{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Python Data Science packages to manipulate and visualize data\n",
    "\n",
    "In this Jupyter notebook we will:\n",
    "- Go over several popular Python packages used for Data Science\n",
    "- Go through the example of analyzing avocado prices using these popular Python Data Science packages\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1: An overview of popular Python Data Science packages \n",
    "\n",
    "Let's very briefly discuss several popular Python Data Science packages. The packages we will discuss are:\n",
    "- NumPy\n",
    "- pandas\n",
    "- Matplotlib\n",
    "- seaborn\n",
    "\n",
    "We can discuss additional Python packages, particular for modeling and prediction, later in the workshop.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1.1: NumPy\n",
    "\n",
    "[NumPy](https://numpy.org/) is a library that adds support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. In many ways, it's functionality is similar to MATLAB's basic functionality. \n",
    "\n",
    "The core data structure of NumPy is the `ndarray`. ndarrays are similar to Python lists but all elements in an ndarray must of the same type; e.g., all elements are numbers, or all elements are strings, etc.\n",
    "\n",
    "Let's create a few ndarrays below!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 6]\n",
      "[0 1 2 3 4 5 6 7 8 9]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "\n",
    "x = np.array([1, 2, 3])\n",
    "print(2 * x)\n",
    "\n",
    "print(np.arange(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1.2: pandas\n",
    "\n",
    "[pandas](https://pandas.pydata.org/) is a package for data manipulation and analysis that has two main data structures:\n",
    "\n",
    "1. `Series`: One-dimensional ndarray with an index for each value. They are similar to a named vector in R.\n",
    "\n",
    "2. `DataFrame`: Two-dimensional, size-mutable, potentially heterogeneous tabular data. They are similar to an R data frame. DataFrames can also be thought of as multiple Series of the same length with the same index, or as muliple ndarrays with the same index.\n",
    "\n",
    "Here are some documents that show translations between Data 8 datascience package and pandas\n",
    "- [googledoc I created](https://docs.google.com/spreadsheets/d/1GeghI6Md4QjJcugEEa4a_N_jQNGZRdxqFrynvJgq1CM/edit#gid=0)\n",
    "- [babypandas documentation](https://pypi.org/project/babypandas/)\n",
    "\n",
    "Let's load our avocado data as a DataFrame and look at the first three rows using the `df.head(3)` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>12/27/2015</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>12/20/2015</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>12/13/2015</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": [
       "         Date  AveragePrice  Total Volume     4046       4225    4770  \\\n",
       "0  12/27/2015          1.33      64236.62  1036.74   54454.85   48.16   \n",
       "1  12/20/2015          1.35      54876.98   674.28   44638.81   58.33   \n",
       "2  12/13/2015          0.93     118220.22   794.70  109149.67  130.50   \n",
       "\n",
       "   Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  region  \n",
       "0     8696.87     8603.62       93.25          0.0  conventional  2015  Albany  \n",
       "1     9505.56     9408.07       97.49          0.0  conventional  2015  Albany  \n",
       "2     8145.35     8042.21      103.14          0.0  conventional  2015  Albany  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "avocado = pd.read_csv(\"avocado.csv\")\n",
    "avocado.head(3)\n",
    "\n",
    "# More complex data manipulation will be discussed more below\n",
    "#avocado.groupby(\"type\").mean().reset_index()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1.3: Matplotlib\n",
    "\n",
    "[Matplotlib](https://matplotlib.org/) is a plotting library.  Each plot has a figure and a number of different subplots which are called \"axes\". Matplotlib is based on MATLAB's plotting syntax and it can be roughly thought of as being similar to base R's graphics.  \n",
    "\n",
    "Matplotlib has two interfaces for plotting:\n",
    "\n",
    "1.  A \"pylab\" procedural interface based on a state machine that closely resembles MATLAB. Updates are made to the most recent axis plotted on.\n",
    "\n",
    "2.  An object-oriented API. Updates are made to the axis object that is selected. \n",
    "\n",
    "Generally the objected oriented interface is preferred although they are rather similar (a few of the functions/methods are named slightly differently)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# using the pylab interface\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot([1,3,10]);\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot([2,6,12]);\n",
    "\n",
    "\n",
    "#using the object oriented interface\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "ax[0].plot([1, 3, 10]);\n",
    "ax[1].plot([2,6,12]);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 1.4: seaborn\n",
    "\n",
    "seaborn is a visualization library built off Matplotlib, but it provides a higher level interface that uses pandas DataFrames. One can think of it as being somewhat similar to ggplot. \n",
    "\n",
    "There are \"axes-level\" functions that plot on a single axis and \"figure-level\" functions that plot across multiple axes. Figure level plots are grouped based on the types of variables being plotted; e.g., a single quantitative variable, two quantitative variables, etc. The image below shows different categories of plots that can be created in seaborn.\n",
    "\n",
    "<img src=\"https://seaborn.pydata.org/_images/function_overview_8_0.png\" width=500 height=500>\n",
    "\n",
    "Note: to use the seaborn functions below you will have to use seaborn version 0.11 or higher which might require updating your packages in conda. This can be done using: \n",
    "- conda activate facwavdev\n",
    "- conda update seaborn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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wgfLycp544gl27tx5ymv+9re/MXhwz+/yP10H3Nzc3OP6lPIG+/fvZ3fDLiIS0x1WZ9y5I/nw4/84rD7VOe2zXctV6zRfBmwwxmwWkauB8fbjrwHLgftdFIdS6jQ212/HzL3CYfXJjK5HmpYtW0ZgYCC33357+7Hs7Gz27dtHbm4u119/PaWlpVxwwQW8+eabiAjjx4/n6aefJicnh4iICO655x4+/vhjQkND+fDDD0lKSuJf//oXjz76KP7+/kRHR7N06VIeeughjh49ysqVK/nNb35DRUUF27dvZ9OmTcTHx/PEE09wyy23cPjwYQD+8pe/MHbsWJYvX85DDz1EXFwcVVVVjBs3jr/+9a/4+ekMN+X51q9fT2JG/zPeCbAzSYNG8unbT3P8+HECAwMdVq86kfbZru2zXdXj3wS0bUifZIzZAWB/Prv1bZRSHq2tc+3MunXrePbZZykvL6euro5Vq1Z9p8zhw4e58MILKS4uZty4cbzyyiuAbXezxYsXU1xczEcffURQUBCPPfYYN954I0VFRdx4440AFBQU8OGHH/LPf/6TxMRElixZQmFhIfPnz+fuu+9ub2ft2rX8+c9/pqSkhA0bNvDee+854aehlOsVFRURnuyY+cxtgiNiiOmdwerVqx1ar7KeL/fZTk+aRSQIuAr4Vw+vmyki+SKS39DQ4JzglFJubdSoUaSlpeHn50d2dnanNxYFBQW1z6O74IIL2stcdNFF3Hrrrbzyyiun3WjhqquuIjQ0FIDjx4/zi1/8gmHDhnHDDTecMAdv1KhR9O3bF39/f6ZNm8bKlSsd90GVslDBumLCevd1eL2x5+Twn0WLHV6vcl/e3me7YqR5ClBojGmb7LJTRJIB7M+7OrvIGPOyMSbHGJOTkJDggjCVUlYYMmQIBQUFnZ4LDg5uf+3v79/pHLbAwMD2tWU7lnnxxReZPXs2W7duJTs7mz179nTaRnh4ePvrZ555hqSkJIqLi8nPz+fYsWPt505ev9YR69kq5Q6K1pcQner4pDnh3JF8vHCRw+tV1vLlPtsVSfM0vp2aAfARMN3+ejqgK6Ar5cMuvfRSmpqa2r+iA8jLy2PFihVnVe+GDRsYPXo0jz32GPHx8WzdupXIyEgOHjx4ymv2799PcnIyfn5+vPHGGyeMdqxdu5aNGzfS2trK/Pnzufjii88qPmcQkctFpEpEakXkOzdZi81z9vPrRWSE/XiIiKwVkWIRKRORRztcoyseeTFjDNWV5UQ7aLm5juL6DqGmupIDBw44vG5lHV/us52aNItIGDAR6DiR5A/ARBGpsZ/7gzNjUEq5NxHh/fffZ8mSJfTr148hQ4bwyCOPkJKSclb1/vrXv2bYsGEMHTqUcePGcd555zFhwgTKy8vJzs5m/vz537nmzjvv5LXXXuPCCy+kurr6hBGNMWPG8MADDzB06FCysrK49tprzyo+RxMRf+AFbN/uDQamicjJt6tPAQbYHzOBOfbjTcClxpjzgGzgchG50H5OVzzyYps3byYoNJzg8CiH1+0fGEzvfkM6ndeqPJcv99lijDnrSpwtJyfH5OfnWx2GTxMRbnzpS5e0Nf+2sXjC30tvUFFRwaBBg9rfZ6ansrl+u8Pq75OWwqat2xxWn1WWL1/O008/zccff3zacif/PAFEpMAYk+PM+OztjAEeMcZMtr//DYAx5skOZV4Clhtj5tnfVwHj227Oth8LA1YCdxhj1nQsY59St9wYM/B0sWif7Tn+/e9/c+9Df2DkHU87pf7yj//OpIG9ePpPf3JK/b5G++zu6W6fDT3rt1215JxSygN4Q2fpw1KBrR3e1wOju1EmFdhhH6kuAPoDLxhj1tjLnLDikYjoikdepKSkhJCkLKfVHz/gfBYvnYtzUnKlfbZr6SKjSinVhfHjx3drxMJind3lcvJXNqcsY4xpsW9GlQaMEpGhPWpcVzzySPnriolIdvxNgG1is4ZQU1Vx2nmpSjmas/psTZqVUso71AMdt3RLA07+3rbLMsaYfdg2nbrcfkhXPPJiJSWlTlk5o01AUDBJWYN0vWblFTRpVkop75AHDBCRLPv6+DdhW62oo4+An9pX0bgQ2G+fcpEgIjEAIhIKfB+o7HCNrnjkhZqbm9myaQNRvfs4tZ3IjMGsWuWae2KUciZNmpVSygsYY5qBu4DFQAXwjjGmTERuF5G2/W4XAnVALfAKcKf9eDKwTETWY0u+lxhj2r7b1BWPvNSGDRuIiksiICjEqe30yhrCsi90BQ3l+fRGQKWU8hLGmIXYEuOOx17s8NoAszq5bj1w/inq3ANc5thIlTsoKysjOtl5NwG2ies7lNw3n6S1tRU/Px2rU55L//YqpSy3c+dOfvzjH9O3b18uuOACxowZw/vvv39GdT377LMcOXLEwREq5X3KysoISXTu1AyAkKhYgiOiqKys7Lqw8gi+2mdr0qyUapeakYqIOOyRmpHaZZvGGK655hrGjRtHXV0dBQUFvP3229TX15/RZ/CkDlgpKxUUlxDh5PnMbeL6DtWbAZ1A+2zX0ukZSql227duZ8aiGQ6rb+7lc7ss89lnnxEUFMTtt9/efqxPnz788pe/pKWlhQceeIDly5fT1NTErFmzuO2221i+fDmPPPII8fHxlJaWcsEFF/Dmm2/y/PPPs337diZMmEB8fDzLli1j3rx5PPHEExhjuOKKK/jjH/8IcMrjSvmK8rJyMq+b6pK2ItIHs+KLVfzsZz9zSXu+Qvts19KRZqWUpcrKyhgxYkSn5/7+978THR1NXl4eeXl5vPLKK2zcuBGAdevW8eyzz1JeXk5dXR2rVq3i7rvvJiUlhWXLlrFs2TK2b9/O/fffz2effUZRURF5eXl88MEHpzyulK9obm5mswtWzmgTmzmINXm6S6Q38OU+W0ealVJuZdasWaxcuZKgoCD69OnD+vXrWbBgAQD79++npqaGoKAgRo0aRVpaGgDZ2dls2rSJiy+++IS68vLyGD9+PG3rBt988818/vnniEinx6+55hoXflKlrFNXV0dUrwSnr5zRJiatH59vqKGxsZGQENe0qVzDl/psHWlWSllqyJAhFBYWtr9/4YUXyM3NpaGhAWMMzz//PEVFRRQVFbFx40YmTZoEQHBwcPs1/v7+NDc3f6du22IR33Wq40r5ivLycqJTnL9yRhv/wGDiU7NYv369y9pUzuHLfbYmzUopS1166aU0NjYyZ86c9mNtN4VMnjyZOXPmcPz4cQCqq6s5fPjwaeuLjIxs37J39OjRrFixgt27d9PS0sK8efO45JJLTnlcKV9RXl5OcEKGS9uMSj+H/HydouHpfLnP1ukZSilLiQgffPAB9913H0899RQJCQmEh4fzxz/+kRtuuIFNmzYxYsQIjDEkJCR0OY9t5syZTJkyheTkZJYtW8aTTz7JhAkTMMYwdepUrr76aoBTHlfKFxSVlBGemOnSNsNTz2HVV2u58847uy6s3JYv99niLkPep5OTk2P0t1NriQg3vuSabVDn3zbWbb6K8XYVFRUMGjSo/X1qRirbt253WP0p6Sls27LNYfW5u5N/ngAiUmCMybEoJEton+3+hpw3guQpdxDfd6jL2tyzqZzN7z9DdUWpy9r0NtpnO15P+m0daVZKtfO1zlIpX2SMYWNtNQOTM13abkxqf1ZsrNWbAR1I+2zX0jnNSimllA+pr68nKDScoNAIl7brHxhEXHIGZWVlLm1XKUfRpFkppZTyIRUVFfRy4coZHUWn9qe4uNiStpU6W5o0K+XjdP64Y+jPUXmKiooKQhNdu3JGm5CkvhSu06T5bGhf4zg9/Vlq0qyUDwsJCWHPnj3aCZ8lYwx79uzReZrKIxSXlBHq4uXm2sSk9yevcJ0lbXsD7bMd50z6bb0RUCkflpaWRn19PQ0NDVaH4vFCQkLad7tSyp2VllcQNfpHlrQdkzaAgrklGGMQEUti8GTaZztWT/ttTZqV8mGBgYFkZVkzt1EpZY3a6iouvrqPJW2HRMXiFxBAfX096enplsTgybTPtpZOz1BKKaV8xDfffMPRo0cIjYm3LIa49AF6M6DySE5NmkUkRkQWiEiliFSIyBgRiRWRJSJSY3/u5cwYlFJKKWVTVVVFXGqWpVMjQpOyKCkpsax9pc6Us0ea/w9YZIw5FzgPqAAeAHKNMQOAXPt7pZRSSjlZZWUlEUnW3ATYJrx3JoXFmjQrz+O0pFlEooBxwN8BjDHHjDH7gKuB1+zFXgOucVYMSimllPpWeUUlQXHWziWOTulLSalucKI8jzNHmvsCDcBcEVknIn8TkXAgyRizA8D+nOjEGJRSSillt760nMgka5PmqORMNm2ooaWlxdI4lOopZybNAcAIYI4x5nzgMD2YiiEiM0UkX0TydWkVpZRS6uxVVlUS2dualTPaBIaEER4dy8aNGy2NQ6mecmbSXA/UG2PW2N8vwJZE7xSRZAD7867OLjbGvGyMyTHG5CQkJDgxTKWUUsr7HT9+nO1btxCZaP164r1S+1JWplM0lGdxWtJsjPka2CoiA+2HLgPKgY+A6fZj04EPnRWDUkoppWzq6uqIikvEPzDY6lAITuyjK2goj+PszU1+CbwlIkFAHTADW6L+joj8DNgC3ODkGJRSSimfV1VVRbTFUzPaROgKGsoDOTVpNsYUATmdnLrMme0qpZRS6kRVVVWExFs/NQMgOjmLsg8+sDoMpXpEdwRUSimlfMD6sgpCE9xj6+rI3hls2lhLa2ur1aEo1W2aNCullFI+oLyiksje1m5s0iYwJJywyBi2bNlidShKdZsmzUop5SVE5HIRqRKRWhH5zhKfYvOc/fx6ERlhP54uIstEpEJEykTkng7XPCIi20SkyP6Y6srPpBxnQ201URbvBthRr5RMKioqrA5DqW7TpFkppbyAiPgDLwBTgMHANBEZfFKxKcAA+2MmMMd+vBn4b2PMIOBCYNZJ1z5jjMm2PxY683Mo5/jmm29oamwiJDre6lDahcSnU1lZaXUYSnWbJs1KKeUdRgG1xpg6Y8wx4G3g6pPKXA28bmy+AmJEJNkYs8MYUwhgjDkIVACprgxeOVdVVRVxKX0QEatDaReamEFxSanVYSjVbZo0K6WUd0gFtnZ4X893E98uy4hIJnA+sKbD4bvs0zleFZFejgpYuU5VVRXhie4zNQMgqncfSsp1eobyHJo0K6WUd+hsCNH0pIyIRADvAvcaYw7YD88B+gHZwA7gz502LjJTRPJFJL+hoaGnsSsnq6ysIijOvb48iErOZENNtdVhKNVtmjQrpZR3qAc6rieWBmzvbhkRCcSWML9ljHmvrYAxZqcxpsUY0wq8gm0ayHcYY142xuQYY3ISEhLO+sMoxyopryQyyT2Wm2sTEhXHsaYm9uzZY3UoSnWLJs1KKeUd8oABIpJl34X1JuCjk8p8BPzUvorGhcB+Y8wOsU10/TtQYYz5344XiEhyh7fXAjoJ1QNVVVUR6UYrZwCICHGpWVRVVVkdilLdokmzUkp5AWNMM3AXsBjbjXzvGGPKROR2EbndXmwhUAfUYhs1vtN+/CLgFuDSTpaWe0pESkRkPTABuM9FH0k5SGtrK1s21xGR6F4jzQDhiRmaNCuP4dRttJVSSrmOfTm4hScde7HDawPM6uS6lXQ+3xljzC0ODlO5WH19PaER0QSGhFkdyncExaZSXqHLzinPoCPNSimllBerqqqiV3Ifq8PoVGTvDErKNWlWnkGTZqWUUsqLVVdXExKfZnUYnYpITKe6WqdnKM+gSbNSSinlxcoqKglxs+Xm2kQmpbNtyyZaWlqsDkWpLmnSrJRSSnmx0vJKt1s5o01AUAjhMXFs3rzZ6lCU6pImzUoppZQXq62pdtukGSCmdwbV1brJiXJ/mjQrpZRSXqqpqYndu3YSHp/cdWGLhMSn67JzyiNo0qyUUkp5qbq6OmISU/Dzd98VZkPiUykpr7A6DKW6pEmzUkop5aWqq6uJcrPts08WmZhBRaVOz1DuT5NmpZRSyktVV1cT7KYrZ7SJTEpjw4Zaq8NQqkuaNCullFJeqqS8klA3XaO5TVhsb/Y27KKxsdHqUJQ6LU2alVJKKS9VUVlJpJtPz7tXzgMAACAASURBVPDzD6BXUiobNmywOhSlTkuTZg+Vmp6BiLjsoZRSyvPU1da69XJzbSIT03XZOeX23Pd2WnVa2+u3cuNLX7qsvfm3jXVZW0oppc7egQMHOHToIKHR8VaH0qXguFRNmpXbc2rSLCKbgINAC9BsjMkRkVhgPpAJbAJ+ZIz5xplxKKWUUr6mpqaGuOQMxM/9v1QOTUijtELXalbuzRX/kiYYY7KNMTn29w8AucaYAUCu/b1SSimlHKimpoYID5iaARCZlE5FZaXVYSh1Wlb8+nk18Jr99WvANRbEoJRSSnm1qqoqgmJTrA6jWyIT06mr1WXnlHtzdtJsgE9FpEBEZtqPJRljdgDYnxOdHINSSinlc0rKKwlPdO+VM9qERsdz+PAhDhw4YHUoSp2Ss5Pmi4wxI4ApwCwRGdfdC0Vkpojki0h+Q0OD8yJUSimlvFBlVTWRHpI0i58fcckZ1Opos3JjTk2ajTHb7c+7gPeBUcBOEUkGsD/vOsW1LxtjcowxOQkJCc4MUymllPIqxhg21XnGcnNtIhLTqampsToMpU7JaUmziISLSGTba2ASUAp8BEy3F5sOfOisGJRSSilftHv3bhA/giOirQ6l24JiU3TZOeXWnLnkXBLwvn1jjADgn8aYRSKSB7wjIj8DtgA3ODEGpZRSyudUV1cTm+w5o8wAYbrsnHJzTkuajTF1wHmdHN8DXOasdpVSSilfV11dTViCZ8xnbhOZlE5l7hKrw1DqlHRHQKWUUsrLVFRWEeghy821iUxMJ7/Oe24ELCsrY8mSJURERPDDH/6Q2NhYq0NSZ8n9twlSSimlVI+Ulld61E2AAMGRvTh27DjffOPZmwQfOnSIa667nqFDh3Lffffxi1/8gri4OB588EGrQ1NnSZNmpZRSystUV1cTmeRZ0zNEhNiUDI9eQaOhoYHsC3L48L13CQkJ4Zlpg1j6P6PITAhn9uzZzJ492+oQ1VnQpFkppZTyIq2trWzdstFj1mjuKCIhzWNX0Dh06BCXTZzMhuoq4mIiaHjmEu6d1JfLhiSw8anxjMyK4cEHH6SgoMDqUNUZ0qRZKaWU8iJbt24lPDKGgOBQq0PpsaC4VKo8MGlubW3l+hunUVK8jpDQUCofG0NEyIm3ja196CIAcnJyrAhROYAmzUoppZQXqaqqIrp3H6vDOCPhHrrs3MOPPsqKz79A/AN47/ahxEcGAVD79T4umb2cpF8uYsyjy5j3iyEA3HnnnVaGq86QJs1KKeVmRORdEblCRHrUR4vI5SJSJSK1IvJAJ+dFRJ6zn18vIiPsx9NFZJmIVIhImYjc0+GaWBFZIiI19udeZ/8JlTNVV1cTEp9mdRhnJDIpg6oqzxppXrx4Mc+98CKtEsB1IxKYMjwRgJUVO8h++EvqIqMZ+qtL+Do5nhlzy0mMDGTOnDkYYyyOXPWUJs1KKeV+5gA/BmpE5A8icm5XF4iIP/ACMAUYDEwTkcEnFZsCDLA/ZtrbAWgG/tsYMwi4EJjV4doHgFxjzAAg1/5eubGyikpC4lOtDuOMRCSms3njBo9JKOvr65l28y2Epg8hoOUIb83MBqDhwBGufaGIPlPOZeIfJtNnbB8mPHwZg36czf4jxwG44447rAxdnQFNmpVSys0YY5YaY24GRgCbgCUi8qWIzBCRwFNcNgqoNcbUGWOOAW8DV59U5mrgdWPzFRAjIsnGmB3GmEJ72weBCiC1wzWv2V+/BlzjoI+pnKSsotLjVs5oExwehZ9/ALt27bI6lC41Nzdz7fU/IumCiTSUreblnwwkKMCWVk1/MR//pBhG33HhCddk/zibpBG2f1ovvfSSy2NWZ0eTZqWUckMiEgfcCvwcWAf8H7Yk+lRbpqUCWzu8r+fbxLfbZUQkEzgfWGM/lGSM2QFgf07s0QdRLldbU0Nkomet0dxRr96esezcb3/3exoahX27d9E/MZSbx9j+KS0v28ay2kOMnz2x0+su+d2lhAQKAHPmzOm0jHJP3UqaReSi7hxTSil19kTkPeALIAz4gTHmKmPMfGPML4GIU13WybGTv+M+bRkRiQDeBe41xhzoYcwzRSRfRPIbGhp6cqlyoMbGRnbv2kl4fLLVoZyxsET3X3Zu6dKlvPz3f5A27kZ2Fq/gvTuGAdBqWrn7rQpSvpdFWGxYp9cGhAQw9KcXAHpDoKfp7kjz8908ppRS6uz9zRgz2BjzZNsor4gEAxhjTrVeVT3Q8Tv5NGB7d8vYp328C7xljHmvQ5mdIpJsL5MMdPq9uTHmZWNMjjEmJyEhoTufUTlBTU0NvZJS8fMP6LqwmwrqlUqlG98MuHv3bqbdfAvn3/I7Sj/+G5MG92JIaiQA/yncyoa9x7no3tOPKw67YRiBgbYUzBNG1ZXNaZNmERkjIv8NJIjIrzo8HgH8XRKhUkr5ns62DVvdxTV5wAARyRKRIOAm4KOTynwE/NS+isaFwH5jzA4REeDvQIUx5n87uWa6/fV04MOefBDlWlVVVUT19typGQARSemUlldaHcYp/fy2O0jKnsDRpiYO1Vfxz5nntZ97/N8b6D02k4Cgrn9pGXaz7abByZMnOy1W5VhdjTQHYfsqMACI7PA4AFzv3NCUUsq3iEhvEbkACBWR80VkhP0xHttUjVMyxjQDdwGLsd3I944xpkxEbheR2+3FFgJ1QC3wCtD23fBFwC3ApSJSZH9MtZ/7AzBRRGqAifb3yk1VVlYSFOeZy821iUxKd9vpGQsWLODLtYWc+4OZrP/Xs/z8oiRiI2xrMufV7qJoWyOj7xjdrbqG/cg2pWPjxo1Oi1c51ml/FTLGrABWiMg/jDGbXRSTUkr5qsnYbv5LAzqO+B4EftvVxcaYhdgS447HXuzw2gCzOrluJZ3Pd8YYswe4rOvQlTtYX1ZBeGKm1WGclYiENLZurqO1tRU/P/dZr+DQoUPM+uU9DL35d9R8tYTWQ3v5y83j28//8eMa4gcnERIV0q36/Pz86Pf9fmxYuoFnnnmG++67z0mRK0fp7t/GYBF5WUQ+FZHP2h5OjUz5Lr8ARMRlj9R0z/4qU3kPY8xrxpgJwK3GmAkdHledNM9YqU5VVFYRmeTZfVpgSBhhkTFs3bq168Iu9OhjjxPd9zzi+g2n4qMX+c3kNALsS8ztPdjIx6X7uGDmqB7VOfp226j0r371K4fHqxyvu3cK/At4Efgb0OK8cJQCWpu58aUvXdbc/NvGuqwtpU5HRH5ijHkTyBSR7/wv2sl8Y6XaGWPYuKGG/rd4dtIMEJOcSXV1NX36uMd24F9//TUvvvQSE377OmW57xJsmnjwqv7t5//6aSWR8RHE9Y/rUb3BEcGEx4dxePcRDh8+THh4uKNDVw7U3ZHmZmPMHGPMWmNMQdvDqZEppZTvafsfM4IT7yNpeyh1Srt27UL8/AmOiLE6lLMWGp9GVVWV1WG0e+IPfyRj1GSCI2OoWTSXR6amt08daTWtzPn8a7KuHnRGdY+7/xIArr9ebxVzd90daf63iNwJvA80tR00xux1SlRKKeWDjDEv2Z8ftToW5XmqqqroleIeI7NnKzg+lbIK91hBo6Ghgblz5zLht69T+tl7hPo1c9/krPbzn5VsY1+TYeI1J+9a3z29h/UGYNGiRQ6JVzlPd0eapwO/Br4ECuyPfGcFpZRSvkxEnhKRKBEJFJFcEdktIj+xOi7l3iorKwlP8Mzts08WmZTuNknz3199lZTzxhEaE0/dkre4f2LqCTco/mXpZmKHJZ/VTYuZ4zIBqKioONtwlRN160/YGJPVyaOvs4NTSikfNcm+I9+V2DYkOQfbwIVSp1RSWkZwvOfPZwaITOpDTbX1m360trbywl9fIn3sD6j5aily/DC/uaJf+/lvDjeyqHw/I/7rgrNqp20zlMsu04Vq3Fm3pmeIyE87O26Med2x4Sjl/VLTM9he76K7wsUPTKtr2gICgwI51nTMZe15sUD781RgnjFmr23/EaVOrbi0nKjB3rFRRnhcb/Y07KSxsZGQkO4t4eYMubm5NPsHEZc1hDWv/5FbR8efMKL86mc1hMeGEde3ZzcAniwoLAjxE3bs2HG2ISsn6u6c5pEdXodgW7OzENCkWake2l6/1WWrg8y/bSwzFs1wSVsAcy+f67K2vNy/RaQSOArcKSIJQKPFMSk3V11dxQWXzrQ6DIfw8w+gV1IqNTU1DBs2zLI4Xnl1LimjrmD31lqONGzhqd9POOH8y198Tdr3z3VIW6Nnjear57/iww8/5Oqrr3ZIncqxujs945cdHr8Azse2W6BSSikHM8Y8AIwBcowxx4HDgP4vqk7p8OHDfLO7gfD4ZKtDcZio3hmWrqDR1NTEwk8WknbBeCo+nceIPlFEhQW2ny+s28WWb45z3rTzTlNL9w26wrb6xg033OCQ+pTjdXek+WRHgAHdKSgi/thuGtxmjLlSRGKB+UAmsAn4kTHmmzOMQymlvNUgbOs1d+yn9ds91amqqipikzPw8/O3OhSHCY7PoLLSupsBlyxZQlx6PwJCItlZtIw37jkxOf7r0jpi+scREHSmqdR3BUcF03SgqeuCyhLdGmkWkX