SMUMSE_ProgWorkshop_Lesson4.ipynb

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    "# SMU Master of Science (Economics) Programming Workshop in Python\n",
    "\n",
    "\n",
    "## Introduction\n",
    "In today's class, we will be learning more about plotting with Python. The key libraries used in this section are [Matplotlib](https://matplotlib.org/) and [Seaborn](https://seaborn.pydata.org/).\n",
    "\n",
    "---\n",
    "\n",
    "### Plotting\n",
    "\n",
    "There are many different kinds of plots: line plots, histograms, 3D plots, violin plots, bar charts, pie charts, scatterplots and heatmaps. Knowing how to use plots effectively can help you to explain certain phenomena in a much clearer manner. \n",
    "\n",
    "Consider the following questions you are trying to answer:\n",
    "\n",
    "Suppose we are trying to find out the relationship between how much a pair of jeans cost, and how long they last, and we have some data. What is the most effective way to plot them? One might suggest the use of a scatterplot, as it is the best way to plot the relationship between 2 numeric variables.\n",
    "\n",
    "What if one is interested in how GDP growth changes over time? One might argue that the simple line plot works best for viewing temporal relationships. \n",
    "\n",
    "Finally, suppose you are interested in whether certain types of securities, such as equities and REITs, yield a higher return than other types of ssecurities, such as bonds.\n",
    "\n",
    "One way to do so will be to use a lineplot, or a bar chart to illustrate this.\n",
    "\n",
    "In this section, we'll have a somewhat informal discussion about plots, and attempt to answer these questions: \n",
    "\n",
    "1. Why do we need plots?\n",
    "2. How do we use them effectively?\n",
    "3. How do we do it in Python?"
   ]
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    "Once again, we will be learning plotting using an application-based approach. There are 2 examples which we will be exploring in much detail, examples which you should be familiar with by now: the Titanic dataset from Kaggle and the MovieLens dataset from GroupLens.\n",
    "\n",
    "---\n",
    "\n",
    "### Data Plotting with Titanic\n",
    "We begin by importing the dataset. \n",
    "\n",
    "This can be achieved using the functions, `import` and `pd.read_csv`."
   ]
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   "source": [
    "# pandas and numpy are used for data manipulation, cleaning and processing\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Seaborn and matplotlib are used for plotting\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "# style allows us to change the format that matplotlib uses to plot\n",
    "style.use('fivethirtyeight')\n",
    "\n",
    "# This plots everything inside jupyter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "dat = pd.read_csv('data/titanic/train.csv')"
   ]
  },
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   "metadata": {},
   "source": [
    "We can start by taking a closer look at the dataset. Recall that the `head()` method returns the first 5 observations of the dataset, while the `describe()` method provides the summary statistics of each variable in the dataset."
   ]
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
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       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
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       "      <th>0</th>\n",
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       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
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       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
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       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
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       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
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    "dat.head()"
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       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
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       "      <th>SibSp</th>\n",
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       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891</td>\n",
       "      <td>891</td>\n",
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       "      <td>891.000000</td>\n",
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       "      <th>unique</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>681</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>3</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Turja, Miss. Anna Sofia</td>\n",
       "      <td>male</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>347082</td>\n",
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       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>577</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.204208</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.526497</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.693429</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.125000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.910400</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.454200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>512.329200</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "        PassengerId    Survived      Pclass                     Name   Sex  \\\n",
       "count    891.000000  891.000000  891.000000                      891   891   \n",
       "unique          NaN         NaN         NaN                      891     2   \n",
       "top             NaN         NaN         NaN  Turja, Miss. Anna Sofia  male   \n",
       "freq            NaN         NaN         NaN                        1   577   \n",
       "mean     446.000000    0.383838    2.308642                      NaN   NaN   \n",
       "std      257.353842    0.486592    0.836071                      NaN   NaN   \n",
       "min        1.000000    0.000000    1.000000                      NaN   NaN   \n",
       "25%      223.500000    0.000000    2.000000                      NaN   NaN   \n",
       "50%      446.000000    0.000000    3.000000                      NaN   NaN   \n",
       "75%      668.500000    1.000000    3.000000                      NaN   NaN   \n",
       "max      891.000000    1.000000    3.000000                      NaN   NaN   \n",
       "\n",
       "               Age       SibSp       Parch  Ticket        Fare Cabin Embarked  \n",
       "count   714.000000  891.000000  891.000000     891  891.000000   204      889  \n",
       "unique         NaN         NaN         NaN     681         NaN   147        3  \n",
       "top            NaN         NaN         NaN  347082         NaN    G6        S  \n",
       "freq           NaN         NaN         NaN       7         NaN     4      644  \n",
       "mean     29.699118    0.523008    0.381594     NaN   32.204208   NaN      NaN  \n",
       "std      14.526497    1.102743    0.806057     NaN   49.693429   NaN      NaN  \n",
       "min       0.420000    0.000000    0.000000     NaN    0.000000   NaN      NaN  \n",
       "25%      20.125000    0.000000    0.000000     NaN    7.910400   NaN      NaN  \n",
       "50%      28.000000    0.000000    0.000000     NaN   14.454200   NaN      NaN  \n",
       "75%      38.000000    1.000000    0.000000     NaN   31.000000   NaN      NaN  \n",
       "max      80.000000    8.000000    6.000000     NaN  512.329200   NaN      NaN  "
      ]
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   "source": [
    "dat.describe(include='all')"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histograms: Plotting Numerical (non-continuous) Features\n",
    "\n",
    "Note that there are many variables in the dataset, allowing for many different hypotheses to be tested by means of plotting. Let's begin with the Survived variable. What percentage of the passengers on the Titanic survived? We can use a histogram to show that - we can use the `plt.hist()` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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tnhQAAMBmthIwZyb5ienUsZEkY4yPJ/maQ92wqm5VVX9bVe+sqvdU1S9P46dU1SVVtaeq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9acee550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "# Hist counts the number of times the Survived variables takes on a value of 0 or 1\n",
    "plt.hist(dat['Survived'])\n",
    "\n",
    "# Put x and y labels on the plot\n",
    "plt.xlabel('Survived')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "# Put x and y limits, restrict x limit to 0 and 2 since Survived = {0, 1}\n",
    "plt.xlim([0, 2])\n",
    "plt.ylim([0, 600])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That was a poor use of a plot, since we could always use the method, `mean` to find out the mean survival rate. In addition, there aren't any interesting hypotheses to test based on this observation alone. That said, this is one way of showing your audience what you are trying to say.\n",
    "\n",
    "#### Functions:\n",
    "\n",
    "Without further ado, let's take a closer look at the previous code chunk, and dissect what's happening:\n",
