IPA</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>0.077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2261</td>\n",
       "      <td>Black Exodus</td>\n",
       "      <td>Oatmeal Stout</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>0.045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2260</td>\n",
       "      <td>Lake Street Express</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>0.065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2259</td>\n",
       "      <td>Foreman</td>\n",
       "      <td>American Porter</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2258</td>\n",
       "      <td>Jade</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.086</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2131</td>\n",
       "      <td>Cone Crusher</td>\n",
       "      <td>American Double / Imperial IPA</td>\n",
       "      <td>177</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0    abv  ibu    id                 name  \\\n",
       "0           0  0.050  NaN  1436             Pub Beer   \n",
       "1           1  0.066  NaN  2265          Devil's Cup   \n",
       "2           2  0.071  NaN  2264  Rise of the Phoenix   \n",
       "3           3  0.090  NaN  2263             Sinister   \n",
       "4           4  0.075  NaN  2262        Sex and Candy   \n",
       "5           5  0.077  NaN  2261         Black Exodus   \n",
       "6           6  0.045  NaN  2260  Lake Street Express   \n",
       "7           7  0.065  NaN  2259              Foreman   \n",
       "8           8  0.055  NaN  2258                 Jade   \n",
       "9           9  0.086  NaN  2131         Cone Crusher   \n",
       "\n",
       "                            style  brewery_id  ounces  \n",
       "0             American Pale Lager         408    12.0  \n",
       "1         American Pale Ale (APA)         177    12.0  \n",
       "2                    American IPA         177    12.0  \n",
       "3  American Double / Imperial IPA         177    12.0  \n",
       "4                    American IPA         177    12.0  \n",
       "5                   Oatmeal Stout         177    12.0  \n",
       "6         American Pale Ale (APA)         177    12.0  \n",
       "7                 American Porter         177    12.0  \n",
       "8         American Pale Ale (APA)         177    12.0  \n",
       "9  American Double / Imperial IPA         177    12.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantitative vs. categorical data\n",
    "\n",
    "As you can see in the nice table that `pandas` printed for the dataframe, we have several features for each beer: the label `abv` corresponds to the acohol-by-volume fraction, label `ibu` refers to the international bitterness unit (IBU), then we have the `name` of the beer and the `style`, the brewery ID number, and the liquid volume of the beer can, in ounces.\n",
    "\n",
    "Alcohol-by-volume is a numeric feature: a volume fraction, with possible values from 0 to 1 (sometimes also given as a percentage). In the first 10 rows of our dataframe, the `ibu` value is missing (all those `NaN`s), but we saw in the previous lesson that `ibu` is also a numeric feature, with values that go from a minimum of 4 to a maximum of 138 (in our data set). IBU is pretty mysterious: how do you measure the bitterness of beer? It turns out that bitterness is measured as parts per million of _isohumulone_, the acid found in hops [2]. Who knew?\n",
    "\n",
    "For these numeric features, we learned that we can get an idea of the _central tendency_ in the data using the **mean value**, and we get ideas of _spread_ of the data with the **standard deviation** (and also with the range, but standard deviation is the most common).\n",
    "\n",
    "Notice that the beer data also has a feature named `style`: it can be \"American IPA\" or \"American Porter\" or a bunch of other styles of beer. If we want to study the beers according to style, we'll have to come up with some new ideas, because you can't take the mean or standard deviation of this feature!\n",
    "\n",
    "**Quantitative data** have meaning through a numeric feature, either on a continuous scale (like a fraction from 0 to 1), or a discrete count. \n",
    "**Categorical data**, in contrast, have meaning through a qualitative feature (like the style of beer). Data in this form can be collected in groups (categories), and then we can count the number of data items in that group. For example, we could ask how many beers (in our set) are of the style \"American IPA,\" or ask how many beers we have in each style.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing quantitative data\n",
    "\n",
    "In the previous lesson, we played around a bit with the `abv` and `ibu` columns of the dataframe `beers`. For each of these columns, we extracted it from the dataframe and saved it into a `pandas` series, then we used the `dropna()` method to get rid of null values. This \"clean\" data was our starting point for some exploratory data analysis, and for plotting the data distributions using **histograms**. Here, we will add a few more ingredients to our recipes for data exploration, and we'll learn about a new type of visualization: the **box plot**.\n",
    "\n",
    "Let's repeat here the process for extracting and cleaning the two series, and getting the values into NumPy arrays:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Repeat cleaning values abv\n",
    "abv_series = beers['abv']\n",
    "abv_clean = abv_series.dropna()\n",
    "abv = abv_clean.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Repeat cleaning values ibu\n",
    "ibu_series = beers['ibu']\n",
    "ibu_clean = ibu_series.dropna()\n",
    "ibu = ibu_clean.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's also repeat a histogram plot for the `abv` variable, but this time choose to plot just 10 bins (you'll see why in a moment)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gIv4FfBHYCNgdQNIuwG7ApyJifp7nFGAUcEKjEkkbA5OB0xsXAo2I84A7gdOG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\nB/sIHixzk7epAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1081850b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(6,4))\n",
    "pyplot.hist(abv, bins=10, color='#3498db', histtype='bar', edgecolor='white') \n",
    "pyplot.title('Alcohol by Volume (abv) \\n')\n",
    "pyplot.xlabel('abv')\n",
    "pyplot.ylabel('Frequency');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can tell that the most frequent values of `abv` fall in the bin just above 0.05 (5% alcohol), and the bin below. The mean value of our data is 0.06, which happens to be within the top-frequency bin, but data is not always so neat (sometimes, extreme values weigh heavily on the mean). Note also that we have a _right skewed_ distribution, with higher-frequency bins occuring in the lower end of the range than in the higher end.\n",
    "\n",
    "If you played around with the bin sizes in the previous lesson, you might have noticed that with a lot of bins, it becomes harder to visually pick out the patterns in the data. But if you use too few bins, the plot is also unhelpful. What number of bins is just right? Well, it depends on your data, so you'll just have to experiment and use your best judgement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's learn a new trick. It turns out that `pandas` has built-in methods to make histograms directly from columns of a dataframe! (It uses Matplotlib internally for that.) The syntax is short and sweet:\n",
    "\n",
    "```\n",
    "dataframe.hist(column='label')\n",
    "```\n",
    "\n",
    "And `pandas` plots a pretty nice histogram without help. You can add optional parameters to set these to your liking; see the [documentation](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.hist.html). Check it out, and compare with our previous plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Uxgoe78bO4+2+Xu1KWgFsU1G+DbCqvLVgZmbd06vEsBAYqCjfHbi1u10xM7O8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\nwczMCpwYzMys4P8DL87SZtIJwm8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108162cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "beers.hist(column='abv', edgecolor='white')\n",
    "pyplot.title('Alcohol by Volume (abv) \\n');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which one do you like better? Well, the `pandas` histogram took fewer lines of code to create.  And it doesn't look bad at all. But we do have more fine-grained control with Matplotlib. Which method you choose in a real situation will just depend on the situation and your preference."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring quantitative data (continued)\n",