+LyEf2xydAFfBhN9u4B+h4O+gDQK4xZgCQa3+vlFLKTkTeAJ4GLsY2PW4kkGNpUMqtVVRUENk70+owHCo8MZ3i0nLL2n/r7XeIH3YJNV99SkRoEJcNSWg/d6y5hXcK9zLkRseMMreZ9MQkAB5//HGH1qsco7u/Hj3d4XUzsNkYU9/VRSKSBlwB/H9A2+5WVwPj7a9fA5YD93czDqWU8gU5wGBjjLE6EOUZyssrCI5PszoMh4pMyqB80UJL2m5paeGTjz9m/G/m8sUrj/CDIVEnnH//q41IYAAZYxy7Wkl8/3gAHnroIR588EGH1q3OXnfnNK8AKrHtSNUL6O7t8c8C/w/oePt+kjFmh73eHUBit6NVSinfUAr0tjoI5TmKSsqI7O0dG5u0iezdh40barDid8e8vDzCeyXgHxTK/k2lPHx1/xPOv/T5NuJznPNLSq+sXoBtuTvlXrq75NyPgD9hGxUW4HkR+bUxZsFprrkS2GWMKRCR8T0NTERmAjMBMjK8Y91JpQD8A4OYf9tYl7Qlfo6ba6dcKh4oF5G1nLgL61XWhaTcWVl5Gedm/9DqMBwqODyKgKBgduzYQUpKikvbXrRoMbEDc6j6chGxUWEMSIpoP7dr/xFW1R3iqt9e7pS2J/9xMm//6G2mT5/OG2+84ZQ21Jnp7v+ovwNGGmN2AdiXP1oKnDJpBi4CrhKRqdiWqYsSkTeBnSKSbIzZISLJwK7OLjbGvAy8DJCTk6NfUSqv0XL8GGbuFS5pS2Z84pJ2lMM9YnUAynM0NTWxfesWRiZ53wBTbHImlZWVLk+aP1q4iISxN1H877lcM/jEqRn/WFZLVFw4kcmRTmk7NCoUgDfffFOTZjfT3T0f/doSZrs9XV1rjPmNMSbNGJMJ3AR8Zoz5CfARtm25sT9394ZCpZTyCfYpcZuAQPvrPGxr4yv1HdXV1fRKSsE/0PtWgg1NdP0KGvv376eirITYzMHs31zOfZMzTzj/6uqdpFzar/OLHSR1ZCoAjY26PLs76W7SvEhEFovIrSJyK/AJcKaz8/8ATBSRGmCi/b1SSik7EfkFtm/yXrIfSgU+sC4i5c7KysqISu5rdRhOERKfxvrSMpe2uXz5cpIHDGdreQEhwUEMT49uP1e2ZQ8b9x5n2I+cu+HKpQ9eCsCUKVOc2o7qmdNOzxCR/thu3Pu1iPwQ2/JHAqwG3upuI8aY5djmQ2OM2YNtR0GllFKdmwWMAtYAGGNqRERvmladKikpJSTRu24CbBOVnMX6oo9c2uayFSuIzBxO/boVjEw/cQvvvy+vIyY9hqAw547qt639vHz5cqe2o3qmq5HmZ4GDAMaY94wxvzLG3IdtlPlZZwenlFI+qskY075KkX2DE723Q3WqsLjE69ZobhOVkkl1ZUXXBR0od9nnxPUfzp7KNfzXxaknnJtfsJe+Uwe6JI5+37dNATl06JBL2lNd6yppzjTGrD/5oDEmH9uOfkoppRxvhYj8FggVkYnAv4B/WxyTclPl5WVEpWRZHYZThPVK4tChg+zbt88l7R0+fJiaynICw3vRfPQgN1/4bdK8tmYn3xxtYeAU1yTNF//qYgAuvfRSl7SnutZV0hxymnOhjgxEKaVUuweABqAEuA3bt3u/tzQi5ZYaGxvZsa2eSC9cOQNARIhP60tFhWtGm9esWUNi5kC2lueRGBNOQMC3adI/Pt9MdFYcfgHdvR3s7Pj52drJy8tzSXuqa139yefZb0g5gYj8DChwTkhKKeXbjDGt2G78u9MYc70x5hXdHVB1prKyktjeafgHBFoditNEJPVxWdL8+edfEJU5jN2VeVyUeeK44XvFe+l3pWtGmdsMuHwAAEePHnVpu6pzXSXN9wIzRGS5iPzZ/lgB/By4x/nhKaWU7xCbR0RkN7ZdWKtEpEFEHrI6NuWeSkpKiE7t33VBDxackMH60lKXtJW74gt69R3KN3XrmTb626kZhXW72N/YSv9LXfuzHnu3bSOsSZMmubRd1bmu1lreaYwZCzyKbc3QTcCjxpgxxpivnR+eUkr5lHuxbQw10hgTZ4yJBUYDF4nIfdaGptzRuqIiQpK8cz5zm6jkTIrWO3/ZudbWVooK8gkIj6Gl6QjXnJ/Ufu7tL7cQlRbjsqkZbdqmaKxcudKl7arOdetP3xizzBjzvP3xmbODUkopH/VTYJoxZmPbAWNMHfAT+zmlTpBXWEx0mnM32rBaVO9MqlywgkZtbS1BYRHs2lj1nfnM7xbtJWOCNT/nzO9lAtDc3GxJ++pbrv2VSSml1OkEGmN2n3zQGNMAeO+kVXXGyktLiPHy6RnhCSns3d3AwYMHndrO2rVricsazO7aInJSv12HeePOA9TvO86gqwY5tf1TGffAOABuuukmS9pX3zrt5iZKKccLCfRDZnzikrYC9ddiT3PsDM8pH9TQ0EBjYyOhvbx73xs/P3/i0/tSXl7O6NGjndbOqtVfEZ46kK2f/YupV3z7M/0gbwtR8REEhFiTMvn7+wPw7rvvWtK++pYmzUq5WOPxVmYsmuGStuZePtcl7SiHOU9EDnRyXDj9EqDKB5WUlJDQ5xxExOpQnC4yOYvS0lInJ81riLl4Go37G7hp9PD24x+X7CFmWG+ntdsdsX1j2Vu319IYlE7PUEopt2GM8TfGRHXyiDTGdDk9Q0QuF5EqEakVkQc6OS8i8pz9/HoRGdHh3KsisktESk+65hER2SYiRfbHVMd8WnW21q9fT3hv774JsE1IQiZFxd/Za81hjh07RnVFGUcOHyEsNJTYCNv0jOaWVr6sO8S5PzjXaW13x+QnJwMwe/ZsS+PwdZo0K6WUFxARf+AFYAowGJgmIoNPKjYFGGB/zATmdDj3D+DyU1T/jDEm2/5Y6NDA1RlbvTafsBTvvgmwTXRqXwqLS5xWf0lJCTFJaezeVEG/+OD245+XbScw0J+EgQlOa7s7QqJtXzQ9+uijlsbh6zRpVkop7zAKqDXG1BljjgFvA1efVOZq4HVj8xUQIyLJAMaYzwH9/teDFBauo1f6OVaH4RLRqf2oKHPeWs2FhYXEZJzDvrr1XNI/sv34v9ftIDytl9Pa7YnA8EBdQcNimjQrpZR3SAW2dnhfbz/W0zKducs+neNVEXGPDMLHNTY2smVTHdEpfa0OxSVCYxJobGqkoaHBKfWvXpNHWMoADn298YT1mZdWHSBpZHf+iTjf9/77ewCUl5dbHInv0qRZKaW8Q2d3g5289XZ3ypxsDtAPyAZ2AH/utHGRmSKSLyL5zkps1LdKS0uJS+mDf2BQ14W9gIiQkN6fUiftDLgmL5+gyDhajzcxYVAcAI3Hmqna2ciAyQOc0mZP9RnbB4Arr7zS4kh8lybNSinlHeqB9A7v04DtZ1DmBPadYVuMMa3AK9imgXRW7mVjTI4xJichwdr5n76gsLCQqDT3SOZcJSy5L+vXO/5mwGPHjlFbXcHRw4eJighr34Xvi/IdhIYEEJkU2UUNrrVx48auCymn0KRZKaW8Qx4wQESyRCQIuAn46KQyHwE/ta+icSGw3xiz43SVts15trsWcN7EUtVta/MLCEvx7k1NThaR3I+1BescXm95eTm9EtPYW1/LOfHfrsS7tHQnoclRDm/vbKSPSe+6kHIaTZqVUsoLGGOagbuAxUAF8I4xpkxEbheR2+3FFgJ1QC22UeM7264XkXnAamCgiNSLyM/sp54SkRIRWQ9MAO5zzSdSp7M2v9BnbgJsE5M+gIJCxyfNBQUFxGQM5OCWci7q+22SnFt9gITzUxze3tkY/8B4AH7/+99bG4iP0s1NlFLKS9iXg1t40rEXO7w2wKxTXDvtFMdvcWSM6uwdP36cmspyBkz3rekZ0an9+HxDNcePHycw0HG7yq/Jyyc0uT+H8pZx9Y22TU1aWlop3X6UqZPc62ccEGxL2/70pz/pms0W0JFmpZRSyoOUl5cTnZBMYGi41aG4VEBQCDEJyVRWVjq03jV5BQRF9qK1+RjjBtoWh1m3cTcBAX7EZMQ4tC1HCAgJ4NixY1aH4ZM0aVZKKaU8SF5eHjEZ1u5QZ5WYtAEUFxc7rL7m5maqyktpbGwkKrzDTYCVuwiLdc9fSkbOHAnAnj17LI7E92jSrJRSSnmQVavXEJbqW/OZ24Qk9SW/sNBh9VVVVREVl8S+7ZvpG/ftjNVVtfsIzXLPJcnPnWr7hemGG26wOBLfo0mzUkop5UG+WptHbB8fHWlOH8CaPMclzYWFhfTKOIeD9ZWM6vPtyPLazYdJG5nmsHacYdmyZVaH4HM0aVZKKaU8RGNjI3U1VcT42MoZbXplDKS0uAjbPa1nb21ePqHJAzi0YyOThyYCcPDIMXYcaCbze5kOacMZwhPcc+qIt3Na0iwiISKyVkSKRaRMRB61H48VkSUiUmN/ds/vP5RSSik3U1xcTFxqJgFBwVaHYomQqFgCQ8Ooq6tzSH1r8gsJi0/h+NEDTB4SD8DKqq8JDwskOMJ9f8bj/t84AKftkKg658yR5ibgUmPMedi2X73cvpj+A0CuMWYAkGt/r5RSSqkurF27lmgfvQmwTVzmIPLz88+6ntbWVspKijl2vJnQ0FDCQmxzmtfU7iE4PuKs63em3sN6A3D99ddbHIlvcVrSbGwO2d8G2h8GuBp4zX78NeAaZ8WglFJKeZPlX6wiMmOw1WFYKix5AGvWrj3rempqagiNiGbf11tJjfl2VDlv8yEi+nrGl+BVVVVWh+BTnDqnWUT8RaQI2AUsMcasAZLatm21Pyc6MwallFLKW6xZs4a4LN9OmmP6DOTLNWc/0lxQUEBsn3M5sLWK81OC2o8X1x8hJdu9dgLsTEym+60h7e2cmjQbY1qMMdlAGjBKRIZ291oRmSki+SKS39DQ4LwglVJKKQ/Q0NDA3j17iOqdaXUolortcy4lxetobW09q3q+WruW0JQBHNxey/iBcQAcbTrO1weayRib4YhQnWr8b8cDsGLFCmsD8SEuWT3DGLMPWA5cDuwUkWQA+/OuU1zzsjEmxxiTk5CQ4IowlVJKKbe1du1aevcfgvj59sJXwRExhEREU11dfVb1rF6TT0zaABr37eKq85MAyKttICzE361vAmzTK8M2hWT69OkWR+I7nLl6RoKIxNhfhwLfByqBj4C2P+HpwIfOikEppZTyFl9+uZrwNN++CbBNfN9hrF69+oyvb7sJsMUvgICAQNJiQwFYu2E3IW66E+CpbN682eoQfIYzf11NBpaJyHogD9uc5o+BPwATRaQGmGh/r5RSSqnTWL7yS3plDrE6DLcQkTGIFV+sOuPrq6urCYuK4ZsdW4iLCmk/nrf5IKHpnjNXOH5gvNUh+BRnrp6x3hhzvjFmuDFmqDHmMfvxPcaYy4wxA+zPe50Vg1JKKeUNWlpaWFeQR1xfTZoB4voO5YtVZ540r127ltg+g9m3pYpz4/3bjxfVHyFpeJIjQnSJtnnNixcvtjYQH+HbE6OUUkopD1BaWkpkrwSCIzxnFNSZYtL6s71+K/v37z+j6z9f9SXh6YM4VF/F2H62n2mraWXznmOk5bj39tkdRSZFAvDzn//c4kh8gybNSimHEhGXPYKCg7oOSCkv8OWXXxLbt9sLUHk9P/8AkrIGsWbNmjO6fuWq1cT1HcLhhnqmDrctNlC38wCIEJPheb+Y1NfXWx2CTwiwOgCllHeZsWiGy9qae/lcl7WllJWWfb6SiD46NaOjyD5D+PzzL5g0aVKPrjt8+DCbNtSQFdGL1uZjjO1vW4WioHY34RGe94t4/MB4dlfttjoMn6AjzUoppZSb+/LLL4nXkeYTxA3I5j9Lcnt8XUFBAQl9BtCwsYrI8DD87Ev4FW/ZT6Cbb5/dmfG/Gw/Af/7zH2sD8QGaNCullFJubOfOnXyzd6/Pb2pysvh+wykrKebIkSM9um716tVEpg9i96YKsmK//cJ9Xf1hIjM9Y/vsjiITdV6zq2jSrJRSSrmxVatW0XvAcJ/f1ORkAcGhJGYOZFUPV9HIXf4FMZlDOLi1kpEZ367JXPZ1o0etnHGy7du3Wx2C19N/gUoppZQbW/HFF0Rk6HzmzkT3y2Zp7mfdLt/S0sLqVStJOOd8Dn1dx8QhtnWOjzW3sGP/cY9aOaOjxKGJVofgEzRpVkoppdzYshUries3zOow3FL8gPNZ9OnSbpcvKioivFcCAcFhHDu0n6nDbclmxdZvCArwIyw2zFmhOtWE304AYMGCBRZH4t00aVZKKaXc1NGjR6muKCM2a7DVobil+H7Dqa2poqGhoVvlc3NziR1wPl9vKCMkNJSIENuc5qLNewmNDHZmqE7VluzPmjXL4ki8mybNSiml699uWwAAIABJREFUlJvKy8sjPqM/AUEhXRf2Qf6BQaQMHsnChQu7VX7hp7n06p/Nrg0lpER/u7xcWf0BAuLCT3OlZ9i1a5fVIXg1TZqVUkopN7Vy5UqidX3m04odPJZ/vfdBl+WOHz9O3prVJJ5zPvs2lXNB2rcjy8XbjhDlgStndJRyQYrVIXg9TZqVUkopN7Vk2efE9PX8+czHm5rYt3MbTYcPOrzu5KFjWPZZLk1NTact99lnnxGb2pfgiBgObqth0pCE9nNVuxpJGJRwmqvdX9u85ueff97iSLyX7giolFJKuaHW1lby13zFpZPusjqUM1ZX+AUVH/+NQzvq8Pv/27vv+KiK9Y/jnyeNkIQAIYQOAQwI0kFQQbALNrwqInjFLthFvYr12sXeFfypqBcRREFAkSISqnRpBpSA1ISaUNOz8/tjT3CJCQlk95zdzfN+vUJ2T5vv2ZDJ7Nk5MyGhuFwFVI2rR+I519L2vKuR0Ipfu4usVpNajZKYMWMGl19+eanbfT1uPPFtzyY/N5fcA3u5qnN7AAoLXezYn0/Xzg0qnMVJEdHu7iZPPPEE9957r8NpgpNeaVZKKaX8UEpKCpHVqlO1ei2no5yw/Nxsfnl/GCtGPc21zbLZ8fo5FP7fhRx47wIe6V6VzVNHMuWZgRzY7Z2xhet0uYiPPv6k1PWFhYVMmvQ9DTr0YufGtVSJjCTOmjJ7w879hIcKMQmBNxtgcRIqHDrk/av5yk0bzUoppZQfWrBgAbUCsGtGbtYRpr14K6Fpv7H55e58ekt76td038gYGxXOM1e25OA7vTizdi4zXhhE2p+rK1xm487nM2dOMrt27Spx/bx586haozYxtRuwO3UNDWp49Gf+K4OomMAdOcNTi94tAPebBOV92mhWSqkgISK9ReQPEUkVkWElrBcReddav1pEOnms+0xEdovI2mL7xInITBHZYH0P7LulAsgvc+ZSLbGN0zFOSH5uLjNfHUy9kP1sG96DhnFVS9wuLCyEWQ934b5edVjw7gPs+GNVhcoNrxpNow69+OKLL0pc/+4HH1K30wUAHNiSQqcGfzeS12w/QFiAjs9cXLe7ugEwePBgh5MEJ200K6VUEBCRUOADoA/QGhggIsUH9+0DJFlfdwAfeaz7HOhdwqGHAbOMMUnALOu5ssG8eQuIb97O6RjlZoxh7sgnqZa7h5TnziQsrOwmxlsDWnPfufVY+MGDZKRtqVD5jXtexauvv/mP7gkbN25k5syfadqjLwAHtq47OqkJwNr0bKIaVa9Q2f4iNDQUgFGjRjmcJDhpo1kppYJDVyDVGLPJGJMHjAX6FtumL/ClcVsE1BCRegDGmLlARgnH7QsUXb77ArjSJ+nVMdLT0zlwYD+xdZs4HaXcVkz5gkMbl/PbU12JKEeDuchbA1pzWesazHnr3gqNrhHXuCVxLTrx8iuvHLP8peGvkNijL+FVo8k5coi8w5n0O73e0fXrdmYT3yL+pMv1N5HVI3G5XE7HCEraaFZKqeDQANjm8Xy7texEtymujjEmHcD6nlDG9soLFixYQN2kdkhIYPyZ3vXXOjZOH8WEIW2pW+PEJ2KZeE8HGkUXkPzRYxhjTjpHy8tu5733P2TatGkAjB49mgmTfqD5uf0A2J6ynJjo6KMzAQJsy8ynXod6JR4vEPV8tCcAa9euLWNLdaIC47dRKaVUWaSEZcVbH+XZ5uQKF7lDRJaJyLLyTmmsSjdn7jxiGgfGpCaFBfn8OvIJ+ndO4JL2J/eeKiQkhCWPn07O9hRWTD75rgXRcXXpesfL9L/+Btp06Mxd9z3AGXe9RmQ1d1f83RtW0iI+/Oj2aRmHKXAZagb4xCaeGnRyvw/u27f4B02qorTRrJRSwWE70MjjeUOg+Hhe5dmmuF1FXTis7yXO02uM+dgY08UY06V27cCeJMIfzJ47n1rNA2PkjKXfjaBq4WG+uqNi/a/jYiL4/s62bJzxBTs3ppz0cWqf0o5ej3xK3d5DOP/J/1G9frOj6w5sWsV5Lf/uv7xqSwbRUeGEBMgV/ROxadMmpyMEHZ3cxEsaNGpM2vZtZW+oyhYSxrjBZ9lWXGh4hG1lKeVDS4EkEWkK7ACuAwYW22YycI+IjAW6AQeKul4cx2TgRmC49X2SV1Orfzhy5Aipf6zj1NuK38fpf/Zu28jWud/yy4OdvdLwvLhtAjd2q8OYkY9x2fPfEF7l5IaCi6pZm6iax755M8ZwKP0vBtzS9eiy37ftJ6J6ySN8BLKkPkls+GkD+fn5hIeHl72DKhdtNHtJ2vZt9B+50Lby7GxU2s5VgBl1qW3Fyc0/2laWUr5ijCkQkXuA6UAo8Jkx5ncRGWKtHwFMBS4BUoEs4Oai/UXka+AcIF5EtgP/NcZ8irux/I2I3ApsBfrZd1aV05IlS0ho0oKwCP8eO9gYw5LPX+Di1nGc08p7E7B8ektbfvrPfBaNeYOzb37ca8fdvWk9ISFCp8S/rzT/nnaE8LrVvFaGv+hxfw82/LSBK664gp9++snpOEHDZ41mEWkEfAnUBVzAx8aYd0QkDhgHJAKbgWuNMZm+yqGUUpWFMWYq7oax57IRHo8NcHcp+w4oZfk+4HwvxlRlWLBgAdUS/b8/87q5P5C7Zwvjn+jl1eOGhITw89AOtH92Olu6nE+Ttt28ctytqxfQNP7Y8ZjXpucQd2ZgT599PEU3RCrv8OWV5gLgIWPMChGpBiwXkZnATbjH/BxuDb4/DHjUhzmUN9jZZSJEPwBRSlVePyfPo+Yp5zgd47jyc7JJmfg+r/RNPGYkCm9p0zCW+89ryPufP0f9l74lvErFu1DsW7+E60+NPWbZX3tz6HhanQof2x8lnJbA7t93U1hYeHT8ZlUxPmudWP3kioYpOiQi63APbdQX90eA4B7zMxltNPs/G7tMaHcJpVRl5XK5WL5kMef3vs/pKMe19NuPSIgSHurT3GdlvDmgNeNWzOPX0W/Q89YnK3SswsJCDm7/g9tv+vuq9ZHcPPZnu6jXLniGm/PUe3hvvrz8S/r168eECROcjhMUbLldVEQSgY7AYnTMT6WUUqpEKSkpRFarTmRsnNNRSrV/1w62L5zE2Nt9f6Pi9Pvbs2vFTLb9vqxCx9n552rCw8Lo0MSjP/PWTKpWCSXMB1fK/UFouPvq8sSJEx1OEjx83mgWkRjgO+ABY8zBE9hPx/xUSilVqSxYsMDvh5pbOuZ1ujWLpWdL7938V5o2DWMZ0rM+yz5/joK8vJM+ztaVc2mRcGwXjzVbM6la7cQnYgkkzc9zfxKQlZXlcJLg4NNGs4iE424wf2WMKfpsQMf8VEoppUrwy5y5fj2pyfZ1K9if+hvf3dnBtjLfu7411UNzWTz27ZM+xu7Vcxh0xrEfbKfsOERofHRF4/m1no+4Zwds165iY2grN581mkVEgE+BdcaYNz1WFY35CTrmp1JKKXXU/PkLiffTK83GGH77+jUGdq1N/Zr2XaENCQlh2n3tSVv8IzvWrzzh/TPTtpJ7YC93n9vkmOVr0rOp5tFdI1iFhIewceNGp2MEBV9eae4O3ACcJyIrra9LcI/5eaGIbAAutJ4rpZRSldrOnTvZn5lB9XpNnY5SonVzf6DwwG4+vdn+q5adEqtza/e6LPnsv+Tn5p7QvqmLptE0IYaoYn2X/9ydQ8KpwX9b1eVvXw7Ao4/qmAsV5bNGszFmvjFGjDHtjDEdrK+pxph9xpjzjTFJ1vcMX2VQSimlAsXChQupm9QO8cMpnQvy8lg36UP+e2ljIsKcyTdiUBtqhOSwaMwbJ7TfzhWzGNgl/phlLuMibX8+9ToG58gZnuKau28qffXVVx1OEvj87zdTKaWUqoTmzJvnt/2ZV0z5jBrhLh677BTHMoSEhDDrwQ7sXDaDv1bML9c+6RvWkpO5i8cuOXZovE27DhIaIlSrE3yzAZak+0PdAXj8ce/NsFgZaaNZKaWU8gPJc+b75cgZWfsz2DJ7HJ8OauF0FNo0jOXpS5uw/IvnOJxZ9sha66Z9yTktqv+ja8bqLRlERUf4KqbfaXGh+2f38ssvO5wksGmjWSmllHJYdnY2f6z7nbimvh/7+EQtGfc2LetEcUXHuk5HAeC/V7agY4MqJL/zEIUF+aVudzhzD3vXLeLdga3+sW7t1gOE14wqYa/g1ef1PgA0btzY4SSBSxvNSimllMOWLl1KfONTCIvwr3GD92z+k92r5/Ddnf51BXzBsK5EHNnJnJH/xRhT4ja/fv4S7RrFclqDf3bBWJOWRWSD2BL2Cl5129QlvmU827Zt44UXXnA6TkDSRrNSSinlsPnz51M9sY3TMf5h2ZjXuLh1HK3q+1ff34iwEFY+fTqHUxcz9/+eo7Cw8Jj1fyyYzoGNK5l2f8cS9/99ZzbxLeJLXBfMLn/nciREeOqpp7j99tudjhNwtNGslFJKOWzm7LnUbOpfV3M3Lk0mKz2VcYPbOx2lRI1rRbHq6a4c+XM+Pz1/E1vXLiXrYCYrpnzJqjEv8Va/5tStUfKV+60ZedTrEPwjZ5Tkpqk3EVollE8++QQRYejQoU5HChjaaFZKKaUc5HK5WLZkMfGn+M+sbYWFhawe/xb3nVuf2Khwp+OUKqlODLtf70HPhMMs+/hRpjxyOTvnjGbine2498KSx7vetT+L3AJDrSTfTwPurwZNGkTnmzsD8PbbbyMiiAgRERFMmqRzzpUmrOxNlFJKKeUrKSkpRMZUJzI2zukoR62a+hVVCrN47douTkcpU2REGFMfcOd0uVyElDHO9cq/9hEdFV7mdsGuXf92tOvfjvycfJZ9uowNMzaQn5vPlVdeCUBeXh7h4f77hskJ2mhWSimlHDR/vn8NNZdz6CAbZ3zOqEEtAq5hWZ68a7ZlUqV6VRvSBIbwyHDOvPtMzrz7TACWjVrGmnFriIiIYN++fcTF+c+bOacF1m+DUkopFWRm/pJMbFP/6ZqxdPx7NI6LZFD3Rk5H8Ym1O44QXs+/bmz0J11u7kLfEX0BqFWr8nZhKYk2mpVSSimHGGOYN28etU/xj5vtMnZsJn3ZDL4b7H8jeXjLmvRsap2ijcHjiUuMo/sD7lkEW7X65zjXlZU2mpVSSimHbNmyhdy8fGISGjodBYClY16nZ8uadEqs7nQUn0ndk0v9jvWdjuH3WvRuQVStKNavX09GRobTcfyCNpqVCgk7euewHV+E6K0E3mTXzy2iSuWZclfZZ+7cudRp0cFdNzhsy+pFHNqylm+H+MdVb1/YdzCbrDwXdU6r43SUgHDNl9cAUL++vskAvRFQKXAV0H/kQtuKGzf4LNvKqgxunnazLeWM6j3KlnJU5TJr9hyqJTp/E6AxhpVj3+CWs+oQXy143yAu37TXPXJGmF4zLI/Q0FDqtq/LzlU72bt3L/HxlW9CGE/6v0YppZRyyOzkZOL9oD/z2l8mIlkZfPjv4O3LDLBqSyaRNaKcjhFQ+rzSB9C+zaBXmpVSSilH7Nixg4yMDGo0aO5ojvzcbP6YMpJXr2hCWJBfgV21/TDhfjYleCCoUq0Ke/fudTqG44L7t0MppZTyU8nJydRr2QlxeCzkFZNHERcJD/Z2tvFuh9Vp2dQ+tbbTMQLOlSPdE54MGDDA4STO0kazUkop5YDpM2dRrZmzXTOy9mewJfkbPr/xVEdz2GXT3lzqd9Kb2k5UVJy7S8vYsWMdTuIsbTQrpZRSDvhl9mwSWnZyNMPS8e/Rom4Ml7RPcDSHHdIyDpNXaIhvUblvZjtZp1xwCgCHDh1yOIlztNGslFJK2Wzbtm0cOHCQ6vWaOpYhI20Lu377hbG3tXYsg50WbdhNTEyVgJsa3F+c/fDZAHTv3t3hJM7R/zlKKRUkRKS3iPwhIqkiMqyE9SIi71rrV4tIp7L2FZFnRGSHiKy0vi6x63yC2c8//0zdUzs72p95xbi3ObN5dTo0Cd6JTDwt35RJRHy00zEC3po1a5yO4BhtNCulVBAQkVDgA6AP0BoYICLFLyH2AZKsrzuAj8q571vGmA7W11TfnknlMGXqNGq06OxY+ekb1pKZuoJxg9s5lsFuS7ceIba5Tp9dEe0HuvvgV9YZArXRrJRSwaErkGqM2WSMyQPGAn2LbdMX+NK4LQJqiEi9cu6rvMTlcjH7l1nUbXW6YxlWfvM2l7WNo2FcVccy2G1tWrZOn11BnQa5P5w677zzHE7iDG00K6VUcGgAbPN4vt1aVp5tytr3Hqs7x2ciUtN7kSunVatWER5Vjeha9Rwpf/OqXzmSnsoXt1Weq8xHcvPYfbiAhl0bOh0lKKxatcrpCI7wWaPZqlx3i8haj2VxIjJTRDZY37XyVUop75ASlplybnO8fT8CmgMdgHTgjRILF7lDRJaJyLI9e/aUL3ElNX36dGq3dOYqszGGNd++y6BuCdSICt7psotbnrqX6MgwIqIrzzn7StEoGvn5+Q4nsZ8vZwT8HHgf+NJj2TBgljFmuHWjyTDgUR9mUKpMEhLGuMFn2VqeUj6wHWjk8bwhkFbObSJK29cYs6tooYj8H/BDSYUbYz4GPgbo0qVL8ca68jBx8o/U6uRM75cNv86k4MAuRtx4riPlO2Xppn1E1tTps73hrKFnkfpzKldddRVTpkxxOo6tfPbX2xgzV0QSiy3uC5xjPf4CSEYbzcphxlWAGXWpbeXJzT/aVpaqVJYCSSLSFNgBXAcMLLbNZNxdLcYC3YADxph0EdlT2r4iUs8Yk27t/y9gLeqkZWZmsmbVSi4Z+LztZZtCFymTPuT+c+sREeTTZRf368aDVE3UD7e9ITQ0FIAffijx/XNQs/uSV52iyteqqIN/NHWllLKBMaZARO4BpgOhwGfGmN9FZIi1fgQwFbgESAWygJuPt6916FdFpAPu7hqbgcH2nVXwmT59OvVO7URYRBXby147eyKheYcZ3s+5UTucsmTrERoNqhzjUduhZtOaZP6V6XQM2/nt58QicgfuIZFo3Lixw2mUUsr/WcPBTS22bITHYwPcXd59reU3eDlmpTZ+wvfEtTrD9nILC/L5c+onvHRZ40o3ucfhnDzSDuRz9tmJTkcJGhe9eBHjBo7jvffe495773U6jm3s/s3ZZQ1vhPV9d2kbGmM+NsZ0McZ0qV27tm0BlVJKKV/Iz89n5owZ1Gtr3z0URVb+OJqY0EL+06e57WU7beH6XcRUDaNKjP1X94NVVJy7f/jDDz/scBJ72d1ongzcaD2+EZhkc/lKKaWUI2bPnk31Oo2Iqmlvz8TcrCNs+nk07/U/xdZy/cWCP/cQmVDN6RhBJ6xqGHl5eU7HsJXPumeIyNe4b/qLF5HtwH+B4cA3InIrsBXo56vyg57NIz6gIz4opVSFjB4zlvh2vWwvd8X3H1O/egT/Pqv4sN2Vw8JNh4hNcmZM7GDWfWh35rw0hw0bNpCUlOR0HFv4cvSMAaWsOt9XZVYqOuKDUkoFjPz8fCZN+p6ej3xia7kH9+5i24JJ/PxgR1vL9ScrtmXR8Tq9N8rbmvVsxpyX5tCnTx9SU1OdjmOLynU3gFJKKeWAX375hdiERrbPArhs3Du0axjDea3ibS3XX2xI38/hPEPDbjoToK9s3LjR6Qi20UazUkop5WMff/IZCR0vsLXMXRvXsS9lAd8NqTzTZRc3fdUOYuOiKt2IIXZp2qspAIWFhQ4nsYf+L1JKKaV8KDMzk2nTptG420W2lWmMYenol+nbPp7mdaJtK9ff/Lwuk+ikynmV3Q5nP3I2ANdff73DSeyhjWallFLKh8aMGUP9Nt2oEh1rW5nr5v5A/r7tjL6t8l5lBli48fDRq6HK+4pmBxw3bpzDSeyhjWallFLKR4wxvPfhCOp37WNbmblHDpEy4T1e6ptIVGTlHflo856DHMgppMlZTZyOEtSqN6rudATbaKNZKaWU8pHk5GQyDmVRt1VX28pc9NUbNKoRxkOVcCITT5OWbCW2VjQhYdrU8aWLXnJ3O/rwww8dTuJ7+j9JKaWU8pGXXnmNJj37ITbdiLZlzWJ2r5rN1Hsrd7cMgIkr91GjvY7P7GsxtWMAuP/++x1O4nvaaFZKKaV8ICUlhcWLF9PkjN62lJeXncXyUc/ywPkNaVW/cs+Al5tfwKLNh2n9r9ZOR6kUIqtHUlBQ4HQMn9NGs1JKKeUDjwx7nFMuGEhYRBVbypv/ybPUjTK8cZ02FKf/tp0qVcKp1ayW01EqhQtfuBCA8ePHO5zEt7TRrJRSSnnZ0qVLmb9wEc16XWVLeWtmjmf/n0tYOKyzLeX5uwnL04luFud0jEoj3hrWb9CgQQ4n8S1tNCullFJe5HK5uPOe+0nqfRNhEZE+L2/b78tY9/0HjL61NQ3jqvq8PH9XUOhi4qpMWv3rNKejVCoRMRHk5OQ4HcOntNGslFJKedGnn35KeuZhmvW43Odl7dvxF4tHDmPYxY24tmt9n5cXCH5YtgVCQ3WoOZv1ed09rOI777zjcBLf0UazUkop5SVbt27lP8Meo03/h30+YkZm2lZmvzaEazrU5KVrTvVpWYHkk3nbqdFWR82wW1yiuzvM0KFDHU7iO9poVkoppbygoKCAq6+9jmbn9qdmoySflrVr4zpmvXIbl7euxtghHXxaViDJPJLDz+sP0uGGjk5HqZRqJNbAGON0DJ/RRrNSSinlBfc9MJQ92ZB0wUCflrN+3k/Mfesubjsjjon3aOPQ01s/rCMmPoZap+ioGU647O3LALjgggscTuIblXd+TaWUUspL3nzrbb6dPJUeD37ks24ZedlZLPzfK+xdPYePBrbkjnMa+6ScQJVXUMh7c3bS5s6znI5SaYVHhgMwa9Ysh5P4hl5pVkoppSpgxIiRPP/yq3Qb8hoRUd6fVMQYw/oFPzHl8asI37qYdc+fqQ3mEoycsR4THkbSRb7tGqOOr9djvQD4+uuvHU7ifdpoVkoppU7SG2++xWNPP8tZ971DdLz3bz7bunYpPz57AynjXuOZ3nXZ9urZJNWJ8Xo5ge5gVi5PTdlKqxs6OR2l0mvWqxkAAwf6tpuSE4K6e0aDRo1J277NnsJCwxg32MaPhEKC+kenlFJ+rbCwkAcfepgx302mx9D3ia7lvQazMYbNvy0g5YdPyN69meu61OaTZ3oRGaH1fmkeH7eKsBpRtLq8ldNRFND6X61JmZjCtGnT6N3bnmnk7RDUv4Fp27fRf+RCW8oaN/gszKhLbSkLQG7+0baylFJK/S0zM5Nr+g9gQ3omPR78kCrRsV45ril0sX7hNDZM+5z8g3u4rks8Hzx+DrFR4V45frCavWYHnyzcywWvX+J0FGXpNrgbKRNT6NOnT1CNpqHdM5RSSqlyWr58OW3bd2QnNTnjrte90mAuyMtj5bSv+f7RK9gw4U0GdwzjyAfn8b/bO2iDuQy79mfRb8RqTunbmoRTE5yOozycdrV7RsYXX3zR4STeE9RXmpUqj/AQe6/ch+tbVaUCjsvl4o033uT5l4bT9tqhNOp8XoWPmZt1hFU/fcXWueOpFiE8e3EDHunTjBAfT4oSLDKP5NDjxflENK5F19u7Oh1HFdP19q78/t3vPPnkkzzxxBNOx/EKbTSrSi/fBTdPu9m28kb1HmVbWUqpitu4cSM33HQL2/Yeoud/RhITX7Hpqg/t283qHz8nbclP1ImN5LN/N2dQ90ZeSls5bN5zkAtfXcSB6GgueVO7Zfirqz+7mu9u+Y6qVauSnZ3tdJwK07ezSimlVAny8vJ4+eXhdOx8Ojl123Pm/e9WqMG8c2MKv7z3CD89dQ1RW+Yy9d52pL3eQxvMJ+i7XzfR4ekF5DaM55KP+uqVeT8WWz+WpIuTyMnJCYoJTxy50iwivYF3gFDgE2PMcCdyKKVUMCmrbhURsdZfAmQBNxljVhxvXxGJA8YBicBm4FpjTKYd5+MUYwwTJkzgoUeGEVK9Hmc/PJJqCQ1P6lj5Odmsm/cDW+ZNIGdfOmefEssHz57FaQ28P55zsFu3PYP7R69h3qYjtL6+I+2va+90JFUOPYb2YMfyHcyaNYtzzz2X2bNnOx3ppNneaBaRUOAD4EJgO7BURCYbY1LszqKUUsGinHVrHyDJ+uoGfAR0K2PfYcAsY8xwERlmPX/UrvOyU3Z2NuPHj+eFl1/hUJ6hxWV3Ue+0M074OHnZWWxaNofty2aQsWEFNWIiGdwtnuf+dS4xkdor8kQUFrqY9ts23v55M3NTD1O7dV36fnEpUXFRTkdTJ6D/6P58c+M3JCcnEx4eTlZWFuHhgXeTqxO/vV2BVGPMJgARGQv0BbTRrJRSJ688dWtf4EvjHgNqkYjUEJF6uK8il7ZvX+Aca/8vgGSCqNGcmZlJcnIy306cxJTJk4lv1ppGF95Gx9O64b4wX7bDGXtI/3M1e1JXkZm6gsO7thAbHcV5SVE89WQ3OiVW9/FZBJftew8za20aU9fsYXrKQQpFqNW+AX1H9SEmQSd2CVTXfnEt89+Zz4afNhAREUHNmjWZO3cubdq0cTpauTnRaG4AeM44sh33FQ+llFInrzx1a0nbNChj3zrGmHQAY0y6iPhsXK/vv/+e5cuXl2vb4