    "\n",
    "`plt.figure(figsize=(20, 10))` alters the figure size to fill the whole screen. The first coordinate represents how wide the image is, and the second coordinate represents how tall you want the image to be.\n",
    "\n",
    "`plt.hist()` takes a series as an argument and returns the histogram. In our case, since \"Survived\" only has 2 values (0 and 1), it returns a histogram with 2 unique bins.\n",
    "\n",
    "`plt.show()` starts an event loop, looks for all currently active figure objects, and opens one or more interactive windows that display your figure or figures i.e. it \"shows\" the plot.\n",
    "\n",
    "---\n",
    "\n",
    "Let's take a closer look at the `plt.hist()` function. Recall that we can use the '?' feature in Jupyter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "?plt.hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Histograms for numerical but non-continuous features\n",
    "2. Countplots are histograms for categorical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Countplots: Histograms for Categorical Variables\n",
    "\n",
    "Apart from a histogram, there are many other ways one can use to plot relationships. We can use a countplot (`sns.countplot` from seaborn) to plot the count the number of observations for categorical variables, such as the 'Embarked' and 'Pclass' variable.\n",
    "\n",
    "In addition, we use the function, `plt.subplot` to tell Python and MatPlotLib that we want to have more than just 1 plot. Note the first 2 arguments of `plt.subplot` refers to the \"height\" and \"width\" of the plots - since we intend to have 4 plots, I decided to have a 2x2 layout.\n",
    "\n",
    "What if you wanted to have 2 plots (side by side)? What would be the appropriate arguments for `plt.subplot`?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAK1ECQAA0EqUAAAArUQJAADQSpQAAACtRAkAANBKlAAAAK1ECQAA0EqUAAAArUQJAADQ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\npwuksHoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9bc0e710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "\n",
    "sns.countplot(dat['Embarked'])\n",
    "\n",
    "plt.savefig(\"MyFavoriteFigure.jpg\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Ffitqppp+/0Yg4Dy1Kq6q7XdDZxaVTcm+QBwWFW9vG17ErAXOKaqdszxvvcA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\nJEmSOmVRIkmSJKlT/wP6d2N0mG0MRAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9b5f6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.countplot(dat['Embarked'])\n",
    "\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.countplot(dat['Pclass'])\n",
    "\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.countplot(dat['Survived'])\n",
    "\n",
    "plt.subplot(2, 2, 4)\n",
    "sns.countplot(dat['Sex'])\n",
    "\n",
    "plt.savefig('coolplots.png') # plt.savefig saves the current figure to an image, called \"coolplots.jpg\" on your local drive\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scatterplots: Plotting relationship between 2 numerical variables\n",
    "Sometimes, we are interested in the relationship between 2 numerical features, for example Age and Fare, To do so, we can use a scatterplot (`plt.scatter` from matplotlib) to plot this relationship. \n",
    "\n",
    "In addition, we can use the `plt.xlabel` and `plt.ylabel` functions to label the x and y-axes. Also, we can use the `plt.title` function to label the title of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "?plt.scatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WL6yOuSIARuF8DgDT6WqCxrLp+bftQB0A0OzoDUczc3Drnw6eOTiTuUNzY64I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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9bb5edd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "# argument 1 is for x-axis, argument 2 is for y-axis, s: size of marker\n",
    "plt.scatter(dat['Age'], dat['Fare'], s=50, marker='o', c='g')\n",
    "\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Fare')\n",
    "plt.title('Age against Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is there a relationship between Age and Fare? We can't really tell with this graph. However, it turns out that we can change the code to add a best fit line into the scatter plot, using seaborn's regression plot (`sns.regplot`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "?sns.regplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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gHL4PAHoNQQmAVWHzaFq5YvX0RLmia/NoOlCKgHD4PgDoNQQlAFaFG7aOKl+S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\nK88f1gvOG66aoG5ytrRozURtZ+ulZrp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ezh+K8eR4mYIDFT/LEZCrwHdeynO4RQjAWB6u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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9ac95c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "sns.regplot(x=dat['Age'], y=dat['Fare'])\n",
    "\n",
    "plt.xlim([-10, 90])\n",
    "plt.ylim([0, 600])\n",
    "\n",
    "plt.title('Relationship between Age and Fare')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There seems to be a really weak relationship between the two variables. Interestingly, the regression plot yields the 95% confidence interval for that regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arguments for Plots\n",
    "\n",
    "This section is dedicated to the various arguments we could include when plotting:\n",
    "\n",
    "1. `plt.figure` creates a new figure, and accepts arguments such as figure size, resolution of the figure etc.\n",
    "2. `plt.xlabel` creates the label for the x-axis\n",
    "3. `plt.ylabel` creates the label for the y-axis\n",
    "4. `plt.title` creates the title for the plot\n",
    "5. `plt.xlim` sets the \"x\" limits of the current axes\n",
    "6. `plt.ylim` sets the \"y\" limits of the current axes\n",
    "7. `plt.legend` creates a box of the legend (to be used in conjunction with \"label\" in plots)\n",
    "\n",
    "We will use the following examples to illustrate how you can use some of these arguments to \"beautify\" your plots.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Plots: Histogram Plots for Continuous Variables\n",
    "Suppose we are interested in the relationship between a categorical variable and a numerical variable, say Age and Survival. \n",
    "\n",
    "We want to explore how Age affects Survival rate: how can we do so? One way to do so is to plot 2 distribution functions; 1 for those who survived and 1 for those who did not. We can use the kernel density plot from seaborn (`sns.distplot`) to do so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gfAEAAACABwhfAAAAAOABwhcAAAAAeIDwBQAAAAAeIHwBAAAAgAcIXwAAAADg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\nERERERHJAYUvERERERGRHPh/19E7OzhJRNQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9b9bd2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 10))\n",
    "# first arg - variable of interest\n",
    "sns.distplot(dat[dat.Survived == 0]['Age'].dropna(), bins=range(0,81,5), color = 'red', label=\"Did not survive\")\n",
    "sns.distplot(dat[dat.Survived == 1]['Age'].dropna(), bins=range(0,81,5), color = 'blue', label=\"Survived\")\n",
    "\n",
    "# Sets \"x\" limits, \"y\" limits and title of the plot\n",
    "plt.ylim([0, 0.06])\n",
    "plt.xlim([0, 100])\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Age Distribution of Survivors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OrxV/clzwGhx1jm5uJGe8qmJmGtJxiO5Pvl9ntjgzZFmZK1NaVppm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bzo3kCNWRJx1NEzMqj58Y/xjhzZqM7yzTe1KemzsvzQMkmSJKk9Air4ystN46uV38HjCfIpT0lf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\nSZIkSVIP/g0ekawrLCz3FAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9bd0e358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13, 10))\n",
    "# first arg - variable of interest\n",
    "sns.distplot(dat[dat['Embarked'] == 'C']['Age'].dropna(), bins=range(0,81,5), color = 'red', label=\"Embarked at C\")\n",
    "sns.distplot(dat[dat['Embarked'] == 'Q']['Age'].dropna(), bins=range(0,81,5), color = 'blue', label=\"Embarked at Q\")\n",
    "sns.distplot(dat[dat['Embarked'] == 'S']['Age'].dropna(), bins=range(0,81,5), color = 'green', label=\"Embarked at S\")\n",
    "\n",
    "# Sets \"x\" limits, \"y\" limits and title of the plot\n",
    "plt.ylim([0, 0.06])\n",
    "plt.xlim([0, 100])\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Age Distribution by Embarkation Point')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the observations made here, is that children under the age of 10 are typically more likely to survive than perish. For all other age groups, there are no observable trends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Histograms for Numerical/Integer variables\n",
    "2. Countplots which are Histograms for Categorical Variables\n",
    "3. Scatterplots for 2 different numerical/float variables\n",
    "4. Distribution plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pairsplot: Exploring Multiple Numerical Features\n",
    "\n",
    "Given that there are **so many** relationships to explore, how can we fully explore them in detail? One way to do so is to plot the relationships between *any* 2 numeric features in our dataset, and it turns out that there is a very convenient way of doing so. We can use a pairplot.\n",