    "\n",
    "In the [previous lesson](http://go.gwu.edu/engcomp2lesson1), you learned how to compute the mean of the data using `numpy.mean()`. How easy is that? But then we wrote our own custom functions to compute variance or standard deviation. It shouldn't surprise you by now that there are also NumPy functions for that!\n",
    "\n",
    "\n",
    "##### Exercise:\n",
    "\n",
    "* Go to the documentation of [`numpy.var()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.var.html) and analyze if this function is computing the _sample variance_. \n",
    "**Hint**: Check what it says about the \"data degrees of freedom.\"\n",
    "\n",
    "If you did the reading, you might have noticed that, by default, the argument `ddof` in `numpy.var()` is set to zero. If we use the default option, then we are not really calculating the sample variance. Recall from the previous lesson that the **sample variance** is:\n",
    "\n",
    "$$\n",
    "\\begin{equation*}     \n",
    "     \\text{var}_{sample} = \\frac{1}{N-1}\\sum_{i} (x_i - \\bar{x})^2\n",
    "\\end{equation*}\n",
    "$$\n",
    "\n",
    "Therefore, we need to be explicit about the division by $N-1$ when calling `numpy.var()`. How do we do that? We explicitly set `ddof` to `1`.  \n",
    "\n",
    "For example, to compute the sample variance for our `abv` variable, we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.000183378552053\n"
     ]
    }
   ],
   "source": [
    "var_abv = numpy.var(abv, ddof=1)\n",
    "print(var_abv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can compute the standard deviation by taking the square root of `var_abv`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0135417337167\n"
     ]
    }
   ],
   "source": [
    "std_abv = numpy.sqrt(var_abv)\n",
    "print(std_abv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might be wondering if there is a built-in function for the standard deviation in NumPy. Go on and search online and try to find something.\n",
    "\n",
    "**Spoiler alert!**\n",
    "You will. \n",
    "\n",
    "##### Exercise:\n",
    "\n",
    "1. Read the documentation about the NumPy standard deviation function, compute the standard deviation for `abv` using this function, and check that you obtained the same value than if you take the square root of the variance computed with NumPy.\n",
    "\n",
    "2. Compute the variance and standard deviation for the variable `ibu`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Median value\n",
    "\n",
    "So far, we've learned to characterize quantitative data using the mean, variance and standard deviation.\n",
    "\n",
    "If you watched Prof. Sainani's lecture [Describing Quantitative Data: Where is the center?](https://youtu.be/tQ5slNYRcC4) (recommended in our previous lesson), you'll recall that she also introduced the **median**: the middle value in the data, the value that separates your data set in half. (If there's an even number of data values, you take the average between the two middle values.)\n",
    "\n",
    "As you may anticipate, NumPy has a built-in function that computes the median, helpfully named [`numpy.median()`](https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.median.html). \n",
    "\n",
    "##### Exercise:\n",
    "\n",
    "Using NumPy, compute the median for our variables `abv` and `ibu`. Compare the median with the mean, and look at the histogram to locate where the values fall on the x-axis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Box plots\n",
    "\n",
    "Another handy way to visualize the distribution of quantitative data is using **box plots**. By \"distribution\" of the data, we mean some idea of the dataset's \"shape\": where is the center, what is the range, what is the variation in the data. \n",
    "Histograms are the most popular type of plots in exploratory data analysis. But check out box plots: they are easy to make with `pyplot`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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x+uPlzHppG4cCz0bEGkkPAueSnIL7a6An/eXfq4h4TdL3gDmSvgBMAcZHxG/T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\n/wOpkdOWZBfOAAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108162c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.boxplot(abv, labels=['Alcohol by volume']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S1GIwSJJaDAZJUovBIElq2WHUBUizWZIZGaeqpjyONCwjC4YkDwX+Chhrmq4CTqiqH46q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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1109544e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.boxplot(ibu, labels=['International bitterness unit']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is going on here? Obviously, there is a _box_: it represents 50% of the data in the middle of the data range, with the line across it (here, in orange) indicating the _median_. \n",
    "\n",
    "The bottom of the box is at the 25th _percentile_, while the top of the box is at the 75th percentile. In other words, the bottom 25% of the data falls below the box, and the top 25% of the data falls above the box. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Confused by percentiles?_\n",
    "The Nth percentile is the value below which N% of the observations fall. \n",
    "\n",
    "Recall the bell curve from our previous lesson: we said that 95% of the data falls at a distance $\\pm 2 \\sigma$ from the mean. This implies that 5% of the data (the rest) falls in the (symmetrical) tails, which in turn implies that the 2.5 percentile is at $-2\\sigma$ from the mean, and the 97.5 percentile is at $+2\\sigma$ from the mean.\n",
    "\n",
    "The percentiles 25, 50, and 75 are also named _quartiles_, since they divide the data into quarters. They are named first (Q1), second (Q2 or median) and third quartile (Q3), respectively. \n",
    "\n",
    "Fortunately, NumPy has a function to compute percentiles and we can do it in just one line. Let's use [`numpy.percentile()`](https://docs.scipy.org/doc/numpy-dev/reference/generated/numpy.percentile.html) to compute the `abv` and `ibu`  quartiles. \n",
    "\n",
    "** abv quartiles **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first quartile for abv is 0.05\n",
      "The second quartile for abv is 0.056\n",
      "The third quartile for abv is 0.067\n"
     ]
    }
   ],
   "source": [
    "Q1_abv = numpy.percentile(abv, q=25)\n",
    "Q2_abv = numpy.percentile(abv, q=50)\n",
    "Q3_abv = numpy.percentile(abv, q=75)\n",
    "\n",
    "print('The first quartile for abv is {}'.format(Q1_abv))\n",
    "print('The second quartile for abv is {}'.format(Q2_abv))\n",
    "print('The third quartile for abv is {}'.format(Q3_abv))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** ibu quartiles **\n",
    "\n",
    "You can also pass a list of percentiles to `numpy.percentile()` and calculate several of them in one go. For example, to compute the quartiles of `ibu`, we do:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The first quartile for ibu is 21.0\n",
      "The second quartile for ibu is 35.0\n",
      "The third quartile for ibu is 64.0\n"
     ]
    }
   ],
   "source": [
    "quartiles_ibu = numpy.percentile(ibu, q=[25, 50, 75])\n",
    "\n",
    "print('The first quartile for ibu is {}'.format(quartiles_ibu[0]))\n",
    "print('The second quartile for ibu is {}'.format(quartiles_ibu[1]))\n",
    "print('The third quartile for ibu is {}'.format(quartiles_ibu[2]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK, back to box plots. The height of the box—between the 25th and 75th percentile—is called the _interquartile range_ (IQR). Outside the box, you have two vertical lines—the so-called \"whiskers\" of the box plot—which used to be called \"box and whiskers plot\" [3]. \n",
    "\n",
    "The whiskers extend to the upper and lower extremes (short horizontal lines). The extremes follow the following rules: \n",
    "\n",
    "* Top whisker: lower value between the **maximum** and `Q3 + 1.5 x IQR`. \n",
    "* Bottom whisker: higher value between the **minimum** and `Q1 - 1.5 x IQR`\n",
    "\n",
    "Any data values beyond the upper and lower extremes are shown with a marker (here, small circles) and are an indication of outliers in the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Exercise:\n",