mO/Fj03xuByuXC5XBQWFlJQUEBeXh45OTlkZWVx8OBBMjIySd+1m/S0HRTk5xETX5/YZh1IvPJ+wqNrcji/kD+XL6AgPx9Xfi4F+TkU5GSTn32YvKyD5B/OJO/AHvIO7CH/UAbGVUiVKlVoXD2Efs1j+Pd1bahfo4qVLI/1W/d482XyO8VH4XUZgzEGY8AYcLkM+YUu8gtd5BW4yMor5EhOPvuz88k4lMeug7lszsgjdW8um/flkVMAUVHhVGlckzZDzqRBlwZHj31k7xF7T055VcfrO9Lu2nZMuW8KmZmZtG3b9ui6sLAwatSoQUJCAgkJCcTFxRETE0N0dDTh4eGEh4cTGhqKiBASEnL0+/FATjubAAAK60lEQVT069ePdu3aeS2/2D3otIj0Ay42xtxmPb8B6GqMubfYdncAd1hPWwJ/eKyOB/baENcXNLszNLszNDs0McbU9sJxjqs8dauI/Ai8bIyZbz2fBTwCNCttXxHZb4yp4XGMTGNMzRLKP16dXV6dT2KfCirlanLxxeYfD8o6glcYHx/fTnL0n78ZU9IrSmEJuwbPDBknLpjPPwTf/xcv3zvxY5VYbztxpXk74HmrcEMgrfhGxpiPgY9LOoCILDPGdPFNPN/S7M7Q7M7Q7LYqT91a2jYRx9l3l4jUs64y1wN2l1T48erskxWAPwOvE5FlLn0NKvX/g8p+/uA/r4ET47QsBZJEpKmIRADXAZMdyKGUUsGkPHXrZGCQuJ0BHLC6Xhxv38nAjdbjG4FJvj4RpZTyR7ZfaTbGFIjIPcB03EMbfWaM+d3uHEopFUxKq1tFZIi1fgQwFfdwc6m4h5y7+Xj7WoceDnwjIrcCW4F+Np6WUkr5DUfGvjHGTMVdeZ8sr34EaDPN7gzN7gzNbqOS6larsVz02AB3l3dfa/k+4HzvJi23gPsZ+IC+BvoaVPbzBz95DWy/EVAppZRSSqlAo3NPKqWUUkopVQa/bDSLyGcisltE1nosGyciK62vzSKy0mPdYyKSKiJ/iMjFzqQ+mqWk7B1EZJGVfZmIdPVY5+/Z24vIryKyRkSmiEisxzq/yC4ijURktoisE5HfReR+a3mciMwUkQ3W95oe+/h79n7Wc5eIdCm2j79nf01E1ovIahGZKCKew5X5e/bnrdwrRWSGiNT3t+zBoqT6xlp+r/Ua/y4ir3osD7rXP5D/XnhLINff3hLIdak3lHb+HusfFhEjIvEey5w5f3N0EHL/+QJ6Ap2AtaWsfwN42nrcGlgFVAGaAhuBUH/KDswA+liPLwGSAyj7UqCX9fgW4Hl/yw7UAzpZj6sBf1r5XgWGWcuHAa8EUPZWuMe6TQa6eGwfCNkvAsKs5a8E2Ose67HNfcAIf8seLF+l1DfnAj8DVaznCcH8+gfy3wsvvgYBW3/b8Br4fV3qy/O3njfCfYPyFiDe6fP3yyvNxpi5QEZJ60REgGuBr61FfYGxxphcY8xfuO8K71rSvnYoJbsBiq7QVufv8U8DIXtLYK71eCZwtfXYb7IbY9KNMSusx4eAdbhnOOuLe9pfrO9XWo/9PrsxZp0xpqTJIQIh+wxjTIG12SLcY/5CYGQ/6LFZNH9PKOA32YNFKfXNncBwY0yutU3RmNBB+foH8t8Lbwnk+ttbArku9Ybj/B8AeAv3BEyeN+A5dv5+2Wguw9nALmPMBut5adPC+pMHgNdEZBvwOvCYtTwQsq8FrrAe9+PvCRD8MruIJAIdgcUUm/4XKJr+NxCylybQst8C/GQ9DojsIvKi9bt6PfC0tZlfZg9CLYCzRWSxiMwRkdOt5ZXp9Q/kvxcVEsj1t7cEcl3qDZ7nLyJXADuMMauKbebY+Qdio3kAf19lhpKnX/S3IUHuBIYaYxoBQ4FPreWBkP0W4G4RWY77Y5M8a7nfZReRGOA74IFiVwz/sWkJyzT7SSotu4g8ARQAXxUtKmF3v8tujHnC+l39CrinaNMSdve339VgEAbUBM4A/oN7fGihcr3+gfz34qQFch3oLYFcl3qD5/njPt8n+PvCxTGblrDMlvMPqEaziIQBVwHjPBaXa1puh90ITLAej+fvjxH8PrsxZr0x5iJjTGfcb1Y2Wqv8KruIhOP+ZfvKGFP0Wu8S97S/yLHT/wZC9tIERHYRuRG4DLjeWJ3QCJDsHsbwd3ckv8oexLYDE4zbEsAFxFO5Xv+A/XtxsgK5/vaWQK5LvaGE82+Ou7/yKhHZjPscV4hIXRw8/4BqNAMXAOuNMds9lk0GrhORKiLSFEgCljiSrnRpQC/r8XlAUdcSv88uIgnW9xDgSaBoogS/yW5difoUWGeMedNjVWnT/wZC9tL4fXYR6Q08ClxhjMny2CUQsid5bHYFsN567DfZg9z3uOtIRKQFEAHspXK9/gH79+JkBHL97S2BXJd6Q0nnb4xZY4xJMMYkGmMScTeUOxljduLk+XvzrkJvfeG+opkO5Fsv1K3W8s+BISVs/wTuK6B/YN117E/ZgR7Actx3ey4GOgdQ9vtx38n6J+7pdMXfsluvrwFWAyutr0uAWsAs3H90ZgFxAZT9X9bPIBfYBUwPoOypuPubFS0bEUDZv8Pdj381MAX3zTh+lT1YvkqpbyKA0dbPYAVwXjC//oH898KLr0HA1t82vAZ+X5f68vyLbbMZa/QMJ89fZwRUSimllFKqDIHWPUMppZRSSinbaaNZKaWUUkqpMmijWSmllFJKqTJoo1kppZRSSqkyaKNZKaWUUkqpMmijWSmllFJKqTJoo1l5lYjcJyLrRGSHiLxvLRsiIoMcyrNZROK9fMxEERno8fymonNVSqlAonW2UuUX5nQAFXTuAvrgntGqC4AxZsRx96ggazYhMca4fFmOh0RgIO5plpVSKpBpna1UOemVZuU1IjICaIZ7isuaHsufEZGHrcfJIvK2iCwUkbUi0tVjm/+JyC8iskFEbvfY/z8islREVovIs9ayROvqyIe4Zw7znIe+tHz/FpElIrJSREaKSKi1/LCIvCgiq0RkkYjUsZY3t54vFZHnROSwdajhwNnWcYZay+qLyDQr+6tl5DgsIq+IyHIR+VlEulqvyyYRucLa5iYR+V5EpojIXyJyj4g8KCK/WZniyv6JKKVU6bTO1jpbnRhtNCuvMcYMAdKAc4HM42wabYw5C/cVjs88lrcDLgXOBJ4WkfoichHueeW7Ah2AziLS09q+JfClMaajMWbL8bKJSCugP9DdGNMBKASuL8oDLDLGtAfmAkWV/zvAO8aY063zKjIMmGeM6WCMecta1sE6flugv4gc7w9CNJBsjOkMHAJeAC7EPXX2cx7btcF9daQr8CKQZYzpCPwKOPLRqVIqeGidrXW2OjHaPUM54WsAY8xcEYkVkRrW8knGmGwgW0Rm4654egAXAb9Z28TgrpC3AluMMYvKWeb5QGdgqfuTQaoCu611ecAP1uPluCtDcP8huNJ6PAZ4/TjHn2WMOQAgIilAE2BbKdvmAdOsx2uAXGNMvoiswf0xYpHZxphDwCEROQBM8din3XGyKKWUN2mdrXW2QhvNyhmmlOclLRfgZWPMSM8VIpIIHDmBMgX4whjzWAnr8o0xRWUXcnK/F7kej8s6hmd5rqJ9jTEuEfHcz/OYLo/nrpPMqJRSJ0PrbK2zFdo9QzmjP4CI9AAOFL3bB/qKSKSI1ALOAZYC04FbRCTG2qeBiCScRJmzgGuK9hWROBFpUsY+i4CrrcfXeSw/BFQ7iQxKKRWItM5WCn3no5yRKSILgVjgFo/lS4AfgcbA88aYNCDN6tv2q/UR3WHg37ivDJSbMSZFRJ4EZohICJAP3A0cr1/dA8BoEXnIylX0h2I1UCAiq4DPOX5fQKWUCnRaZyuFe8gXpzOoSkREkoGHjTHLii1/BjhsjDleHzRbiUgUkG2MMSJyHTDAGNPX6VxKKWUXrbOV+pteaVaqdJ2B98V9uWQ/x15hUUop5V+0zlY+pVeaVVAQkcVAlWKLbzDGrHEiD/hnJqWU8gf+WD/6YyblX7TRrJRSSimlVBl09AyllFJKKaXKoI1mpZRSSimlyqCNZqWUUkoppcqgjWallFJKKaXKoI1mpZRSSimlyvD/BDBaEDv2lMkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 444.125x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "penguins = sns.load_dataset(\"penguins\") \n",
    "\n",
    "# axes-level plots\n",
    "\n",
    "# create two subplots and set the figure size\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 5)\n",
    "fig, ax = plt.subplots(1, 2)\n",
    "\n",
    "# create two axes-level plots of a distribution of a single quantitative variable\n",
    "sns.histplot(data=penguins, ax = ax[0], x=\"flipper_length_mm\", hue=\"species\", multiple=\"stack\");\n",
    "sns.kdeplot(data=penguins,  ax = ax[1], x=\"flipper_length_mm\", hue=\"species\", multiple=\"stack\");\n",
    "\n",
    "\n",
    "# create a figure-level plot of a distribution of a single quantitative variable\n",
    "sns.displot(data=penguins, x=\"flipper_length_mm\", \n",
    "            hue=\"species\", multiple=\"stack\", kind=\"kde\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 2: Revisiting manipulating and visualizing the avocado data\n",
    "\n",
    "Let's revisit manipulating and visualizing the avocado data but using popular Python Data Science packages. \n",
    "\n",
    "Below we reload these packages, although not really necessary if one has loaded them already. It is recommended that all packages are loaded at the top of a Jupyter notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing packages that we will use \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# make sure we can display figures in the Jupyter notebook\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.1: Loading the avocado data\n",
    "\n",
    "Let's reload the avocado data as a pandas DataFrame using the `pd.read_csv(\"csv_or_url\")` method. We will also convert the `Date` column to a `datetime` data type. \n",
    "\n",
    "**Exercise 2.1**: Please show the first 5 rows of the avocado DataFrame using `df.head(5)` method.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>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>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>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>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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.08</td>\n",
       "      <td>78992.15</td>\n",
       "      <td>1132.00</td>\n",
       "      <td>71976.41</td>\n",
       "      <td>72.58</td>\n",
       "      <td>5811.16</td>\n",
       "      <td>5677.40</td>\n",
       "      <td>133.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>conventional</td>\n",
       "      <td>2015</td>\n",
       "      <td>Albany</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.28</td>\n",
       "      <td>51039.60</td>\n",
       "      <td>941.48</td>\n",
       "      <td>43838.39</td>\n",
       "      <td>75.78</td>\n",
       "      <td>6183.95</td>\n",
       "      <td>5986.26</td>\n",
       "      <td>197.69</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": [
       "        Date  AveragePrice  Total Volume     4046       4225    4770  \\\n",
       "0 2015-12-27          1.33      64236.62  1036.74   54454.85   48.16   \n",
       "1 2015-12-20          1.35      54876.98   674.28   44638.81   58.33   \n",
       "2 2015-12-13          0.93     118220.22   794.70  109149.67  130.50   \n",
       "3 2015-12-06          1.08      78992.15  1132.00   71976.41   72.58   \n",
       "4 2015-11-29          1.28      51039.60   941.48   43838.39   75.78   \n",
       "\n",
       "   Total Bags  Small Bags  Large Bags  XLarge Bags          type  year  region  \n",
       "0     8696.87     8603.62       93.25          0.0  conventional  2015  Albany  \n",
       "1     9505.56     9408.07       97.49          0.0  conventional  2015  Albany  \n",
       "2     8145.35     8042.21      103.14          0.0  conventional  2015  Albany  \n",
       "3     5811.16     5677.40      133.76          0.0  conventional  2015  Albany  \n",
       "4     6183.95     5986.26      197.69          0.0  conventional  2015  Albany  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado = pd.read_csv(\"avocado.csv\")\n",
    "\n",
    "# convert the Date column to a \"datatime\" data type\n",
    "avocado['Date'] = pd.to_datetime(avocado['Date'])\n",
    "\n",
