{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "J8Isz2FwDONC"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "D-zPDF33DONE"
   },
   "source": [
    "# Plotting with Pandas and Matplotlib\n",
    "\n",
    "-----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "I4ag5MBwDONH"
   },
   "source": [
    "### Learning Objectives\n",
    "*After this lesson, you will be able to:*\n",
    "- Implement different types of plots on a given dataset.\n",
    "\n",
    "<!--\n",
    "Talking points:\n",
    "We will use Pandas and Matplotlib to create simple plots.\n",
    "We'll cover plotting line plots, scatter plots, bar plots, and histograms, and a brief introduction on how to manipulate the style of your plots with Matplotlib.\n",
    "-->\n",
    "\n",
    "---------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "qDihbOCjDONK"
   },
   "source": [
    "## Recap\n",
    "\n",
    "In the last lesson, we learned about when to use the different types of plots. Can anyone give an example of when we would use a:\n",
    " * line plot?\n",
    " * bar plot?\n",
    " * histogram?\n",
    " * scatter plot?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "yjT2l0BuDONM"
   },
   "source": [
    "### Pandas and Matplotlib\n",
    "\n",
    "<!--\n",
    "Talking points:\n",
    "In this lesson, we will introduce how plotting works in Pandas and Matplotlib. It is important to know that Pandas uses Matplotlib behind the scenes to make plots. So, you will notice that Pandas plotting methods often use similar parameter names as Matplotlib methods. Further, you can use Matplotlib functions in combination with Pandas methods to alter the plots after drawing them. For example, you can use Matplotlib's `xlabel` and `title` functions to label the plot's x-axis and title, respectively, after it is drawn.\n",
    "-->\n",
    "\n",
    "As we explore different types of plots, notice:\n",
    "\n",
    "1. Different types of plots are drawn very similarly -- they even tend to share parameter names.\n",
    "2. In Pandas, calling `plot()` on a `DataFrame` is different than calling it on a `Series`. Although the methods are both named `plot`, they may take different parameters."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2HqUvXmYDONP"
   },
   "source": [
    "*Sometimes Pandas can be a little frustrating... perserverence is key!*\n",
    "\n",
    "<img src=\"https://media.giphy.com/media/EPcvhM28ER9XW/giphy.gif\" width=\"200\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SN6Z5XUNDONT"
   },
   "source": [
    "## Lesson Guide\n",
    "\n",
    "- [Line Plots](#line-plots)\n",
    "- [Bar Plots](#bar-plots)\n",
    "- [Histograms](#histograms)\n",
    "- [Scatter Plots](#scatter-plots)\n",
    "- [Using Seaborn](#using-seaborn)\n",
    "- [OPTIONAL: Understanding Matplotlib (Figures, Subplots, and Axes)](#matplotlib)\n",
    "- [OPTIONAL: Additional Topics](#additional-topics)\n",
    "\n",
    "- [Summary](#summary)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bqte2ZN4DONV"
   },
   "source": [
    "## Plotting with Pandas: How?\n",
    "\n",
    "`<data_set>.<columns>.plot()`\n",
    "\n",
    "`population.states.count().plot()` creates:\n",
    "\n",
    "![](https://exceljet.net/sites/default/files/styles/original_with_watermark/public/images/charttypes/line%20chart2.png?itok=lG1hqRu4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WNoBS5juDONW"
   },
   "source": [
    "## Plotting: Visualization Types\n",
    "\n",
    "Line charts are default.\n",
    "\n",
    "`# line chart`\n",
    "\n",
    "`population.states.count().plot()`\n",
    "\n",
    "For other charts:\n",
    "\n",
    "`population.states.plot(kind='bar')`\n",
    "\n",
    "`population.states.plot(kind='hist', bins=3);`\n",
    "\n",
    "`population.states.plot(kind='scatter', x='states', y='population')`\n",
    "\n",
    "Let's try!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MxzMTLp3DONY"
   },
   "source": [
    "### Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0CkXJSdxDONa"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set the plots to display in the Jupyter notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# change plotting colors per client request\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "# Increase default figure and font sizes for easier viewing.\n",
    "plt.rcParams['figure.figsize'] = (8, 6)\n",
    "plt.rcParams['font.size'] = 14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "h0ZGCLuEDONf"
   },
   "source": [
    "### Load in data sets for visualization\n",
    "\n",
    "- [Football Records](https://www.kaggle.com/martj42/international-football-results-from-1872-to-2017): International football results from 1872 to 2018\n",
    "- [Avocado Prices](https://www.kaggle.com/neuromusic/avocado-prices): Historical data on avocado prices and sales volume in multiple US markets\n",
    "- [Chocolate Bar Ratings](https://www.kaggle.com/rtatman/chocolate-bar-ratings): Expert ratings of over 1,700 chocolate bars\n",
    "\n",
    "These have been included in `./datasets` of this repo for your convenience."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avocado.csv  chocolate_ratings.csv  international_football_results.csv\r\n"
     ]
    }
   ],
   "source": [
    "!ls ./datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "foot = pd.read_csv('./datasets/international_football_results.csv')\n",
    "avo = pd.read_csv('./datasets/avocado.csv')\n",
    "choc = pd.read_csv('./datasets/chocolate_ratings.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CrFBAr-yDONk"
   },
   "source": [
    "<a id=\"line-plots\"></a>\n",
    "## Line plots: Show the trend of a numerical variable over time\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's focus on the football scores for starters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>home_team</th>\n",
       "      <th>away_team</th>\n",
       "      <th>home_score</th>\n",
       "      <th>away_score</th>\n",
       "      <th>tournament</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>neutral</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>England</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>London</td>\n",
       "      <td>England</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>England</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Friendly</td>\n",
       "      <td>Glasgow</td>\n",
       "      <td>Scotland</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         date home_team away_team  home_score  away_score tournament     city  \\\n",
       "0  1872-11-30  Scotland   England           0           0   Friendly  Glasgow   \n",
       "1  1873-03-08   England  Scotland           4           2   Friendly   London   \n",
       "2  1874-03-07  Scotland   England           2           1   Friendly  Glasgow   \n",
       "\n",
       "    country  neutral  \n",
       "0  Scotland    False  \n",
       "1   England    False  \n",
       "2  Scotland    False  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# taking the head of the foot dataframe. #dadJokes\n",
    "foot.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can extract the year by converting the date to a `datetime64[ns]` object, and then using the `pd.Series.dt.year` property to return the year (as an `int`). We'll learn more about this in future lessons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1872-11-30</td>\n",
       "      <td>1872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1873-03-08</td>\n",
       "      <td>1873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1874-03-07</td>\n",
       "      <td>1874</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "         date  year\n",
       "0  1872-11-30  1872\n",
       "1  1873-03-08  1873\n",
       "2  1874-03-07  1874"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "foot['year'] = pd.to_datetime(foot['date']).dt.year\n",
    "foot[['date', 'year']].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then get the number of games played every year by using `pd.Series.value_counts`, and using the `sort_index()` method to ensure our year is sorted chronologically."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1872    1\n",
       "1873    1\n",
       "1874    1\n",
       "1875    1\n",
       "1876    2\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foot['year'].value_counts().sort_index().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this date, we can use the `pd.Series.plot()` method to graph **count of games** against **year of game**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SQRNn8NbDONs",
    "outputId": "b042fdea-b151-4287-9525-0038cb9dcfbc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42cde6b6a0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foot['year'].value_counts().sort_index().plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YMx-ggN0DONx"
   },
   "source": [
    "### Knowledge Check <img src=\"http://cdn.shopify.com/s/files/1/1061/1924/products/Up_Pointing_Hand_Emoji_grande.png?v=1480481048\" width=\"30px\" align=\"left\">\n",
    "\n",
    "Why does it make sense to use a line plot for this visualization? \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "C54zib1EDONy"
   },
   "source": [
    "### Another example\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rGlS563hDONz",
    "outputId": "90f67495-1fe1-4a3f-930b-8281a43192fe"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brazil       552\n",
       "Argentina    535\n",
       "Germany      495\n",
       "Mexico       494\n",
       "England      483\n",
       "Name: home_team, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foot['home_team'].sort_index().value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FQHr3oqpDON3"
   },
   "source": [
    "### Knowledge Check <img src=\"http://cdn.shopify.com/s/files/1/1061/1924/products/Up_Pointing_Hand_Emoji_grande.png?v=1480481048\" width=\"30px\" align=\"left\">\n",
    "\n",
    "Why would it **NOT** make sense to use a line plot for this visualization?\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JaFxqHmuDON7"
   },
   "source": [
    "<a id=\"bar-plots\"></a>\n",
    "## Bar Plots: Show a numerical comparison across different categories\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "aib7n5nMDON8"
   },
   "source": [
    "Count the number of games played in each country in the football dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0NKT6cViDON8",
    "outputId": "20117947-0dda-4db3-8bd5-1510caa7a005"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USA         1087\n",
       "France       775\n",
       "England      659\n",
       "Malaysia     634\n",
       "Sweden       632\n",
       "Name: country, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "foot['country'].value_counts().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's view the same information, but in a bar chart instead. Note we are using `.head()` to return the top 5. Also note that `value_counts()` automatically sorts by the value (read the docs!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "-um6OqVnDOOA",
    "outputId": "4726c40f-b1a8-4bc4-ad4a-d24e6c63e1a0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42cddabd68>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "foot['country'].value_counts().head().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KdwuxIbXDOOE"
   },
   "source": [
    "<a id=\"histograms\"></a>\n",
    "## Histograms: Show the distribution of a numerical variable\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's change to the chocolate bar dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Company \n",
       "(Maker-if known)</th>\n",
       "      <th>Specific Bean Origin\n",
       "or Bar Name</th>\n",
       "      <th>REF</th>\n",
       "      <th>Review\n",
       "Date</th>\n",
       "      <th>Cocoa\n",
       "Percent</th>\n",
       "      <th>Company\n",
       "Location</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean\n",
       "Type</th>\n",
       "      <th>Broad Bean\n",
       "Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Agua Grande</td>\n",
       "      <td>1876</td>\n",
       "      <td>2016</td>\n",
       "      <td>63%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.75</td>\n",
       "      <td></td>\n",
       "      <td>Sao Tome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Kpime</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>2.75</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Atsane</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.00</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company \\n(Maker-if known) Specific Bean Origin\\nor Bar Name   REF  \\\n",
       "0                   A. Morin                       Agua Grande  1876   \n",
       "1                   A. Morin                             Kpime  1676   \n",
       "2                   A. Morin                            Atsane  1676   \n",
       "\n",
       "   Review\\nDate Cocoa\\nPercent Company\\nLocation  Rating Bean\\nType  \\\n",
       "0          2016            63%            France    3.75              \n",
       "1          2015            70%            France    2.75              \n",
       "2          2015            70%            France    3.00              \n",
       "\n",
       "  Broad Bean\\nOrigin  \n",
       "0           Sao Tome  \n",
       "1               Togo  \n",
       "2               Togo  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lbRp1-zJDOOG"
   },
   "source": [
    "### How would you split the `Rating` values into 3 equally sized bins?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "3GA8RVSRDOOG",