    "\n",
    "Calculate the end-points of the top and bottom whiskers for both the `abv` and `ibu` variables, and compare the results with the whisker end-points you see in the plot. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A bit of history:\n",
    "\n",
    "\"Box-and-whiskers\" plots were invented by John Tukey over 45 years ago. Tukey was a famous mathematician/statistician who is credited with coining the words _software_ and _bit_ [4]. He was active in the efforts to break the _Enigma_ code durig WWII, and worked at Bell Labs in the first surface-to-air guided missile (\"Nike\"). A classic 1947 work on early design of the electonic computer acknowledged Tukey: he designed the electronic circuit for computing addition. Tukey was also a long-time advisor for the US Census Bureau, and a consultant for the Educational Testing Service (ETS), among many other contributions [5].\n",
    "\n",
    "##### Note:\n",
    "\n",
    "Box plots are also drawn horizontally. Often, several box plots are drawn side-by-side with the purpose of comparing distributions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing categorical data\n",
    "\n",
    "The typical method of visualizing categorical data is using **bar plots**. These show visually the frequency of appearance of items in each category, or the proportion of data in each category. Suppose we wanted to know how many beers of the same style are in our data set. Remember: the _style_ of the beer is an example of _categorical data_. Let's extract the column with the style information from the `beers` dataframe, assign it to a variable named `style_series`, check the type of this variable, and view the first 10 elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "style_series = beers['style']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(style_series)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0               American Pale Lager\n",
       "1           American Pale Ale (APA)\n",
       "2                      American IPA\n",
       "3    American Double / Imperial IPA\n",
       "4                      American IPA\n",
       "5                     Oatmeal Stout\n",
       "6           American Pale Ale (APA)\n",
       "7                   American Porter\n",
       "8           American Pale Ale (APA)\n",
       "9    American Double / Imperial IPA\n",
       "Name: style, dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style_series[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Already in the first 10 elements we see that we have two beers of the style \"American IPA,\" two beers of the style \"American Pale Ale (APA),\" but only one beer of the style \"Oatmeal Stout.\" The question is: how many beers of each style are contained in the whole series? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Luckily, `pandas` has a built-in function to answer that question: [`series.value_counts()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) (where `series` is the variable name of the `pandas` series you want the counts for). Let's try it on our `style_series`, and save the result in a new variable named `style_counts`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "American IPA                      424\n",
       "American Pale Ale (APA)           245\n",
       "American Amber / Red Ale          133\n",
       "American Blonde Ale               108\n",
       "American Double / Imperial IPA    105\n",
       "Name: style, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style_counts = style_series.value_counts()\n",
    "style_counts[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(style_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(style_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `len()` function tells us that `style_counts` has 99 elements. That is, there are a total of 99 styles of beer in our data set. Wow, that's a lot!\n",
    "\n",
    "Notice that `value_counts()` returned the counts sorted in decreasing order: the most popular beer in our data set is \"American IPA\" with 424 entries in our data. The next-most popular beer is \"American Pale Ale (APA)\" with a lot fewer entries (245), and the counts decrease sharply after that. Naturally, we'd like to know how much more popular are the top-2 beers from the rest. Bar plot to the rescue! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we'll draw a horizontal bar plot directly with `pandas` (which uses Matplotlib internally) using the [`plot.barh()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.plot.barh.html) method for series. We'll only show the first 20 beers, because otherwise we'll get a huge plot. This plot gives us a clear visualization of the popularity ranking of beer styles in the US!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4AmgmGkvqHZQEaCCOoZy8d4mk/VufpSRpVeBrlOVnO7UdRtGTycD7ge9IGme79VlE91A+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dM6yrqGtO92jYhhwL8AP5Z0VJ11OQj4VUQsAi4E1sppnZJ0OKl37PQ8rHBHYDgwRZI6WX0PUqBS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\nzMzMzMysxIGSmZmZmZlZyf8HM9xsOyl1ooIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110975828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style_counts[0:20].plot.barh(figsize=(10,8), color='#008367', edgecolor='gray');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing multiple data\n",
    "\n",
    "These visualizations are really addictive! We're now getting ambitious: what if we wanted to show more than one feature, together on the same plot? What if we wanted to get insights about the relationship between two features through a multi-variable plot? \n",
    "\n",
    "For example, don't you want to know if the bitterness of beers is associated with the alcohol-by-volume fraction? We do!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter plots\n",
    "\n",
    "Maybe we can do this: imagine a plot that has the alcohol-by-volume on the absissa, and the IBU value on the ordinate. For each beer, we can place a dot on this plot with its `abv` and `ibu` values as $(x, y)$ coordinates. This is called a **scatter plot**.\n",
    "\n",
    "We run into a bit of a problem, though. The way we handled the beer data above, we extracted the column for `abv` into a series, dropped the null entries, and saved the values into a NumPy array. We then repeated this process for the `ibu` column. Because a lot more `ibu` values are missing, we ended up with two arrays of different length: 2348 entries for the `abv` series, and 1405 entries for the `ibu` series. If we want to make a scatter plot with these two features, we'll need series (or arrays) of the same length.\n",
    "\n",
    "Let's instead clean the whole `beers` dataframe (which will completely remove any row that has a null entry), and _then_ extract the values of the two series into NumPy arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "beers_clean = beers.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1403"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ibu = beers_clean['ibu'].values\n",
    "len(ibu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1403"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abv = beers_clean['abv'].values\n",