    "\n",
    "We can use the function, `sns.pairplot` to plot the relationship between any 2 features across all features in a particular dataset. Note that `sns` stands for seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ngbolin/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:447: RuntimeWarning: invalid value encountered in greater\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n",
      "/Users/ngbolin/anaconda3/lib/python3.6/site-packages/statsmodels/nonparametric/kde.py:447: RuntimeWarning: invalid value encountered in less\n",
      "  X = X[np.logical_and(X > clip[0], X < clip[1])] # won't work for two columns.\n"
     ]
    },
    {
     "data": {
      "image/png": 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k7T4+eVI57WEvpHXngW6+fk5NsrKy3xDUBXxsf7aF5pDDbWvrWFbuozlkM88H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\n3q++RHELRSvpudmsOhxaLwIAAAAAgOYwtk2nxZieni6b8ZF/O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PxzC6YOOPr18faOcO3tSDDc06vnV6Gj+xuTWw39F3bcHYgo33f2MIp6cyeNe2VvzR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uuv37zqf59NlLve7Op+9qJubDfimVulsK3wXLmBuZlpF1tzBY/txLVnc5pTRDZ86cKXYR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fsxXUH+11ocjm+c8ycu6pUPcT8WjK/5VLrfuXkarJOVoR4TC5aLFUeGztNFpm4P1tFRy6rXweWD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\nGBzdVtWLq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IiIiohqWEtF3WtKLY0NbkRERERERERERdgR7WoW29soBBvciIiIiIiIiIiK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KuGi/sik+SxEXLprqcxjTjeWWNQzDMAzDMAzDMAzDMIxJpGCXlBqGYRiGYRiGYRiGYRhGPmIe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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9aaa2128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(dat, diag_kind='kde')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we set the argument, `diag_kind` to \"kde\" and [KDE](https://en.wikipedia.org/wiki/Kernel_density_estimation) stands for Kernel Density Estimation - a non-parametric way to estimate the probability density function of a random variable. This means that we want the diagonal plots to be the probability density functions of the variables themselves.\n",
    "\n",
    "---\n",
    "\n",
    "#### Correlation Plots & Heatmaps: Exploring Correlation Across Multiple Numerical Features\n",
    "\n",
    "While some of the relationships provided aren't very informative, it gives us a good idea of what we should be looking out for. In addition, we can further explore the underlying relationships in the dataset with a correlation plot (in the form of a heatmap to get a better idea)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PassengerId</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.005007</td>\n",
       "      <td>-0.035144</td>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.012658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <td>-0.005007</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.257307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <td>-0.035144</td>\n",
       "      <td>-0.338481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.549500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Age</th>\n",
       "      <td>0.036847</td>\n",
       "      <td>-0.077221</td>\n",
       "      <td>-0.369226</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.096067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SibSp</th>\n",
       "      <td>-0.057527</td>\n",
       "      <td>-0.035322</td>\n",
       "      <td>0.083081</td>\n",
       "      <td>-0.308247</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>0.159651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Parch</th>\n",
       "      <td>-0.001652</td>\n",
       "      <td>0.081629</td>\n",
       "      <td>0.018443</td>\n",
       "      <td>-0.189119</td>\n",
       "      <td>0.414838</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fare</th>\n",
       "      <td>0.012658</td>\n",
       "      <td>0.257307</td>\n",
       "      <td>-0.549500</td>\n",
       "      <td>0.096067</td>\n",
       "      <td>0.159651</td>\n",
       "      <td>0.216225</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PassengerId  Survived    Pclass       Age     SibSp     Parch  \\\n",
       "PassengerId     1.000000 -0.005007 -0.035144  0.036847 -0.057527 -0.001652   \n",
       "Survived       -0.005007  1.000000 -0.338481 -0.077221 -0.035322  0.081629   \n",
       "Pclass         -0.035144 -0.338481  1.000000 -0.369226  0.083081  0.018443   \n",
       "Age             0.036847 -0.077221 -0.369226  1.000000 -0.308247 -0.189119   \n",
       "SibSp          -0.057527 -0.035322  0.083081 -0.308247  1.000000  0.414838   \n",
       "Parch          -0.001652  0.081629  0.018443 -0.189119  0.414838  1.000000   \n",
       "Fare            0.012658  0.257307 -0.549500  0.096067  0.159651  0.216225   \n",
       "\n",
       "                 Fare  \n",
       "PassengerId  0.012658  \n",
       "Survived     0.257307  \n",
       "Pclass      -0.549500  \n",
       "Age          0.096067  \n",
       "SibSp        0.159651  \n",
       "Parch        0.216225  \n",
       "Fare         1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following code, we use the \"Blues\" color map, and set `annot` to True. This means that we want the correlation coefficient to appear on the heatmap.\n",
    "\n",
    "One downside of the heatmap is that it doesn't really show the relationship between \"string\" categorical variables, against numerical variables, but this can be circumvented through the use of other plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "?sns.heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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tItrOWtY3t5LoH11LjAqn3j/K1v7Dc1NpI+cYiIsM63RmTCRUrVjyEqve/i8A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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9c96fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "sns.heatmap(dat.corr(), cmap='Blues', annot=True, vmin=-1, vmax=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heatmap above, we note that Fare and Passenger Class are good indicators of predicting who survives. Also, we note that PassengerID is not very indicative of whether or not a passenger survived, but we should have expected that to begin with."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In-class assignment\n",
    "___\n",
    "\n",
    "1. Plot the Fare distribution for passengers that survived, and for passengers who died.\n",
    "\n",
    "1. Plot the Age distribution for passengers that are Male and passengers who are Female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ngbolin/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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3AAAAFCS8AQAAoCDhDQAAAAUJbwAAAChIeAMAAEBBwhsAAAAKEt4AAABQkPAG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\nERERERERkYC48SYiIiIiIiIS0D8VkbGCK8XDwgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8235f1beb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "## Your code here\n",
    "\n",
    "\n",
    "# Sets \"x\" limits, \"y\" limits and title of the plot\n",
    "# plt.xlim([-100, 600])\n",
    "# plt.ylim([0, 0.1])\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Fare Distribution of Survivors')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ngbolin/anaconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:545: UserWarning: No labelled objects found. Use label='...' kwarg on individual plots.\n",
      "  warnings.warn(\"No labelled objects found. \"\n"
     ]
    },
    {
     "data": {
      "image/png": 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mlBgOfOmY6zQk5jsNiflOQ2K+81XXrl275fZeSwzvZs2apXHjxqmsrKyxvLKy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\nDHwtQBy4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f82379620b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10))\n",
    "## Your code here\n",
    "\n",
    "# Sets \"x\" limits, \"y\" limits and title of the plot\n",
    "# plt.xlim([-10, 100])\n",
    "# plt.ylim([0, 0.1])\n",
    "\n",
    "plt.legend(loc='upper right')\n",
    "plt.title('Age Distribution by Gender')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Violin Plots: Conditional Relationship between Categorical and Numerical Variables\n",
    "\n",
    "There are many other plots for different purposes, and we will be exploring some of them here. One example is a conditional plot, which plots the relationship of feature 1 against feature 2, controlling for feature 3. \n",
    "\n",
    "For example, one might be interested in plotting the relationship between where a passenger Embarked, how much he or she paid and how likely he or she is to survive. In this case, we can use the violin plot (`sns.violinplot`). \n",
    "\n",