    "outputId": "6fd9da5c-9695-45e2-893e-50a97dea8db0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.75, 2.75, 3.  , 3.5 , 4.  , 3.25, 2.5 , 5.  , 1.75, 1.5 , 2.25,\n",
       "       2.  , 1.  ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc['Rating'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use a histogram! The `bins=n` kwarg allows us to specify the number of bins ('buckets') of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "NX3B8D50DOOM",
    "outputId": "d6d35a0e-66c3-4ae3-8e0f-beca1ee6be2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42cdd90630>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choc['Rating'].plot(kind='hist', bins=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is helpful to increase this number if you think you might have an outlier or a zero-weighted set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SvtT0px2DOOQ",
    "outputId": "6f0840c3-e0f0-4c4f-f9e3-199921b567b5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Chocolate Rating')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choc['Rating'].plot(kind='hist', bins=20)\n",
    "plt.xlabel('Number of Ratings')\n",
    "plt.ylabel('Chocolate Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ok1NR7AgDOOU"
   },
   "source": [
    "### Knowledge check: \n",
    "What does the y-axis represent on a histogram? What about the x-axis? How would you explain a histogram to a non-technical person?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "St7-JnIMDOOV"
   },
   "source": [
    "### Making histograms of an entire dataframe:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9Z_Qlm-DDOOW",
    "outputId": "9167113b-c728-494c-852c-70feffa51be8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cdc44f60>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cdbef048>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cdc156d8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cdbbcd68>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choc.hist(figsize=(16,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JgMCXRAwDOOa"
   },
   "source": [
    "### Why doesn't it make plots of ALL the columns in the dataframe?\n",
    "\n",
    "Hint: what is different about the columns it plots vs. the ones it left out?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BDjFPIqXDOOa",
    "outputId": "381b4346-18b3-4437-c1bc-52bb1d80d901"
   },
   "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>Company \n",
       "(Maker-if known)</th>\n",
       "      <th>Specific Bean Origin\n",
       "or Bar Name</th>\n",
       "      <th>REF</th>\n",
       "      <th>Review\n",
       "Date</th>\n",
       "      <th>Cocoa\n",
       "Percent</th>\n",
       "      <th>Company\n",
       "Location</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Bean\n",
       "Type</th>\n",
       "      <th>Broad Bean\n",
       "Origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Agua Grande</td>\n",
       "      <td>1876</td>\n",
       "      <td>2016</td>\n",
       "      <td>63%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.75</td>\n",
       "      <td></td>\n",
       "      <td>Sao Tome</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Kpime</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>2.75</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A. Morin</td>\n",
       "      <td>Atsane</td>\n",
       "      <td>1676</td>\n",
       "      <td>2015</td>\n",
       "      <td>70%</td>\n",
       "      <td>France</td>\n",
       "      <td>3.00</td>\n",
       "      <td></td>\n",
       "      <td>Togo</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Company \\n(Maker-if known) Specific Bean Origin\\nor Bar Name   REF  \\\n",
       "0                   A. Morin                       Agua Grande  1876   \n",
       "1                   A. Morin                             Kpime  1676   \n",
       "2                   A. Morin                            Atsane  1676   \n",
       "\n",
       "   Review\\nDate Cocoa\\nPercent Company\\nLocation  Rating Bean\\nType  \\\n",
       "0          2016            63%            France    3.75              \n",
       "1          2015            70%            France    2.75              \n",
       "2          2015            70%            France    3.00              \n",
       "\n",
       "  Broad Bean\\nOrigin  \n",
       "0           Sao Tome  \n",
       "1               Togo  \n",
       "2               Togo  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the data types of all the columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Company \\n(Maker-if known)            object\n",
       "Specific Bean Origin\\nor Bar Name     object\n",
       "REF                                    int64\n",
       "Review\\nDate                           int64\n",
       "Cocoa\\nPercent                        object\n",
       "Company\\nLocation                     object\n",
       "Rating                               float64\n",
       "Bean\\nType                            object\n",
       "Broad Bean\\nOrigin                    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It looks like it included `REF`, `Review Date`, and `Rating`. These have datatypes of `int64`, `int64`, and `float64` respectively. What do these all have in common, that is different from the other data types?\n",
    "<br><br>\n",
    "<details>\n",
    "    <summary>Click for the answer!</summary>\n",
    "    They're all **numeric!** The other columns are **categorical**, specifically string values.\n",
    "</details>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can filter on these types using the `select_dtypes()` DataFrame method (which can be very handy!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "REF               int64\n",
       "Review\\nDate      int64\n",
       "Rating          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc.select_dtypes(include='number').dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LtmF6np5DOOe"
   },
   "source": [
    "### Challenge: create a histogram of the `Review Date` column, with 10 bins, and label both axes\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Company \\n(Maker-if known)', 'Specific Bean Origin\\nor Bar Name',\n",
       "       'REF', 'Review\\nDate', 'Cocoa\\nPercent', 'Company\\nLocation', 'Rating',\n",
       "       'Bean\\nType', 'Broad Bean\\nOrigin'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choc.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kTjiIv9sDOOf",
    "outputId": "5c261790-d7de-432e-dcb8-35fbe018b450"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Number of Reviews')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "choc['Review\\nDate'].plot(kind='hist', bins=10);\n",
    "plt.xlabel('Review Date');\n",
    "plt.ylabel('Number of Reviews')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1HkkekvDDOOj"
   },
   "source": [
    "<a id=\"scatter-plots\"></a>\n",