    "len(abv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that both arrays now have 1403 entries—not 1405 (the length of the clean `ibu` data), because two rows that had a non-null `ibu` value _did_ have a null `abv` value and were dropped."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the two arrays of the same length, we can now call the [`pyplot.scatter()`](https://matplotlib.org/devdocs/api/_as_gen/matplotlib.pyplot.scatter.html) function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gI4CFoccm+28GwZrm9fDX2kfq9aC/76fhzZh+a+QYX4A3oU3hxV3og8V66sg+dvr/XgSv\nt2K3/0bwXQAvDZWb5+9/IHTOmyL7Wgjg6/557YX3ZvoJRD7o/LJT/bIH/Ov09fAfdUJ9o/dsP4AH\nAFwZKfdBv74j/r9gmdWUlP1P8t9gHoc3QWwnvMbJjf55HwLwiF/2EZxoQAz4+58V2tcZ8L4l7/If\n2+G/bk6POW7qtYM3kTJ6D77mP/a1mLpUQs+d75/HHn//u/y6neFyj2PqfbX/nKDn7YXwPkye9vfz\nIwCvTnj+FHiv3Y/mfA/o9V9LlYQyhVwDeBP/7oG3JHkngNUAfgvj/w5f4V+LPn+b+q/VT/uvC9vt\n/xw59pXwlk8+hxMN0T8F0F7kNYP3jf198BqAu+C9Tz0F70vNb0bK/iW8v5UPWBzz4sj1PeT/Ps9/\n/PM48XerOPF32wfvfekpeF/cXpvyN/weeKuCnvWv49P+tboZMe9HZfon/kkQlYaI7AQAVZ1T35pQ\nK/LHyHfD+/CxnRxJ1LQ4R4GIWp6MD5l8NYD1bCQQeaLrPYmIWtFTIjIXXhf7+1H/GfREpcEeBSoN\n8ZNCYXzSpA+kPY+oADvgTTx+GF546mjEQqKWxTkKREREZMQeBSIiIjJiQ4GIiIiM2FAgIiIiIzYU\niIiIyIgNBSIiIjJiQ4GIiIiM2FAgIiIiIzYUiIiIyIgNBSIiIjJiQ4GIiIiM2FAgIiIiIzYUiIiI\nyIgNBSIiIjJiQ4GIiIiM2FAgIiIiIzYUiIiIyIgNBSIiIjJiQ4GIiIiM2FAgIiIiIzYUiIiIyIgN\nBSIiIjJiQ4GIiIiMOupdgbKYOnWqzpkzp97VICIiqokHH3zwgKpOSyvHhoJvzpw52Lx5c72rQURE\nVBMi8pRNOQ49EBERkREbCkRERGTEhgIREREZsaFARERERmwoEBERkVFdGwoicoaIfFtE1LL8X4mI\nisg1Va4aERERoY4NBRFZBuBHAM60LP8bAP48pcz7RGSLiDwiIj8RkTcWUFUiIqKWVc8ehZUALgXw\ngGX5WwCsNz0oIisBfBjAUlU9B8AHAdwpIkvyVpSIiKhV1bOhcL6qbrMpKCIvA3A+gL83PH4qgI8A\nWK2qTwCAqn4XwHcAfKqY6hIREbWeujUUVHXYofjfAPgQgCHD45cBOBnAvZHt6wEsEJEXudeQiIiI\nSr/qwZ9n0AngqwnFzvF/7ohs3xF5nIiIiByUOteDiEwC8P8D+GNVVRExFZ3q/zwc2d7v/zytCtUj\nIiJqemXvUfgTAD9X1R9kfL6xZQEAInKtiGwWkc379+/PeAgiIqLmVdqGgj9B8f/CW72Q5oD/szuy\nPfj92bgnqeptqrpQVRdOm5aaaZOIiKjllHno4bcBDMNb4hhs6/J/flRE3gfga6r6UQCP+NvnANgZ\n2sdc/+cjICIiImelbSio6rcBzApvE5GL4K1suEFVbw899G0ARwBcBOC+0PaLAWxR1ceqWFUiKpHe\nbf1YvfE57B0YxvSuDqxYPAVL5vfUu1pEDau0Qw8uVPUggI8B+FMReQEAiMhrALwOwAfqWTciqp3e\nbf245f796BsYhgLoGxjGLffvR++2/tTnElG8uvUoiMgn4UVmnO3//pD/0GJVPRYpezq84EnRoYd3\nqOpmAFDVVSJyFMA3RWQYwAiAN6tqb/XPhojKYPXG53B0eHzqmKPDitUbn2OvAlFGdWsoqOpfOJTd\nB+AlFuU+A+AzeepFRI1r70B8HDfTdiJK1xRDD0READC9K/67j2k7EaVjQ4GImsaKxVMwuWN8+JTJ\nHYIVi6fUqUZEjY/NbCJqGsE8BK56ICoOGwpE1FSWzO9hw4CoQBx6ICIiIiP2KBDVGQMEEVGZsaFA\nVEdBgKBg7X8QIAgAGwtEVAoceiCqo6QAQUREZcCGAlEdMUAQEZUdGwpEdcQAQURUdmwoENURAwQR\nUdnxawtRHTFAEBGVHRsKRHXGAEFEVGYceiAiIiIjNhSIiIjIiA0FIiIiMmJDgYiIiIzYUCAiIiIj\nNhSIiIjIiMsjiYhyYgZQamZsKBAR5cAMoNTsOPRARJQDM4BSs2NDgYgoB2YApWbHhgIRUQ7MAErN\njg0FIqIcmAGUmh2bvEREOTADKDU7NhSIiHJiBlBqZhx6ICIiIiM2FIiIiMiIDQUiIiIyYkOBiIiI\njDiZkYjIEXM7UCthQ4GIyAFzO1Cr4dADEZED5nagVsOGAhGRA+Z2oFbDhgIRkQPmdqBWw4YCEZED\n5nagVsMmMBGRA+Z2oFbDhgIRkSPmdqBWwqEHIiIiMmJDgYiIiIzYUCAiIiIjNhSIiIjIiA0FIiIi\nMmJDgYiIiIzq2lAQkTNE5NsioumliYiIqNbq1lAQkWUAfgTgzIQyZ4jIzSKyUUR+KiKPicjXROTF\nhvLvE5EtIvKIiPxERN5YrfoTERG1gnr2KKwEcCmABxLK3Ajg9wEsV9WXAngJgBEAP442FkRkJYAP\nA1iqqucA+CCAO0VkSTUqT0RE1Arq2VA4X1W3WZT7hKruBgBVPQqvgdEJ4NqggIicCuAjAFar6hN+\n2e8C+A6ATxVdcSIiolZRtxDOqmqTk/XdAEYj2/b4P38ttO0yACcDuDdSdj2AT4nIi1T1sUwVJaJS\nWbVhL9ZuPYxRBdoEWHZ2N1ZeML3e1aq63m39zC9BdVHqVQ+qOqyq0YbCWf7P+0LbzvF/7oiU3RF5\nnIga2KoNe3HXFq+RAACjCty15TBWbdhb34pVWe+2ftxy/370DQxDAfQNDOOW+/ejd1t/vatGLaDU\nDQWDawH8HMBXQtum+j8PR8oGf0WnVbtSRFR9a7dG/8STtzeL1Rufw9Hh8YvDjg4rVm98rk41olbS\nUNkjReQSAFcCuFBVh2yekrK/a+HPdZg9e3b+ChJRVY0aFlKbtjeLvQPxI7Wm7URFapgeBRE5F8CX\nAbxBVbdEHj7g/+yObA9+fzZun6p6m6ouVNWF06ZNK66yRFQVbYamv2l7s5jeFf+dzrSdqEgN0VAQ\nkXMAfB3AW1T1hzFFHvF/zolsnxt5nIga2LKzo98Fkrc3ixWLp2Byx/jW0OQOwYrFU+pUI2olpW+O\n+o2EbwD4Q1X9gb/tDAA3qep1frFvAzgC4CKMn+R4MYAtXPFA1ByC1Q1lW/VQ7RUJwb646oHqodQN\nBT+o0vcA/CeAOSIyx39oKoAXBuVU9aCIfAzA+0Xky6r6pIi8BsDrALyhtrUmompaecH0ujcMwoIV\nCcFkw2BFAoDCGwtsGFA91K2hICKfhBeZcbb/+0P+Q4tV9Zj//5vhNQre5f8L+374F1VdJSJHAXxT\nRIbhRXB8s6r2VukUiIgSVyTwg52aQT0DLv2FRZnfc9znZwB8JnOliIgccUUCNbuGmMxIRFRWXJFA\nzY4NBSKiHLgigZodm7xERDlwRQI1OzYUiIhy4ooEamYceiAiIiIj9igQNamypSUuW31qVadanXcZ\nry81BzYUiJpQrYIANWp9alWnWp13Ga8vNQ8OPRA1obKlJS5bfYDa1KlW513G60vNgw0FoiZUtiBA\nZatP0rGLrFOtzruM15eaBxsKRE2obEGAylafpGMXWadanXcZry81DzYUiJpQkUGAerf1Y+manVh8\n63YsXbMTvdv661qfotSiTrU67zJeX2oebG4SNaGiggAVNUmujEGJalGnWp13Ga8vNQ9R1fRSLWDh\nwoW6efPmeleDqFSWrtmJvphx7hldHVh31ZzaV4iICiMiD6rqwrRyHHogIiNOkiMiNhSIyIiT5IiI\nDQUiMuIkOSLi1wIiMuIkucbBEM5ULWwoEFEiZkYsP4Zwpmri0AMRUYNjCGeqJvYoEFHLdluv2rAX\na7cexqgCbQIsO7sbKy+YXu9qOePqFKom9igQtbig27pvYBiKE93WWSIwNpJVG/biri1eIwEARhW4\na8thrNqwt74Vy4CrU6ia2FAganGt2m29duthp+1lxtUpVE1sbhK1uFbtth41BKU1bS8zrk6hamJD\ngajFTe/qiA3T3Ozd1m0S3yhok4nbGgFXp1C1cOiBqMW1arf1srO7nbYTtarm/spARKlatds6WN1g\nu+qhVVeGEDF7pI/ZI4nIJBrQCPB6Xa6/cBobC9SwmD2SiKggrboyhAhgQ4GIKFWrrgwhAthQICJK\nxYBG1MrYUCAiStGqK0OIAK56ICJK1aorQ4gANhSIiKwwoBG1Kg49EBERkRF7FIgoEQMNEbU2NhSI\nyCgaaChIQQ2AjQWiFsGhByIyYqAhImKPAlHJlKmrn4GGaqtM954owIYCUYmUrau/VVNQ10PZ7j1R\ngEMPRCVStq5+BhqqnbLde6IAvxYQlUjZuvobMdBQGbrvk+pgeqxs954owIYCUYmUsau/kQINlaH7\nPqkOAIyPlfHeEwEceiAqFXb151OG7vukOiQ9xntPZcWmKlGJNGJXf5mUofs+Sx32Dgzz3lNpsaFA\nVDKN1NVfNmXovk+rQ9JjvPdURnUdehCRM0Tk2yKi6aWJiJKVofs+qQ5lqB+Rq7r1KIjIMgB/C+B4\nSrlJAG4A8GYAwwD6Afylqv4gpuz7AFzrlxsG8FFV/XrBVSeikipD971NHapRvzKs9qDmJKr1+TIv\nIj8G8AcAPgTgj1RVDOU+D+ASAOer6n4ReQeAvwfwO6r6UKjcSgAfAHCeqj4hIpcC+BaAN6hqb1p9\nFi5cqJs3b859XkREtRZdaQF4PRXXXziNjQUyEpEHVXVhWrl6Dj2cr6rbkgqIyAvh9RCsUtX9AKCq\nXwTwJICPh8qdCuAjAFar6hN+ue8C+A6AT1Wn+kRE5VCG1R7UvOo29KCqNtOQlwEQAPdGtq8H8C4R\n6VLVAQCXATjZUO5TIvIiVX0sb52p+TR6d+2Kdbuxac/Q2O+LZlaweumsCeXSznPVhr1Yu/UwRhVo\nE2DZ2d1YecH01Mfqcf2S6gNMvCYdAowoYutXi/rX4hhlWO1Bzavsqx7OATAKYFdk+w54dV8AYKNf\nLtgeLRfshw0FGqcMwXnyiH4gAsCmPUNYsW73uMZC2nmu2rAXd205PFZ+VDHud9Nj587orPn1S6rr\nygumx16T4It2tH61uP+1eo2JAHGjyBI7oEvkpuwBl6YCOKKqI5Ht/f7P00LlAOBwSjmiMY3eXRv9\nQDRtTzvPtVujfzYY2570WD2uX1J9APM1CYTrV4v61+oajRqmmpm2E7koe4+CiW07ObGciFwLbw4E\nZs+enbdO1GAasbs23I1tK+08s3zIjGp9rl8RH4hB/Yqsf3R44fzZnXhg12BszISkYzT6UBg1p7L3\nKBwAcLKItEe2d/s/nw2VC283lRtHVW9T1YWqunDatGm5K0uNxRSEp6yx9YNu7L6BYbh8UUw7zzZD\nc7pNkh+rx/VLqo+toH5F1T96X/oGhnHXlsPGRoLpGHH7ueX+/ejd1j9xB0Q1VPaGwiPw6hidnTUX\nXpyEraFyADAnplz4caIxjRb8Jq4bO86imZVxv6ed57Kzo+1rjG1Peqwe1y+pPsDEc48K16+o+tve\nl7Rj5BmmmNoZ31IybSdyUfaGwloACuCiyPaLAXxHVYMBy28DOGIot4UrHijOkvk9uP7CaZjR1QEB\nMKOro9Trzm26xONWPaSd58oLpmP5gu6xb+VtAixf4K0kSHqsHtcvqT4AsHrprAmNhQ5BbP2Kqr/L\nUEXSMfIMhfRefSai3a7t/naivOoWcGmsAiK3Iz3g0sXw4i4cEJG3A/gHxAdcej+8gEtPishrAPSC\nAZeoSSxdszO2O3tGVwfWXTWn9hUiAOb7EpV2n159+xPoH5r4ftxTEXzvmuQP/LjVHoB5uSwR0AAB\nl0TkkyLyEIA3+L8/5P87KVL0PQDuBPCAiPwMwDsBvDbcSAAAVV0FLwjTN0XkEQCfBPBmm0YCUSNo\ntKGSVhF3X6Js7pMY5l6btofZroAhyqKeAZf+wrLccQAf9v+llf0MgM/krBpRKZUhj4GrVpjFH3df\nglUPLufdPzTqtJ2oVso5vZuIYjVSGuJGD2jlooj7UoYU2URxyj6ZkYgaVKMHtKq1PENLptUeaatA\niGywoUBEVdGIAa3qKc8qjLjVHpzISEVhnxYRVQW70t3lGcJgo4CqhT0KRFQVXKVB1BzYtCdqErVa\nYWB7nGppHR/cAAAgAElEQVSt0miFlRREZcKGAlETqNUKA9fjFL1Ko5VWUhCVBYceiJpArVYY1Hsl\nQ72PT9SK2KNAVGLR0LymmezVXGEQ7uo3BXyv1UqGVl5JwSEXqhf2KBCVVFz8/k17hrBi3e4JZauV\n8tk2tXWtVjI0WmrwojAFNdUTGwpEJeUSv79aKwxsUyj3DQzjvNu2Y9WGvcYyvdv6sXTNTiy+dTuW\nrtmZ6UOuVVdS2Ay5FHF9ieI0dzOcqEVUa4WBS5f+qAJ3bfEyvwdpnwNFTUJsxHwXRUgbcuEkT6om\nNhSImkQ18kCYgibN6OrAvl8NYzSms2Ht1sMTGgpJ34hd69xI+S6Kkha8qsjrSxTFoQeikipD/P64\nrv4OAQaPj8Y2EgDEbm/lSYhJbIcL0oZceH2pmthQICqpMsTvj+Yf6KkIRIBDCamP22TitladhJjE\nZYJiWh6Inkr8W7lpO5GL1v0rJWoAZYjfH+7qX7pmJ/qHkr+lLju7e8K2FYunjBtDB1pjEmIS1+GC\npCEXNaxJMW0ncsGGAhFZS+rKbhOvkRCdnwC03iREm5gHRQ4XHB6KbxCYthO5YEOBiKwlTW5cd9Wc\nxOe2yiRE2xUIRWbXZKZOqiYOYBGRtVaNY+DCNsx0kdeS94Wqic1NoiYR7e6e1dOOB58ZwqgmDwvE\nPbcaGSHLEIJ41Ya9WLv18Ng1ef4p7Xjq0IjVNbJlE/MguA49lTac1O4NEeS5Jkvm9+DhvsFx53b5\nWV0t0YOTVRlej42CDQWiJhDX3R3uii4yGFKWIYQyBARatWHv2DUAvGuy4+DIuN9N18hF0jBA9Doc\nGhrF5A7BzZecnus69G7rx92PD4wtTR1V4O7HB3DujE5++MUow+uxkXDogagJ2IZaXrv18IRttcjI\nWIasj3HnnqecSdIwQLWuQxmubyPh9XLDhgJRE7CdKV+vYEhlCAhkChCVtZxJUsyDal2HMlzfRsLr\n5YZDD0RNwNTdHWUKhlTtGfPdFUF/zFK97kpMhaqkTewaAXHXyJVpeKZa15qrHtzwerlhjwJRE4jr\n7o5jCoZU7Rnzgvi6mbZXQ9y55ymXRbWuNVc9uOH1csPmE1ETiFuNYLvqIW0lQxGzw/sNIZ9N25Nk\nrU9w7kWseshah2oFnmq1gFZ58Xq5EVVG7gKAhQsX6ubNm+tdDaJSic4OB7xvXuE8AzaWrtmZOVBT\nNeqTRxnqQFQEEXlQVRemlePQAxEZFTU7vKiu3jLMVi9DHYhqiUMPRGTkEjxoelcHzp/diQd2DU7o\nzi2qq7cMs9XLUAeiWmJDgYiMXIIH9Q0MjwtoFA1iU0SuhzLMVi9DHYhqiUMPRGTkGjwoqugu+TLM\nVi9DHYhqiU1gIjJKGjK4cf0+q30U2SVfdE6DaO4Hm1UPnDFPrYYNBSJKZBoyMAVRiiqyS77InAZx\nuR9scz20SspsIoBDD0SUkU2wpKK75ItccWDK6ZA31wNRs2GPAlELsl2tAEzsnn/5GRXs7h/BIYtg\nSS8+/aSx/Vzx1R3jsjXOPbUdd1w516neRa44MIVzDrYv+fITODB4olClDTiuqGpwJpf7EhWt79RO\nQe/VZ6bWjygNAy75GHCJWkVcwKCoIIDQw32D47rns1i+oBs/2XNkXCMh4NpYePXtT8QOd/RUBN+7\nxu1DcdGt242PTe2UcR+6JssXJDcWXIIzudyX6HOjjYTwebCxQCYMuEREsVxWKxTRDb926+HYRgIA\n43aTWuWMsGkkAOnDFC5DJXlWkZjqa3seREk49EDUYmy76fcODKOIj5m8aZvDiswZUYS0c3MZKnG5\nL0S1xB4FohZjuwpheldHISmXi9hHwFT3LCsrTPVyqW9aWZf6utwXolpiQ4GoxdikpA5WKxSRcnnZ\n2d2Ye2p77GOm7SZFBjsynduys7sxtdOutZB2fVzq63Jfokz1tT0PoiRsKBC1mCXze3D9hdMwo6sD\nAi+D4/IF3eN+DybMrbxgOpYv6B775twm3od78LsA6GzH2POiH/xzT23HA7sGsfPgCNojn1lZVj3E\n1T1r1sa4cwsmJ/ZefeaED9lKG2LLFlVfl/sSFVdfTmSkonDVg4+rHojSJc3iB5B51j4R1Z7tqgcO\ndhGRtbRZ/Laz9tlQIGocbCgQNZA8wXuy5iMI78fUDHCZiV/PWftFXROiVsKGAlGDiEvrHE7jnLWs\nyzFNpnd14MjxkZrnfnBR1DUhajWczEjUIPIG78mSE8EmCFAwE78euR9cFJkngqiVlL6hICILRaRX\nRLaKyKMislFE3hwpM0lEPiYij4nIz0TkhyLyynrVmagaigjeY9Pt37utH0vX7MTiW7ejL6F8dCZ+\nUtCjvCsUilBkngiiVlLqoQcRmQPgewD+C8BSVR0WkXcBuENE3qCq6/yifw/gEgDnq+p+EXkHgO+K\nyO+o6kP1qDtR0aZ3dcR+cJuC99iWDbMdapjR1YF1V82xOmZc2XrIek2IWl3ZexReD6AHwN+o6jAA\nqOrnAfQDeCsAiMgLAVwLYJWq7vfLfBHAkwA+Xo9KE1VD3uA9Nt3+LkMNRR2zVspeP6KyKntTOmj+\nj9VTRAReAyeI7LIMXs/mvZHnrgfwLhHpUtWBaleUqNqWzO/BuscOYdOeobFt4TTO0bKf/dF+HA19\nge6alD5pz6YbXkc1NQV1kSstovtZsW73uGuwaGYFq5fOSn1u8Pxarxqh5tYKr5NSB1wSkR4APwbw\nOICrABwB8H8B3AjgMlVdLyL/BuBKACep6kjouX8O4NMAzlPVjWnHYsAlKrtVG/bGpnyOixB4xVd3\nZErrvHTNzsR5CYGuScDr5ndb18dFUlCnaEMpEDQWXNI6Z61Ds30IUHaN/jppijTTqtoP4NUAJgM4\nAGAfgLcDuFRV1/vFpgI4Em4k+Pr9n6fVoq5E1WZKaRy3PWtaZ5t8AwAwcNytPi6SVifENRIAjG2v\n5moPrpCgqFZ5nZS6oeDPP9gI4CkAUwCcDuBDAL4mIkvSnm6x/2tFZLOIbN6/f3/u+hJVkymlcZFp\nnKP5BupRnzyrE4pa2cAVEmSjVV4npW4oAPgYgFMBvFdVj6jqqKr+B4D7AfyLiHTA62k4WUSiaeiC\ntG7Pmnauqrep6kJVXTht2rRq1J+oMEWkRbaxZH4P1l01Bxuvm1eX+uRJJV1UGuoi01lT82qV10nZ\nGwovBvC0qg5Gtj8OYBqAuQAegXcesyJl5sKbDLm12pUkqoWktMhRRaV17ppk3u5SHxdJqxMWzazE\nPifYXtTKBq6QIBut8jope0NhH4Az/J6DsOcDUAC/BLDW//9FkTIXA/iOquYbMCUqiaS0yFF3XDk3\nNuWza1rne98+b0JjoWuSt92lPnHCgZ2WrtmJ3m3etKKk1Myrl86a0FgIr3ooKg11kemsqXm1yuuk\n7Kse3gTgTgB/DeBDqqoicjGA/wZwl6r+vl/u8/AaBuer6gEReTuAfwBgHXCJqx6IaqfRZ4sTNYOm\nSDOtqv8pIpcBWAlgi4iMABiFN6Hxs6Gi74G3ZPIBETkO4DCA1zIqI1E5Jc0WZ0OBqFxK3VAAAFX9\nb3g9CElljgP4sP+PiFC+QDDRQElxyjxbvGzXk6hWSt9QICJ3ZUupbNNIAMo7W7xs15Oolso+mZGI\nMihbIBibRkKZZ4uX7XoS1VI5m+9EZMXUHd5IgWAEKH1XfhmvJ4dCqFbYUCBqUEnd4Y2UUjktsFMZ\nlO16ciiEaolDD0QNKqk7vGyBYNICJZVd2a4nh0KolkodR6GWGEeB6iXahTyrpx0PPjM0lrp52dnx\nQYwW37odSX+9XZO85E1Rcemgk44ZzUQ5tVPQ0d6OvQPDE44fBGOKE91PlgBQ9ZTU1R9Ntx2+fi5D\nBLZlk+59IwzlUDkUFkdBRL6uqm8splpEFBbXhRzu4h5VjKVyjjYWuiuC/iFzUyGukRDsMzy5MOmY\nP9lzZELGyQODCi86evwxL/7S9gmNhd5t/XhmYHTctmcGRtG7rb9hPsyWzO+JrWs0/Xf4+p07o9N6\niMBlOME0FAJ4YWo5FEFFshl6eKmIXG349xYRebWIlHOqMlHJxXUhx4lL3SzpCVJzWbv1cGpa6jhx\nDZRm7ipPSrftct4uZW3SgTfL9aX6s5mJ0wUvj0Lcq3ISvORMLxWRtQBWqKr7OwtRi7KdNR+Xurl/\naHTixgIVkb466Eo3ffs1nX9SV37ZJKXbdlkt4VI26CUIhilMt6qMq1yo8dg0FHap6tuTCohIBV5I\n5Q8CuKWIihG1gqQu5LC41M22z82qTfI1FuLyOUTFrRpI6sovY2PBdJ3aBDj9efarJVxXVoSHQpau\n2VmqVRnUXGyGHl6dVkBVhwD8GYD/k7tGRC3EpgsZiE/dbPvcrJad3e2clho4kZo6bVilQ4DB46MT\nskcmdeWXUVK6bZfVEnlWVpRtVQY1l9TmpqpaDXKp6hER4XJLIgfRLmSXVQ/R53YIcDz0uTy1U/Dc\nUXXuFQgfs3dbP25Yv29CmZ6K4PCQJq56SOr27qkIBo8rDvnDJ+HJd0ld+WUU3JukoRKblQxxrwXb\nlQt5nkuUptDlkSKyRVUXFLbDGuLySGoV59223dhV/uNrx69WMHVpz+jqwLqr5iQeJ+m5AIyP7fvV\nsHX9iCi7IpdH/pGq/otFuRfCm9xIRAnyhN7NuiZ/codgMGV1RVwXep7QxSsWT4mdo5A0r2LvwDB+\nb0H3uDkK0fo1WujiRqsvUZTNTJf3AohtKPhDDdMAXABvEmNvcVUjaj55Qu+6PDdaNq6RIPDW3CcN\nb+QJXRzuDreddDm9qyOxK7/RQhc3Wn2J4qQOPYjIKJAYAA7w3nMeBXCx7ZyGsuHQA9VCtbryo881\nlQ2z6cqPW7kwuUNw/YXTnD7obOpjs988168eGq2+1FoKG3oA8EsA/2V47BiA/QD+B8C3VZWLdokS\n5OnKL2JNftioeqGAqzXBzrY+Qcjh82d3YvXG53Dj+n3G45Qxi2OSRqsvURzbOApvq3pNiFpAnq58\nl+f2VNrGVhQksQn3awpd7MJU9+CbtW0XfdmyOKZptPoSxbFZzvjaqteCqEXUaq38xIWLyaod7jet\n7rbhixstXkCj1ZcoTu7lkSIyDcBBVTWkoGkMnKNARYrOdD9/dice2DWIvQPD6K4IBIL+odEJj6XF\nUVjy5Sf8pEyerklAV6VjwrBAWmbJNJ0dgqPDavxGvOm6ebHn6VL3qZ2C3qvPBGDOhigANl43fh5F\n0n7KiKseqKxs5yhYNRREZB6ASwGMArhDVX8pIlcA+ByA0wAMAfgigPepanUD0FcJGwpUFJvQxcHE\nPQCpZQFg+YLu2EyOpv3GBUkq2kcvOT133YNU07aT/qKpqqP7ISJ7RcZReDW8yYyd/qa/FJFl8JZM\nPgrgewBmA3g3gB0A/jZrpYmagU1GyHC3um32SJvIhLXMGOiS+dJU9+BDPy7mQlwXvamhlCXLJRHZ\nsZlRcyOAHwD4R3gBlf4cwBcAfEhV/yYoJCJvAfABsKFALc52RrvLzHeX8MW1mlFvGxvBpu71CkHM\nYQGidDYNhRcAeJGqDgCAiPwIXs/B+eFCqvofIsLMkdTybLM6BjPfbcoGwZFsRHM+NIoiVle4YDAk\nIjs2qx76g0YCAKjq0wC2G2ImHCqsZkQNyiarY9CtbpsBstIO60yOxzU+LXW9dHaIse6u2SlNp5Xl\ndG1XWhC1OpuGwtGYbYOGsg34PYaoWEvm9+D6C6dhRlcHBN6EvEUzK2Mf3m0CXH5W19g36HBZk6ER\n4I4r546lcE6TNEd5RlfHhP24J5O2d3RYcceVcyc0CqZ2CgaHZUKa6bx6t/Vj6ZqdqftlMCQiOzZD\nD6eJyB9ifKN9Ssw2AODiYCKM70YPuriDsfpRBe5+fADnzugcKxeUNc3+n97VgVUb9mLAchFyWoCj\nsKB+IxYTE02SMkIGQyzhVQlj3f6Dw2PPs+n2NwWS6qm0jd+vxXACgyER2bHpUZgF4PbIv9nwVj1E\nt88qtnpEjc+lizspQM/arRMzKsZZNLPiFOjHdvWCSdIwissxbbr9TYGkgu1FXWsiOsGm6fwEgHdY\nlBN4qyGImkYRs+JduriTZv/bxEZYNLOC1UtPtNdt6p61qz3I0RDdb55jptXl8FB8QyHYXtS1JqIT\nbBoK96jq9212JiL35KwPUWkUNSvetYvbNPu/TeKXGpqyQNquIrBdpREVjZhYxDHTuv3TnlfUtSai\nE2yGHjY57M+lLFGpFTUrvqgu7mVndzttt2W78iLMsbjVMW2uSdrzOJxAVDybHoV3A/iS5f5cyhKV\nWlGz4vN0cUeHPhbNrBhzKWQdJlkyvwfrHjuETXuGrM9pJOf6pqzXZMn8HjzcNzgW7TG8giTPfonI\nzKah8BIRYXxUajlFzorP0sUdN/Rx8OgIbrr49An7yjNMsmrDXqdGAlDMyoCs1+TuxweMK0iy7peI\nzGz+2n8JL9dDGgHwu/mqQ1QetvkHqiVp6CP6QehSNsp2NUWgnl35ec6TiLKxaSjsUtW32exMRH6a\nsz5EpeHSjV1kzoBgX6YJhn0Dw1h063YAXqCkaQmTEcNlgfj00GmjCJU2IBy64MWnnzR2bivW7R7X\nGxFddRF3XnGpt+N+j7uGNsNBzN9AVKzUNNMiMk1V91vtzKFs2TDNNGUVl1Y6SPecd7ihGmzTQydZ\nvqAbuw4eix2yiGssZDmvuGv4mtufjA24dEqlDfdc84JC7wVRs7NNM5266sHlg79RGwlEeRSZMyBv\n8KNaHWPt1sPGeQ1x27McM+4aFhlwiYjs2CyPJKIEReYMqEWegSKO4ZL2Os8xo88rMuASEdlhQ4Eo\np8mGjEqm7UkqBWRn6qkkBzkoYsWCa3bKrMeMPs+0n3DApSKPT0RsKBDldtSweNi0PclQAQuRJSXp\ncpYAS1HLzu7GopmV2Mfitmc5ZtzqCgZcIqo9NhSIcjL1wmeZBVDE7IT+odGx1Q1Rm66bF5sGu5Lw\nTjC1U8alyF6+wAvytHrprAmNAtOqhyXze3D5WV3j9rNoZmVcHZYv6B73e9wExLi6h8ulPU5E7tgf\nR5RTUg6GovYVLfPja+clpqQGYGwsAMUFJTIthYyKC5T06L5jmT7E0+rOgEtExWKPAlFOReZgsHlO\nUKaRutm5GoGocbFHgVpaEcF5glwL4fwD4RwMefYlODEc0SbAy8+o4IFdg1h863ZM7+rA5Wd1jQUp\nmtwhGBxW3LB+H266dx9efkYFu/tHrM7N5TpkuWZcjUDUuFIDLrUKBlxqPY0WnCepvg/3DeKuLcmh\nmE3n5nIdsl4z0zDJjK4OrLtqTmK9iag6Cgu4RNSsGq07PKm+NvkaTOfmch2yXrNGGiYhovEaYuhB\nRJYDeC+A5wH4NQDPAfg7Vf2K//gkADcAeDOAYQD9AP5SVX9QnxpTI2i07nBTvUx5Hmz3kXYdbPJC\npF0zpn8malylbyiIyJ8B+EMAb1DVp/1Gwb8AeDWAr/jF/h7AJQDOV9X9IvIOAN8Vkd9R1YfqUnEq\nve6KoD8m0l93SsCiejHV10Vc4KGkdNq2ORpsAhpxNQJRYyr10IOIzAGwCsB1qvo0AKjqcQAfAPA5\nv8wLAVwLYFWQa0JVvwjgSQAfr32tqVGYAhOlBSyql7z1MnX1Jw0L2ORo4BACUXMre4/CHwI4qKqb\nwhtVdQ+APf6vy+BNDr838tz1AN4lIl2qOlD1mlLhqp0uuD8mC2HS9iSrNuy1XvWw5MtP4MCg+cO3\nHUDeAI1tAkyZLOOOE04PHa3v809px1OHRsZ+v/ysLiyZ34Mb1+9LPdbR0EqL6HmH72FPpQ0KxeEh\nrcrKiqK51MHl/ldLGepAzansDYVXANjpz1F4H4Bp8OYnfFFVv+SXOQfAKIBdkefugHd+CwBsrE11\nqSjRLu++gWHccr+XnLSoD4ykLncXqzbsHbfiYFQx9nv0jTqtkQDkbyQA3odEdBXEpj1DWLVhLwBM\nqO+OgyPjfr/78QGcO6PTKVJk9Lyj9zCcHjrpftbi3qdxqYPL/a+WMtSBmlephx4AzALwm/CGGt4M\n70P/bwHcJiIf8stMBXBEVaPvr/3+z9NqUVEqVi1WJBQ1E9+04iBue1ojoQidHZJYpzwrJGwE+08b\ntih6ZUWRXOrgcv+rpQx1oOZV9obCZHgrHf5CVftUdVRV7wTwDQDXi8jJCc9NHdAVkWtFZLOIbN6/\nf39BVaYi1GJFQlF5AUwhl11TMRfl6LAm1sm2XlmvdbB/m+dnWYVRCy51KMP9L0MdqHmVfeghaA5H\nVy78FMDvwethOADgZBFpj/QqBLFwnzXtXFVvA3Ab4AVcKqTGVIiihgXSFDETv8hcD0WY3tWBfb8a\nTqyTzQeI6R6kCY5h83zXVRi14lKHMtz/MtSBmlfZexQe839G6zkS2v6I/zOanWYuvJgKW6tWO6qa\nRgrQ45LrYWpndd+5g2uUVCebfBLBfromudchKRdF3DGiynDvXepQZK6PrMpQB2peZe9RWAfgLfAm\nLIaDJ/0WgEEAPwdwCMAtAC4CcHuozMUAvqOqHKRrQI0UoMcm10N4Rnoa06qHuNUJpnwOwXVKqlP4\nsaT9vPIL2xFeCCIAxP8GG81FET5G9B7arnoow713qUORuT6yKkMdqHmVOteDiLQD+BGAIwB+V1UH\nROQCAPcA+Kiqftwv93l4DYPzVfWAiLwdwD8AsA64xFwPVC3RGelRjZpfooz1JSJ7TZHrwZ9zcBmA\nbQB+LiL/C68B8O6gkeB7D4A7ATwgIj8D8E4Ar2VURiqDtJnnjZpfgohaQ9mHHqCqz8H74E8qcxzA\nh/1/RKViM9yQdUZ/tYPslGEFAhHVV6l7FIiagc3M8ywz+oMhjaAhEgTZCYIqFcFUr1quQCCi+mJD\ngajK0maeZ53RX4sgO2VYgUBE9cWvBURVFp2RLgAmtwNHR5BrRn8tguyUYQUCEdUXGwpENbDygumF\nL1WrVZAdpocmam0ceiBqUAyyQ0S1wB4FqqlapA92OUae+oSf210RCAT9Q6Ox4X8FwMbr5sUec1ZP\nOx58Zmhs5cIkwbgAR1M7Bb1Xnwlg4iqHqZ3jU0kvmlkZ67ko6jq4PBY9Fwb9IWp8pQ64VEsMuFR9\ntQje43KMPPWJe24aAXDzJac7Pw/wGgSvmtuVGLgJOFF/AIVch6T9xD0WZ/kCNhaIysg24BIbCj42\nFKpv6ZqdsYl2ZnR1YN1Vc2p+jDz1MT03zYyMiZYA85yEuGMAKOQ6JO3H9FhUmwA/vnZeajkiqi3b\nhgKHHqhmahG8x+UYeeqTtc55zrWI9NBFXQeX8yg61XEthq+I6AROZqSaqUXwHpdj5KlP1jrnOVfb\n1QzTuzoKuw5ZHosqchVGMEzSNzAMhdejccv9+9G7rb+4gxDROGwoUG692/qxdM1OLL51O5au2Wl8\n065F8B6XY+SpT1oK5TiS8XkBl/TQRV0H18eS6r1qw16cd9t2LLp1O867bXumCJLMPUFUexx6oFyi\nE+GCb3gAJnQH1yJ4j8sx8tQn+lyXVQ/RY4ZXCiSJSyVsSg8dd5w818H2MdOqh2gGzSDcdPi8bDD3\nBFHtcTKjj5MZs6nFBMVWsujW7cbHNl3XuBMCz7ttuzE4lMtER77eiIrDyYxUE630DS9rrAGbx6th\nxbrd2LRnaOz3RTMrWL10VlWPaVJUuOkVi6fELtfMOnzVrBMjm/W8qD7YUKBc4rrZg+3NJGmIBUDi\n8IvL8MwplTYcCkdbCm13EW0kAMCmPUNYsW53XRoLRYWbLnL4yuW+NJJmPS+qH05mpFxaJbtg0iS6\ntAl2LhPwFPFfsU3bTaKNhLTt1VZkuOkl83uw7qo52HjdPKy7ak7mD79mnRjZrOdF9dNcX/uo5lol\nu2CeWAMuzz08FN8gMG2vhmi39fmzO/HArsFc93flBdOx6+CxCUMh9YzY2GzDZsF9MwXBatTzovpj\nQ4Fya4XsgmlDLEmPuQzP1HsoJ67bOrxaIWs3du+2fjy679i4bY/uO4bebf11e+3U+1oXySakeCOe\nF5UDhx6ILGSNNZD2XJfjuFg0s+K0PRDXbR2VpRu7jN3hzTRslnbfGvW8qBzYxKSGFM2iGM5SWGT2\nyOikwEkCDCsmlH24b3BcfbomATes34cb1u8DAMw9tR2Dw2IV0+CzP9qPo6Evul2TzN/eTfVfvXRW\nbN037xnC0jU7jce37Z4OytkOU9Srmz/p/i6Z3zPhvl1+VlfmiZFZV8QUIek6zmjS4UCqHTYUqOEk\nBe85d0an9YzvtNnhcSsHjuvEZYa92/px9+MDY7P6RxXjUj8DwI6DI1g0s5K61v+Kr+6Y8NwDg4or\nvroDd1w516n+QR1dZsGbuuOjpnd1OA1T1KObP+284+7b3Y8P4NwZnc7DKllXxBTFdH0ZX4KKwKEH\najhrt8anWl679bBTF3daWduVAzbd9Un7C9txcMR6u+25ulwTm7DMQTe2yzBFPbr5i1yNkvU4tRpy\naaZhFCof9ihQw0kK3lOr7JHh7nvX7vOiuqJt6+9ynnGrWEzDCTf6wyo29azH6pi08y5qOKTI7JtZ\nXxutsvqI6oMNBWo4ScF7Tn9ebVYY2HSrxykyGI5t/V3P03YVi8swhct+i5J23kUNh+RZEROW97XR\nCquPqD449EANJyl4T5ErDNISIyZ1q8dZNLOS2hVt+oOM2257rtXqlnYZpqiHIlejZD2OyzHKuDKE\nCGCPAjWguCyK4VUPwMSMhjfd661ACJdN664dsYhx1DcwPLayobNDcHRYMb2rA50dOm5eQTABcrEh\n6VPQFW06ZNx22+7manVLuwxT1EPaeRd1XfJm3ww0WwAoah7MHulj9sjmFF0hEVi+oDs1KqApU2GS\ntP2mZT989e1PoD8mCmNPRfC9a850qgs1FmbGpFqzzR7JoQdqakkrJNLYDim47DetK1oQfzzTdmoe\nXHfYv94AACAASURBVLlAZcWhB2pItrPD86Q3Dncp2/YsBPtNCkTUU2nDSe1e/obgsdUbn8ON6/cZ\nhx76YzJKpnEJAjSrpx0PPjNkHMqh6uPKBSorNhSo4bjMDhfEj+/bfj8PZpIvMswtiGqT9HwJh4ZG\nMblDcPMlpwNAaox+wH0mvmsQoHBDKBzAio2F2uLKBSojDj