    "The violin plot function, `sns.violinplot` takes 4 arguments, 2 arguments for the x and y-axes, 1 for the \"hue\" argument and 1 for the \"split\" argument. Play around with the arguments to see what each of them does."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SACjExo0bZ7zWSUHkv8mybGdK6SURcWdK6YmpF7Msy+oh5YLN9i8AAADK5rHHHpv1+vnn\nnx8XXHBBi6oBAJjQMUuzsyzbWf+4NyL+OSLeGBF7ji+5rn/cW799Z0ScO+Xh59TnAABg2atWZ+83\n6O7ublElAAAndEQQmVLqTymtPv55RFweET+JiNsi4gP12z4QEbfWP78tIq6rn579pog4NGUJNwAA\nLGtzBZFzXQcAKEKnvAI5KyL+ub5PTTUi/j7LsjtSSv8SEV9KKX0oIrZFxLX1+2+PiCsjYktEDETE\nB1tfMgAAdCZBJADQiTriFUiWZc9ExM82mX8xIt7aZD6LiA+3oDQAAFhy5lp6bWk2ANAOHbE0GwAA\nWDw6IgGATiSIBACAkunp6Tml6wAARRBEAgBAycy19FpHJADQDoJIAAAomdk6Hnt6eqJ+SCQAQEsJ\nIgEAoGRm64h0UA0A0C6CSAAAKJne3t4ZrwkiAYB2EUQCAEDJzBY2OqgGAGgXQSQAAJTMbB2Rs10D\nACiSIBIAAEpmrsNqAADaQRAJAAAloyMSAOhEgkgAACiZarUalUrzl/o6IgGAdhFEAgBACc3U+agj\nEgBoF0EkAACUkCASAOg0gkgAACihmZZgW5oNALSLIBIAAEpIRyQA0GkEkQAAUEIzBY46IgGAdhFE\nAgBACc0URPb19bW4EgCACYJIAAAoIUuzAYBOI4gEAIASclgNANBpBJEAAFBCOiIBgE4jiAQAgBKa\nqfNREAkAtIsgEgAASsip2QBApxFEAgBACVmaDQB0GkEkAACUkKXZAECnEUQCAEAJWZoNAHQaQSQA\nAJTQTIGjIBIAaBdBJAAAlJAgEgDoNIJIAAAoIUEkANBpBJEAAFBCgkgAoNMIIgEAoIS6u7sXNA8A\nUDRBJAAAlFCzzseUUlSr1TZUAwAgiAQAgFJq1vnY3d0dKaU2VAMAIIgEAIBSahZE2h8SAGgnQSQA\nAJRQs9DR/pAAQDsJIgEAoISa7QUpiAQA2kkQCQAAJTTTHpEAAO0iiAQAgBISRAIAnUYQCQAAJdRs\naXazOQCAVhFEAgBACQkiAYBOI4gEAIASEkQCAJ1GEAkAACXU1dU1bS6l1IZKAAAmCCIBAGCZEEQC\nAO0kiAQAAAAACieIBAAAAAAKJ4gEAAAAAAoniAQAgGUiy7J2lwAALGOCSAAAAACgcIJIAAAAAKBw\ngkgAAFgmUkrtLgEAWMYEkQAAAABA4QSRAACwTDisBgBoJ0EkAAAsE5ZmAwDtJIgEAAAAAAoniAQA\nAAAACieIBACAZcIekQBAOwkiAQAAAIDCCSIBAGCZcFgNANBOgkgAAFgmLM0GANpJEAkAAAAAFE4Q\nCQAAAAAUThAJAAAAABROEAkAAAAAFE4QCQAAAAAUThAJAAAAABROEAkAAAAAFK6jgsiUUldK6V9T\nSl+pjy9IKf0gpbQlpfTFlFJPfb63Pt5Sv35+O+sGAAAAAGbXUUFkRHwkIh6fMv7TiPh0lmU/HREH\nIuJD9fkPRcSB+vyn6/cBAAAAAB2qY4LIlNI5EfHLEfE39XGKiLdExKb6LZ+PiKvqn7+zPo769bfW\n7wcAAAAAOlC13QVM8RcR8bsRsbo+PiMiDmZZNlYf74iIs+ufnx0R2yMisiwbSykdqt//QrMn3rx5\nc1E1AwDAkjE4OOi1MQBQqI0bN854rSOCyJTSv4uIvVmWPZRSevNiP/9s/wIAAGC5WLFihdfGAEDb\ndEQQGRGXRsT/mlK6MiL6ImJNRPxlRKxLKVXrXZHnRMTO+v07I+LciNiRUqpGxNqIeLH1ZQMAAAAA\n89ERe0RmWfZ7WZadk2XZ+RHx3oj4VpZl/1tE3B0R19Rv+0BE3Fr//Lb6OOrXv5VlWdbCkgEAAACA\nBeiIIHIWH4+Ij6WUtsTEHpA31edviogz6vMfi4gb2lQfAAAAADAPnbI0e1KWZfdExD31z5+JiDc2\nuWcoIt7d0sIAAAAAgJPW6R2RAAAAAEAJCCIBAAAAgMIJIgEAAACAwgkiAQAAAIDCCSIBAAAAgMIJ\nIgEAAACAwgkiAQAAAIDCCSIBAAAAgMIJIgEAAACAwgkiAQAAAIDCCSIBAGCZSCm1uwQAYBkTRAIA\nwDKRZVm7SwAAljFBJAAAAABQOEEkAAAAAFA4QSQAACwT9ogEANpJEAkAAMuEPSIBgHYSRAIAAAAA\nhRNEAgAAAACFE0QCAAAAAIUTRAIAAAAAhRNEAgAAAACFE0QCAAAAAIUTRAIAAAAAhRNEAgAAAACF\nE0QCAAAAAIUTRAIAAAAAhRNEAgAAAACFE0QCAAAAAIUTRAIAAAAAhRNEAgAAAACFE0QCAMAykVJq\ndwkAwDImiAQAgGUiy7J2lwAALGOCSAAAAACgcIJIAAAAAKBwgkgAAFgm7BEJALSTIBIAAAAAKJwg\nEgAAlgmH1QAA7SSIBAAAAAAKJ4gEAAAAAAoniAQAAAAACieIBAAAAAAKJ4gEAAAAAAoniAQAAAAA\nCieIBACAEhofH582l2VZGyoBAJggiAQAgBIaGxub1xwAQKsIIgEAoISahY6jo6NtqAQAYIIgEgAA\nSqhZ6CiIBADaSRAJAAAlJIgEADqNIBIAAEpoZGRkXnMAAK0iiAQAgBJqFjrqiAQA2kkQCQAAJdQs\ndNQRCQC0kyASAABKaHh4eNrcyMhIZFnWhmoAAASRAABQSs26H2u1WoyNjbWhGgAAQSQAAJRSs47I\n2eYBAIomiAQAgBKaKXC0TyQA0C6CSAAAKKGZgsihoaEWVwIAMEEQCQAAJSSIBAA6jSASAABKaKbA\n0R6RAEC7CCIBAKCEBgcHm87riAQA2kUQCQAAJTRT4DhTQAkAUDRBJAAAlNBMQaSOSACgXQSRAABQ\nQjN1PuqIBADaRRAJAAAlJIgEADqNIBIAAEpIEAkAdBpBJAAAlNDAwMCC5gEAitYRQWRKqS+l9EBK\n6UcppUdTSv+pPn9BSukHKaUtKaUvppR66vO99fGW+vXz21k/AAB0GkEkANBpOiKIjIjhiHhLlmU/\nGxGXRMQ7Ukpviog/jYhPZ1n20xFxICI+VL//QxFxoD7/6fp9AABA3UyBo6XZAEC7dEQQmU04Wh92\n1/9kEfGWiNhUn/98RFxV//yd9XHUr781pZRaVC4AAHS0sbGxGB4ebnrt2LFjLa4GAGBCRwSREREp\npa6U0g8jYm9E3BkRT0fEwSzLxuq37IiIs+ufnx0R2yMi6tcPRcQZra0YAAA602zLrwWRAEC7VNtd\nwHFZlo1HxCUppXUR8c8R8crFeu7Nmzcv1lMBAEDHe+GFF2a8dujQIa+PAYDCbNy4ccZrHRNEHpdl\n2cGU0t0R8QsRsS6lVK13PZ4TETvrt+2MiHMjYkdKqRoRayPixZmec7Z/AQAAUDaz7Vo0MjLi9TEA\n0BYdsTQ7pbS+3gkZKaUVEfG2iHg8Iu6OiGvqt30gIm6tf35bfRz169/KsixrXcUA83fs2LG4//77\nY8uWLe0uBYBlYral2YODg1Gr1VpYDQDAhE7piNwQEZ9PKXXFRDj6pSzLvpJSeiwi/r+U0o0R8a8R\ncVP9/psi4u9SSlsiYn9EvLcdRQPMZXh4OD7+8Y/Hjh07IiLi+uuvjyuvvLLNVQFQdkePHp3xWq1W\ni8HBwejv729hRQAAHRJEZln244h4bZP5ZyLijU3mhyLi3S0oDeCU/Ou//utkCBkRceuttwoiASjc\nXAfSHDt2TBAJALRcRyzNBiir559/vmG8Z8+eNlUCwHIyVxA5W8ckAEBROqIjEgBgKdu6dWt89rOf\njd27d8frX//6+K3f+q3o7e1td1ksY3MFjXMFlQAARdARCQBwiv76r/86nn766Th27Fh8+9vfjrvu\nuqvdJbHMzRVE6ogEANpBEAkAcIoeffTRhvETTzzRpkpgQr7jMf+iXxAJALSDIBIAYJGNj4+3uwSW\nuXzQ+JJqahhbmg0AtIMgEgDgFGRZNq85aKVpQWR3mvU6AEArCCIBCtQsjKjVam2oBCjK6OjotLmx\nsbE2VAIn5Dse11cFkQBA+wkiAQo0MjIyba5ZaAEsXUNDQ9PmhoeH21AJnKAjEgDoRNV2FwCL5eGH\nH45vfvObsWHDhrj22mujt7e33SVB066o0dFR359QIoODg/Oag1bJsmxa0Li+2th/YI9IAKAdBJGU\nwu7du+PGG2+cXPI6ODgY119/fZurguZdUc26JIGlq1noODAw0IZKYMLQ0FDDNiA9KWJtV+M9OiIB\ngHawNJtSuP/++xtecN9+++1trAZOaLYM29JsKJdmnWWCSNopHzL2VyL6K07NBgDaTxBJKRw8eLDd\nJUBTzbofdURCuTQLdIQ8tFM+iFxZSdOCSB2RAEA7CCIphWYnE0MnEERC+TULHYeHh3U/0zb578n+\nSor+JkuzvX4CAFpNEAlQIEuzofyOHDnSdF7HGe2SDyJXViK6U4qeKU2RtVrN6e4AQMsJIiktv+Wn\nE+iIhPKbKYg8fPhwiyuBCc06IiMmAsnZ7gMAKNq8g8iUUk9K6T+llJ5KKe2vz70tpfSbxZUH8yPs\noVPpiITymylwFETSLtM6IuvLslc6sAYAaLOFdER+KiJ+LiL+9ymPezwiPrzYRcFCNTud1ItrOsHY\n2Ni0OUEklMtMB6YdOnSoxZXAhOlLs1PDx5nuAwAoWnUB914dERuzLDuaUqpFRGRZtiOldE4xpcH8\nNQsiBwcH21AJNGoWOjYLJ4Gla6bAcaaAEoqWDxhX1FsI8kuz7WMKALTaQjoiR/P3p5TOjIj9i1oR\nnARBJJ1qfHx8XnPA0nXgwIGm84JI2iX/umhlmuiEXJHriPRaCQBotYUEkZsi4r+nlM6NiEgprY+I\nz0TEF4soDBai2amPXlzTCQSRUG5Zls0YOM4UUELR8q+BjndErqjMfh8AQNEWEkT+XkQ8HxFPRcS6\niHguIl6MiP+rgLpgQZodTGMfPjpBrVab1xywNA0MDDT9ZViEIJL2yXdEHu+EXJHsEQkAtNe89ohM\nKVUi4ucj4j9GxP8RES+NiD1Zlnk3TUdotueeU7PpBFmWTZsTREJ57N8/8w41s12DIumIBAA61byC\nyCzLaimlr2ZZtro+tavAmmDBmoU9AFC02cLGF198sYWVwAn5jsi+SvM9IpvtsQ0AUKSFLM3+bkrp\njYVVArBMCM6hPF544YUZrx05cmTGZdtQpKGhoYZxXz1/7E2z3wcAULR5dUTWPR0RX0sp/XNEbI+I\nyXfSWZb934tdGCxEtTr9W7nZHLRayu3HFRFRqSzkd0BAJ5ur63H//v2xYcOGFlUDE/IBY2+9E7Kv\nMvt9AABFW0hSszYibo+I3oj46SnzWntou+7u7nnNQas1CyKbzQFL01xB5AsvvCCIpOVm7ohMs94H\nAFC0eQeRWZa9v8hC4FT09fVNm1uxYkUbKoFGzbofBZFQHvkgMosUacrvaO0TSauNj4/H6Ojo5DhF\nRM/xIFJHJADQZgteu5pSWhERZ8bE65qIiMiy7LnFLAoWqlnoKIikE3R1dU2bs20AlEd+j8ihdWfH\nioM7ZrwORcvvS9qbTvwCTEckANBu896oLKX0ypTSv0TEsYjYWv/zbP0PtNXKlSunzQki6QTNgshm\nc8DSlO94HFp79qzXoWgjIyMN4+4p2WNP7pX/1M5JAIBWWMiJCf9PRNwXES+JiMMRsT4i/iYi/v3i\nlwUL0yyI7O/vb0Ml0MhBSlBew8PDceTIkclxlioxtOalDffoiKTV8h2RPVO6IHvS7PcCABRtIe+G\nL4mId2RZNpJSSlmWvZhS+lhEPBIRf1dMeTA/vb2985qDVhNEQnnlux3HelfF2Iq1s94DRct3OXZP\naTvoTrPfCwBQtIV0RA7HieDyxZTSuTGxT+SZi14VLIJmh4RAq9kjEsprehC5OsZ6VzfM7d+/v5Ul\nwbSl2VO7IHtye0Tm7wUAKNpCkprvRsQ19c9vjoivRsTdEXHPItcEC+YUYjqVjkgor3zIONa7KsZ6\nV005Mzvi0KFDMTY21trCWNZm2yMy3xEpiAQAWm3Od8MppZdmWbY7Iq6dMn1DRDwWEasj4r8XVBvA\nkieIhPI6cOBAw3i8b3VEpSvGu1dGdXQgIiKyLIuDBw/GmWdaQEJr5IPvapxIH6tp9nsBAIo2n47I\npyIisropxs2tAAAgAElEQVSU0j9lWVbLsuxvsyz7bJZlRwuuEWDJEkRCeTXriIyIGO9b1TCfDyyh\nSOPj4w3jqeFjfrOQWq0WWZYFAECrzCeIzK95fXMBdQCUUrM9Iu1fCuVw8ODBhvFYz6qGj8cJImml\n/AE0XVNeyaeUpoWRuiIBgFaaz7thvyYFOEnNgshmc8DSkw8ix3v6Gz4ed+jQoZbVBLN1REY0BpMR\ngkgAoLXmsz6wmlK6LE50RubHkWXZt4ooDmCpa9b9qCMSyuHw4cMN4/GelQ0fjxNE0kr5YDH/E6ea\nIkamtBnkg0sAgCLNJ4jcGxH/bcr4xdw4i4gLF7MoWCinZtOpmn1vCiKhHI4cOdIwnimIzN8HRarV\nag3jrtzPofxPoPz9AABFmjOIzLLs/BbUAQCwpEwLIrtXNHyc6T4oUj5YzP86LD8WRAIAraQth1Jw\n4iMArTQ6OhojIyOT4yxVIuvqjoiIWndfw73Hjh1raW0sb/lgMf9iv5JLIgWRAEArCSIBCtQsJPem\nD5a+fLhYq/ZG1JfAjld7G64NDAy0rC7I/9zJB4/5F/9+mQsAtJIgEqBAzQ4BEETC0jc4ONgwrk0J\nH2uCSNpormAx5RZn+5kEALSSIJJScFgNnarZGzwnlMLSNzQ01DCu1ZdlR8TkEu3jhoeHW1ITNDPX\nHpEAAK0kiKQULCuiU42Ojk6bGxsba0MlwGLKh4tTg8haLojMh5YAALBcCSIBCtQsdGwWTgJLS/7v\ncVbpmvJ5teGaXz4AAMAEQSSlYGk2nWrqqbqzzQFLy7QtFtKUIDJVZr8XAACWKUEkQIEEkVBO+XCx\nIXwURLKE2N4GAGglQSRAgZqFjg6uAKBV8jFjfmxVCQDQSoJIgAI1O6RCRyQsLzrOaKW5gsUsF0UK\nIgGAVhJEUlre+NEJmnU/OkEXlr5KpfElVMpqJwZTP4+Irq6ugFbJB4v5l0M6IgGAdhJEUgq1Wm1e\nc9BqzYJIHZGw9FWrjSdjTw0fUy6InHYvtJCl2QBAJxFEUgqCSDpVs+5HHZGw9HV3dzeMU22s6ecR\ngkhaa1pHZO56vkNSEAkAtJIgktLywpp2Gx8fj7GxsWnzgkhY+np7exvGlfHRpp9HRPT19bWkJoiY\nzx6RC7sfAGAxCSIBCjLTEmxLs2Hpy4eLU8PHlAsi86ElFCkfLM61PkQQCQC0kiCSUmh2EIDDAWi3\nZvtDRggioQxWrFjRMK6MDTf9PCJi5cqVLakJInREAgCdTRBJKTR7Ee2FNe02Ojq6oHlg6ejv728Y\nV8aGJjff6xpt3H5BEEkrzblHZOtKAQCYRhAJUJBm+0PONg8sHd3d3Q1LrlOWRWV8otu5MtYYRK5a\ntaqltbG8TQsi86fTzHE/AECRBJGUwlwvsqEdxsfHm84LIqEc1qxZ0zCujAxERERX/eNM9wEAwHIl\niAQoyExBZK0219EBwFKwevXqhnFVEAkAALMSRAIUZKZOXUEklMO6desaxl0jxyIiolr/ONN9AACw\nXAkiAVrMflxQDqeddlrDuDp8NCIiuuofjxNEAgDABEEkAMBJyAeRxwPIai6IzN8HRVrovtn22QYA\nWkkQSSnoMKMTdXV1NZ2vVPyvF8rg9NNPbxhXh49EZNnExynOOOOMVpbFMpff/qMr9xop/xNIEAkA\ntJJ3wwAFmSmInGkeWFqmBZFDR6IyNhyV8dHJuZ6enli1alWrS2MZyx+Uln+xX8n97namg9UAAIrQ\nEUFkSunclNLdKaXHUkqPppQ+Up8/PaV0Z0ppc/3jafX5lFL6TEppS0rpxyml17X3n4B289t8OpEg\nEsrtzDPPbBhXh4807YbUtU8r5Tsi88Fj/sW/A9QAgFbqiCAyIsYi4j9mWfaqiHhTRHw4pfSqiLgh\nIu7KsmxjRNxVH0dEXBERG+t/ro+Iz7W+ZIDZdXd3L2geWFqmBZFDh6M6dLhhzrJsWm1aEJm7nl+q\nrSMSAGiljggisyzblWXZw/XPj0TE4xFxdkS8MyI+X7/t8xFxVf3zd0bEF7IJ90fEupTShhaXDTCr\narXadF4QCeWwZs2ahr/nXWPD0XP0hYZ78mElFC0fLHbN0REpiAQAWqkjgsipUkrnR8RrI+IHEXFW\nlmW76pd2R8RZ9c/PjojtUx62oz4H0DF6e3ubzvf09LS4EqAIlUplWsdj38EdDWNBJK02MjLSMO7O\nBZH58ejoaAAAtErzdp02SSmtioibI+L/zLLs8NQ9lbIsy1JKJ7UR4ObNmxepQjrVwYMHp8357067\nzbTv1rFjx3x/Qkn09/c3jFfkgsharebvOy21a9euhnFPbil2Ty6IfPrpp6eFlwAAp2Ljxo0zXuuY\nIDKl1B0TIeT/yLLsn+rTe1JKG7Is21Vfer23Pr8zIs6d8vBz6nNNzfYvgHI47bTTps35704nqFar\nMTY21jD3kpe8xPcnlMR5550XzzzzzOS4OnKs4fqrXvUqf99pqQcffLBhPL0jMkXEid/tn3XWWb5H\nAYCW6Yil2Wmi9fGmiHg8y7JPTbl0W0R8oP75ByLi1inz19VPz35TRByasoSbZcip2XSqZsuwLc2G\n8phr6bWl2bTa8PBwwzjfAZkPJnVDAgCt1CkdkZdGxPsj4pGU0g/rc78fEf8lIr6UUvpQRGyLiGvr\n126PiCsjYktEDETEB1tbLsD8NDuYxmE1UB5znYotiKTVpu8RmVuanWtDyAeXAABF6oggMsuy70ZE\nmuHyW5vcn0XEhwstiiUlpZm+faC9mp2cLYiE8pgtiOzp6Zm2hyQU7dixxu0BVuSCxxW510wDAwNF\nlwQAMKkjlmYDlFWzILLZHLA0nX766TNeO+OMM/yijJbLB4srK43fg/lgUhAJALSSIBKgQF1dXdPm\nKhX/64WymC2IbHaQGhQtHyzmg8d8MJnvoAQAKJJ3wwAFahY6CiKhPNauXTvj32lBJO2QDxbzweNK\nHZEAQBt5NwxQoGbLMi3VhPLo6uqK1atXN722bt26FlcDzYLIyI0bfwYdPXq06JIAACYJIgEKVKvV\nps1NnLcFlMXatWsXNA9FybIsDh482DC3pivNOs7fDwBQJEEkQIGaBZHN5oCla6aOyJnmoSgDAwMx\nOjo6Oe5JEX25Jvw1ua2LBZEAQCsJIgEKNDY2Nq85YOkSRNIp8qHi2q40bTuQdToiAYA2EkQCFKhZ\n6Di1WwVY+vr7+5vOr1q1qsWVsNxNDyKn35Nfmn3o0CFbhgAALSOIBCjQ8PDwvOaApWvlypULmoei\n7N+/v2G8tmv64Wh9ldSwXHtsbCwOHTpUdGkAABEhiAQoTJZlMTQ0NG2+2RywdK1YsWJB81CUPXv2\nNIzPrE4PIiMizsjN79u3r7CaAACmEkQCFGRkZKTpwTSDg4NtqAYoSl9fX9P5np6eFlfCcpcPItdX\nm7/UX58LIvOPAwAoiiASoCDHjh1b0DywNHV3dzed7+3tbXElLHd79+5tGM/UEbm+u3E+/zgAgKII\nIgEKcuTIkabzR48ebXElQJFmCiKr1WqLK2G5yweK+cBxcr4qiAQA2kMQSSk47ZFONFMQOdM8sDRV\nKs1fTnV1NTmyGAoyOjo6PYicqSMyt2T7+eefL6wuAICpBJEABTl8+HDTeaeTQrmk1DzsmWkeirBr\n164YHx+fHJ/WFbGi0vx78GW5Tsnt27cXWhsAwHGCSErBmz060UyB40wBJVAuuvVppeeee65hfE7P\nzC/zz+pOMXXjgAMHDujWBwBaQhBJKXizRyc6ePBg0/nBwcEYHh5ucTVAUWq12oLmoQj5rsZ81+NU\nXSnFhtz1fJAJAFAEQSRAQWZbgj1TSAksPWNjYwuahyIspCMyIuLs3HXLswGAVhBEAhRktrDRPpFQ\nHiMjIwuahyJs3bq1YXz2LB2Rza4/++yzi10SAMA0gkiAgswWROqIhPIQRNJuR44ciV27dk2OKxFx\n3hwdkRf0Nl7fvHlzEaUBADQQRAIUZLaw8cCBAy2sBCjSwMBA0/nBwcEWV8JytWXLlobxOT0pemc4\nMfu4C3NB5LZt2+xfDAAUThBJKTTbh2t0dLQNlcAJsy2/dnI2lMexY8cWNA+L7amnnmoY50PGZlZ3\npVhfPRFWjo+PW54NABROEEkpHD16dNrckSNH2lAJTBgeHp61G8rSbCiPZj+DIvwconXyy6ovmkcQ\nGTE9sLQ8GwAomiCSUmjWeabjjHaa6/vP9yeUx0zdz/6e0wq1Wi2efPLJhrkL5tgf8riLehuXbz/+\n+OOLVhcAQDOCSEqh2Zs9pxLTTnN1QgkooDxm+nnj5xCtsG3btoafOSsrEef2zL4/5HGv6OtqGD/6\n6KORZdmi1gcAMJUgklLQEUmnyQeR+feElmxCOWRZFvv372967cUXX2xxNSxHP/nJTxrGr+yrRCXN\nL4j8qZ4UfVNuPXToUGzfvn0xywMAaCCIZMnLskxHJB0nf0jFWdU063VgaRoYGIihoaGm1wSRtML0\nILJrhjun60opXtnX+HbgkUceWZS6AACaEUSy5I2MjDQ9IXumwwOgFfLff2d2p1mvA0vT3r17Z7y2\nb9++FlbCclSr1eLRRx9tmLu4b2Ev71+5QhAJALSOIJIlb6aTiWfqUIFWGBgYaBif0dUYRA4ODtqH\nC0pgz549s16r1WotrIblZsuWLQ2/2FpZiThvnvtDHveqXAflj3/84xgbG1uU+gAA8gSRLHkzBY6C\nSNopH5Cv6kpRnTKu1WoxMjLS2qKARbdr164Zr42OjlqeTaEefPDBhvGrV3TNe3/I436qJ8XqKe8I\nBgYG4oknnliM8gAAphFEsuTN1G2iC4V2ygfhvSkiv1pOWA5L344dO2a9vnPnzhZVwnKUDyIvWbHw\nl/aVlOJ/WtnYFZl/XgCAxSKIpLQse6Wd8t2OvZUUPbkuFR2RsPTlg8ixnv5Zr8Ni2b9/fzzzzDOT\n4xQxLVCcr3yA+dBDD51KaQAAMxJEsuR1d3cvaB5aYXh4uGHcnSJ6KrPfAywtWZbFc8891zB39CWv\naBhv27atlSWxjOTDwgt6U6ztWtiy7ONes6Kr4U3B9u3bZ93/FADgZAkiWfJ6e3ubzvf19bW4Ejgh\nf5J7d4rIR+PNTnsHlo69e/c27Ac7Xu2NY+t/uuEeQSRFuf/++xvGl6w4uW7IiIj+rhQbc/uH5J8f\nAGAxCCJZ8lauXLmgeWiF/Imj1ZSimma/B1hann322YbxyKr1Mbz6rIa5rVu3xvj4eCvLYhk4evRo\n/OhHP2qYe33/yQeRERE/l1vW/f3vf/+Ung8AoBlBJEtetVptGjquWbOmDdXAhHzI2BURXbk9IgWR\nsLQ9/fTTDeOhNS+N8d5VMdZz4mfSyMiIfSJZdA888EDDz5AN3SnO6W6+LPv9zw42/JnJG/sb3xY8\n+eSTsW/fvsUpGACgThBJKTQLHVevXt2GSmBC/tT2apoII2e7B1haNm/e3DAeXrMhIqWJj1Ns2bKl\nlWWxDOS7Fd/Y3xUpndz+kMedUa3ET/c2Psd99913Ss8JAJAniKQU1q5dO685aJX8UsxKRFQszYbS\nqNVq04LIoXoAOZQLIp966qmW1UX5HT16NH74wx82zL3hJE/LzntDbnn39773vUV5XgCA4wSRlIIg\nkk6TZVnDOCX/w4Uy2blzZxw7dmxyPF7ti9H+MyIiYmjd2Q33Pvnkky2tjXL7/ve/3/CLrJdWU5zX\nc2rdkMe9MRdoPvnkk7Fr165FeW4AgAjviymJZsuwLc2mnfLLrisRkX+baGk2LF1PPPFEw3ho7csm\nfuNw/PMptm3b1hBawqm4++67G8ZvWnXqy7KPO7O7Ei/vbXx78O1vf3tRnhsAIEIQSUn09/fPaw5a\nJd8RGTGZUcx6D7A0PPbYYw3jwdPOmfy81r0ihvvPnBxnWTYtuISTsXv37nj88ccb5v7NqsVZln3c\npbnnu+eee/y8AgAWjSCSUmjWCdDVtbgvzGEhpi3Nnsc9wNKRDyKH1p3bOD6tcZy/H05GvjtxY28l\nzupe3JfzP9/fFVMP4N69e7ftBQCARSOIBGiBFNPDSEEkLE379u2LPXv2TI6zVJm2HHswF0Q++uij\nLamN8sqybNqy7MXuhoyI6O9K8drcXpHf+ta3Fv3rAADLkyASoE0Wa08voLV+8pOfNIyH1r4ssq7u\nhrnB085rGG/evDmGhoYKr43y+slPfhK7d++eHFdjonuxCPmA8957743BwcFCvhYAsLwIIgFaIKv/\nAZa+fBCZDx0jIsb61sTIytMmx+Pj4/aJ5JR84xvfaBj/XH9X9HcV8wut16yoxNopWeTg4GB873vf\nK+RrAQDLiyCSUtBZRqfJf082CyF938LSlA8iB07/qab3DZ7WOP/II48UVhPldvjw4bjvvvsa5i5b\nXdxe2NWU4hdXVRvm8kEoAMDJEEQCFKBZyJjfElIQCUvP3r17G/aHrKWuGFp3TtN7B3MBZT7AhPm6\n5557YmxsbHL8kmqKi/uKfRn/i7mg86mnnoqtW7cW+jUBgPITRAIUoFJp/N9rLZveFSmIhKUnHyYO\nN9kf8riBJvtE2mePhcqybFo34ptXd0Wl4J8hL+muxM/kwk5dkQDAqRJEAhSg2dLsWu6efFgJdL78\n8uqZlmVHRIz3rY6RladPjmu1Wjz++OOF1UY5PfLII7Fjx47JcVdE/C+5ZdNFuWxN49e5++67hekA\nwCnxLphSyPJrXqHNpnVExvQgUkckLD2PPvpow3jw9OkH1TRebwwq84+Hudx+++0N49etrMS6amt+\nfvzcyumH1tx9990t+doAQDkJIgEKkA8is5i+R6SOSFha9u3bF3v37p0c11JXDK09e9bH5JdnCyJZ\niH379sUDDzzQMHf5mtZ0Q0ZMHFpz2erGr3f77bf7BTAAcNK8C6YUdJbRafLfk7XM0mxY6h577LGG\n8fDaDTPuD3ncYC6I3LJlSwwPDy96bZTT17/+9ajVTvz0OLc7xSsKPqQm77LV1YY3DDt27HACPABw\n0rwLBihAs6XZWe64GgE6LC1PPPFEwzgfMjYz3rc6RlesmxyPjY3Fli1bFr02ymdkZCTuvPPOhrlf\nWlNt+c+O06spXt/feIJ2frk4AMB8CSIBCtDsjWJ+IZuOSFhapgWR686Z1+Py9+WfB5q5995749Ch\nQ5PjlZWI/3lV1yyPKM4vrW78ug888EDs2bOnLbUAAEubd8EABZi2NDtiehIJLBlDQ0Oxbdu2xrm1\nL5vfY9c17iO5efPmRauLcsqyLG677baGuX+7qiv6Ku3ppH9lXyXO7TnxtWu1Wnz1q19tSy0AwNIm\niAQoQH4j/0pEhJXYsGQ988wzDXv1jaw8PWo9K+f12PyBNoJI5vLII480BN8pWntITV5KKd6R+/p3\n3nlnDAwMtKkiAGCpEkQCFKDZiaL5HHJqqAF0tvy+jkNrNsz7scOr1ketciLEefHFF2P//v2LVhvl\n8+Uvf7lh/PqVlVjf3d6X7W/q74o1U0oYHByMb33rW+0rCABYkgSRAAXIh4yViKjkosjx8fEWVgSc\nimeffbZhPLzmpfN/cKUrRlatb5jaunXrIlRFGT3//PPx4IMPNsy9fW37uiGP66mkeGuuK/LLX/6y\nn2UAwIIIIgEKMDo62jDuThHVXEvk2NhYCysCTkU+OBxe/ZIFPX549VmzPh8cd9tttzV01V/Qk+Ll\nvZ3xkv2ta6oxNYrcs2dPPPDAA22rBwBYejrjVQ1AyeSDyGqaCCNnuwfoTOPj47Fjx46GuZEFB5GN\n9z/33HOnXBflc/jw4WnLnd++tjrtALR2WduV4hdyJ3ffeuutbaoGAFiKBJEABRgcHGwY91VS9KbZ\n7wE60969ext+cTDWvSLGe/oX9Bwj/Wc0jPPBJkRE3HHHHTEyMjI5Pr0rxc/3d83yiNa7IrdM/Ikn\nnognn3yyTdUAAEuNIBKgAMeOHWsYr6xErKw0JpFOG4WlYefOnQ3j0f4zF/wcI7nH7Ny5s+mhVixf\nIyMjcfvttzfMXb6mK6od0g153Lk9lXjNisa3ELoiAYD5EkQCFODIkSMN4/5Kiv6uxjeThw8fbmVJ\nwEnavXt3w3ik//QFP8d476qoVbonx4ODg/4fQIPvfOc7cfDgwclxX4p48+r2H1LTTL4r8v7775/2\n9wQAoBlBJMAiGxkZaeh2rEREfyViTW513aFDh1pbGHBS9uzZ0zAeXbFu4U+SUoyubHyc4Ibjsiyb\n1lX4i6u7pv0Cq1O8uq8S5/acqK1Wq8VXvvKVNlYEQKd66qmn4m//9m/jC1/4Qjz77LPtLocOIIgE\nWGT79+9vGK/piqikFOtybyjz9wGdad++fQ3jsRVrT+p58gFm/nlZvh5++OHYvn375LgSEW9f05nd\nkBERKaW4IlffN7/5zWmrAQBY3nbu3Bm///u/H7fcckv80z/9U3z84x+PF198sd1l0WaCSIBFlu+e\nOrNaqX9Ms94HdKYXXnihYTzat+aknmesb/Wsz8vydcsttzSM39DfFeu7O/tl+i+s6orTpnT6Dw0N\nxde//vX2FQTL3MDAQDz44IPxox/9qOHQK2inBx54IMbGxibHIyMj8eCDD7axIjpBx/yqNaX03yLi\n30XE3izLXl2fOz0ivhgR50fE1oi4NsuyAymlFBF/GRFXRsRARPz7LMsebkfdAHn55Zbr6wHk+mpl\n2n1ZlkXqsIMIgEb539yPnXQQ2fg4XdFERDzzzDPxyCOPNMxdubZjXqLPqJpSXL6mGl88cOIN5le/\n+tV45zvfGd3d3bM8ElhsAwMD8bGPfWzyNejFF18cf/InfxKVSmf/QoPye+655+Y1x/LSSf9n+tuI\neEdu7oaIuCvLso0RcVd9HBFxRURsrP+5PiI+16IaAeaUP2H3rO6JoHFdV8SULbXi2LFjDQcTAJ1n\nfHx82n6u472rTuq5xnobOyIPHDhw0nVRHvm9IV/ZV4kLezvpJfrMLltdjd4pP9cOHDgQ3/nOd9pX\nECxTP/jBDxp+Ef7444/HU0891caKYEKz78PNmze3oRI6Sce8ysmy7DsRkW8NeGdEfL7++ecj4qop\n81/IJtwfEf8/e3ceH1V974//dc6ZPZmZ7JOVBJIQEMIioIILCARcqFippVVr763+Wq3V26u39nr9\n3tprq9X7tdpq+6NWpIq2ShUUUHaQRXYEQoAQErLv+0wms8853z9CJjmzhCyTWd/PxyOP5HzOycwn\nkMyZ8z7vz/sdxzBMWmBmSgghQ3O/y5d5NRDJMgwypMyQxxJCQovBYADP865tp1QJgR1dtpp7AJMy\nIkl7ezu+/vpr0Vg4ZEP2i+EYLFKLO7Ft3rwZgiAEaUaERKfq6uphjRESSJ2dnR4JGgBw5coVmM3m\nIMyIhIpQf6ejEwSh6erXzQB0V7/OAFA36Lj6q2NN8IIi7pHPW1YJ/b+TYBAEARUVFaKxdNnAPZ8M\nGYsqm9O1ferUKahUqoDNjxAyMu5voB2ymFE/lvv3tra20rkqyn355ZdwOgfOCWlSBjOVIZMnMCzL\nNRLsMjjRH3qsra3Fl19+iYKCgqDOi5Bocu7cOY+xU6dOITc3NwizIaTPyZMnvY47nU7s2LED06dP\nD/CMSCDl5+f73BfqgUgXQRAEhmFGdXt1qH8AEhni4uI8xuj/nQRDR0cHjEaja1vGAOmDsiCzZSy+\nxsBFp8FgoN9VQkLY4L9nAHCOIRDp/r0mk4n+/qOY2WzGiRMnRGN3aCRgw6xucLKUxbwYDid6B85t\n33zzDVasWBHEWRESPcxmM+rq6jzGa2pqkJeXR7XISdBs2rTJ576GhgZ8+9vfDuBsSCgJ9VuuLf1L\nrq9+br063gAga9BxmVfHSJSiJUAkVLhnQ06QMeAGvQHMkTFDHk8ICS3udVyd8rEEIpUQMPAa0Nvb\nC7vdPurHI+Ft7969MJlMrm01C9wSyw3xHaHrTo04t+HMmTOoqakJ0mwIiS7nzp0TdSXu19bW5jVA\nSUgg2O12nDlzxuf+U6dOiUrfkOgS6oHILQB+ePXrHwLYPGj8YabPTQD0g5Zwkyjk7eRrs9mCMBMS\n7dwLMk90aziQI2cxOBTZ0NCAnp6eAMyMEDIa7qU/xrI0GwwLp0xcioEaVkUnp9OJrVu3isaWaCSQ\nseGZuZSnYJHvdr7bsmVLkGZDSHTxtfwV6Av2EBIMpaWlsFgsru1YFlAMOsV1d3ejqqoqCDMjoSBk\nApEMw3wE4CiAAoZh6hmGeQTAKwCKGIYpB7D06jYAbANQCaACwDsAfhqEKZMQ4q1GJDUBIMFQVlYm\n2s5zuzBTsAwy3bIiqashIaHLnxmR3r6fOmdHp5MnT6KlpcW1LQGwVBM2FZO8utOtyc7Bgwc9Os4T\nQvyvuLjY576hMtIIGU/nz58Xbc9WcZjuVgPZ/RgSPUImECkIwvcFQUgTBEEqCEKmIAjvCoLQIQjC\nEkEQ8gVBWCoIQufVYwVBEJ4QBCFXEIRCQRDoVk+U89aNy9sYIePJ4XB4NJ5Y02bHD6rMrg8AHlkj\nly5dCtgcCSEj435TyyGL9XHk8Lh/P900i05ffPGFaHtBLActF57ZkP3mqFgkSQZ+Brvdjp07dwZx\nRoREvvb2drS1tfncf/nyZVFDLEICxb381FQFiykKbshjSPQImUAkIaNlt9vR2NjoMU61iUigVVVV\nwWq1ura1Pkp9USCSkPDR0dEh2nYo1GN6PPfvp0Bk9KmurvbIAlkW5tmQAMAyDIo04hPf9u3bqQ4q\nIePIvQakWZsOh1Tp2rZarWhvbw/0tAhBc3OzaHuCjMUEt1Vh7seQ6EGBSBL2ysvLvdaIpOAOCbTS\n0lLR9mS595fYyQrx+OXLl73+DhNCgs/9Am7MgUi5+PuHymQhkcm9NuQUBYtsH+eLcLMwVgL5oOvM\nrq4uHDlyJHgTIiTCGQwG0bZDqYVDqRWNUYkEEgzuNfDjJAziJMyQx5DoEf63X0nUO3funNfx8+fP\nw74pmb4AACAASURBVOl0guPCswMlCT8egUgFh5Mmz25wyRIGWg7QX10pY7VaUVVVhfz8/EBMkxAy\nTHa73UsgMm5sj6mKF20PrhNIIp/BYMDBgwdFY8v9nA3ZXwZkuPs/mKj0ceTIxXAMbo3lsKdnYCno\nl19+iYULF/rtOQghQxCufhASZAwT3uVGyPiKjNuvJKodO3bM67jJZEJJSUmAZ0OilSAIXgKR3l9i\nGYbxyJa8ePHiuM2NEDI6LS0tEISBKzqHPBYCN7agkV0pDmR6Ky1CIteePXtES5WTJQyuV0XW23H3\nZeaXL1+mOmCEjJO4OPE5RWruhtQizoDUasUZkoQEglotXgHS5RDQ5RCGPIZEj8h650OiTl1dHaqr\nq33ud886IGS8NDc3i7rryhh41EEZrMCtWLN7EJMQEny1tbWibVtM0pgf0xaTKNpuaGigRgJRgud5\nj+YtS9Qc2AjLGkmTsSh064y6Y8eOIM2GkMiWlZUl2lYYmsDZB7KeFQoFkpOTAz0tQpCWlibarrHx\nqLGJA5Hp6emBnBIJIRSIJGFt9+7dQ+4/cuQIent7AzQbEs3cMxrz5SwkQ1xcumdLlpaWijKvCCHB\n536jyxo79os5XqaCQxbj2vbVcI1EnjNnzoiW4ksZ4DZ1ZFZJWuL2cx08eBBGozFIsyEkciUkJECn\n0/ncX1BQAJalS34SeLm5uaLtUguPSxbxjde8vLxATomEEHpVImHLarVi3759orGWaXfDIY91bVss\nFuzfvz/AMyPRyD2jMd/Hsux+E2SMqKC/Xq9HU1PTeEyNEDJKZWVlom2bOsUvj2t1exz35yGRafv2\n7aLtG2I4qLnIyobsN0vFImHQz2az2fDVV18FcUaERK5Zs2b53Dd79uwAzoSQAYWFhaLt071OnDeL\na+dPnz49kFMiIYQCkSRsud9dd0qV6EmdBn2m+IS7fft2yjQj4869S7uv+pD9OIZBntwzK5IQEhqc\nTqdHgNAcl+mXx7a4PQ797Ue+trY2fPPNN6KxpRGaDQn0neNuV4tLkOzcuZPejxEyDubOnTuqfYSM\np4KCAqhUKte2WQAGr8xOSEhATk5O4CdGQgIFIklYEgQBX375pWhMnzETAieBPmMWBGbgV7u+vt5n\nZ21C/MFgMKC+vt61zQAeQUZv3LMmKRhBSOgoKyuDxWJxbTukKthVCX55bHOcuKbX2bNnKUAT4fbt\n2yf6P86WMciVR2Y2ZL9FagkGhyLr6+sp+5eQcTBjxgxIpVKPcZ1Oh4yMjCDMiBBAKpUOmZE7d+5c\n6qwdxSgQScJSWVmZqHaXAAb6rOsBAE6FGkZdgeh49+VQhPiTezbkBBkDJXvtE6u3OpGEkNBw/Phx\n0bY5MQfw0xtmS1wGeHbgorGjowNXrlzxy2OT0MPzvEcpmUVqScRfgMVJGMx26wi+d+/eIM2GkMgl\nl8sxefJkj/HCwsKIf50hoW3evHmj2kciHwUiSVhyr/vYm5wHhzLOtd2dJV6GcOrUKfT09ARiaiQK\nuTequday7H55clb0ItzQ0CDqvE0ICQ6n04kjR46IxowpBT6OHjmBk8KUNEk0dujQIb89PgktFy5c\n8GhSMz+GG+I7IsdCt+Xnhw4dEmUaE0L8Iz8/f1hjhASSr/qlEonEo4YkiS4UiCRhx+l04vDhw6Ix\nQ8ZM0bYlLhO2QUvoHA4Hjh49GpD5kehz4cIF0XbBMJZlA4CSZZAtE9+pdg9qEkIC7/Tp02hra3Nt\n86wEvW6Bw7Fyz9zft28frFarX5+DhAb3LMC5Kg4xEdqkxl2hkkXcoJirxWKh92OEjIOsrCyPscxM\n/9Q1JmS04uLivP5u5uXlQaFQBGFGJFRQIJKEnaqqKlF2o1OiQG9SrvgghkFP2jTRENWJJOPBZDJ5\nLKmcohx+pssUhfjY8+fP+2VehJDRc69BbEy9DoJE7tfnMKYUwCkZeBPe09NDWZERyGw2e2TX3qqO\njmxIoK9pzS2x4qxIWp5NiP8lJSV5jCUnJwdhJoSIecvMzcvLC8JMSCihQCQJO+519MwJ2QDr+abe\nlCjOXqH6e2Q8XLx4ETzPu7bTpQy0I8h0maoUvwyXlJT4bW6EkJE7f/48zp49Kxrrzprj9+cROKlH\nNv+GDRtgs9n8/lwkeE6cOCH6P03kGEwbZvmOSHFbrPg92oULF9DZ2Rmk2RASmdRqtceYRqMJwkwI\nEZswYcKwxkh0ia53QiQi1NbWirbNcd67wVk0qeAHBSg7OjpgNBrHdW4k+pw5c0a0PXWEF5gFCnGd\nyLq6OrS3t/thZoSQkeJ5Hu+//75ozByXBas2bVyer3vCHAjMwCtAW1sbtm3bNi7PRYLj66+/Fm3P\nj+XARlnziDQZi4mDypAIguCRJUoIGRu53DNr39sYIYGWkpLiMabT6YIwExJKKBBJwo5erxdtOxRa\n7weyHJyyWNGQwWAYr2mRKOUeiJwxgmXZAKBiGeS61ZR0z8YihATGtm3bUF5eLhprn3z7uD2fQxmH\n7qzrRWMff/wxmpqaxu05SeAYjUaPc8SNUdKkxt2NblmR7gFaQsjYeOuOTR2zSSiIj48f1hiJLhSI\nJGGnt7dXtO2UKn0e65SJ91FGJPGnhoYGNDY2urY5eC61Ho7pbt9z4sSJsU6NEDJCdXV1WL9+vWjM\nmFIAS9z4FvvvmnQznJzMtW2xWPCHP/wBTqdzXJ+XjL/jx4/D4XC4tnUSzwZl0eJGlTgQeenSJVFD\nKEIIIZEpNjZ2WGMkulAgkoSdEd3dEwTRJsvSrzzxn+PHj4u2pyhYKNmRX2Re73aBdvbsWeqeS0gA\nGY1G/O///q+olp9TIkdbwdJxf26nLAbtBUtEY2VlZR5LxEn4OXz4sGj7xhguajOUkqQs8uTin52W\nZxNCSOTz1h1bqfSdSESiA0VlSNiRyWSibYZ3+DgSYN32uX8vIWPhvrRsziiX3GXLGCQOanBjs9lw\n8uTJMc2NEDI8drsdv/vd71BXVycab5u6HA6lj9IffmbImAVjkriD5JYtW/DFF18E5PmJ/1mtVo/m\nY+7Lk6PNjTHi7tmnTp0K0kwIIYQEirfrb4lE4uVIEk0oEEnCjvtdFdbpu8Mo4xDvo6LNxF9qa2tR\nWVnp2mYAzFGN7iKTYRjMixG/HO/fv38MsyOEDIfdbscbb7yBCxcuiMYNqdPQkzotcBNhGLROuwsO\nuXip0rvvvosDBw4Ebh7Eb0pKSmC3213bSRIGWdLozIbsd71KfJ4rLS2F2WwO0mwIiSzRmm1NQh/H\neV4fUSCSUCCShB33VG7W4XsJq3uQUqVSjcucSPTZt2+faPs6BYsEyejfBN4cKz4hnz59Gp2dnaN+\nPELI0CwWC373u995LA81x2WiddpdQIAv6pzyWDTO/i54TuoaEwQBf/jDH6iTdhg6ffq0aHumko36\nQEGKlEXqoGCsw+FAcXFxEGdECCEkGKL9fEgoEEnCkFQqFW0zvO+C/u776O4L8QeLxYLdu3eLxm4Z\n45K7bBmDjEEXaDzPY8eOHWN6TEKIdwaDAb/+9a89gkU2VQIaZ30HAif18Z3jy6pJRdPM+yAMeoMu\nCAL++te/4qOPPoLgVveYhCZBEPDNN9+IxmaOMmM+0sx0a87m/jdICCGEkMhHgUgSdgZ3oAQAgfX9\n5t59H3UhJf6wf/9+Uff2WBa4YZT1IfsxDIMlGnGgfOfOndS0hhA/u3TpEp5++mlcunRJNG5TxaNh\nzvfBy0aXOZ+/62XRx2iZknLRXLgSAiN+i7Zhwwa8/PLLMBqNo35sEhiNjY1oaWlxbUsATFUE7y03\nx3HIzs5GYWEhsrOzvS6TC5QZSvFznz59mgLshBBCSJShQCQJO+4XYUNlrrjv6+npGZc5kehht9ux\nceNG0dgitQSyUXTLdndLLAfloIfR6/XYuXPnmB+XENKXpbZlyxY8//zzaG9vF+2zqHWon/eDgDWn\nuRZj6nVonH0/eFZ8c+LkyZN4+umnUV5eHqSZkeG4ePGiaLtAwULhh3PEaCQnJ+Pxxx/H2rVr8eab\nb2Lt2rV4/PHHkZycHJT5TFGwkA36p2hvb0dbW1tQ5kIIIYSQ4KBAJAk7DQ0Nom2bMt7nsXa3fe7f\nS8hI7dy5U3TRJAFQpPHPkn8ly+B2t8fauHEjFfMnZIxaW1vxm9/8BuvWrfPIjDfFT0DD3AfhdGsU\nE2ympFw0zH0ATqm4LnJrayuee+45fPTRR6JmKCR0XL58WbQ9JUjZkBzHYfXq1Vi1apWrNI1EIsGq\nVauwevXqoGRGylgGE+Xifw/3fy9CCCGRg+pBEm8oEEnCislkQmNjo2jMHpPo83ib274rV66My7xI\ndNDr9diwYYNobLGGG1OTGncrtBIo3LIi3Z+TEDI8TqcTW7duxVNPPeW1Fl1X9o19y7GliiDM7tos\ncZmovelfYdGkicYdDgc2bNjgdYk5Cb6ysjLRdm6QApGZmZlYuXKl130rV65EZmZmgGfUJ88tEEm/\nw4QQQkh0oUAkCSvnzp0TZbPYVAlDXkBa4jJE22fOnBm3uZHIt27dOtHyfhkD3BPn36YWao7BHVpx\nVuSWLVtQWVnp1+chJNJdvnwZzz33HN59911YLBbRPqdEjsZZq9BesAQYos5wKHAo41B3w8PonjDX\nY19dXR2ee+45vP322zAYDEGYHXHX29uLuro61zYDIFcenLfbGo3GZ5M+iUQCjUYT4Bn1cQ9EUkYk\nIYQQEl0oEEnCyvHjx0XbvUm5Qx5vSpwo2r58+TI6Ozv9Pi8S+Y4fP44DBw6Ixr4dJ4GW8/9yg7u1\nEiRy4g7ab775JjWuIWQYWlpa8Pvf/x7PPvus1wCHKT4LdTf9CL0pBUGY3SixHNqmLEPjzPvgcGum\nIwgCtm/fjsceewybNm2CzWYL0iQJAFRUVIiar6RLGaiCVB/SYDB4NPjr53A4gha8dg9EVlZW0u8t\nIYREKGpIRryhQCQJGxaLBUePHhWNmZLzhvweh0IDi1rn2uZ5HgcPHhyX+ZHI1dTUhD/+8Y+isSyZ\nZ+aivyhYBj9MEmdaVldX45133hmX5yMkEhiNRrz33nt44okncOjQIY/9TokcLdfdiYa5D8Gu8l1b\nOJT16qag5uafQJ8+w2OfyWTC+vXr8cQTT+DAgQPgeT4IMyQ1NTWi7UlByoYEgPr6emzevNnrvs2b\nN6O+vj7AM+oTJ2FEN9scDodH2R1CyMhQsIcQEk7G5yqakHFw+PBh0fI6hzwWpoRsAED+rpdFx5Yv\n+y/X1z1p06HoaXFt79mzBytXrqTCuWRYzGYzXn31VZhMJtcYB+CRJBkk4/g7NFvFYX4Mh6O9A6UI\n9uzZg/z8fCxfvnzcnpeQcGMwGLBlyxZs27ZN9Hc6mDFlMlqnLIdToQ7w7PyPlyrROn0FetKmIeXi\ndsjM3aL9bW1teOONN/Dpp5/i/vvvx8033xyUpiTRqqmpSbSdLg3eew2n0+mqMbxy5UpIJBI4HA5s\n3rwZGzZs8GjcFEjpMgYd5oHASXNzM3JycoI2H0IIIYQEDgUiSVgQBAFbt24VjRnSpgPMtTMNetKm\nIal8H5irdwrr6+tRXFyMWbNmjctcSeSwWq146aWXUF1dLRr/XoLUZ82vH1QN3eHaff8HE5U+jgT+\nNUmKKhuPZvvAxdrbb78NtVqNBQsWXGP2hES27u5ufP7559ixY4dHDch+VnUK2iYvgdmtTEckMCdO\nRO2CH0NbdwoJlYfBOcSlG+rq6vD666/j448/xv3334/bbruNApIB4B6I1EmDu/iora0Na9aswdat\nW6HRaGAwGFBfXx/UICQApLg1eXP/dyOEjAwlWBBCwgkFIklYOHv2rCgYJICBPvP6YX2vUx4LY0oB\n1C0DXRk3bdpEgUgyJIfDgddeew3nz58Xjd8Yw2G5JjAX80qWwVMpMvy60Qrb1Vgkz/N4/fXXoVQq\nMXv27IDMg5BQ0tTUhK1bt2LPnj0+68rZ5Wp05C1ET/rwbliFK4GToDvnJhjSZyCh8jDi6r4BI4iX\nZDc2NuKPf/wjPv74Y9xzzz1YvHgxlErfN0DI2HgGIoMfHHA6nR5LxoOtL0A7EAylpdmEjA0tzSaE\nhJPIfXdOIobT6cT7778vGutNmQyHKm7Yj9GdfYNo+9y5c9RBm/hkNpvx0ksv4eTJk6LxSXIGjyZJ\nA3rXOUvG4qfJMtGLtcPhwEsvvYTDhw8HbB6EBJMgCLhw4QJefvll/PSnP8W2bdu8BiEdUiXa8xeh\n5pbH0JMxI6KDkIPxMhXapxSh5uYfw5A2HQI8X6NaWlrwzjvv4NFHH8X777+P9vb2IMw0sjkcDrS1\ntYnG3DP/SJ9UKWVEEuJP3rKc7XZ7EGZCiJi3mtW+GqmR6EEZkSTk7dy502NpbOfEkS1LtcRlwpSQ\nDVXnQEbAu+++i9///veQy+X+mCaJEN3d3fjtb3+LiooK0XiGlMEvdHIogtD9dE4Mh/8vSYq32wfe\nUPZnbHZ1dWHFihUBnxMhgWC323HkyBFs2bIFV65c8XmcQxaDrpyboM+cDUEiC+AMQ4tdlYCWwnvQ\nOekWxFcdgaapxFWWpF9vby8+++wzbNmyBQsWLMC3vvUtTJ48OUgzjix6vV50waVhEZRzRjhIcgvQ\ndnV1BWkmhESG7u5ujzG9Xo+kpKQgzIaQAVar1WPMZrNBIqFQVDSj/30S0ioqKrBu3TrRmCF1Gqza\ntBE/VvvkxZhw7G+u7fr6evzlL3/BU089RXVVCACgqqoKr7zyClpaWkTjKRIGv0yVI5YL3u/JLWoJ\nzAKwvmMgGCkIAtauXYvm5mb8y7/8C53QScRobW3Fzp07sWfPHuj1ep/HOeSx6MyZD0PmLAic1Odx\n0cYek4DW6SvQOekWJFQfgabhnMeSbafTiUOHDuHQoUPIy8vDnXfeiVtuuYVuzo2B2SyuAayiIKRP\nKrdkZfd/O0LIyNTV1Xkdo0AkCbbe3l6vYyqVKgizIaGCrlpJyGppacGrr74qSt3mOSk68heN6vGs\nmjTo02dA23jONfbVV18hKysL991331inS8LcV199hTVr1ngs95woY/BMqhzaIAYh+xVpJFAwwNp2\nOwaHFL744gtcuXIFv/jFL5CQkBC0+REyFjzPo7i4GNu3b8epU6e8LuXpZ1Hr0J19I3pSpwIsNWDx\nxaGKQ+t1d6Ej9zZo675BXN1pcHbPgE9FRQXeeust/O1vf8PixYtxxx13ID09PQgzDm/uwTRFdFQG\nGBWF2w1gCkQSMjYnTpzwOkb1xEmwecvW7erqQnJychBmQ0IFBSJJSKqursb//M//eCzVabnuLjiU\n2lE/btuUZVDoGyDv7XCNrV+/Hr29vXjooYcoMzIKWa1W/O1vf8OOHTs89hUqWTyZIoMyhLJablVL\noOEYvNlqczWwAYDS0lI8/fTTeOaZZ1BYWBi8CRIyQt3d3di3bx927dqF5uZmn8cJAHqT89GdfQPM\n8RMAer0eNqc8Fp15C9E1cQHUTecRX3MCskHnwX5GoxFbtmzBli1bMHPmTCxfvhzz5s2DVErZpsPh\nGYik31Ff3IO0FosFgiDQ+zBCRqG4uBhnz571GN+7dy9WrFiBjIyMIMyKkD7empE1NjZSWZgoR4FI\nEnKOHTuGt956yyONuztrDoxp08b02IJEhuaZ9yHr+HtgnQNLXDdu3Ii2tjb85Cc/QUxMzJieg4SP\nyspKvP7666ivr/fYt1jN4QeJUkhC8KJoporD/0mT448tNnQ4B6KR3d3d+NWvfoWVK1fiwQcfpOAB\nCVk8z+PcuXPYtWsXjh8/7rXIfj+nRA5DeiH0WXNgj0kM4Cwjj8BJYcicDUPGLKjaryCu7huo2q94\naW3Td2FbXFwMrVaLJUuWoKioCGlpIy+LEk08ApGhd/oIGRzDQMbAdUNNEARYLBbq6E7ICFVVVeG1\n117zus9ms+G3v/0tXnzxRco+I0FTWlrqMXbp0iUsWrQo8JMhIYMCkSRkGI1GvPvuu/jqq6889hlS\np6GtYKlfnscWm4zG2fcj/cynYJ0Dy3APHjyIixcv4sknn8TMmTP98lwkNPE8j88//xz/+Mc/PLq2\nSRngXxOluFUd2i+PE+UsfpMhx5o2G0rMA0tYBUHA559/jrNnz+Lpp5/GhAkTgjhLQsS6urpc2Y/u\ntVjdWdQ66LOuR0/qtKhuQDMuGAam5DyYkvMgMXVBW38G2oZir8u29Xo9Nm3ahE2bNmHGjBlYtmwZ\nbrzxRrrR4YV7QJ0aZg/NvajCUDckCCGeDhw4gDVr1sBisfg8pqmpCc8++yz+/d//HTNmzAjg7Ajp\nu0F36tQpj/GjR4/ikUceofcSUSy0r7RJVBAEAUeOHMG6devQ0eG5VKx7wly0FRT5dRmeOSEH9XMf\nRPrpjyEZdOHV3t6OF154AcuWLcMDDzyAuLg4vz0nCQ1NTU148803vd6dS5EweCpFhmx5eBT2UnMM\n/kMnw6ZuB7Z0OzC4N251dTWeeeYZPPDAA7jnnnvAcVRHjwRHf+3HXbt24cSJE0MGG3iGgzF1CvRZ\nc2DRZtDy6wBwqOLRMXkxOnNvQ2xLKbR130Cp91xGBQDnzp3DuXPnoNVqsXjxYixbtoyyJAdRKBSi\nbYvvMqdRTxAEWMQN3T3+/Qgh3rW0tODdd9/1WheyI/dWSE1d0DSdd411dXXhV7/6FYqKivCDH/wA\nGo0mkNMlUezzzz/3WgNYr9dj+/btuOeee4IwKxIKKBBJgur8+fN4//33UV5e7rFPYFi05y1Cd86N\n43IxatWmof6Gh5F2diPkve2ifbt27cLBgwfx7W9/G/fccw8tFYoAPM9j27ZtWL9+vUdDGgC4LZbD\nQ4nSkKoHORwsw+A78VJMU7B4u80uWqptt9vx/vvv49ixY3jqqaeoRhAJqM7OTuzduxd79uy5Zvaj\nTZUAfdb1MKRNBy+jLorBIHAS9KQXoie9ELKeFmjrz0DddAGcw+pxrF6vx2effYbPPvsMhYWFWL58\nOWVJAh7vFSyC4ONIYhUgunkmlUohkdBlCSFDMRgM2LRpE7788kvY7XaP/V3ZN6Bz0i2AwIN12hHb\nWibav3v3bhw+fBirVq3C3XffTcF/Mq4uXLiAjRs3+tz/4YcfYvr06Zg0aVIAZ0VCBSNE6JskvV4f\nmT9YhKioqMDHH3/sNVUbAKyxyWiefg9sGt2wHi9/18ui7fJl/zXsuTBOBxKuHER89TGvdbLi4uLw\n3e9+F0uWLIFcLh/245LQ0dLSgrfeegvnz5/32BfDAj9KkuGGGP9kDP6gamSdPz+Y6L8gd69TwHsd\ndhzr9cw4k8lkeOihh7BixQqwbHhkfJLwIwgCzp8/j+3bt1+z9iPPcDDqpkCfORuW+Kywz34cy3ko\nVDEOG9QtpdDUn/GZJdlPq9Vi6dKlWL58OVJSUgI0w9BSXV2Nn//8567tDCmDVzIDf6EfzPPQcHU7\nBDxZN7CcVKPRYP369QGfByHhoL+R2NatW71ml/Esh/aCpdBnzRkYFHgkVhxEfNURn9c3q1atwrJl\ny+j6hvhdaWkpfvOb38BkMg15nFarxQsvvEDByCig1WpFL0UUiCQBIwgCzp49i02bNqGkpMT7MQyD\nrpyb0Jl7KwR2+HfG/XEBqOiqg+7Cl5CZOr3u12q1uOuuu3DnnXfSkoYwIQgCdu/ejXXr1nmtnzND\nyeKRJBkS/FjIKxQuAI8aHXi/w45eL8sCp02bhqeeego63fCC/IQMR29vL/bv34/t27d7bf40mC0m\nEfqMWTCkF0ZU9mMkBiIH68uSPAt103mvWZL9WJbFnDlzcOedd2LWrFlRdeOjpaUFP/nJT1zbiRyD\nP0ygQKQ3zXYev6gf+D3S6XR4++23Az4PQkKZ2WzGli1bsHnzZp8BHbM2A63X3Qmb2vsNIEVnLXQX\nt/m8vklISMB3vvMdFBUVRX1WOxk7QRCwfft2rFu3zqMO/7+lyGDhBbzdLs7mlclk+MlPfoLFixeD\nCfOb0sQ3CkSSgLPZbDhy5Ag+//xzVFdX+zzOmFKA9vxFo+qK6rcLQN4Jbd1pJFR+LaodOZhcLsfS\npUuxYsUKqo0Vwjo7O/HnP/8Z33zzjcc+JQM8mCjFbbGc3094oXIB2O0QsK7DhjMmz2ikQqHAj370\nIxQVFdEJn4xJfX09tm7digMHDgxZLJ9nORh1U6HPnAVLXPhnP3oT6YHIfozDhtiWS9DWn4FS3zDk\nsampqbjzzjtRVFQElSpygs6+9Pb24sEHH3RtSwCszVGAC/Dve6ich4ZSanbi5eaBMim5ubn4/e9/\nH/B5EBKKnE4ndu7ciQ0bNkCv13s9xiGPRXveIvSkF17znMrwDmhrTyGh8rDPG0k6nQ4PP/wwFixY\nQO8NyajU1tbinXfe8ZpwtCpOgnvj+wLd/+iwY7vB4XHM9ddfj0cffRTp6enjPlcSeBSIJAFTW1uL\n3bt3Y//+/ejp6fF5nDkuE+2TF8MSlznq5/L3BSDrsCK+6ijiak6A5T1fKPsVFhaiqKgI8+fPp7uI\nIeTs2bN44403vL55K1SyeCRJikTJ+GTphNIFoCAIOGx04oNOO7zEI7FgwQI88cQTiImJGbc5kMgj\nCAIuXryIzz//HCdPnhzyWGtMIvSZ16MnfTp4aWTV2nU/71xLJAYmZT2t0DachbqxZMgsSZVKhWXL\nluFb3/oWEhNHfrMxnPzwhz8UnXv+b6YcqdLAZoWG0nnIlz2Gvsz9frfeeiueeeaZgM+DkFBTUVGB\nP//5z6iqqvK63ylVonPifOiz5kDgRnbtwdrNiK8+jriak2B5zxqTADBjxgz89Kc/RWpq6ojnTqJT\nU1MTPvnkE+zfvx8873nBsSpOgpVxEleAWxAEfNTp8BqM5DgORUVFWLVqFZKTk8d97iRwKBBJxpXZ\nbMaRI0ewa9culJWVDXmsKT4bXRNvgilx0pizY8YrE4WzGhFXcxLa+tNDXmSp1WosWrQIRUVF1rz8\n3gAAIABJREFUmDBhgl+em4yc0+nEhg0b8Mknn8D9tU3OAN9PkGKx2v9ZkIO5XwByHIfMzExoNBoY\nDAbU19eL6uYF4gKw0yFgbbsNJWbPNwepqal49tlnqTYLuSan04njx4/js88+89pgrJ/AsDCmTIY+\naw7M8RMiMvsRoEDkYIzDBnXzRWjrvoGix3djIo7jcNttt2HlypXIyckJ3AQD6Pnnn8eFCxdc2/+e\nIsP1fqpBPFzhEIhc32HDbsPAufCBBx7Ad7/73YDPg5BQIQgCNm/ejA8++MBrfWWnRIGunBvRPWEu\nBMnYajpy1l7EVx+Dtu4brwkXCoUCTzzxBG699dYxPQ+JXDzPo6SkxFUT3FtMScEAjyRJcVOs93Jr\nB3ocWN9hh81L1IZlWdx888244447cN1111GWbgSgQCTxO6PRiFOnTuHo0aM4c+aM147E/QQwMOoK\n0JVzE6xa/6Vdj/eSONZhhab+DOJrTkBiNQ557IQJE3DTTTdh/vz5yMnJoRfOADGZTHj11VdRXFzs\nsW+ynMWPk6XQBSArZfAFYHJyMlavXo2VK1dCIpHA4XBg8+bN2LBhA9ra2gAE7gJQEATs63Hio047\nrG6vjlKpFD/72c+wcOHCgMyFhBdBEHD8+HF8+OGHQ9Z/dMhjoc+cDX3GLDgV6gDOMDgoEOmFIECh\nb4S27hvENpeCFXw3K1qwYAEeeuihiFuC9Ze//AU7duxwba+Ol2BFXGBXTIRDIPKVJisuWAZujj37\n7LNYsGBBwOdBSChwOp1Ys2YN9uzZ47GPZ6XoyrkB3dk3gpf6t+YsZzUi4crX0DacAeMlJvDAAw/g\n/vvvp2sZ4tLQ0IBDhw5h//79aG5u9nncdAWLHyVJkXyNa68mG4+17XZctnpZunVVZmYmFi1ahFtu\nuYUydcOYeyBy+N1ACBlEr9fjxIkTOHr0KM6dO+dRjNadU6qEIW069BPmwK5KCNAs/YeXyNGdcxO6\nJ8yDuunCkLWxamtrUVtbi3/+859ITU3F/PnzMX/+fOTn59OJfJwYDAa8+OKLqKioEI0zAL59dTkA\nG+B/e47jsHr1aqxatco1JpFIXNtr1qwZsqOwvzEMgyUaCaYpWfyp1YaaQbcf7XY7/vCHP6C3txd3\n3XVXwOZEQt+FCxewfv36ITPcLZpUdOXcBGNKAcAGNvOLhBiGgSUuA5a4DLQXLIG27jS0tae81lw+\ncuQIjh07hmXLluG73/0uEhLC772BN5mZ4jIzDXa6L+5No1180ZmVlRWkmRASXE6nE2+++SYOHDjg\nsa8n9Tq0TV4ybjf3nPJYtF13B7qz5yKldBdUndWi/f/4xz/gcDjwwAMPjMvzk9AnCAIqKytx4sQJ\nnDhxwmfJgH7pUgb3x0sxR8UO67o3Tcbi/6TJcLTXiY1dDrQ6PM+Z9fX1+PDDD/Hhhx8iPz8fN9xw\nA+bNm4fs7Gy6tg5jlBFJhsVisaC0tBTnz59HSUkJKioqvNaAcGeKnwBD5mwYUwogcOMX9w5GkwBZ\nTys0DcXQNJaAc/hu0tAvPj4ehYWFKCwsxPTp05Gamkovnn7Q0dGBF154wSNTS8sBjyfLME0ZnCVx\n2dnZWLt2LSQSz997h8OBRx99FDU1NUHJRLHxAv7eace+Hs9A6IMPPoj7778/4HMioaWlpQXvvPMO\nTp065fOY3qQ8dOXcGNHLr4dCGZHDwzjt0DSWIK7mOGSmLq/HyOVy3HvvvfjOd74T9vWWi4uL8cIL\nL7i2UyQMfp8V2M7ZoZ4R2Wrn8cygjtkSiQQfffRR2P/fEzJSdrsdb731Fg4ePCgad0oUaJm+Ar0p\nkwM3GUGAtv40ki7t8chmX7VqFR588EGwbGDr3ZLg6OzsxLlz51BcXIyzZ8+iq8v7uXuwbBmDO7US\nzI/hRp384bha236b3oHGYdzES0xMxKxZszBr1iwUFhYiLi5uVM9LAoMyIsmw2Gw2lJWVoaSkBCUl\nJSgvL79m1mM/u0KDntTrYMiYBXvM+GQ4XOsCMBCBSZs6Be1TitCRfztiW8ugaSiGsqvG69IGAOjq\n6sLBgwddbzaSk5Mxffp0V3CSCvKOnN1ux0svveQRhJwkZ/DvKXLESYIXHNFoNF6DkEDfRZdGownw\njAbIWAb/miTDZIUD77TZMfjt5t///nckJSXh9ttvD9r8SPAIgoD9+/fjr3/9K8xmz2CGAKAnbTq6\nJs6HLZZes8i1CZwU+qzroc+chZjWciRUfu1RR9JqtWLDhg04ceIEnn766bDOjpsyZYqrFAcAtDoE\ntNj5gJQGCRfn3eoVFxQUUBCSRJ1Lly7hr3/9KyorK0XjdoUGDXO+D3tMgBt7MQz0WXNgjU1B+pl/\nimrjb9y4ERUVFXj00UfD+vWZeBIEAY2NjSgrK8PFixdx8eJFNDY2Dut7JQDmxXC4Xc1himJ4GZBD\nPh7DYKFagltjOZw389jf48BpEw9f68c6Ojqwd+9e7N27F0DfioRp06bhuuuuQ0FBAXQ6HSX9hDAK\nRBIIgoC2tjZUVFSgvLwcly9fxuXLl2G3e++m5o1NlQCjbgqMKQWwalKjKjtG4CToSZuGnrRpYG0m\nxLaVI6alDKqOqiHrY7W1teGrr77CV199BQDQ6XSYOnUq8vLyMHnyZOTk5EAmkwXqxwhLGzZs8HgD\nN03B4uc6GRRscH8HDQYDHA6Hz4xIg8EQhFmJ3RwrgZpl8MdWm6hQ9DvvvIPp06dTcDzK9PT0YM2a\nNThy5IjX/cbkfHTkLYRNnRLgmZGIwLDo1RWgN2UyYpsvIrHiIGRmcZZFVVUVnnnmGTz88MO4++67\nw/ICQi6XY+rUqSgpKXGNnTdTIHKw82bxe6NZs2YFaSaEBJbZbMbx48exd+9e0WtEP7tcjfq5D8Gh\nGnlml7+SMCzxWWi4/nvI+OYjcM6Buv/FxcX4t3/7N8ydOxdLly7F7Nmz6QZCmBEEAV1dXaioqMCV\nK1dc1/1G49D9DwZjAExRsJgfw2FeDIdYzv/naZZhMEPFYYaKg94p4ESvE8eMziHrSAJ9S7jr6+ux\nc+dOAH1JIZMnT0Z+fj5yc3ORl5dHWZMhhAKRUai7u9sVdKyoqEBFRQX0ev2IH8camwKjrgBG3RTY\nYpKiKvjoCy9TwZAxE4aMmWAdVqjaKhDbWoaY9itgnUMHdltaWtDS0oL9+/cD6KsxmJ2djfz8fOTl\n5SE/Px9ZWVngOKrBBgCXL1/Gpk2bRGMzlSyeSpFBFuQgJNB3Mty8ebOoRmS/zZs3D9n0I5BmqDg8\nmyrD/zYPBCNNJhPefPNNvPjii2EZCCAj19nZieeeew4tLZ4dj81xmWjPvx2WeMqCIH7AMDCmTYNR\nNwXa+rNIqPwaEluva7fNZsPatWtRWVmJJ598Mixfg2bNmuUWiHRiiYbecgMALwiiJjUABSJJ5LLZ\nbKisrERpaSlOnz6N0tJSnyvMbMp4NMz5/qiCkP5mjctAw9wHkHF6A7hBNX55nnfVCpTL5SgsLMTs\n2bMxdepUZGdn0zVKCHE6nWhqakJ1dTWqq6tRVVWFK1euoLu7e8SPJWeA6UoW16s4zFRx0I5D8NEX\nLcegSCNBkUaCLoeAMyYnTpucKLXwXrttD2YwGHDq1ClRmaGEhATk5uYiJycHEydORE5ODnQ6Hf3u\nBgG9K4pwer0elZWVqKqqcgUf+7v1jpRNFQ9zfDZMCdkwJ2TDKY/182wjCy+R911spU0DeCcU+kao\nOmug7KyBQl8Plh+6UYnT6URlZSUqKytdd3bkcjkmTZqEvLw8TJo0CZMmTUJmZmZUvnh+8cUXojql\n8RzwWHJoBCGBvv+/DRs2AIDXrtmBbFRzLQUKDt9LkGJ9x0CwvKSkBNXV1Zg4cWIQZ0YCwWQy4cUX\nX/QIQvKsBO2Tl0CfdT3daCL+x3LQT5iDnrTrkHJxB9QtpaLd+/btQ0JCAh566KEgTXD0Zs6ciQ8+\n+MC1fdHCwyEIkNDfEa5YeZgGxSHVajUmTZoUvAkR4ie9vb2oq6tDTU0NampqUF5ejqqqqmGVturR\nTUXrdXeAlwa+brgvVm06am/6EXTnt0DVVee532oVBXlkMhny8vKQl5eH7OxsZGdnIysrC3K5PNBT\njyqCIKCjo8PVLLW2thY1NTWoq6uDzWa79gN4wQHIkTOYpuAwXckiX8GGxPkrXsJgsUaCxRoJbLyA\nciuP82YeF8xOVNsEDKdBSGdnJzo7O3Hy5EnXmFwux4QJEzBhwgRkZ2e7vo6Pjw/Lm6HhggKREaJ/\neXV/0PHKlSuoqqpCR0fHqB/TrtDCnDAQeHQoglfTLuyxHCzxWX0ZRbm3gHE6oNA3QNlZA1VnDRT6\nBjDCtZv/WK1WlJaWorR04IJNJpMhOzvbFZicNGkSsrOzI35Z94ULF0Tb/5IoG5flAWPR1taGNWvW\nYOvWrdBoNDAYDKivrw+pIGS/JWrOY9nDxYsXKRAZ4ZxOJ1599VVUV1eLxi3qVDQX3gN7bFJwJkai\nBi9VonnGvehtykfypZ2iumSffvopkpOTsXz58iDOcOQmTZrkes0HABMPnDXxmBsTfTcN3R02is9/\nhYWFUXkzlYQnp9OJ1tZWNDY2oqmpyfW5rq4O7e3tI348c1wWOvJugzkhexxmO3YOpRYNcx9CTGsZ\nEq8cgtzoO5nFZrO5agz2YxgGqampyMjIQHp6OtLS0pCeno709HQkJiZS85sREAQB7e3tqK+vR11d\nHWpra1FXV4e6ujqYTKYxPbaSASbJ+wKOUxQscuVs0EtcXYuMZTBNyV1tSiqFmRdQbuFRZuFRbuVR\naeVhHWbrYqvVivLycpSXl4vG1Wo1MjMzkZWV5fqgAKX/UCAyDAmCgJaWFly+fNmVMVdVVYWenp5R\nPybPSmHV6GDRpsOiSYNFmxESSwMilcBJYL4a4O1EX1dRhb4Rcn0TFIZGKPRNkFqGt1zeZrN5vHiy\nLIvMzEzk5uZi4sSJyMvLQ25ubsTclWxvbxcF2aUMUKgKzTczTqcTNTU1wZ7GNbEMg+tVrCgQeenS\nJdx9991BnBUZb/v370dxcbFozJicj6aZ9wEsBQdGguM4ZGZmhvxNh5DEMOhJnw6LNh2ZJ9ZDYh+4\nqFq3bh3mz58f1AZfI8WyLG6++WZs377dNXbQ6AhYINK9C7Z7F+1Ad8nuZ+MFHO0V/03ceuutQZkL\nId7wPI/u7m60traipaXF9bn/6/b29jG/rttUCTCmTIYhfUZ43OxjGPTqpqA3pQByfSM0jecQ21YO\nifXadQUFQUBTUxOampo89kmlUiQnJ0On00Gn0yElJQUpKSnQ6XRITk6GVquNymBPf3LR4EBjXV0d\n6uvrvTYRHCkp09fhOlvGYqKcRZ6cRZqUGXWn61ChZAfqSgJ9ZUAa7AIqLDyqbTyqrDzqbAKG13q3\nT09Pj0cCEACoVCpRcLL/IykpKSp/Z0eLApFhwG6348qVK7h06RIuXbqEsrIydHV1XfsbfRAYFlZ1\nCiyaNFivBh5tMUkA3ZUKGoGTugKT/Thb79XAZBMU+ibIDY2Q2IZ3x4vneVd6fn8zHIlEgkmTJmHK\nlCmuj4SE8elqPt6sVqtoW8r0LSMgY+N+99P935lEnqNHj4q2LZp0NM+4l4KQI5ScnIzVq1d7LcMw\n2nIo0cgek4DG67+LzJMfguX7LhesVivOnDmDhQsXBnl2I7NkyRJRILLYxKPbISBOEr0XKadNTo9l\n2XPnzg3ehEjUMZvNaG9vd330Bxfb2trQ1taGjo6OYS2lHgm7QgtLXDrMcVkwJeXCror36+MHDMPA\nGpeBtrgMtAl3QGZsg6q9Esrueij0DaJav8Nht9vR2Njos0OzTCZDUlISkpOTkZSUhJSUFCQlJbnG\nEhMTwz7BQq/Xo7q62rWcvz/T0WKx+OXx1SwwQcZe/WCQLWeRLmXARUGwjGUYZMkYZMkG4hsOQUCD\nTUCNjUetjUetTUCdjYfx2osSRUwmE8rKylBWViYa7w9Q9i/tzsnJQU5ODtRqtT9+pIhDgcgQ1N3d\n7Qo6Xrp0CRUVFaM+KQoMC2tsMqyaVFjVur6go1oHgYus//pIzERxymJgSs6DKTmvb0AQILEY+jIn\ne1ogNzRD3tM87OCkw+FwdUTfsmULACAlJUUUmAyXQtOpqamIjY11dXkz8UC5lUeBIvTnHsrOmsR/\nM/n5+UGaCQkEu92O8+fPi8Zapt0JgaMumCPBcRxWr14takwlkUhc22vWrAn781EgWbXp6M6eh4Sq\ngSD52bNnwy4QmZubiwkTJqC2thYAwAM43OvA3dro/fs65LYse+HChdR1l/iN0+lEV1eXK6jY/zE4\n8DiS7sAjJTAMbKoE2GKTYYtN6Uv60KZHZk19hoFNnQKbOgXdwKBrlAbIjG2Q97RBZmyD1NyF0Ya8\nbDbbkIFKANBqtUhMTHQFK5OTk0UfWq02ZJZ/d3R0uFawVVVVoaqqakyJRYMpGCBDxiBTyiJTxiLz\n6tfRfOPLGwnDIFveF5DtJwgCupxAvY1HvZ1HvU1Ag41HvV24ZjMcd74ClImJiZg4cSImTpyI/Px8\n5OfnIz4+TG9I+FFkRaPCXHl5OT799FOcOHECgjDC33wAPCeDVZ0Cq1oHqyYVFnUqbLFJEZ/ZEjWZ\nKAwDh1ILo1ILY+rUvjFBAGc19gUme5qhMPQFKIe7rLu1tRWtra04ePAggL5/y5UrV6KoqCik7zJy\nHIeZM2fi8OHDrrF32+34n3QWyhCvaRKqvu5x4KxZfEvw+uuvD9JsSCD09PSI7roLDNuXHU9GJDMz\nEytXrvS6b+XKldi6dWtYlGcIJVa1TrTd3NwcpJmMHsMwWLx4Md577z3X2IEeJ+7USMJ+CdxotNl5\nlLidYxYvXhyk2ZBwJAgC9Ho9Wlpa0NzcjObmZtGy6c7OzoDc9HHIYmBXxcOmSoBdldD3dUwi7DEJ\nENjAXVrn73p5RPvLl/3X+E1m0DWKaNhhg6y3A1JTJ2SmTkhNXX1f93aCc4w960+v17sas3ojlUpd\nWZSpqalITU2FTqdzfR0bOz5BYkEQ0NjYiOLiYpw7dw6XL19GZ2fnmB+3P+CYIWVFnxM5JqSXBIdK\neRBvGIZBggRIkHCYMWh9HS8IaHcIaLQPBCYbbDwa7cKwa0/26+joQEdHh6h7d3JyMvLz8zFz5kzM\nnDkTqamp/vqRwgYFIoNMEAScP38en376qUedrqHwrLSvnqM2zZXtaFclRF1n06jPRGEYOBVqmBTq\ngcxJAKzdfDVrsi8wqdQ3QGruvubDtbW1Ye3atfjkk09wzz334I477kBMTMx4/gSjtnDhQlEgssku\nYE2bDU+myCCNsr+Dsaqw8Fg3qGM2AGRlZVGjmginVquhUChcwUhG4KHqqBK9lpBr02g0kEi8v52S\nSCRhVdswVMS0VYi2dTqdjyND26JFi/DBBx+43oc02QWcjtKmNdv0DlFH04kTJ1K3bOJVf1OOwUtV\n+2vk+WvJ6lCcEgUcSg3syjjYFVrYlXFwKONgV/Z9LUgiuxmkPwkSGazaNFi1aR77WLsFUnM3JOZu\nSM16SK9+lpi7IbUYwDpH1/F5MLvd7qpRWVJS4rE/NjbW1YxkcNfkuLjR9UloamrC7t27cejQoTEl\nw6hYIEPal9mYIe1bTp0hYxHPIaQDjr64r1yssLchTzrypKtAYhkGKVIGKVJglkocoOx0Cmi0CWiw\n82i4+rneJsAygh+pP2P7yJEjAPpW+912220oKipCcnKyv3+ckESByCAyGo149dVXvb4wurMrNLDE\nZcIclwFLXCassTqq6QjKRPGFlyphTsiBOSHHNcZZjVB010PZ3QBFdz0Uhiafnbr1ej0++OADbNy4\nET/72c+wYMGCAM18+ObNm4fbb7/dVQMTAM6YeLzcZMWTKXIkBGk5Qqg2CfDlYI8D73XYYR908pTJ\nZPj5z38eMstZyPiQSqW4+eabsXfvXtdYysXtaJj7IOwx4Vk/NhgMBgMcDofXYKTD4XB1TibDo6k/\nC3WTuGTAokWLgjOZMYqLi8Mtt9yCAwcOuMa2dtsxR8WG5cXkaOmdAg64LctesWJFkGZDQpHJZMLx\n48dRUlKCkpKScVvRJDAsHAoN7AoNHFc/7ErtwNcKDQRJ6K4IGkq4lanipQpYpamwarxkggkCWIcV\nEouhLzhp0fd9bTFA0v9h7QEzihWEgxmNRlcptMEyMjJQWFiIGTNmYN68edcsIWGz2fCXv/wF+/bt\nG9HzS9CX4ThBxiLratAxM4wDjv0GX/skJyfj8cc9Vy7+/4NWLobatdFQWIZBkoRBkgSiDEpBENDh\n7KtBWW/jUWcXUGvj0WgTMJy/wubmZvzzn//Ep59+iuXLl+ORRx7xeZM7UkT2Txfi1q9f7zMIaY1N\nhikhB5argUeHgjIqvKFMlOFzymP7ut7ppgAAGKcDckMTFN0NUHbXQ9VR6WoO0M9kMuG1117De++9\nF3L/lgzD4LHHHkNtbS2uXLniGq+wCvhVowU/S5ZhijL6sk6Gyy4I+HuHHXt7PE+Pjz/+OHJzc4Mw\nKxJoS5cuFQUipdYeZJ76EPVzHgiPbp4hoL