    "## Scatter plots: Show the relationship between two numerical variables\n",
    "---\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scatter plots are very good at showing the **interaction between two numeric variables** (especially when they're continuous)!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>0.93</td>\n",
       "      <td>118220.22</td>\n",
       "      <td>794.70</td>\n",
       "      <td>109149.67</td>\n",
       "      <td>130.50</td>\n",
       "      <td>8145.35</td>\n",
       "      <td>8042.21</td>\n",
       "      <td>103.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046       4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74   54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28   44638.81   \n",
       "2           2  2015-12-13          0.93     118220.22   794.70  109149.67   \n",
       "\n",
       "     4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  \\\n",
       "0   48.16     8696.87     8603.62       93.25          0.0  conventional   \n",
       "1   58.33     9505.56     9408.07       97.49          0.0  conventional   \n",
       "2  130.50     8145.35     8042.21      103.14          0.0  conventional   \n",
       "\n",
       "   year  region  \n",
       "0  2015  Albany  \n",
       "1  2015  Albany  \n",
       "2  2015  Albany  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avo.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42cb84d748>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BWdqx7M2bjW26EEIIITa9toIz3/ffAfwWSebrp4CfB/qAXwe+r41LvAH4J8Bx3/efqN72y8AhAK317wBfJimj8TxJKY2OZe06ywKKCIfTMwpPQY8HKdcSxYuzYz1eMpQ5WnDYmzfX7ZCmEEIIIdrXbubsQ8D/qrV+wvf9n6je9iRwdzsP1lo/TPM5ZfXnWODn2mzPBqr/NhxcG9PjWTKuZShrrsmO9XhWhjKFEEII0bZ2g7NdJMEYLEzStzSZsL/91YIvBwfDYNZw62BI2gWvRfhZjJRk0IQQQgixrOXqnNU8RjIsWe+dwCOdbc5W4AAKhaHHtTgOnJlOsbPHYEkCsEa1VZtfOpfnUyd7KUZSrFYIIYQQzbWbOfvnwF/6vv9Pgbzv+18BbgW+v2st2+QsilKscEJwFHznUobX7q40nfQ/WnAoRopKDOORw7lplzuHow1otRBCCCE2u7aCM631M77v3w68FfgSSbHYL1V3DbgOqer/Db2eoS9j8ZRlfz5sevZgxvL0RIpLRRcFDGYNRwau35pnQgghhFha26U0tNZFQHexLVtIElTtyMbszUc8O50miBQvz/Xz/EyJ/+PuxYHXVEWxPx9Rrg5nTpZdqXkmhBBCiKbaLaXxEM0n/1dIisd+Xmv9551s2OaWBFnFyCG2ijBSRFZRDBWPjGY4e7jEsbphy715w2DGMBM4GAsXCy4ZF85Mu7JAQAghhBCLtLsg4OvAYeBvgT+o/nsD8F3gMvBJ3/d/sQvt29TKkeKFWY+KVRgUEZa5wGEmUJyZducn/vd4lrfcUOLenRXeuL/Mkf6Qjz3VxxfPyAIBIYQQQizW7rDm9wNv0Vqfqt3g+/5ngE9prV/r+/7nSfbd/C9daOOmFQFTgctC7TOHUmz40tkcu/Ms2k/zxoGYfXlDIVI8MZ5GKZgoW24dDGWIUwghhBDz2s2c3Q6cbbjtReA2AK31IyS10K4zte5bKP2WcmAucqjEUIgWSmvUdgt41a4ydw0H5D1LOU5WcC61rVMxWpyBazwWQgghxPbTbubsG8B/933/V0nmmB0A/i3wMIDv+3cBm3AfzPWS7LcJEFmYKLtMBx5pxzKYWZhP1uNZXrMn5NREhpsHQ4IYfuau5qs2a7XRApPsy+nfWkCfzs8f1zJyQgghhNhe2g3OHiDZW/Mk4JKM6H0eeHf1/gD4yU43bmtJslnWGnrcmMMDJpmTNu0yVVEMZixTFcXevFlyv836XQRGC04SiLmWl2ccPn0yR2AUw7lkI3UZChVCCCG2p3brnE0A7/R93wF2Ale11qbu/me71L5N7trMVYjD05MZ5qIIT8GHn+jn6I6IF2Y8bhsKyXtJFqxRs0yZwvKt0TQvz3pkPYur4E37yvSn7ZJDoUIIIYTY2tquc1aVB3qAw77vA6C1bpyLdh1ZGM6sv00BYQyOp5gouzw55oC1FCOYrjh87Kk+ejxLEMO77ihQiZOVn4FR5FNJZmyqonjzwRKnJlNkveS6WTfmUF/I224qy5CmEEIIsU21W+fsKPAZ4G6SaKQ+KnG707StwJB0hSFZHJB0jedYjvSHjJbSeMoyEyisVXznUpZeL8l49aaTBQEf+EaG2wYDduQMWMuFOYehjJnPjB3uD3lmIoVF4SiHPfmIs9MuNw7EEqAJIYQQ21C7mbPfAv4G+F7gHEnNs/8E/F13mrVVLARkNTlifvGVk7x6T8zHj/dSCBXnZlLcMpgEWa4DY2WHyYrCUVCOoRBm2ZmLuaE/wFMLl+vxLD98uMRU2SUyMFp0+cTTA2Qcyz27At57pywKEEIIIbabdktp3A38G631FKC01tPAvwZ+rWst2zLqoikUIYqRHOQ8y9tuLPJjNxV53d4K/WlL2rW4DhzojelNGRQGrMIC0xVFMXAIYiiFCyU49uYNKMOlgkMlUmQci+MoJsvO/DlCCCGE2D7azZyVgRQQAmO+7x8CJoHhbjVsa1nInkUozk25PHo5Oz+5/8dvKvL8lMuP3ljkj5/rwVVwfDzFdMXFYCmFiv4ew+NXM2Q8SDmWn769QDFSfOLpPN8azRGZZLg040WksQxljSwKEEIIIbahdoOzhwAf+D3gT4D/SbKv5te606ytRi36+nNn8tyzK8RRUIoUnz6VJ5+Cp8aSbZwuzCmeuJom7YGKLTk3yaRNBzCUifEceH7K5WAfnJ7wqBiFg8IYOLqjzLHhmP15KaMhNk4xUpybdrEg8x+FEKLD2i2l4dcd/jJwAugDPt2NRm09tQUBAJYIh6fG0qRcMMZybDgk7Vq+cynNS3MeeS/Gcy1esoiTUuxQiCA04CjIupa7RpKN02dDB5IBUDxlma64fPFMFhTcMxLw3mMy70ysr2Kk+MSJXp4YS4NF5j8KIUSHLRuc+b7vAl8l2VuzUq1v9gddb9mWsrAwwMUyW1HcvTNkJGcwVhFbuDjncLXk4ijFmHE40h8RxTGnp1OkHIunLPeOVLh7Z4UfPlJhOGv47uUUBkXOMRgDdw5XSLsKx1FgLZMVR4rRinU3WnCYrDg4QKwUL824nJt2uXM42uimCSHEtrDsjHKtdQwcaefc61tSXSTjQk/KkHUNGRcG0oZ33VHg5oGA4azBWktgFN+7v8hdIxXuHanQm7KMV1wuzHk8N53ihRmX83Muv/9MnitFDwOkPcurd5ZRyhLFFgOLSm4IsV725g1DGUNk4WrRYazs8tXzOdnzVQghOqTdOWf/Dvht3/c/SLK35vz4Rf1OAUJRiS2eSuqYfd/BEnvyBn06TyFSOMpSjhQp1/IHz/Zzy2DEC7MeI9mYmYqDVfCd0SynJ9Io4HLRJbTgWYV1LF95uZeMZznUH/G2I0XuGI5kKEmsux7P8t5jczx0Ic3DF3Ps640JYtlSTAghOqXdbNgngHcBZ0n20QxJ9tcMu9SuLcuSbH5+ciINwFQlqfw/mLEc7I3Y3xdz+46QyCbB2k0DETf1Bygso3MuhchlsuJyqehStkmZjghLxrFkXEtkkoK2g1kjgZnYMD2e5U37A/blk8As7VgGM5Yz0y7FSFGM1PzXy1nJuUIIcT1oN3N2pKut2PIWFgQYHK6UFOXYUI4VRwZi0k6yJVNvKrnNGkg7FmMVZ6Y9Cj1JUJdLWSqxITagFCirsFgckkButODiOJBxXQYzEpiJjdXjWR44OsdowWEwY9Gn8wRGobCgwNokaHvg6LWLBYqRuuZxS50rhBDXm3ZXa74IUN34fLfWerSrrdpyFicgTTWYqg3+/oP9JaYqik+e7KcYKZ6L4FdeNU0hUkCWigEs9KYMaQW37wjpS1u+cSFLOXbo8QxKKbKepT9juGUwZKqiGM6u+zcqxCI9nuWmgZgz0+783rAX5pK/h/29hkJ47XBnMVJ86mQvgVEUQpK5mRnb9FwhhLgetbu35iDJFk5vJxnKzPu+/6PAa7TW/2cX27dFWcqh4qvnc3xz1GJRjBaSoZ4rJZcoVvzG3w/wH14/xXNTKUaLLtZCbKAvbbhY9PilO2aYC+DxK1mKkUM5hrxnmK0oChHXLASoZSL25mW4U6y/vXkznyEeyhhQUAiTbFjj7+powZkP5EKjqMRLnyuEENejdoc1f4dkR4AbgJPV274F/D/AssGZ7/ufBN4KXNFaH2ty/33An5Ls2wnwea31h9ps2yaUzDN7etxjfz5mMGvpS1lKEUSxwnPAU/AXL2TY3RNTCBUzFUUUO8yFLoUIPn4iz46MZaTHcKGgMBZmQoesUbw8m6IUqfkgrD4TIUNDYiPUD3HWAqylPizUB3J5z/KeowWmKko+WAghRFW7wdn9wD6tdej7vgXQWl/1fX9Xm4//PeCjtC5a+5DW+q1tXm/Ti3B4bjrN1ZJhOGfIupZfedU0v/H3A2BhrORwpehyYc7lctEltorQJhm3rGPZmTWopJwZxiZ11CyK3fkIi+L4mMd9BwJgcSZChobERqkNcdYs9TvYGMj1eFaG6IUQok67qzWngZH6G6r7a7Y190xr/Q1gYmVN2/pcACw7MhE3DYTsyBn+w+unCI1CKcWT41l63JieVJJJAAcLpFxLZOGHj5T4wRtmyToxaWUBQzFQeGphBwFYyERMV5I5PLJYQGx2tUBOMmVCCHGtdjNnnwA+5/v+rwCO7/uvB/4jyXBnp7ze9/0ngYvAL2itn252ku/77wPeB6C1ZmRkpNlpm4dyOF/IMh1ZXiynUQoGcy6qAqXI4UolQxBDMXZQQFop+jIO2UyWhy7nePg8hFTvcyy7ex3u3qM4sHsH+dTC07y/H37z2w4DWcWXzmd5/yvNovs3mud5m/9ntclJHzZXCOH8DBzoZ9nfeenDtZM+XDvpw87Yzv3YbnD2n4Ey8DEgBXwS+DjwkQ6143HgBq31nO/7PwR8Ebil2Yla6weBB6uHdmxsrENNaGYtP3TLcDZiouwSRpZLBcVvPQa9KQsqJO8pXJKVal462SA951kcBQfzFTxj+O7FNJfnPAzJ7Wll2ZcvEVbgxEuFRcNGT497zJby5HoMU3Pqmvs32sjICN39WW1/0ofXWul8S+nDtZM+XDvpw87Yiv24b9++ts5rt5SGBf5r9b+O01rP1H39Zd/3f8v3/RGt9dbq9UUsldghRmGMxZKsxgyN4o17C+RTcKAv5g9O9YGnSCnDob6Q3lSyTdOz00lGzVVJYGZMMpx5bjrFK3YGi1a1FSPF117O8eKsx4szyUbUsupNtLJZVveutR0y31IIsR21W0rjSZLNzj+rtT7f6Ub4vr8HuKy1tr7vv4ZkLtx4p5+nu2rB0MIm6CnXkoogm4qpBEn1/6kKfHO0B8+Bg70he3oiHh7NglWcnkrT41iem04zko25eSAg7aaYLilSWbhjKKQYKr5nb3nRG9lowcGiePXugCtFh/sPlGQuz3VmJUHOZlnd24l21K/8lFIcQojtot1hzX8L/CTwQd/3HwP+EPhjrXVbk/x93/8scB8w4vv+eeCDJMOjaK1/h6R+2s/6vh8BJeCd1WzdFuIAtU/sSdNnA0VP2vDWGwpcLmeYLCsuFjyMVYyVHIqhYq4CprpNU2wVM7FlyDXMBYpXHKmQSylmexRPT6Z5/GoGV8HfXow5WrevZu0NKogVu3KGI5I5uK6sNMjZLNmmTrSj2cpPIYTY6tod1vwC8AXf9/uAHycJ1D7s+/5XtdY/2sbjf3KZ+z9KUmpji3MW/duXCsl6sL/PgopJOQ4XCjAbQCWGvAfl2MVUy2TUgrrpwMGm4anxLHnP4jiKlLK4LniO5dmJFKcmPO7YEc2/Kckb1PVrpUHOZsk2daodjSU8mqllFnMDq3oKIYRYV+1mzgDQWs/6vv+HwBRJ5uuHutKqLau2cXMSahVDh8DAqYkUP35zif/r7wa5WlDEOGRdy0TZpWzB1g2FKgx9nuHVuwIyLjx6Jc1sxaEUKxSKyMIlZfnIk/3cPRxgUQQx/MxdczLX5jq10iBns2Sb1qsd9ZnFh686vOOwkg8wQohNrd05Zwp4M/BTwI8BLwKfBd7dtZZtYQ6WWwYqjJVSZD3LE1czeCriQsElqmbVSrEhXd3WPJEEdhbFbOTw3atpco4hiKEcK9IOVGJDRinyaZgLHM7PupRil1Ks+PjxXj5w7yw9nt00k73F+lhNkNNOtmk9rEc76jOLlVgWDQghNr92M2cXgTngj4A3aK1PwfxG6KKBweHF6TRGKaaCZLumr17onQ/MaueUq9myxRQGxWTFYRJDrwuRVZg4uS+0lshahjOGvrRhYs4j61oy7sJ2OZ94upfJssNQ1vDeO2Urp+vBegQ5WzXor88sDmZk0YAQYvNrNzj7R1rr79QOfN+/C3gX8NNAe0U7rjMBqjaFDKylHDkkKzrr49nGwKyRg+tEZK2DseB5loyyHOmvcMtgxPceDPnzc+CqZA6bsYo/fCbLQ+czeC5kHMvZAy7HhqNlnkeI1pZadLAVArb6zOKxQ2lK05uznUIIUdPugoDv+L6/k2RY8wHgbuBh4F90sW1bmK0W1kgCsopVOLFlJBMxVvGoL7ex3HVAkU8bpisOldBhDhgfy/P4mOUrLxt+/Xsm+cLZHuYCxQe+sYNKpCjEirSy9KYslWi55xBiec0WHezNm01RkqOJ5/vuAAAgAElEQVQdtcxiPpUsB++EYqQ4O+2igD15I5u3CyE6pmVw5vt+CvhRkrllbwGeJ5lrdgPwDq31lW43cOtJ9sB0cBYFaKCIjMNIKmYytMTVnTeXu1ZvyjBadDENAZ1BUQgU//OFNC9Me8yFDrOBQ9q1KJId03tThkz1jWIrZDjE5tVs0cFmKcnRbc3+doqR4hNP9/LElXSSD1dwdEdI3tvcQaoQYmtYLnN2mSSy+D3gg1rrxwF83/9nXW7XFpZsYL54VouDweAC+/tDSpNZCma5zJlh0DOMlWqBGdXz7fy/sYXLRQ8DZLxkyDSMkllrrgMZL3kT3SxFR8XW1WzRwXqU5NjoDxVL/e2MFhwmyw4oRSFI/iKNhcBs3yBVCLF+lpvQ/xQwCLwWeLXv+0Pdb9JWVP+mUR9Ambr/HCZDh7GSRz5dC7eWerOxOCiCWOEqW3de/b+WHg+ullPs7QkJomSOmwVcFDt7DI5KhqPqMxy1Nw8hVqo2NFgLkmoB21uPFLoS8NcCoy+dy/Opk70UOzREX4wUZ6bdtq631N/O3nyyIOdy0aEQORSjZF6o7FIghOiElu/SWuv7gJuAvwR+Abjk+/6fA3mqFf4FLARkNfXFaJNAza0GXKVQcVN/QMpplTlLrpdLGTKuJTV/7eR8D4tXrW82GyhetTtgIGvIpiDrWAyW2CRDLZVIUYoUiiTDoZRlquxwYtzr2JuduH7VrxJtN+Bp10o+VLQbcBVCVhTwLZUd7PEsbzlU4pU7K7xxb5k37qvwuj3lDc9KryTwFEJsXssuCNBavwj8GvBrvu+/kWSVpgGe9H3/k1rrX+xyG7cI1fBvwkFhSDZ2crDEOLxcSOPYWkas/vyFIUuDw3gAOSxpxxIbMz/vLKo+JrIOkxU4MZ6iEimCSBFa8FDEFg73BXz5xR56PEvKtbxhT4m/vZDlY8f7wSYbpEupDbFW3Ro2b3fYdCXPf36GFc2Ta1VD7shAzN68qT6v4TV7whV9350espXpC0JsHyvdIeBh4GHf9/85STHad3WlVduKwgN6U4aUa8l7lrlIkXENpagxE9AY4DnEGDJuDMoljA0RChdDWF1QEBrFSzMpDvXF7MoZnpvyyHmWIIZvX8yQTcFg1nIgHzFeVsyFTpLXcxSTZUfmx4g1KUaKRy6lKERJDbFOLgxot7juShYmHOhnRfPkWgVQPZ7Fv7XA8TGPu0aiFQdmnQ6kGvvh7LRLrjo3UII0IbaWFQVnNVrrMsmqzc92tjnbjSXlWKxVhFaRdw2TFQcLBLHCwWKaDm0ubOWUdS0ZDyoVi6MUjrWEi4rZwljJYX9fzHDOcqloiKxirOwSWAciw0QFSpFiMGvoTZlkFlxsQVkGM/KiLVanFmAUI8WzkyluG0pWK3ZyzlU7xXVbZdgag6t8imUDvtpjBjMWfTq/ZABVjNT8/acmVhZgtQooV5tRq+8HpSxfezmHRbJoQmxFqwrORPtSjiWIFbGBMITYQhAnk4d73YhizKKdAxKW3ZkAq1yynmWi7FAyzvx9i4M6RcUowlgRKMNP31bgM6dz1bWhyf3WWG4ciJgNHN56pMh9+8v8jxd7yHugT+ebvulI2Q2xnFqAMZCx3D4U8spd5RUP7XXCUhm2Ztmp2vm1UiCNv+P1jymEkHFhoJoRPDed/D3WHrOWUiJLBZRryajV90M5Uvz1yz3bvsyJENuVBGcdVdsBoDZ3TBFXs2ZpLJNB8uJubDIHrRC7JFXJbHWr9AWRccinLbMVRTlKruVUz8t7MbPV22o11C6XXCLjcXIyTTGA2Kr5ZQqea/nu5TS7ewxfP5/llsGQMFb05AxBfO2ndpm3cv1ZTUBeH2D0eHZDArOaZhm2ZsHTIVr/jtc/JjSKSkySicLy1fM5rE0e499aoBwlGarVlBJpHBKFZEFFOVIdqR2XBJTJtIf6bKZ88BJia5DgrKNq4dDCRP/YJNP7K7Z6HFGtgaaIoRqYNWbOHMZDGA+Tr2srQU31+qXIJe8aduVCdmUtBeMyGySlN4qRQ2SqQVn1mfpShkLkEht4cizDo5czzIUOL8/FvGJXsOhN5XopLCoWrDYgX82G6+tpqexUq9/x+sfkPct7jhaYqiQrnr/6cg9p13JxzuWjT/YymEn+fu8/WOTGuhIj7agfEn1q3FbL4CTBXm1ldbsBX+MwbKE6zHzTQEQQJ99Dbast+eAlxNYgwVlHXbtiM7SLb1s8x0wtWels6T04FQbDUNYwnLWkUnBzNmSirDgxkaYQKuLqVS0KRyVfp1xLIYIgUoz0WHZ6McPZmPsPlBa9QK9HYVGxuawlIF+PDddXohglw48WuHEgbho8tvodbxZwDmeT637tvOXRS2kqRpFyLK/dExDEipxnVxzk1Pf5hbnkb31/r6EQKu4/WLxmIv9SGa/GYdi0mxTDLceKuVAxlLFMVRTD2cXPOVVRPHQhxf5es2RgKVk2ITaOBGcdZfHmS12o6i3L7QSw1H1LldlIlgpMVxwKUZqRbEzaMZybSTEXOPNlNiApYDucjVFARhkmyi5ZD16ehYN9EYf6Yo40vLFu9myI6LztEpAXI8UnTvTyxFh6UamYxuBxud/xZgFnj2e5/0CJybLDUNbwxJU0V4oOu3JmVf1V3+dD2STVXev/xmCp3WHYqDoM6yqYqjiAx+XiwqKf2nNOVRQnJ1KcGE/h0LykjmTZhNhYEpx1lCKq/psw87d34to1PW7Enh5DJXapVIcw5oLF5zgYXAX7eiOmKi7j5WRLqZFsTGgU33ewyNtvKQPJXJf6N6nNlg0R3bVdAvLRgsNkpb1SMcv9jjfLGh0ZSMrVBEZxbCTgzh0VXrV7Yb5Yq75rvF5jn9fa3+wa7Q7D9niWdx9N5rHZpAtwFPOZs9pzPnIpxUxFcbHggWreTzK9QYiNJcFZxy0egswSk4RAK9kyydb9d+1KztC4nJlxm5ThWFzENjSGtLKMl12KoUNk4eKcw+GBmB8+UgGQT8cC2B4B+d68YShjeHEWMJah7OqyWktljWrBzdlpl6+9nOOZySzPTi3MF1vqb2i87PDx472kXRZtjN7Y50v1/3LDsPULC4azSTHcUxOZ+fY3nv+aPSFPjWU4X1i6n7ZLNlWIrUqCs65SVFCsLDBLHndtti0J1lwUjgMmvjZoqy9eC+AoxXToknEtcwHzqz135yJemHEZKymKkZovFSCfjsVW1uNZ3ntsbtGcs9V82GiVNerxLDmvumq6yXyxxr+hYqT4+PFenp1Kk3MtNw+G8+e0O6erVWZzqVpryw3bLtdP2yWbKsRWJcFZl1lcrp0/thpJUdqMayldE5jR9PoGy85szFyQFL41OHiO4ZmpNL/xWIqsZ3EU3DoYYawUpBVbX49nuXM4WtM1lssatZov1nju2WmXUuSQdiylONkPd2/etD2nqz6Aa/bBaalAcrlMaDv9tB2yqUJsVRKcrbuVBmoL51sclInocS2V2K1O/l/6epGFO4Yq5FIwXXGYiRSF2CEoK/rShh4vZk9PxFQFRnLNC9IKsR0tty3TcpmnduaLFSPF117OcaHgEhrYmYu4b39p/vzl5nS1E8Bt1PBjffmOqYraltk1Wa0qNpIEZ+smKRa78gxafQCmKFgXFdcqny2/EvTrF3IUYpd82hAZF4PBU5ZSqLiKw1DaEuFQDJNrdWIbGSE2k/GyMz8na4TFQY/C8j17yygFWddypJp1qs8aLfd3sFSGabTgYFHcszPgWxczXJzz+MTJfu68HHDfvvKyBWzbCeDWsr/nahVC5rfteqa6bVfasbz5YGnVQ8k1m+U1R1ario0mwdmWUNtrMylY62CI54O25ufWzEYuGReiGFKepRg6BKa6jrTi8NhYbn6XgsP9Ee85uvTWN/LiJLaa8bLDL31zaP73+MEfWQh60q7lO5fSfGs0w0zosDMX84qdAe89NjdftLU2+b/2N/LmgyX25s18sdcghp+5a47h7LXBVS2rdaXkoBTkPMDCY5cyzAUOQxnTsoBtO1mxtezvuRrjZYdvnofpwMFRlsAoAgMnJ9JMVpLSIqttw2Z6zZHVqmKjrXSmuliV1SwKqElmraWISVVnjhmcavnaZlmzxatFrxQcPMeyJx8zkg1RyhKTfKIvAYFRuICnoN+LmaokiwQeuZSiECUvToFJXpyE6IRipDgz7VKM1jYPs53rfPeyx2ygyLqWcqz489MwmLFJ0FR0CCKFcgAUkVFMVpKyErVA4XPP53liLI1Slsevpvm9U3187Kk+pgPF81Mpnp1K8/HjvYvaUGsXJJusv/3mAq/aXQFrqRhF2rPs6jFYli5gW8sg/ciNRe4eKePfWmh6Xq2tlXjhMa36ZS19Xwt0v/CsxzdHM4TVIKoUKrCwq8cseq1Y6XPVB0Qb/Zojq1XFRpPMWce0M5esdn875y4Mg8bArmyMUorhXMiZ6TSOgtgoSqb1UGmI4uVpF8dxqRhFtGjHApVsLVW9xlSYZNlqQxbPVocs6vfmE2ItVpMdaTa/Ca4tAwOL534VI8XTExlmQ4eZMPl4dHZacWEij39rgUsFh794KcfxsTRg8RzLUMYwmLE8cilFMVLs6jG8OAPnplyullwcpRgrOYzkIkpxEvRlXOYzK83KZhwZiHnLoRL/cH8Za+HvRrMEcets2KdO9s5vw3TzQMjfjTbP0A1mLM9Mpub7ofb326x/15qZOj7mERjFYA6i2HBjf8D775rlUsHhq+dzi76n1TxXs4Boo4Y5ZbWq2GjrEpz5vv9J4K3AFa31sSb3K+AjwA8BReDdWuvH16NtnbOwCXnz+mQ1psV9jddbCKTKxmFXT8w7byvyu0+7hHHyPOMll6JxWDw3bfF1SlbhxfXXTaSr5TmyKUPWhcGM4cR48gI8kLHcPhTyyl3lDd3QWmwvKx0uagxUbh8KSbmW2waDRWVgzk27fONCblEwMFpwsFbx2t0VLsw5DGQscZzhYsHhUsHhzuGIIwNJSYlyNdDaUzdkWXu+e3YFHO6rMBm4WAtBrPjhG4p861KOjJu8kdcCicayGSfHPf7ypYXzHjg6x9Hh1m/6tT6qbcN0ciKFQfF/P97PL7xiZj5AK0aK42MeNw+EqGrB2een3CX7t1nf782btgOQu0Yi0k5SlifrWr7/hgrDWcNw1nBkYPH3dGZ66XYspdlCi40c5pTVqmIjrVfm7PeAjwKfXuL+HwRuqf73WuC3q/9uIbbu306l45Ngz8GScS2F0AGreMXOkNjClZLLlaKLQ1I2w8HOb6p+beuS2kwephqSKRzHUjYuQWiYCRXlSLE7l6Yvs1BxXAIz0UkrHS6qD1QCo6jEiqfHU1wtOlwqetw+lPx+WqAQVQOa6rD8zYPxfFbJwXKpCCcnFdZkUMryL/JJJqq+pEQtqBjMWG4bCjk2XGZ/b1Kk9bmpgMevZki7luMTWd51R4Hnp9z5ifhnppPMc9ZNymYUIvgfL+Q4O5Mm61puratx1upNv9ZHoVFYCxGK2SDZjunjx3v5wL2zQBK4zASKE+Mp7hyO6EsZ7hqJODXRvH8b+34wY1cU/AxnDb/+hkleqOzgcGZyURavMZBZ7bBg/XVWE+AJsV2sS3Cmtf6G7/uHW5zyNuDTWmsLfNv3/UHf9/dqrUfXo32dUT9kuVCp/9r7Ww1n2obzkmzcwXzAWDkNwP97oo9X76ngojg/6xJaVd0pwNLjGkqxIsZpuE7y5pV1LIGBFFDGUjYOC8GkJYjgxESGX7h3msFq1XAJzLavjRgyWulwUX2gknYspUiBgn29hoHMQma3VM10lWPFVMUhMvAXLypu6ItIu5bZQDEXOuDARBFOT6bnA536NjQGFc9OZXhmMvn6H+4vMxc67OpJCs5++lSefIr5ifi17+fWwZBKDG85VOJvLuTI1YK1EEpRMqez3aKz77y1wMee6uP8nEfes6SrQ6iQ/PzOzqRAKaYq8L8fK5DzLG/aX0LB/MrT+uvWr+ycqqgVBz/DWcNtB2BsrHWw1YlhQZn3Ja5nm2XO2X7g5brj89XbrgnOfN9/H/A+AK01IyMj69LA9jTO/1Ls6rEUAkshaic4a3afw4uFDMlG5skqqXv2J6c+NOrNP0ahqMRudRXnAk+BsTCUVcQW4lDRm7KUK43tUYS4zAQO084AbzwE+dTKvvtWPM/bZD+rraeTfVgI4VOPOVRiRca1vP+VZs0/70II52fgQP/yvzuHVnDdfzmcXHdHDi7MevzF8w6GHgYylpv3pBmu3n/PXpfxEpyZUJyZ68FauFS2vHov7