1Qw3GZHV5pj9+HabtJWo6EwLKzu50C\nEdmUzdL97BoEKI7N8AwRNT/2KFDDSZsdHu5WN30cDsUHQDSKy50wtVPw3FGd0F1vWtkQZTOckTX/\nQBEz6E3DKOwOJ2otbChQw0kKcGOTbjco6yqc3yFJT6UNhzLMKYiTNf9AliBAUaZhlGrkKiCi8uLQ\nAzWcpNnh1erKd6HGfoxssgTdcQ0CFMc0jMIgQESthXEUfIyjkKwe3c/hY/ZU2qDQsZUC0Vn6Lz+j\ngt39I4nflAWY8NyoShsQ7gyYe2r7WNbGaGrr55/SjqcOjVitoLBhmngZPLbxunlO92HJl58Yl4ly\naqeg9+ozU88lOoySVKfuikAg6B8arerrIimtOBFlYxtHgQ0FHxsKZnHd+ZM7BNdfOK1qjQXbIQRb\nQdAaUwCmJHNPbcfLZp7s/DwXne3A/e+Yh9fc/mTssMUplTa8//yp1vfhiq/uiM04OffUdrztZadZ\n78dUH5NqvC7yBM0iIjMGXKLC1KP72XZmvo3wUEOWmfw7Do5UfQXAUf8z3TRsoVCn+5CUrtplP67D\nKNV4XeQJmkVE+bGhQKnqEYO+qH3P6OrA5Wd1YfXG57D41u2ZhwmyPk/8OiyaWUFbwrSAYPeHY8I3\nB9uLug+m4Zm4/Zjqk6To10WeoFlElB8bCpTKtEIgy8qBvMd0McMfM7/78QH0JSyVtJH0IZ9k43Xz\nsGLxFDy671jiB1uw+6RrXe37YErFXcR+8jBd+6z3hIjcsKFAqeoRgz5LnoUwl1UQaeae2m5MbZ2k\n4v91Wa3E8ANAFZWy2JVLKu4s+8kjKa04EVUf4yhQqnrEoM+SZwE4sbIhqN+NfgrorMKrHnYdPGYM\nuBRVaQN+8M55AOy64oM5Ckvm9+DhvsFxM/zDAZeij7349JPG8kRkuS/R6xWVlkq6FqsebNKKEwNj\nUfWwoUBW6hGDPjimbbrnuHS8psBDSdoE+PG188Zt693Wj0f3HRu3beA4cNPFp6deF5s6BCGle7f1\n4+7HB8YaH+GASwAmPBZuuISDIbVJ+hh+3HnGKUP+gZUXTGfDIAEDY1E1ceiBSs+m+7uornMgvks7\nz8oPmzoEIaXz5mgIytp0y7PrvnkwMBZVE3sUqKZcUh8Hjy2Z34N1jx0a9+157qntGByW2P3E5WQ4\nNoIJwZn2Dgyj0u59SCviu7TDgX7i9A0MT8gsGd1PXP2jFEjMUOmykmDvwPCE7vpwMCfXrnt2aZdf\nPVYmUetgQ4FqxjX1cfDYw32DEz5kdxwciQ24E20kABgXnXBUgUf3HbMKCpQlOFNwjHCa5lUb9iY2\nEmxM7+rAkeMj6LdYrhhMjCyiu55d2o0hLbcHUR4NN/QgIhtEREVkTr3rQm5cu9WDx1wC7th8INt2\nyeYN6BM8P+9+gmEVgd0QylHHzJhJ2KXdGOqxMolaR0M1N0VkOYBXGh7rArAKwKUARgA8DeDPVPXn\ntashJTF1gyZN9EtKFZ0n4I5Nl2zegD6jisRcCWkEGMtxceP6fdb7KTIOEbu0G0M9ViZR62iYhoKI\nnATgrwF8C8DrY4rcCaAHwEtV9YiIfAzAfSLyElX9RQ2rSgZZViBM7+rAvl8Nx35o5wm4Y9Mla1o5\nYLOiIJBULG0/N19yeqZ8F0UGImKXduMow+oUak6NNPTwpwA2A9gUfUBELgVwGYCPqOoRf/PHALQD\nuL5mNaREWYP3uATcWTSzYr3fNEnHtTmOzf7nntoe+9jcU9szB4sqcjUDu7SJqCGyR4rIFAA/A/AK\nANcAuBHAXFXd6T/+eQBvB9ClqsdCz1sHYJGqzkg7BrNHFsdmZYNNOujw86JphsMrF9JWPSStkPjt\nW7cjOqQffNNvE2CSjE87HS4zZbKMmyiZVTS1ddck4MiwXa9FOOhTtQIRJaV4LnJFBFNJE9VWU6WZ\nFpHPADimqn8pIjdhYkPhhwBmquqcyPM+C+A9AKaramKIPjYUimGbktoURCkuaFLWY6SJayS4Wr6g\nG1/bcjj3vIBFMytYvXRWppUW1Uy3nHStARSWfpyppIlqr2nSTIvIPABXAPh4QrGpAOLeXfv9n6cV\nXS+KZztLPk+XdlEz8YtYHLB2a/5GAnBitUaWFRLVTLecdaWKK6aSJiqvRpiR9AkAq1T1UIbnJg6I\ni8i1AK4FgNmzZ2fYPUWlzZIPd1V3VwSV9jbnPAFlmolfdKrjLPurZrrlLNc6y31gKmmi8ip1Q0FE\nLgDwWwCuTCl6AMDMmO3BrK5n456kqrcBuA3whh4yVpNCkmbJR7ux+4cUkzu82f0uXdVlmonvsgIi\nz/6ClQxFr/5Ik3ati7oPaedNRPVT9qGHS+GtXNgkIg+JyEMA3uU/9i1/2+sBPAJgpr+EMmwugL1p\n8xOoOElDCkV1VRc1Ez9+vYGbZWd3W4ZBSjZJTuzPdJx6pFuuVdprppImKq9S9yio6g0AbghvC01m\nfH1oMuMwgOvgrYq4z992kv/7f9SswpQY+MWU8tmmqzo6u/7ys7rGUh2nDVuYZtP/z3XzEvMrAN4K\nhIHjE7eH97PygumxgZVmdHWM1a+zQ7HjoHlWRNB+WnnB9AnprBfNrIyb0FfLlQE2gXyKWPXAVNJE\n5dUQqx7C4lY9+Nu/DeB5AF7nB1y6GcAKAFYBl7jqofqyrnTIs8ohaTY9gNQVBlln8Zu85vYncShm\nveUplTbcc80LClvRQUSUpmlWPQRE5PVxQw+hIm+GNwTxkIhshdebcBGjMpZH1q7qPEMWSbPpbWbU\nF53XQA1rJILtzK1ARGVT6qGHMFX9FrzwzabHD8OL3kgltWR+D/75J8+O64I/o6st9ZtynlUORcym\nNx0nPBwyud1LxhSkq44GapraKei9+kwcNmR/DLa7nitTQBNRtTVMjwI1vhXrdk8Yp99xcAQr1u1O\nfJ5pFr1tvgbTdtsZ9XHHCYYI+vykVYMjJ/I6jOrEaI4HBhVLvvxE6rm4nGu0DkEK6N5t/RN3QESU\nERsKVDOmFNBpqaHzzK7PsorA5jhZ8jAcGNTUc3E5Vw5TEFEtNMzQA7WuuJn358/uxOqNz+HG9fsS\nu9xtZtOHH4vmb3jx6SdNyFORlPo6zQ0xKz/Cx1gyvwcP9w2Oq9PlZ3XFnlu9Ak9xuKOceF+oWhpu\n1UO1cNVD9SUtRdx03Tzr/VRrZUDSColzZ3RmSvlsK8hp4HJuefJlZMVVGeXE+0JZNN2qB2p8ptTM\nrimbq9XlnrRCImvKZ9dju5xbPVJAc7ijnHhfqJo49EA1s3rprAkpoIOsicDE9NDhx8JMXet9A8NY\nfOt2dFcEAhnLIRENdhRN65xmVONDFRcpWIWRNpwQDR41ZbLgaOgp4WGMotI22wy5ZAmalbVrfMmX\nnxg3PBSsKMl7zEbuui9T/hNqPmwoUE3FffADExsJgDfJccW63ROeU/GXIsZReDkkgjUIcR/wLo2E\nWuuptMUGZOqptE0YGhlVjPvABLxrtmrDXgCYUDb43aWxENelHSdtBUp0P8EKDQBOH8bRRgJwYkVJ\ntLHgcsyi6lcvZcp/Qs2HQw9UCi4rIoaKyA9dUkkBmWxTLicFk3JN22wz5FLtoFlh0UZC0naXYzZ6\n1309hqGodbC5Sbm5dNkW0R3ezNNvkwIy2Z53UjApm0BTtqs7BLDuoq9HICmbYwbHMQ0tNUrXvU1O\nDqKs2FCgXFy6bOO6ztNyLcQpOrVzmZiGVSrtwLFRu/POk5LadqjBdWWFS9d40mvKRdoxbc61kbru\nl8zvYcOAqoJDD5SLS5dtUne4y4qIZkw9PLXT+wQ3DasMjdift01K6t5t/Vi6ZicW37odS9fsHIvm\n6DrUYNpP1IrFUxDpGUeHwDmQVHCdouK2p3XHp50ru+6JPGwoUC4uXcpJeRdWL501oVFgWvWw8oLp\nWL6ge+zbcZt4ZWd0dUAA9FQEp1TaIPC++c49tX3c8yspr/pgP0XrEMTuNzxr3/SxpQDOndGZ+Afb\nJifiMSRJCv2c1NUeXM9gbb5rCGmR5N8DSa+p3qvPnNAoMK16WDK/B9dfOG3sfobrnnQcxJQlamWN\n069GpeTSpWwaMgg+8E0rIuKsvGB6pqV+LpICRGUZ/hjW9MBSSddo9cbnELdgI24YIKn35oFdg8Zv\n7Kb7GXeMpG/+0Q/Y1Rufw/FI5Y+PIrZs6pCBYSlknKTueJdzJWplbChQLisWT4mNCBfXZbvs7O7Y\nOQlZhhKKmkAZfez5p7TjqUMjqY2Aas2RSLpGXzPM53DtvUn6xn7zJadPuJ+T2oAjx0ew+Nbt4661\nS2+SS1mX11QetToOUaNjQ4FycZltfe6MTqzdcnjct+I2f7uLIidQRh+LZrestaTcFA/sGrTuvRHE\nD2MEKxVM+4nez55KG351bNSPTTH+Wrv0JrmUrdUMfq4UILLDXA8+5nqovqJyE7js57zbticOd9Ry\n9USHAD+61j6nRZRLPP8Lv7gdgzFtns524P++amKvQZZ8EqZv5HH7YS4CovKxzfXAHgWqmaLCzBY1\ngbLWhhUTuu/jhIdVouGoLz+rCw/sGkz9BmyKXHl0xO2bdNK1dtmPS1bMNI0capmoEbGhQDVTVJjZ\nIidQ1rrBEF4dAKSHEo6Go7778QGrb+Fp18h2zX1R++nd1o+7Hx8Yu96jCtz9+ADOndHp9CHf6KGW\niRoRl0dSzRQVZtZlP0nxBOoZj8EllLDN86Lqca2TFBUiudFDLRM1IvYoUM2kdVXbdinHdWOf0dWG\nm+7dhxvW7xs3AXDlBdOx6+CxcTkjBNkiQhbNZXWAqYxpRUf0GgHeB+oN6/fhpnv3WYfOLmrCn0s4\n5aTjlDVL4hVf3TFuIuzcU9txx5Vz61gjouKwoUA1Zeqqds30F+3GDr9Jh1c2nDujE4/uOzbu+WWZ\nvtsTE/nJpm7B85JWdJw7o3PcNQpzzSRZRGjgyR2CwZiekqC3wvb+lzFLYrSRAHivxyu+uoONBWoK\nHHqgUsib6S/O2q2HrcvWg32ap/jnJQVVsjlv10ySecQ1EsLbbe9/GbMkmpbU1nupLVFR2KNANWXq\nXi4ieE9UUnChMjBlirR9XpagStFyZWF7/2sZ+4CrK4g8bChQzSR1LxcRvCeqTYDTn2dXNmrTdfMS\nQzgXobuSLaNEcE2SVnTYnHdaJskiJQWAAtwDMlX7A5urK4hO4NAD1UxS97JLl3Jc2TjLzu62Lhun\n2p+jEnOEtGOGr0nSig6b867lqo9Ke/L2sg0puAyFRZOOpW0najTsUaCayRO8J9oNfEZX27gx4Kmd\ngueOKkb1xLfXu7Ycxtqth63zN0SlFTd9S7Z1aGgUS9fstO7SnhG5JknhngPBNau0e6mqFfHlqt3N\nnpQ+GyhfOGWXobA7rpzLVQ/U1NhQoJrJGrwnrhs46sCgYvkC7xtyUfkb0hoCFT8E8brHDo1bfuki\n2qXtmtEwKYumSzCkanez2wwt1GJIwZbr6go2CqiZceiBaiZr97LLKociZ/KbussDQVd01kZCdD9A\nfbrgaxHEqGxDC2karb5E1cQehRZW61ndWbuXXVY5FMnUXR6WZaKkaT9BHgjbfA5FcQ1ilOV1U7ah\nhTSNVl+iamJDoUXVa1Z3lu5l21UOjS7IA2Gbz6EoLt3seV43ZRpasNFo9SWqFg49tKhGipmfZ+VC\nHvUKM1Dr++DSzd5IrxsiKgZ7FFpUGWPmm7q0o93ALh/gbYJMqx6qHUMhTRH3wSV3BpA/7TQRNSc2\nFFpU2WLmp3VphxsMS9fstBqKCJYT3nL//lJFIbSR9z64DhEUlXaaiJoPhx5aVNlmdbt0adsMRQTn\nUuZcDyZF3IdqDRGU7XVDRNXHrwEtqmyzul26tJfM75kQuyAccAkAhvyUyo2g0gYMjZ74XUe9ugf1\nzxK8p1pDBHleN2lDIUWtwjGl3q4Xl/owvwSVERsKLaxMs7q7K4L+mCRJcfkQVm3YOyF2wYHB8c9t\npD6EcCMh7vcsKYt7Km04FN0R4lNbu8ryukkbCilqFU5S6u16NBZc6sP8ElRWHHqgUojLe2DaXsv0\nyGXhGl3SlMI6a2rrvNKGQooaKklKvV0PLvXhihIqK/YoUCn0x3z7NW1vtImJ9WBKYW3antTlXUR3\neNpQSFFDJUmpt6vJdI1c6sMVJVRWbChQKVTagaMxX5rjwijnTcbUCooKogSgkO7wtKGlolZTJKXe\nrpak6+dSH64oobLi0AOVQlp2wbC0HAxUXBClorrD04aWilpNkZR6u1qSrpFLfbiihMqKTVUqBVMP\nQdx2mxwMgTYBTu4ABo5nqVU+M7o6xrqih0dGJky4DOqXNQ02YJ5RX0QQpaRYFa7d4WlDS0vm9+Dh\nvsFx53L5WV3Owx/R1NsCYHI78LUth/HArsGqrCJIGjKwSQUeKNtKJKIAGwpUCi5dtKZu7KhwwKV6\nDFbM6mnHuqvmjHVNh032U1RHPwRcIkKmzajPG0Qp7Tmu5ZO61Xu39ePuxwfGXgOjCtz9+ADOndEJ\nwG34I0i9HVz3wSqvIkg7t6RU4FFlWolEFODQA5WCSxetqRs7rAwBl4IlnNWazV7UDH/XXBpZusPT\nutWrMfxRq1UEHDKgZsceBaopUxfyygumY9fBY+PiIyyaWRn7JhbuYk8iwLj93liCoEtps9nD18RF\n2oz6LLkebHoWzuhqcx4SSBtayDLjv29gGEvX7Kx7XgoOGVCzY0OBaiZtdv2j+46NK//ovmPo3daP\nh/sGx3Wxm5xSacM917xg3LZ2AeodwTlpxn/0mpjEDcEkDddkzfVw3m3bUxtjOw6OYMW63Vj6olOs\nj5E0tLBkfk9q972pAZN0zFquIuCQATUzDj1QzWTtXrbtSo8LJlTPRsKimRUAyTP+bYdG4oZgkoZr\nsna7264O2LRnyOkYaWWTuu/ThkZccoJwSIDIXel7FETkJQD+FMDL4NV3EoB7AHxMVfeHynUBWAXg\nUgAjAJ4G8Geq+vOaV7qKyhgL3rZOWbuXbZmCCdXD1E7Bg88MJU5OPDQ0GhtmOSo8BBOWNFyz2HBc\nU3d9+B52dsjYBMAkLvczraxN933S0IgpJ0jaPokoXekbCgD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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110bb6a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize=(8,8))\n",
    "pyplot.scatter(abv, ibu, color='#3498db') \n",
    "pyplot.title('Scatter plot of alcohol-by-volume vs. IBU \\n')\n",
    "pyplot.xlabel('abv')\n",
    "pyplot.ylabel('IBU');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hmm. That's a bit of a mess. Too many dots! But we do make out that the beers with low alcohol-by-volume tend to have low bitterness. For higher alcohol fraction, the beers can be anywhere on the bitterness scale: there's a lot of vertical spread on those dots to the right of the plot. \n",
    "\n",
    "An idea! What if the bitterness has something to do with _style_? Neither of us knows much about beer, so we have no clue. Could we explore this question with visualization? We found a way!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Bubble chart\n",
    "\n",
    "What we imagined is that we could group together the beers by style, and then make a new scatter plot where each marker corresponds to a style. The beers within a style, though, have many values of alcohol fraction and bitterness: we have to come up with a \"summary value\" for each style. Well, why not the _mean_… we can calculate the average `abv` and the average `ibu` for all the beers in each style, use that pair as $(x,y)$ coordinate, and put a dot there representing the style. \n",