    "# Ignore this (this will remove a warning later that arises in later exercises when plotting with dates)\n",
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "\n",
    "# show the first 5 rows of the avocado DataFrame\n",
    "avocado.head(5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.2: Relabeling columns\n",
    "\n",
    "Let's rename the `4046`, `4225` and `4770` columns. The code below does this using a `df.rename()` method which takes a dictionary of elements to describe how the columns should be renamed. A Python dictionary is a data structure that enables you to look up a value based on a key that is supplied. \n",
    "\n",
    "If you would like to know more about Python dictionary let me know and we can discuss them!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>sold_nonorg_sm</th>\n",
       "      <th>sold_nonorg_l</th>\n",
       "      <th>sold_nonorg_xl</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>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>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>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": [
       "        Date  AveragePrice  Total Volume  sold_nonorg_sm  sold_nonorg_l  \\\n",
       "0 2015-12-27          1.33      64236.62         1036.74       54454.85   \n",
       "1 2015-12-20          1.35      54876.98          674.28       44638.81   \n",
       "2 2015-12-13          0.93     118220.22          794.70      109149.67   \n",
       "\n",
       "   sold_nonorg_xl  Total Bags  Small Bags  Large Bags  XLarge Bags  \\\n",
       "0           48.16     8696.87     8603.62       93.25          0.0   \n",
       "1           58.33     9505.56     9408.07       97.49          0.0   \n",
       "2          130.50     8145.35     8042.21      103.14          0.0   \n",
       "\n",
       "           type  year  region  \n",
       "0  conventional  2015  Albany  \n",
       "1  conventional  2015  Albany  \n",
       "2  conventional  2015  Albany  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado2 = avocado.copy()\n",
    "\n",
    "# rename using a dictionary - requires knowledge of a dictionary\n",
    "avocado2.rename(columns = {\"4046\": \"sold_nonorg_sm\", \n",
    "                          \"4225\": \"sold_nonorg_l\",\n",
    "                          \"4770\": \"sold_nonorg_xl\"}, \n",
    "                inplace = True)\n",
    "\n",
    "avocado2.head(3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.3: Reducing (filtering) the data to a smaller number of rows\n",
    "\n",
    "To filter data using pandas requires creating a \"Boolean mask\". We do this by creating a Series of Boolean values (True's and False's) that meet particular criteria. Once we have this mask we can use it to select only the columns that are listed as True (this is similar to how one can filter rows of an R data frame in base R). \n",
    "\n",
    "**Exercise 2.3**: The code below filters the avocado data to get only the data from the Northeast. Please use the `df.shape` property to see how many rows this DataFrame has. Also play around with the code to explore filtering the data in other ways.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0        False\n",
      "1        False\n",
      "2        False\n",
      "3        False\n",
      "4        False\n",
      "         ...  \n",
      "18244    False\n",
      "18245    False\n",
      "18246    False\n",
      "18247    False\n",
      "18248    False\n",
      "Name: region, Length: 18249, dtype: bool\n",
      "338\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# print the boolean mask\n",
    "the_mask = avocado2.region == \"Northeast\"\n",
    "\n",
    "print(the_mask)\n",
    "\n",
    "# filter the data based on a boolean mask\n",
    "avocado3 = avocado2[the_mask]\n",
    "#avocado3 = avocado2[avocado2.region == \"Northeast\"]\n",
    "\n",
    "# print how many rows the \n",
    "print(avocado3.shape[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.4: Selecting a subset of the columns\n",
    "\n",
    "We can select a subset of columns using the syntax `df[[\"col1\", \"col2\", \"col3\"]]`; i.e., we pass a list of columns we would like to select into our data frame `df[]`. \n",
    "\n",
    "**Exercise 2.4**: Create a DataFrame `avocado4` that only has only the columns:\n",
    "- Date\n",
    "- AveragePrice\n",
    "- Total Volume\n",
    "- type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Date</th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1508</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3156360.20</td>\n",
       "      <td>conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1509</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3190120.04</td>\n",
       "      <td>conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1510</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3696551.52</td>\n",
       "      <td>conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1511</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3218494.55</td>\n",
       "      <td>conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1512</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2593780.51</td>\n",
       "      <td>conventional</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Date  AveragePrice  Total Volume          type\n",
       "1508 2015-12-27          1.20    3156360.20  conventional\n",
       "1509 2015-12-20          1.20    3190120.04  conventional\n",
       "1510 2015-12-13          1.09    3696551.52  conventional\n",
       "1511 2015-12-06          1.14    3218494.55  conventional\n",
       "1512 2015-11-29          1.22    2593780.51  conventional"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# select with a list\n",
    "avocado4 = avocado3[[\"Date\", \"AveragePrice\", \"Total Volume\", \"type\"]]\n",
    "\n",
    "avocado4.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.5: Creating separate tables for conventional and organic avocados (can skip)\n",
    "\n",
    "To gain more practice with filitering data in pandas, let's create separate DataFrames that have the conventional and organic avocados.\n",
    "\n",
    "**Exercise 2.5**: Please create a DataFrame called `conventional` that only has data from conventional avocados, and a DataFrame called `organic` that only has data from organic avocados. Then print the number of rows in each DataFrame.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "169\n",
      "169\n"
     ]
    }
   ],
   "source": [
    "conventional = avocado4[avocado4.type == \"conventional\"]\n",
    "organic = avocado4[avocado4.type == \"organic\"]\n",
    "\n",
    "print(conventional.shape[0])\n",
    "print(organic.shape[0])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.6a: Supply and demand visualization using Matplotlib\n",
    "\n",
    "Let's create a scatter plot of the volume of avocados sold as a function of their price using Matplotlib. To do this we can use the `plt.scatter(\"x_col\", \"y_col\")` function. \n",
    "\n",
    "**Exercise 2.6**: Please go ahead and create the scatter plot. You can also use the `plt.xlabel(\"label\")` and `plt.ylabel(\"label\")` to add better labels to the axes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting using Matplotlib pylab interface\n",
    "plt.scatter(conventional[\"AveragePrice\"], conventional[\"Total Volume\"]);\n",
    "plt.xlabel(\"Price\");\n",
    "plt.ylabel(\"Volume\");\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.6a: Supply and demand visualization using seaborn\n",
    "\n",
    "Let's also create a scatter plot of the volume of avocados sold as a function of their price using seaborn. To do this we can use the `sns.relplot(data = df, x = \"x_col\", y = \"y_col\")`.\n",
    "\n",
    "**Exercise 2.6**: Please go ahead ans create the scatter plot. Create two versions of this plot that have one additonal argument which is either:\n",
    "- `col = \"type\"`  \n",
    "- `hue = \"type\"`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# using seaborn to create a facet plot of both conventional and organic\n",
    "sns.relplot(data = avocado4, x = \"AveragePrice\", y = \"Total Volume\", col = \"type\");\n",
    "\n",
    "# using seaborn to create a plot of both conventional and organic distinguished by color\n",
    "sns.relplot(data = avocado4, x = \"AveragePrice\", y = \"Total Volume\", hue = \"type\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.7: Joining Tables (can skip)\n",