Oi1hAa+eV4RGYeBrMNA3qWUSnNoePH38CNHmf+z+PLzLr1pmAtgz0iGw7NJnznWMpBVDOWS+0qpDAf64UfuBCzcMgyQ5sSsQy6rKASWvkyWvxvL8dhke/1d66P/uAt+89sOGU/Rm3Y5dij5oPanLzlEOAzkLAeHXEpeis+fdRgvKoZ7LK+6cfFzFEL40vNJ+0/NWm4bNqTTDsZNMoXHDqXb+h3wPI/cwEhbP+uV/JzrFUIYn4H3vw4mSrXnyazyapuPvB52xnbux80SnDWLSpp+1NJaPwg8WDtnbGysa42C1fzQLQNuxEzsYnEYKzZ+c/XZsXYL0ibnxNYSRAZVmebrF3oYL7rzw5gWCBc9xqBQeFjKKMbLCxmySuXafTsVhpFsknn769MRz47GHZ3jMTIyQnd/VttfJ/vwzLTL1FyefMoyVVKceKmwpiGjYqT4xIleJitJiYhaOYpOGQYowgEXfuqmhRIXnzuRZFX8Wws4UZ68UhiTwlhF3rMc6A3Z4YZ8/w3JXrKTlRGOx4ZKZImDgFw4x9hY8wnz/q0F4kqeq6XkeIed4x2HmV+coE/nuTqd3EepwIefzF8zRPiOw0lGqhQpvvpyz6r6WwHvP7qQ5SxNJ+194BaHj5eTxQdOZLk8UeZbL/bjOIrTVy2vH57mWMOQ7dRcHqXgKy9m+dsXIJ+K+Wd3zXDHcERp2lKqe96lMqu5gRE+/FBlRbsbrOR3odligsa2bXXyetgZW7Ef9+3b19Z5myU4Ow8crDs+AFzcoLas2VDWEJZdXGWoRBA0PasWmLW7QCDZG3Mga/ji2TxXCk51h4DFmbrFX1vK1mly37X7cvZ7hsgkc9FGix6uk2w9c6xhi5dipNa8d6HYeJ0eMjo37fLEWBoHeHE2OV7tNkrtFH2t39+yECqmKmp+GO0nboVPn8rjKDgznSLngT7t8ab9JUZyin+wv8KVosObD5YWXb9+wvxUJZls799aWFQBv1YWItcwbHdq3OPMtMuh/ni+DERtG6W9ecO5afeaorPNvs+lvvfadervG84aPnDv7Pxt56bd6p+9BXXtx77az/zMjIcFBjJJeZHZ8Nph1qVWWxYjxWMvJfP7Blvsybvcas1WP2OpMSbE5gnO/gz4ed/3/4hkIcD01ppvVs+yMxfjuYrzcynCptmxWkDWbmBmyBEzmIN8yvLEWIbANHvc4jlrtmny8dpMncKigErs4LmWM1MehRA+/3wPe/Nzi1aHfeJEL0+MpcHCPbsC3nunFKfdijpdKsDW/uck4+irvVqrN/X6N/RmwWX9ZPIP3DvLI5dSpBzmV3QqIONapmLFrpzhxoY3/No1pyrJ/DUFiwq7NmtbrXzGbx3vZ7Tg8swkfO+B0nyw27gbQa3oLCysRFTKcv+B0vxK0dq59RX3m2X1LhWcRR+SjgzE3LkjYLTgsjcfc6TJbgIPHJ3j1ITHx57qp1wtfXHXyLVB9FIrOz91shfjucuW2WkVYC0XuMlcMyHWr5TGZ4H7gBHf988DHySZl47W+neAL5OU0XiepJTGe9ajXd3h8FIhxf922xwPX8zx/KTLeLh4UsaudMRM6FK2bt2trbd3SnsOhQhKkSUyzYI9mjy2vcCvx7H0ZSyXi2CrTZgJHJ6bXjxperTgMFlxkqs6ismyI59qt7BOlgq4cSDmnl0Bk2WHoey1gU+7lnpTb/aG/sDROc5Ou03/YmorjU9NZObf5I8MxLzqRsOJlwot98x85FISmA00ZIaWatvxMY/IKg72xUxXFHcOB/PXbnxMrehsbSVi2rU8einNZNkh5SQT/vMpy6OXF1fcb8zqfezJXl6YTS36kASQcqEvbUm5jT2y8D2+clfIb7xxcn5hQKvdDeoDpFobduZYtsxOqwBrucyY1BgTYv1Wa/7kMvdb4OfWoy3rIYwUh/tjrpYisp7lby+61OZ7KSwzoUvFLn5LScpgGkJUdaOYBbXjUgTWqmoxjMXz1lLVawer2Lszlzb0pizTriXrWVKOrW5Xw6ICm3vzhqGM4cVZwFiGqis6hejxLO+9c+1vqEu9qS+VyXmoVtvswrUZmGZv8vkULQPSZkFdrQ1Lta1W/6scK/rSllfvXpj9udRj5rd2Kjqgkur6xTCp9J+UzKlW3I8XvtfadYIYIqOS3LtSvDTrcXLcYy5UBLFif69Zto7ZcNZw34HmEy6W6rtaG+aChX5a6ufcKsBqJzMmNcbE9W6zDGtuKxOhQzlKtm45N+0SxpZvX84m2zA5EBqnOpC48IIVVLNmnrIMZWLuHgl4YixNxjGMFj2KkSIiKYeRZM4Wh28hlh2ZmIlrVmEuR5FzDHvzEbvzIS7J8voLBY87dwTzL8pQfQM+NidzzkRTnXhDXepNvVUmJ+0mQU6zOZKradNSbVjq9lr9r2aZqOWuVVvYEMTJvK93Hy00rbhff53BjOUzz+R5aQ6ulhwiY/nt4/3cMhhxZtqbH25caR2zxnlgzfruTftLDA5k2MHy0xmW6vtuZMY2y4bpQnSKBGdd4fBf/76ff/v6aRTQn4HbhmLKEUyWFUVT6/bagoDkMQBpJ8lclSLFzpzhcF/E7EWXKIqJcYlN8jgPW10QsPCcSWC20syZIesZzs543Lkj4uyMx4F8xCt2VnjLDaVrArDa3BbZa1N0S7M39aUyOUolw4Io+NrLOW4c6MwcyFaBRbPbW2WiWl3r2HDEjQ3V9ZtV3G+8znuPzfHQhRRfP5/DKrgwl8JRi3f1WMnE+uXm+tVvAD846fCOw0v3XTuBUiczY5tpw3SxdW22AF+Csy65WvL4/073srMnpjdtwCbzSTKugnChSK0HxHXDmKVYsdez/PjNBb57OUspSuqblZSLsg4p12BjQzQ/n6x+UUFjYNa4GME2nJfc/9xMBhdLX9pQihTTgaIn1XxTZnkhFBul8Q29x0sm0k+WnUVDgFttOGypYHS54ddX7Y74k+e9+cK71kJPamG4canhw2ZvQsvN9btScnhx1uPVuwMqLfp5I14fZHWnWKvN+L4mwVmXxJDM3bKK+/aXOLYjYDZQfGs0y0TFIazOOdvTkyy9n644VCwMeDE70jED6WQI8ZFLKS4UXE5PpvEcQ8oxKGsYLadIgqzaqs/FWbNkK6fGemrN995M2qs4PZEmtHCl6PLspOUf31xc9EIO8Mil1DXL6FeyP58QnXRkIGZXzrTcSHy7mqoobh9KtnIzVvHaPYsn6DfLNi71JrTcXL/aBvBXig6Hc0v381oCpdVmLmR1p1irzRjgS3DWFZYYxTNTaV65q8zXz2eZqji8NOsxVXEI7cKQ4FwA9x+a5QtnBwCHqUhxetrhL17K8bN3zfGaPSGPXYk4O53CVD8Z70jH1eCs3sI8NE9ZIlsL2mrhW33w1vyFz3MhihShgTBW/Lcne7l5IE4WBygLFkKjFi2jX+m8Fth86WOxdV3PK/tq32/yt2eaTtBvzMAt9Sa03Fy/IFbcsyvg/gMlXnVjar4QbrM2rSZQWkvm4nr+HRCdsRkDfAnOukLhYkk7Mbf0h3zxXJ5SqBgtuuS8+r03FVNRij89O1CX5VKkXMtc4My/2PzADSVet6fMbOhwbDjid5/Ok3UsZdOYDav+a2svTkkQuPBrthCcLXy18EKWdQ1B7BBbB4Xl+Wqtp9t3xMmqMmB/r1n1vBbYnOljsbVdryv7VhOUtHoTaneuXz7FktX6VxsorTVzsdrfAfmgKGBzBvgSnLXQQ0iR1W04aIGjOyL29cZgk/0tY6Po8QzTQS0oSkKkxkr/1kJv2lyTlapVK/+p24pMlB1OjGcox9QFdskzRw3HSTnaxRP4PSzh/L6clsFUzOG+iJQLT17NEBkoRA7PTGaYDiLuGglIKShUq4kvN69lKY0vwmenk4B1s/xBCLGVrDQoWc2b0GqeY6WB0kZkLuSDoqi32T7kSXDWQrCG7rl//xw/f3cBgNt3BHz1pRyuY5kLHV63q8wLsx6XSt58DitDTE/aEhrF0R3Jqq/vXk5KaAxkksKTHz/eS8aFZyZTHMiHKGtxUQ3BWW0FaP1OAVA/lOlg6c8Y5ioOFkuMQyH2eGbKZXdPQMaNiYyLQjGQjhjKGN5yKFm52W55gaXUvwgrZedXgMmLoxDrY7O9CcHGZC424zwjIWokOGshXnFZigVZb2GS/N0jAY9fydCbtswFivsOFjkxnuGvXnKoGAcX2NtruHdniVLkknIVx8fSTJUdLhY89uVjPNfQ60FsITCKiYqD6yYlNUID8/PNrsmcQS1Qc7HEQF/KMBc4xDC/6jM2isDA2ZnM/LmRhamKS1/62u1x6q3kxb7+RbgcKf66uhm0vDgKcX1b76BxM84zEp2z1YesJThrYcipMGFWN6z5/7d370F21vUdx9/Pc657y+5mNyFXMIQIIaFEakFFaQW1tAqMVn+CVAdrVGbEa0cHL1Nqx3ao1zIjtSJSQ6Hgt7ZWdKhaRx1n1BbIiBISpCaQ+53sZi9n91yep388z0lONmfPXrPnnM3nNXPIPuc85zzf/Db78N3f7fvozg5CkizMhlx3/jAd6Wjibkc6ZO3CgO/sSNOSCCkG0NtSYt9ggjDM0pdP0JoMGCr4LGsrUAiiygBLM3FJlnhV2sJMQCGAMDi1pyybDBktQrRH+alJWjrOU3wAABM7SURBVLmyQH+hXNulehIH5cQ04KLuEZa1hzxxMMny9mBWNp4t34SHi9HO7ro5ishca8R5RlPV7AnImTIfhqyVnNUwEGSm/d4SHtv7E2SSJUZLnLKD+OMHUwzkfToyIX0FOJCLvg07h1L4QDGEYsnnsYMtpP2QC7oKhHhcs2KYbLxC8qkjSXJFj58fyELokySgpyVgeWuBZ46lKZxWGD2sqEow2Q1kPfYNpTheCHnmWBRbZR2/md4U5sPNUUSaVyMO8U7WfEhAzpT5MGStbd5rWNs5yHjbTkzGroEUW46m6MqEJ3YQb0mG/PpwmsM5n92DqTGfHs0fyxV90smQnmyJYghbj6b57bEUS9oCVneW6MlGy+YHix5+vDIUPM5pLXL96hxXrRhlQbIUx15+RJ9/8lsePe8TUl5SkCQgSenE+QkgV/RYkA5IeuDHxc539CfYtLWd7z3Xxqat7QwXpz/8W7456qYiIjJ5lQlIPvBUtaXCfBiyVs9ZDX4yzdTLIUWShOB55AoeB4b8E/X29g/5jJR8zmktcXgY8kFIkcQp7/WJ9hTziTapvai7QCYRbTrZk43OaU2GfHDDIB/4aZrhkgehx3kdAet6imw+FPKKZaNsPphiqJigUIJClTy8K1lidVeB1543zOGhJKu7CxDC/c908GxfmpBoxWY2ETBY8E8UO/eg6X8rERFpZvMhATlT5sOojJKzGtZ2F3jy6PTeW8THKwYM+D4jpZMJ3tK2gO5MQDYZkk2FLEqV2DvonZjE7xOyIBWyvneUK5eN8Pzx9InVjGN/+NZ0FfnUFX08sK2NNd0lwhDu39ZGOgHH83D5kjw7B1IcHvY5NELFdhrRAOdo4LN7IMlvX0jxxgtG+dGulmheXCog44WkklE8l58zQkc6qh94cVxYunKuWFcmZHt/YlI/BJojISIyc/MhATmTmnnIGpSc1ZSs2ks8tj7l+ErAwmxANnFq4fCN6wd5rj/BSMkjm4hqbn53e4Zdg2mOjiTAg85MyGvOzQP5mj98L1lU4DeHi+QDj6EipBPQlYmTrxJc2F1g1QK4almOr27p4GjOJ1dKMBJ4jAaQG03y3efb+cmeNq5YMko6EfLCaJJUMhoqXdRWYtdgmoQXJWIX9wyeclPoyoTYs22nzXuoloRpjoSIyOxp9gRExqfkrIZdA9Wa52SZpMloTRRZMqbHqzUZsi7ugSq79dISm7b6DBejBQTvveRk4jJRAeSxiVJ5o9hbLo42rS3XvnzpOUXSiZBtR5Ps6EswWEwyGnp4IQwVYPPBFG1pGCj4XLNyhEPDPlctG+G5gXTVci+rO0ts70+cNsS5tC04LQmDiSdpqldNREREyVlNPdkCtYqFTyQABosJHvxtGxvXVe8lqkxIpttFXfnb09jPKM9Rq6yRd0F3keUdRZ446LFnKEG0FtSnEIa8uGuEXYMpckWP1Z0lrlyeZ++zqXHnNVSb91AtCTt3nHMr2+HeLe0cG/XpzgRsXK9eNREROTspOavhV4eyjJ+IBUy02DXaiCNa4Vht0ny1Yb7VnSWOjvg8diDFJb1R71p5C47yooJaam0UW5m45YoeG3rzbDmS5Cd7W+ltiepnDhd9LluU5+qVuRN7mtVKGqu9XisJe9XyHB6waswKzef6Ezx5JI0P7ByIjsf2LoqIiJwNlJzVsLQ1z3NDLVVeqSyPNJ6QItA36tORDqqupKnWw9SVCbn9593kg6i8kRdGG12k/ZA7rzw2qQRtPJWbv5bniXl+yOLWgFLosbStxE0vHmJtT/GUxGmieQ1jX6+WsA0VOCURXdU5OKa14v/4HgThDDYwERERaW5KzmrYPTx2E9qTKUMCKFUd8izzWJwtsq63wB+fm6s6RFeth+mxA6koYUuG7B/0KQawrCNguOjxw50Zblg9MuPhvnJSmE6EHBr2ede6AQolJt07V0vlMG1lwrbneO3tN87vLLFhcZ5jIz7d2agSgYiIyNlIyVkNF3WNsHsow8lFACFJIOuF5EJI4NVI0AJWdxVZ1hawapxEo1oP0yW9RdJ+yEDBIx9GRct3DSTwPXjqaIbDI8lx569N1tK2AM8LefxAGjzYfDAzK3O8aq3GXLGAmnvytCZDNq7TsnARERElZzW8fFnAf+89ebyqvcjhXJJC6JHxooLjparzzkLaEwF4cN35wzUTjrFDgj3ZgDuvPMYPd2bYOZCiNRWy5XCS/kKCQ7kkB4YS7FiRYP0M5mO1JkOuWZHj2IjP4taAfGl2NpKttRqzLXX6YoVqcWlZuIiInO1U76EG