    "\n",
    "Better yet! We'll make the size of the \"dot\" proportional to the popularity of the style in our data set! This is called a **bubble chart**.\n",
    "\n",
    "How to achieve this idea? We searched online for \"mean of a column with pandas\" and we landed in [`dataframe.mean()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.mean.html). This could be helpful… But we don't want the mean of a _whole_ column—we want the mean of the column values grouped by _style_. Searching online again, we landed in [`dataframe.groupby()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html). This is amazing: `pandas` can group a series for you! \n",
    "\n",
    "Here's what we want to do: group beers by style, then compute the mean of `abv` and `ibu` in the groups. We experimented with `beers_clean.groupby('style').mean()` and were amazed… However, one thing was bothersome: `pandas` computed the mean (by style) of every column, including the `id` and `brewery_id`, which have no business being averaged. So we decided to first drop the columns we don't need, leaving only `abv`, `ibu` and `style`. We can use the [`dataframe.drop()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.drop.html) method for that. Check it out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "beers_styles = beers_clean.drop(['Unnamed: 0','name','brewery_id','ounces','id'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>ibu</th>\n",
       "      <th>style</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.061</td>\n",
       "      <td>60.0</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.099</td>\n",
       "      <td>92.0</td>\n",
       "      <td>American Barleywine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.079</td>\n",
       "      <td>45.0</td>\n",
       "      <td>Winter Warmer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.044</td>\n",
       "      <td>42.0</td>\n",
       "      <td>American Pale Ale (APA)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.049</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Fruit / Vegetable Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.049</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Fruit / Vegetable Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.049</td>\n",
       "      <td>17.0</td>\n",
       "      <td>Fruit / Vegetable Beer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.070</td>\n",
       "      <td>70.0</td>\n",
       "      <td>American IPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.070</td>\n",
       "      <td>70.0</td>\n",
       "      <td>American IPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.070</td>\n",
       "      <td>70.0</td>\n",
       "      <td>American IPA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      abv   ibu                    style\n",
       "14  0.061  60.0  American Pale Ale (APA)\n",
       "21  0.099  92.0      American Barleywine\n",
       "22  0.079  45.0            Winter Warmer\n",
       "24  0.044  42.0  American Pale Ale (APA)\n",
       "25  0.049  17.0   Fruit / Vegetable Beer\n",
       "26  0.049  17.0   Fruit / Vegetable Beer\n",
       "27  0.049  17.0   Fruit / Vegetable Beer\n",
       "28  0.070  70.0             American IPA\n",
       "29  0.070  70.0             American IPA\n",
       "30  0.070  70.0             American IPA"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers_styles[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now have a dataframe with only the numeric features `abv` and `ibu`, and the categorical feature `style`. Let's find out how many beers we have of each style—we'd like to use this information to set the size of the style bubbles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "style_counts = beers_styles['style'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "American IPA                      301\n",
       "American Pale Ale (APA)           153\n",
       "American Amber / Red Ale           77\n",
       "American Double / Imperial IPA     75\n",
       "American Blonde Ale                61\n",
       "American Pale Wheat Ale            61\n",
       "American Porter                    39\n",
       "American Brown Ale                 38\n",
       "Fruit / Vegetable Beer             30\n",
       "Hefeweizen                         27\n",
       "Name: style, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style_counts[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(style_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(style_counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of beers in each style appears on each row of `style_counts`, sorted in decreasing order of count. We have 90 different styles, and the most popular style is the \"American IPA,\" with 301 beers…\n",
    "\n",
    "##### Discuss with your neighbor:\n",
    "\n",
    "* What happened? We used to have 99 styles and 424 counts in the \"American IPA\" style. Why is it different now?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OK. We want to characterize each style of beer with the _mean values_ of the numeric features, `abv` and `ibu`, within that style. Let's get those means."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "style_means = beers_styles.groupby('style').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>ibu</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>style</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Abbey Single Ale</th>\n",
       "      <td>0.049000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Altbier</th>\n",
       "      <td>0.054625</td>\n",
       "      <td>34.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Adjunct Lager</th>\n",
       "      <td>0.046545</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Amber / Red Ale</th>\n",
       "      <td>0.057195</td>\n",
       "      <td>36.298701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Amber / Red Lager</th>\n",
       "      <td>0.048063</td>\n",
       "      <td>23.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Barleywine</th>\n",
       "      <td>0.099000</td>\n",
       "      <td>96.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Black Ale</th>\n",
       "      <td>0.073150</td>\n",
       "      <td>68.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Blonde Ale</th>\n",
       "      <td>0.050148</td>\n",
       "      <td>20.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Brown Ale</th>\n",
       "      <td>0.057842</td>\n",
       "      <td>29.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>American Dark Wheat Ale</th>\n",
       "      <td>0.052200</td>\n",
       "      <td>27.600000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 abv        ibu\n",
       "style                                          \n",
       "Abbey Single Ale            0.049000  22.000000\n",
       "Altbier                     0.054625  34.125000\n",
       "American Adjunct Lager      0.046545  11.000000\n",
       "American Amber / Red Ale    0.057195  36.298701\n",
       "American Amber / Red Lager  0.048063  23.250000\n",
       "American Barleywine         0.099000  96.000000\n",
       "American Black Ale          0.073150  68.900000\n",
       "American Blonde Ale         0.050148  20.983607\n",
       "American Brown Ale          0.057842  29.894737\n",
       "American Dark Wheat Ale     0.052200  27.600000"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style_means[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking good! We have the information we need: the average `abv` and `ibu` by style, and the counts by style. The only problem is that `style_counts` is sorted by decreasing count value, while `style_means` is sorted alphabetically by style. Ugh."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that `style_means` is a dataframe that is now using the style string as a _label_ for each row. Meanwhile, `style_counts` is a `pandas` series, and it also uses the style as label or index to each element.\n",
    "\n",
    "More online searching and we find the [`series.sort_index()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.sort_index.html) method. It will sort our style counts in alphabetical order of style, which is what we want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "style_counts = style_counts.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Abbey Single Ale               2\n",