    "\n",
    "In order to practice joining DataFrames, let's join the information about conventional and organic avocados into a single wide DataFrame. \n",
    "\n",
    "To join to DataFrames together we can use the `df1.merge(df2, on = 'Date', suffix = ('_left_suffix, '_right_shuffix')` method. Where:\n",
    "- `df1` is the first DataFrame we want to join\n",
    "- `df2` is the second DataFrame we want to join \n",
    "- `on` is the name of a column that both tables have in common that we want to join on\n",
    "- `suffix` is an optional tuple that specifies a string to append to the names of the left and right columns\n",
    "\n",
    "\n",
    "Note: different types of joins are possible, see `? df.merge` for more details.\n",
    "\n",
    "**Exercise 2.7**: Please create an variable `wide_data` that has the data from `conventional` DataFrame joined with the `organic` DataFrame. Then print the first 5 rows of this DataFrame. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Date</th>\n",
       "      <th>AveragePrice_conventional</th>\n",
       "      <th>Total Volume_conventional</th>\n",
       "      <th>type_conventional</th>\n",
       "      <th>AveragePrice_organic</th>\n",
       "      <th>Total Volume_organic</th>\n",
       "      <th>type_organic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3156360.20</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.70</td>\n",
       "      <td>75884.69</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3190120.04</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.77</td>\n",
       "      <td>73826.41</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3696551.52</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.80</td>\n",
       "      <td>76466.85</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3218494.55</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.53</td>\n",
       "      <td>67245.25</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2593780.51</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.59</td>\n",
       "      <td>48901.36</td>\n",
       "      <td>organic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  AveragePrice_conventional  Total Volume_conventional  \\\n",
       "0 2015-12-27                       1.20                 3156360.20   \n",
       "1 2015-12-20                       1.20                 3190120.04   \n",
       "2 2015-12-13                       1.09                 3696551.52   \n",
       "3 2015-12-06                       1.14                 3218494.55   \n",
       "4 2015-11-29                       1.22                 2593780.51   \n",
       "\n",
       "  type_conventional  AveragePrice_organic  Total Volume_organic type_organic  \n",
       "0      conventional                  1.70              75884.69      organic  \n",
       "1      conventional                  1.77              73826.41      organic  \n",
       "2      conventional                  1.80              76466.85      organic  \n",
       "3      conventional                  1.53              67245.25      organic  \n",
       "4      conventional                  1.59              48901.36      organic  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_data = conventional.merge(organic, on = 'Date', suffixes = ('_conventional', '_organic'))\n",
    "\n",
    "wide_data.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.8: Are organic avocados really more expensive? \n",
    "\n",
    "Now we are ready to again address whether organic avocados are really more expensive and by how much! Let's start addressing this question by visualizing the data and overlapping histograms using seaborn.\n",
    "\n",
    "**Exercise 2.8**: Please use the `sns.displot(data = df, x = \"x_col\", hue = \"hue_col\", kind = \"plot_type\")` method to plot overlapping kernel density estimates of the average avocado price where:\n",
    "\n",
    "- `df` is the data frame you want to plot the data from\n",
    "- `\"x_col\"` is the name of the data column you want to plot\n",
    "- `\"hue_col\"` is the name of the column that specifies the type of avocado \n",
    "- `\"plot_type\"` should be set to the string \"kde\" to plot a kernel density estimate\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 460.375x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.displot(data = avocado4, x=\"AveragePrice\", \n",
    "            hue=\"type\", kind=\"kde\");\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.9: Are organic avocados really more expensive? Additional visualizations!\n",
    "\n",
    "Let's use seaborn to create some additional visualizations comparing conventional and organic avocados. \n",
    "\n",
    "**Exercise 2.9**: The code below uses `sns.catplot()` function to create a stripchart of the data for the conventional and organic avocado prices. Please create versions of the plot that plot the same data but that create different plots by setting the `kind` argument to the following values:\n",
    "\n",
    "- `\"box\"` to create a boxplot\n",
    "- `\"swarm\"` to create a swarmplot\n",
    "- `\"violin\"` to create a violin plot\n",
    "- `\"point\"` to create a single connected point at the category means\n",
    "- `\"bar\"` to create a dynamite plot\n",
    "\n",
    "You can also use `? sns.catplot` to see more options. Which type of plot do you think looks best? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# default kind is stripplot\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"strip\")\n",
    "\n",
    "# other options\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"box\");\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"swarm\");\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"violin\");\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"point\");\n",
    "sns.catplot(data = avocado4, x = \"type\", y = \"AveragePrice\", kind = \"bar\");\n",
    "\n",
    "# which is best? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.10: Getting the difference in conventional and organic prices for each date (can skip)\n",
    "\n",
    "Another way we can assess whether organic avocados are more expensive than conventional avocados is to compare their prices on each date. \n",
    "\n",
    "To examine this in pandas, we can pull a Series out of our DataFrame using the syntax: `my_series = df[\"colum_name\"]`. If we have two Series with the same index values in `my_series1` and `my_series2`, then we can create an Series that has the differences between each index value using  `diff_series = my_series1 - my_series2`.\n",
    "\n",
    "\n",
    "**Exercise 2.10**: Please complete the following steps to create a Series called `price_difference` that has the difference in prices of organic and conventional avocados for each date:\n",
    "\n",
    "1. Extract a Series from the `wide_data` DataFrame that has the prices of organic avocados and store it in a variable called `organic_array`. \n",
    "2. Extract a Series from the `wide_data` DataFrame that has the prices of conventional avocados and store it in a variable called `conventional_array`. \n",
    "3. Create a variable called `price_difference` that is a Series that has the difference in prices between the organic and conventional avocados.\n",
    "4. Use the `min()` and `max()` functions to find what the minimum and maximum price differences are. Does this provide evidence that organic avocados are always more expensive? \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.010000000000000009\n",
      "0.9900000000000002\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# these are Series - could use the to_numpy() method to convert them to NumPy arrays but not needed\n",
    "organic_array = wide_data['AveragePrice_organic']\n",
    "conventional_array = wide_data['AveragePrice_conventional'] \n",
    "\n",
    "# can subtract two Series that have the same Indexes \n",
    "price_differences = organic_array - conventional_array\n",
    "\n",
    "\n",
    "print(min(price_differences))\n",
    "print(max(price_differences))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.11: Adding a column that has the difference in organic and conventional prices our wide DataFrame (can skip)\n",
    "\n",