34PWRLR1a9YLaU+HLMxGJcFbUtCaGq93x2O45LPzeJK7nuzgP7dPrcxRTzbghtUjdKZDwtBjRUeJlmQIYQjeqf10w8WohudUSyit6iyxuCVKzGZrd+mJdqxWqSYREZGJqeeshrZkwHDckTMSQotf4tnhNKMhjI56tCUD0gTkq+S4AT67h6AUQN9oyJLWEtteSNKVCSY1bDd2/7IHn2k7sc1EeZh0Jpu6nondpbVjtYiIyMwpOavh0eeznOxc9OkvQFuySLLkMxJEu/tf1J3nicMpqjWlhwd47BpIcDzvcfdvFrBuYeFEEjOZBK08zLdx/elJz0Sbuk7kTAwjamhSRERkZjSsWcNF3acmGQkvqk+ZCzxCfEI8WhLQnRpvSDAkICQkxPegEHiUwmjV4v6hqTV9tSHBasOI0x3mFBERkcYwZz1nzrlrgbuIdqG418zuHPP6LcDngPIU/C+b2b1zFV81rz9/hP/a2crhkSRd6SJrukPO7RzhVwczpBIB6UTI3qFknOGevmozBSzMlOhtCaPi52FIwmPW5niNHUYEVLtSRESkyc1JcuacSwB3A68F9gCPO+ceMbOtY079ppndNhcxTUZLMmR97yjPDSQ4t22U1iQkA49FrQGrO4v05z060wFDRfjR7gSnbanhweK2Ej3ZEvkSvGPtEKMlZnU+VuUwYrU6lxpiFBERaS5z1XN2OfA7M9sB4Jx7GLgBGJucNZStR5P8Yn8LAT77B1v45B8c45zWgK5MSN+od6LQuIdHb7rEkTyUR4p9Qpa3F3nzBblJLwKYqYlWS4qIiEjjm6vkbDmwu+J4D3BFlfP+zDl3FfAs8GEz2z32BOfce4D3AJgZvb29ZyDcyPHDMFxIxAOWCUaSnVyx+tRzbl0IX/ofn9U9Hn37fTygBCzMwoalCV65ppO21BkL8TQf6Yl241+xANpSYysc1E8ymTyj36uzgdpw5tSGM6c2nDm14eyYz+04V8lZtdnpY7uRvgs8ZGajzrlbgU3A1WPfZGb3APeUP+PIkSOzGmilbCEFLCTExyMgW+jnyJHCKefs6E+QCNo4vyPk2aMt5AOPlB9y2aIR3nXhILn+gKMVJY3mYg5YD5Drh9wZv9Lk9fb2cia/V2cDteHMqQ1nTm04c2rD2dGM7bhs2bJJnTdXydkeYGXF8QpgX+UJZna04vBrwN/PQVw1LciELGsvMlJKkU0UWZAZvz5mPvC4ZmWONV15lrcHrF0YFQ+fyV5kIiIicvaZq+TscWCNc24V0WrMG4G3VZ7gnFtqZvvjw+uBbXMU27iWtgVkE4APWY+qc7gm2nh1pnuRiYiIyNllTpIzMys6524DfkC0lcZ9Zva0c+5vgCfM7BHgA86564Ei8AJwy1zEVkvfqMfahQXSmST50QJ9ox492dPPq7Xxqibpi4iIyFR4YdjUQ2zhvn37Jj5rmspDkolMK6XR4WkPSQ7P8ZyzRtSMcwMajdpw5tSGM6c2nDm14exoxnaM55xNuEu8yjfVUB6yzKUytBSmP1dMJY1ERERkslS+aQKtyZALezhre7xERERkbik5ExEREWkgSs5EREREGoiSMxEREZEGouRMREREpIEoORMRERFpIErORERERBqIkjMRERGRBtL0FQLqHYCIiIjIFExYIaDZe868uXg45zbP1bXm60NtqDZshIfaUG3YCA+14VnfjhNq9uRMREREZF5RciYiIiLSQJScTc499Q5gHlAbzpzacObUhjOnNpw5teHsmLft2OwLAkRERETmFfWciYiIiDSQZL0DaGTOuWuBu4AEcK+Z3VnnkJqOc+4+4A3AITNbX+94mo1zbiVwP7AECIB7zOyu+kbVfJxzWeBnQIbovvctM7ujvlE1J+dcAngC2Gtmb6h3PM3GOfc8MACUgKKZvbS+ETUf51wXcC+wnmhLrb8ws1/WN6rZpZ6zccQ3oLuBPwEuBm5yzl1c36ia0jeAa+sdRBMrAn9pZmuBlwHv07/DaRkFrjazS4ENwLXOuZfVOaZm9UFgW72DaHKvNrMNSsym7S7g+2Z2EXAp8/Dfo3rOxnc58Dsz2wHgnHsYuAHYWteomoyZ/cw596J6x9GszGw/sD/+esA5tw1Yjv4dTomZhcBgfJiKH5pwO0XOuRXA64G/BT5S53DkLOScWwBcBdwCYGZ5IF/PmM4EJWfjWw7srjjeA1xRp1hEiJPclwD/W+dQmlLcG74ZuAC428zUjlP3D8DHgI56B9LEQuCHzrkQ+KqZzdsVh2fI+cBh4J+dc5cS/Ux/0MyG6hvW7NKw5viq7eKr37SlLpxz7cC/Ax8ys+P1jqcZmVnJzDYAK4DLnXOaAzkFzrny3NHN9Y6lyV1pZpcRTZl5n3PuqnoH1GSSwGXAV8zsJcAQcHt9Q5p9Ss7GtwdYWXG8AthXp1jkLOacSxElZg+a2X/UO55mZ2Z9wE/RXMipuhK4Pp7Q/jBwtXPugfqG1HzMbF/85yHg20RTaGTy9gB7Knq+v0WUrM0rSs7G9ziwxjm3yjmXBm4EHqlzTHKWcc55wNeBbWb2xXrH06ycc4viFV4451qA1wDP1Deq5mJmHzezFWb2IqL74Y/N7M/rHFZTcc61Oec6yl8DrwO21Deq5mJmB4DdzrkL46euYR7OwdWcs3GYWdE5dxvwA6KtNO4zs6frHFbTcc49BPwR0Ouc2wPcYWZfr29UTeVK4O3AU865J+PnPmFmj9Yxpma0FNgUzzvzATOz79U5Jjn7nAN82zkH0f9//9XMvl/fkJrS+4EH446THcA76xzPrFOFABEREZEGomFNERERkQai5ExERESkgSg5ExEREWkgSs5EREREGohWa4qIiIjU4Jy7DyhvxFxzA2vn3JeAV8eHrcBiM+uayvWUnInIvOacywI5YKWZ7Znlz74W+LKZXTCbnysiDecbwJeB+yc60cw+XP7aOfd+orJ7U6LkTETmnHNusOKwFRgFSvHxe83swRrvnbWEyDm3CRg1s/eMef4K4CfAEpXLEhEz+1lc3/gE59xq4G5gETAMvNvMxm5ufRNwx1Svp+RMROacmbWXv47LAW00sx/VIZRvEG0K+gEzG6l4/u3At5WYiUgN9wC3mtn/xb/Q/SNwdflF59x5wCrgx1P9YCVnItJw4hJLnwfeRNSj9hDwCWABUT3CTEXv23nARcAX4z+HgG8CHzWz4gSX+inQB1wPWHztFPBW4OZasZhZYUzMpw2fOuceBraY2WfKPX5ECeGHiHoL3w2k4ti7gL8zsy/E703Ef+d3xn/vHwDvi2uDikgdOefagVcA/xZXfADIjDntRuBbZlZiirRaU0Qa0aeB3wMuAX6fqATYx8zsKPBGYIeZtcePo0ABuA1YCLwKuA7YONFFzCwE/gV4R8XTfwrkgXJPXtVYpvn3Oi+OdQlwJ3Af8Ob4818D/K1zbnl87keJai++ElgRv+9L07yuiMwuH+gzsw0Vj7VjzrmR6Je5aX24iEijuZmoDusRMzsIfIZoqLEqM3vMzB43s5KZbQfuBf5wktfaBLzOObc4Pn4H8ICZBdOJZQLDwOfiHr2HiWotft7MhszsV8B2oiQQ4L3A7Wa2Lx5y/TTwVuecN81ri8gsiac8POecewuAc85zzl1afj0uzN4N/HI6n69hTRFpKHHysQTYWfH0TmB59XeAc+5i4AvAZUAL0b3t55O5npn9zjn3OPC2eIHA64l6yKYVywQOVyR9ufjPgxWv54D2+LorgUedc5UFkH2gBzgyzeuLyDQ45x4i6jXvdc7tIZrkfzPwFefcp4imJzwM/Dp+y03Aw3Hv/JQpORORhmJmoXPuANEQ4Pb46XOBvfHX1W52XyOaP/YWMxt0zt1ONEw4WZuAW4mSoy1m9vQkY6mUJxp6bK14bgmwZQpxUHHdvcCbzGzzVN8vIrPLzG4a56Vrxzn/r2dyPQ1rikgjegi4wznXEw83fhJ4IH7tILA4npBb1gH0x4nZOqKJ9lPxTWAt8HGiRG2ysZwQ94g9BdzsnEs4564DXj7FOCr9E3Cnc24lgHNucfyZIjLPKTkTkUb0V8BW4GngSaIhys/Gr/0aeATY6Zzrc84tBD4MbIxXcN5NlGxNmpn1A98BlnH6BN5asYx1G9FKz2NECxe+N5U4xvgs0aKEHzvnBoBfEA3bisg854XhtIZDRUREROQMUM+ZiIiISANRciYiIiLSQJSciYiIiDQQJWciIiIiDUTJmYiIiEgDUXImIiIi0kCUnImIiIg0ECVnIiIiIg1EyZmIiIhIA/l/jzHHEB5bi0QAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# note: s is the size of the dots, alpha is the transparency (0: transparent, 1: opaque)\n",
    "avo.plot(kind='scatter', x='Total Volume', y='AveragePrice', color='dodgerblue', figsize=(10,4), s=10, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh snap! What did we just make?! It's a [price elasticity curve!](https://en.wikipedia.org/wiki/Price_elasticity_of_demand)\n",
    "\n",
    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/3/36/Marshall_PED.png\" align=\"left\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cI3W9z6pDOO1"
   },
   "source": [
    "We can also use a thing called a **scatter matrix** or a **pairplot**, which is a grid of scatter plots. This allows you to quickly **view the interaction of N x M features**. You are generally looking for a trend between variables (a line or curve). Using machine learning, you can fit these curves to provide predictive power."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0vTeuwo7DOO1",
    "outputId": "f4645002-dc3b-49f9-d751-42c43c9c58aa"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb7b63c8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb7619e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb7910b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb738748>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb6e1dd8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb6e1e10>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb6bab38>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb66c208>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb694898>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb63df28>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb5ef5f8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb618c88>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb5c9358>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb5709e8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb5240b8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x7f42cb54a748>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.plotting.scatter_matrix(avo.select_dtypes(include='number').iloc[:,-5:-1], figsize=(12,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a very handy parameter, `c`, which allows us to color the dots in a scatter plot. This is extremely helpful when doing **classification problems**, often you will set the color to the class label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Unnamed: 0</th>\n",
       "      <th>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>4046</th>\n",
       "      <th>4225</th>\n",
       "      <th>4770</th>\n",