       "Altbier                        8\n",
       "American Adjunct Lager        11\n",
       "American Amber / Red Ale      77\n",
       "American Amber / Red Lager    16\n",
       "American Barleywine            2\n",
       "American Black Ale            20\n",
       "American Blonde Ale           61\n",
       "American Brown Ale            38\n",
       "American Dark Wheat Ale        5\n",
       "Name: style, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "style_counts[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, we used Matplotlib to create a scatter plot using two NumPy arrays as the `x` and `y` parameters. Like we saw previously with histograms, `pandas` also has available some plotting methods (calling Matplotlib internally). Scatter plots made easy!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BX++/cRbinVHRM6nOCf7+638N1cez25s8zfbfxktoKvx10n1JPefH6vj0NMgT8H7RrEkp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110f963c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style_means.plot.scatter(figsize=(8,8), \n",
    "                         x='abv', y='ibu', s=style_counts, \n",
    "                         title='Beer ABV vs. IBU mean values by style');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's rad! Perhaps the bubbles are too small. We could multiply the `style_counts` by a factor of 5, or maybe 10? You should experiment. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But we are feeling gung-ho about this now, and decided to find a way to make the _color_ of the bubbles also vary with the style counts. Below, we import the [`colormap`](https://matplotlib.org/api/cm_api.html) module of Matplotlib, and we set our colors using the [_viridis_ colormap](https://matplotlib.org/examples/color/colormaps_reference.html) on the values of `style_counts`, then we repeat the plot with these colors on the bubbles and some transparency. _What do you think?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import cm\n",
    "colors = cm.viridis(style_counts.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8LJrH8J3A31P0nSjyF0T9S1/CMmoHi/xXi+ZqxaK5Qef6Zl49VvzP4yXg9vh3cIDoWlqa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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110a76b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style_means.plot.scatter(figsize=(10,10), \n",
    "                         x='abv', y='ibu', s=style_counts*20, color=colors,\n",
    "                         title='Beer ABV vs. IBU mean values by style\\n',\n",
    "                         alpha=0.3); #alpha sets the transparency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like the most popular beers do follow a linear relationship between alcohol fraction and IBU. We learned a lot about beer without having a sip!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_Wait... one more thing!_ What if we add a text label next to the bigger bubbles, to identify the style? \n",
    "\n",
    "OK, here we go a bit overboard, but we couldn't help it. We played around a lot to get this version of the plot. It uses `enumerate` to get pairs of indices and values from a list of style names; an `if` statement to select only the large-count styles; and the [`iloc[]`](http://pandas.pydata.org/pandas-docs/version/0.17.0/generated/pandas.DataFrame.iloc.html) slicing method of `pandas` to get a slice based on index position, and extract `abv` and `ibu` values to an $(x,y)$ coordinate for placing the annotation text. _Are we overkeen or what!_ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N7Cgu54MPPmD16tVceumlgDdYZODAgXvUxowcOZLDDjsMoMmm2b/97W+cfvrp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Tk3STqfQtM08kNZrn56i/Hjwy/sVBVFUR4vFRAqivJE9XsOy7fWKQ7nd30+XUjSqrbRdY3ACzCs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\nrj7OPjtMx4YW4hPTHHjkBA0t0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      "text/plain": [
       "<matplotlib.figure.Figure at 0x102c29dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = style_means.plot.scatter(figsize=(10,10), \n",
    "                               x='abv', y='ibu', s=style_counts*20, color=colors,\n",
    "                               title='Beer ABV vs. IBU mean values by style\\n',\n",
    "                               alpha=0.3);\n",
    "\n",
    "for i, txt in enumerate(list(style_counts.index.values)):\n",
    "    if style_counts.values[i] > 65:\n",
    "        ax.annotate(txt, (style_means.abv.iloc[i],style_means.ibu.iloc[i]), fontsize=12)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What we've learned\n",
    "\n",
    "* You should always plot your data.\n",
    "* The concepts of quantitative and categorical data.\n",
    "* Plotting histograms directly on columns of dataframes, using `pandas`.\n",
    "* Computing variance and standard deviation using NumPy built-in functions.\n",
    "* The concept of median, and how to compute it with NumPy.\n",
    "* Making box plots using `pyplot`.\n",
    "* Five statistics of a box plot: the quartiles Q1, Q2 (median) and Q3 (and  interquartile range Q3$-$Q1), upper and lower extremes.\n",
    "* Visualizing categorical data with bar plots.\n",
    "* Visualizing multiple data with scatter plots and bubble charts.\n",
    "* `pandas` is awesome!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "1. [Craft beer datatset](https://github.com/nickhould/craft-beers-dataset) by Jean-Nicholas Hould.\n",
    "2. [What's The Meaning Of IBU?](https://beerconnoisseur.com/articles/whats-meaning-ibu) by Jim Dykstra for The Beer Connoisseur (2015).\n",
    "3. 40 years of boxplots (2011). Hadley Wickham and Lisa Stryjewski, _Am. Statistician_. [PDF available](http://vita.had.co.nz/papers/boxplots.pdf)\n",
    "4. [John Wilder Tukey](https://www.britannica.com/biography/John-Wilder-Tukey), Encyclopædia Britannica.\n",
    "5. John W. Tukey: His life and professional contributions (2002). David R. Brillinger, _Ann. Statistics_. [PDF available](https://www.stat.berkeley.edu/~brill/Papers/life.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recommended viewing\n",
    "\n",
    "From [\"Statistics in Medicine,\"](https://lagunita.stanford.edu/courses/Medicine/MedStats-SP/SelfPaced/about), a free course in Stanford Online by Prof. Kristin Sainani, we highly recommend that you watch this lecture:\n",
    "* [Looking at data](https://youtu.be/QYDuAo9r1xE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "    display: block;\n",
       "}\n",
       "\n",
       ".text_cell_render h6 { /*use this for copyright note*/\n",
       "    font-family: 'Source Code Pro', sans-serif;\n",
       "    font-weight: 300;\n",
       "    font-size: 9pt;\n",
       "    line-height: 100%;\n",
       "    color: grey;\n",
       "    margin-bottom: 1px;\n",
       "    margin-top: 1px;\n",
       "}\n",
       "\n",
       "    .CodeMirror{\n",
       "            font-family: \"Source Code Pro\";\n",
       "\t\t\tfont-size: 90%;\n",
       "    }\n",
       "/*    .prompt{\n",
       "        display: None;\n",
       "    }*/\n",
       "\t\n",
       "    \n",
       "    .warning{\n",
       "        color: rgb( 240, 20, 20 )\n",
       "        }  \n",
       "</style>\n",
       "<script>\n",
       "    MathJax.Hub.Config({\n",
       "                        TeX: {\n",
       "                           extensions: [\"AMSmath.js\"], \n",
       "                           equationNumbers: { autoNumber: \"AMS\", useLabelIds: true}\n",
       "                           },\n",
       "                tex2jax: {\n",
       "                    inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
       "                    displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
       "                },\n",
       "                displayAlign: 'center', // Change this to 'center' to center equations.\n",
       "                \"HTML-CSS\": {\n",
       "                    styles: {'.MathJax_Display': {\"margin\": 4}}\n",
       "                }\n",
       "        });\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Execute this cell to load the notebook's style sheet, then ignore it\n",
    "from IPython.core.display import HTML\n",
    "css_file = '../style/custom.css'\n",
    "HTML(open(css_file, \"r\").read())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}