    "Now that we have a Series of price differences, we can add these price differences back as a column to our wide data table. To add an Series to a DataFrame, we can use `df[\"new_col_name\"] = series_to_add`; note: the `series_to_add` needs to have the same number of elements as the number of rows in `df`.\n",
    "\n",
    "**Exercise 2.11**: Please add a new column called `Price difference` to the `wide_data` DataFrame which has the difference in organic and conventional avocado prices. Once you have added this column, using the `df.head(5)` to see the first 5 rows of the DataFrame.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>Date</th>\n",
       "      <th>AveragePrice_conventional</th>\n",
       "      <th>Total Volume_conventional</th>\n",
       "      <th>type_conventional</th>\n",
       "      <th>AveragePrice_organic</th>\n",
       "      <th>Total Volume_organic</th>\n",
       "      <th>type_organic</th>\n",
       "      <th>Price difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-27</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3156360.20</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.70</td>\n",
       "      <td>75884.69</td>\n",
       "      <td>organic</td>\n",
       "      <td>0.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-12-20</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3190120.04</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.77</td>\n",
       "      <td>73826.41</td>\n",
       "      <td>organic</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3696551.52</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.80</td>\n",
       "      <td>76466.85</td>\n",
       "      <td>organic</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3218494.55</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.53</td>\n",
       "      <td>67245.25</td>\n",
       "      <td>organic</td>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-29</td>\n",
       "      <td>1.22</td>\n",
       "      <td>2593780.51</td>\n",
       "      <td>conventional</td>\n",
       "      <td>1.59</td>\n",
       "      <td>48901.36</td>\n",
       "      <td>organic</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  AveragePrice_conventional  Total Volume_conventional  \\\n",
       "0 2015-12-27                       1.20                 3156360.20   \n",
       "1 2015-12-20                       1.20                 3190120.04   \n",
       "2 2015-12-13                       1.09                 3696551.52   \n",
       "3 2015-12-06                       1.14                 3218494.55   \n",
       "4 2015-11-29                       1.22                 2593780.51   \n",
       "\n",
       "  type_conventional  AveragePrice_organic  Total Volume_organic type_organic  \\\n",
       "0      conventional                  1.70              75884.69      organic   \n",
       "1      conventional                  1.77              73826.41      organic   \n",
       "2      conventional                  1.80              76466.85      organic   \n",
       "3      conventional                  1.53              67245.25      organic   \n",
       "4      conventional                  1.59              48901.36      organic   \n",
       "\n",
       "   Price difference  \n",
       "0              0.50  \n",
       "1              0.57  \n",
       "2              0.71  \n",
       "3              0.39  \n",
       "4              0.37  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wide_data[\"Price difference\"] = price_differences\n",
    "\n",
    "wide_data.head(5)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.12: Calculating summary statistics (can skip)\n",
    "\n",
    "We can calculate summary statistics, such as the mean and standard deviation, on values in a DataFrame using `df.mean()` and `df.std()`. \n",
    "\n",
    "**Exercise 2.12**: Using the `wide_data` DataFrame, calculate the mean and standard deviation of the conventional prices and organic prices. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AveragePrice_conventional    1.344438e+00\n",
      "Total Volume_conventional    4.077247e+06\n",
      "AveragePrice_organic         1.859408e+00\n",
      "Total Volume_organic         1.433497e+05\n",
      "Price difference             5.149704e-01\n",
      "dtype: float64\n",
      "Date                         342 days 12:16:38.523299392\n",
      "AveragePrice_conventional                        0.18956\n",
      "Total Volume_conventional                  807016.253448\n",
      "AveragePrice_organic                            0.166871\n",
      "Total Volume_organic                       103535.334452\n",
      "Price difference                                0.209086\n",
      "dtype: object\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/conda/envs/wavfacdev/lib/python3.7/site-packages/ipykernel_launcher.py:1: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "/conda/envs/wavfacdev/lib/python3.7/site-packages/ipykernel_launcher.py:6: FutureWarning: DataFrame.mean and DataFrame.median with numeric_only=None will include datetime64 and datetime64tz columns in a future version.\n",
      "  \n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AveragePrice_conventional</th>\n",
       "      <th>Total Volume_conventional</th>\n",
       "      <th>AveragePrice_organic</th>\n",
       "      <th>Total Volume_organic</th>\n",
       "      <th>Price difference</th>\n",
       "      <th>Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.344438</td>\n",
       "      <td>4077247.416982</td>\n",
       "      <td>1.859408</td>\n",
       "      <td>143349.681006</td>\n",
       "      <td>0.51497</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sd</th>\n",
       "      <td>0.18956</td>\n",
       "      <td>807016.253448</td>\n",
       "      <td>0.166871</td>\n",
       "      <td>103535.334452</td>\n",
       "      <td>0.209086</td>\n",
       "      <td>342 days 12:16:38.523299392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     AveragePrice_conventional Total Volume_conventional AveragePrice_organic  \\\n",
       "mean                  1.344438            4077247.416982             1.859408   \n",
       "sd                     0.18956             807016.253448             0.166871   \n",
       "\n",
       "     Total Volume_organic Price difference                        Date  \n",
       "mean        143349.681006          0.51497                         NaT  \n",
       "sd          103535.334452         0.209086 342 days 12:16:38.523299392  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "print(wide_data.mean())\n",
    "print(wide_data.std())\n",
    "\n",
    "\n",
    "# maybe too complex, just print these separately\n",
    "mean_sd = pd.concat([wide_data.mean().to_frame().T, wide_data.std().to_frame().T])\n",
    "\n",
    "mean_sd.index = [\"mean\", \"sd\"]\n",
    "\n",
    "mean_sd\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.13: Calculating summary statistics II\n",
    "\n",
    "We can also calculate summary statistics on a DataFrame using `df.groupby(\"grouping_column\").agg_method()` method. Let's try that approach here as well.\n",
    "\n",
    "**Exercise 2.13**: Use the `avocado4` table to calculate the mean conventional and organic avocado prices. Hint, to calculate the mean of the values in a DataFrame use `df.mean()`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AveragePrice</th>\n",
       "      <th>Total Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>conventional</th>\n",
       "      <td>1.344438</td>\n",
       "      <td>4.077247e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>organic</th>\n",
       "      <td>1.859408</td>\n",
       "      <td>1.433497e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              AveragePrice  Total Volume\n",
       "type                                    \n",
       "conventional      1.344438  4.077247e+06\n",
       "organic           1.859408  1.433497e+05"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avocado4.groupby(\"type\").mean()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Part 2.14: Explore on your own! \n",
    "\n",
    "Try exploring the data further to see if you can find anything else interesting in it! \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://images.fineartamerica.com/images/artworkimages/mediumlarge/2/funny-avocado-lover-gifts-mike-g.jpg\" width=500 height=500>"
   ]
  }
 ],
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