       "      <th>Total Bags</th>\n",
       "      <th>Small Bags</th>\n",
       "      <th>Large Bags</th>\n",
       "      <th>XLarge Bags</th>\n",
       "      <th>type</th>\n",
       "      <th>year</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.33</td>\n",
       "      <td>64236.62</td>\n",
       "      <td>1036.74</td>\n",
       "      <td>54454.85</td>\n",
       "      <td>48.16</td>\n",
       "      <td>8696.87</td>\n",
       "      <td>8603.62</td>\n",
       "      <td>93.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.35</td>\n",
       "      <td>54876.98</td>\n",
       "      <td>674.28</td>\n",
       "      <td>44638.81</td>\n",
       "      <td>58.33</td>\n",
       "      <td>9505.56</td>\n",
       "      <td>9408.07</td>\n",
       "      <td>97.49</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0        Date  AveragePrice  Total Volume     4046      4225  \\\n",
       "0           0  2015-12-27          1.33      64236.62  1036.74  54454.85   \n",
       "1           1  2015-12-20          1.35      54876.98   674.28  44638.81   \n",
       "\n",
       "    4770  Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  \\\n",
       "0  48.16     8696.87     8603.62       93.25          0.0  conventional  2015   \n",
       "1  58.33     9505.56     9408.07       97.49          0.0  conventional  2015   \n",
       "\n",
       "   region  \n",
       "0  Albany  \n",
       "1  Albany  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avo.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's map the `type` field to the color of the dot in our price elasticity curve. To use the type field, we need to convert it from a string into a number. We can use `pd.Series.apply()` for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['conventional', 'organic']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(avo['type'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see we have two unique type labels, `conventional` and `organic`. Although that is the case for this dataset, let's create a function that will store the labels in a dictionary, incrementing the number up by `1` for each new label. This way, if we receive an additional type label in the future, our code won't break. Always think about extensible code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'conventional': 0, 'organic': 1}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mapping_dict = {}\n",
    "initial_class_label = 0\n",
    "for type in list(avo['type'].unique()):\n",
    "    mapping_dict[type] = initial_class_label\n",
    "    initial_class_label += 1\n",
    "mapping_dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can use this `mapping_dict` dictionary to map the values using `.apply()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>type</th>\n",
       "      <th>type_as_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>conventional</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>conventional</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>conventional</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           type  type_as_num\n",
       "0  conventional            0\n",
       "1  conventional            0\n",
       "2  conventional            0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avo['type_as_num'] = avo['type'].apply(lambda x: mapping_dict[x])\n",
    "avo[['type', 'type_as_num']].head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can use this **binary class label** as our `c` parameter to gain some insight:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f42cb47b198>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avo.plot(kind='scatter', x='Total Volume', y='AveragePrice', c='type_as_num', colormap='winter', figsize=(8,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Amazing! It looks like the organic avocados (value of `1`) totally occupy the lower volume, higher price bracket. Those dang kids with their toast and unicycles driving up the price of my 'cados!\n",
    "\n",
    "Here, we can also see a 'more' continuous `c` parameter, `year`, which makes use of the gradient a little better. There are tons of gradients you can use, check them out [here](https://matplotlib.org/examples/color/colormaps_reference.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'avo' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-af1dae00c09d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mavo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'scatter'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Total Volume'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AveragePrice'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'year'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolormap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Blues'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m8\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Volume'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'avo' is not defined"
     ]
    }
   ],
   "source": [
    "avo.plot(kind='scatter', x='Total Volume', y='AveragePrice', c='year', colormap='Blues', figsize=(8,4))\n",
    "plt.xlabel=('Volume');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can save the plot to a file, using the `plt.savefig()` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Enp2scMwDOO8",
    "outputId": "9e479b9b-7a9a-42ce-8268-1788d23330e7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avo.plot(kind='scatter', x='Total Volume', y='AveragePrice', c='year', colormap='Blues', figsize=(8,4))\n",
    "plt.savefig('./avo_price.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Ron6_N0BDOO-"
   },
   "source": [
    "<a id=\"summary\"></a>\n",
    "### Summary\n",
    "\n",
    "In this lesson, we showed examples of how to create a variety of plots using Pandas and Matplotlib. We also showed how to use each plot to effectively display data.\n",
    "\n",
    "Do not be concerned if you do not remember everything — this will come with practice! Although there are many plot styles, many similarities exist between how each plot is drawn. For example, they have most parameters in common, and the same Matplotlib functions are used to modify the plot area.\n",
    "\n",
    "We looked at:\n",
    "- Line plots\n",
    "- Bar plots\n",
    "- Histograms\n",
    "- Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nLX7Hs6zDOO-"
   },
   "source": [
    "### Additional Resources\n",
    "\n",
    "Always read the documentation!\n",
    "\n",
    "- [Pandas Plotting Documentation](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html)\n",
    "\n",
    "- [Matplotlib Documentation](https://matplotlib.org/)\n",
    "\n",
    "- [Matplotlib sample plots](https://matplotlib.org/tutorials/introductory/sample_plots.html)"
   ]
  }
 ],
 "metadata": {
  "colab": {
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