6+Hps3bxZl5vfbvHkz6uvrgzCr8KSpOw1d6Q7RWHJy\nMgoLC4M0o7G77777RIHISpuACxYe06Po/LRD7xDd7EpKSsJtt90WvAmRkHLixAn86U9/8stNG4dU\nCYdC25fRqNB6BBudspiIXD0WcWWqGAa8VAGbVAGbOsX7MTwPic3YF5Q06weClK6v9eAco2u62NDQ\ngIaGBuzYsQM6nQ5PP/00CgoKfB7/+uuv49ixY0M+Zl+3aha5cgYT5X0NZNKkDCQR+PvYL5pWLjKD\nApQzB2VQ9jfIqbXxqLLxuGLlUWv13b2b53ls374dNpsNTz75ZGAmHyQUiAyi3l7v3cWMKQXonHQz\nrLEplPV4DZSJMnoCJ4ElPguW+Cz0WI2IaSuH7uJ2j+N4nofZbA65QCQAyOVy/Od//if++7//W1RD\nTO8EXm62YbGaw/3xUsRwkXuSH42LZife67Cjye55J/n+++/H7bffHoRZkWCYOnUqioqKsHv3bteY\nxGrEhGN/Q0fereieMC/i6wyPldPpxIYNGwDA60VgpLzJHk+cpQfJZbuhbhFnpXAch8cffzwsmqj5\nkp2djRtuuAEnTpxwjW3tdkRNILLXKWCvQXzJde+991KTGgIAKC0txSuvvAKeH17bWp6T9i2ZVsX3\nfVZe/azSwiHXROWy6WgK9oiwrCvQjLhM74fYLa6sSqm5q2/5t6kbUnMXJOZusPy1/11aWlrw61//\nGm+88YbXOn51dXU+g5DTFSymK1lMVXKYIIvsoKM3tHJR3CDn1qtjdkFAjVXARYsT5808Si2er397\n9+7FAw88gMTExMBOOIAoEBlE3/ve93D69GmYzeKlm7GtZYhtLQPPSmHVpMIclwGrNh0WbTplRl7V\nX3C5ydCIz7btxP333O1xzGfbduJY+u0wFqQHenohjXE6+hrb6Buh0DdA0d04ZHObFStWhHR9ruTk\nZLz22mt4/fXXcfr0ade4AGBvjxOnTE48kCDF/BgurJc5+IPeKeCjDjsO93q+8ZLJZHjiiSfCrjMt\nGbvHHnsMPM+LMiNZ3o7ky/ugaSxB69Q7YInPCuIMQ19bWxvWrFmDrVu3hs2yuJDA84irO4XEioMe\n9cAkEgmeffbZiGiatWrVKlEg8qKFR5nFiQJF5AcjdxocMA+656XRaFBUVBS8CZGQUlxcfM0gpF2h\ngTGlAEbdFFjiMgCGkjQGo2CPb0NlVTK8A8rOWsS2XEJsy6Uhm+eYzWaUlZV5DUSq1Wqf39fDCzDw\nQLtDgIYFEiXhveR6pGjloidBENDlENDu4GF0Akan9/ICHMeFbJ8Gf6FAZBBlZWXhtddewyuvvIK6\nujqP/Sxvh7K7DsrugX0OeSws2gxYtOmwxSTCFpMAuzI+ajNWjJp0/PVQMVi7RZSJ8tm2nfjroVIY\nE2YGe4rBIwjgbL1XO9V1QW7oCz7Ke1p81oYcjGVZPPzwwz7f3ISS2NhYPP/88/j444/xySefiPbp\nncCaNjv29zixOkGKXHn0vYG18QJ29ziwpdsBk5f/ep1Oh1/+8pfUOCBKcRyHJ554AhKJBDt37hTt\nkxvbkHXyA/TopqJr4nzvtZyilHsH0vxdL4su9sa1Q2m443moW0oRX3UEcqPnskGpVIpf/vKXmDt3\nbhAm538FBQUoLCwUleP5R6cdv04LTK3IYNXf6nYI2KYXZ0N+61vfglwenjX4iP/NmjULGzZsgDBE\nrT+pxYD42pOIrz3ZV+NRrr661FoLu1Jz9bMWDoUaTrkafJjWeByp/nNMnvXckMEeSU4hygseDOTU\ngk8QwDoskFh6ILH2DCzXNushteghMeshsRrBYHg1JhUKBaZMmeJ1X1xcHBYuXCgqwdGvxiagxjbw\nGqhmgYnyvjqQGVIGmbK+RjQKNrKCk/3nnM+dvUOuXJzs7MWzYVQfcqTM/NWakXa+r/O2TUC1jYdx\nGAngy5cvh0KhGP9JBhEz1At/ONPr9WHzg5nNZrz11luurkkjJYCBQ6mFTZUAuyq+LzipSoBNlQCH\nUhsVdw6n7H1VVKD5WPrtMKqjIxOStZsh7e0LNkpNHVc/d0La2wlulB3ntFotnnnmGcyYMcPPsx1/\nZ86cwdtvvy1aqj3YPBWL78RLkS4LzN9FMJvVOAUBh4xOfNblQKePO25FRUX44Q9/iNjY2IDNi4Qm\nQRCwe/durF+/Hkaj0esxvYkT0ZVzU18jrCi6qz8c+bteFm1TINIT47RD01CM+OrjPjPx8/Ly8NOf\n/jTiboxcuHABzz//vGjsiWQpboqN3JyAd9tt2D+oDrFGo8GaNWsiPsuDjMzXX3+NP/3pT37riM1z\nMjjksXDI1XAo+j475bFXx2LhlPV9jpRl3PO7T+Gjl37hM9jzvf/6vzgWHxk3dfqb2HDdJ50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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9c96fd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.violinplot('Embarked', 'Fare', hue = 'Survived', data = dat, split = False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Factor Plots: Relationship between Categorical and Numerical Features\n",
    "\n",
    "Consider the following hypothesis, where we are trying to plot the relationship between how many Siblings/Spouse (SibSp) one has as well as how many Parents and Children (Parch) one has, and the likelihood of survival. One way of plotting this is to use `catplot`, otherwise known as categorical plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "?sns.catplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcc9cac1c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot('Parch', 'Survived', kind='bar', data=dat, height=10, aspect=2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following:\n",
    "\n",
    "A data scientist walks up to you, and proposes the following hypothesis:\n",
    "\n",
    "\"I think the ticket type is likely to be correlated with the survival rate. To obtain the ticket type, use the **first character of the 'Ticket' column**. For example, if the Ticket Number is 'AC 2015', the ticket type is 'A', while if the Ticket number is '152385', then the ticket type is '1'.\" \n",
    "\n",
    "Prove or disprove this hypothesis, by means of an appropriate plot. You are required to do some data cleaning, to impute missing values and create a new column, 'TicketType'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'ellipsis' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-920ea5b65de6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Step 2: Get Ticket Type, by obtaining first character of Ticket Number\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mdat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket_Type'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ticket'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m...\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# please provide your code in the \"...\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Step 3: Plot Ticket Type against Survival Rate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[1;32m   3589\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3590\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3591\u001b[0;31m                 \u001b[0mmapped\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3593\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'ellipsis' object is not callable"
     ]
    }
   ],
   "source": [
    "# Step 1: Impute missing values with 'N'\n",
    "dat['Ticket'].fillna('N', inplace=True)\n",
    "\n",
    "# Step 2: Get Ticket Type, by obtaining first character of Ticket Number\n",
    "dat['Ticket_Type'] = dat['Ticket'].apply(...) # please provide your code in the \"...\"\n",
    "\n",
    "# Step 3: Plot Ticket Type against Survival Rate\n",
    "plt.figure(figsize=(20, 12))\n",
    "# your code here\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Data Plotting with MovieLens\n",
    "\n",
    "With whatever knowledge we have acquired on the Titanic dataset, let's move on to the MovieLens dataset. By now, you should be familiar with the 3 different data tables of the dataset, and how to combine these datasets. The following chunks of code should be familiar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import user data table\n",
    "unames = ['user_id', 'gender', 'age', 'occupation', 'zip']\n",
    "users = pd.read_csv('data/movielens/ml-1m/users.dat', engine='python', sep='::', header=None, names=unames)\n",
    "\n",
    "# Import ratings table\n",
    "rnames = ['user_id', 'movie_id', 'rating', 'timestamp']\n",
    "ratings = pd.read_csv('data/movielens/ml-1m/ratings.dat', engine='python', sep='::', header=None, names=rnames)\n",
    "\n",
    "# Import mnames table\n",
    "mnames = ['movie_id', 'title', 'genres']\n",
    "movies = pd.read_csv('data/movielens/ml-1m/movies.dat', engine='python', sep='::', header=None, names=mnames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "users.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similar to the last's lesson, we will combine the 3 dataframes together. One neat way to do this is the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = pd.merge(pd.merge(ratings, users), movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that in the previous lesson, we provided an additional argument, `on`, but in this lesson, we didn't have to. What changed? \n",
    "\n",
    "As it turns out, pandas automatically merges the 2 dataframes on the index column if you do not provide the `on` argument. That is, if you're intending to combine on the index, then it doesn't matter whether or not you decide to include the argument. If you have a column to combine on, then you should provide the column name as an argument.\n",
    "\n",
    "---\n",
    "\n",
    "\n",
    "Suppose we are interested in the distribution of the average reviews in the dataset. We can use seaborn's `sns.distplot` function. Including the argument, `vertical` rotates the plots so that the y-axis becomes the x-axis, and the x-axis becomes the y-axis. We can apply this to the \"rating\" variable in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "# plt.hist(dat['rating'])\n",
    "\n",
    "sns.distplot(dat['rating'], kde=False, vertical=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it turns out, most of the ratings given by the users are 4-stars ratings. Which movies have the most reviews? Let's find out by means of the plot. Recall that we can use the pd.DataFrame method, `groupby` in conjuction with the method `size` to get the total number of ratings per movie.\n",
    "\n",
    "In this case, however, we can simply use seaborn's inbuilt `countplot` function. The countplot functions counts the number of occurrence in the dataframe, and plots it. However, there appears to be too many unique films in the dataset. We can include an additional argument, `order` to \"restrict\" the number of films that we are interested in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following code returns the top 5 films, along with the number of times they were reviewed\n",
    "dat['title'].value_counts()[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The following code returns the top 5 films by name\n",
    "dat.title.value_counts().iloc[:5].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "sns.countplot(x=dat['title'], order=dat.title.value_counts().iloc[:5].index) # value_counts() returns by order the highest counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the graph above, we restricted the number of films to be 5, and selected the top 5 films.\n",
    "\n",
    "---\n",
    "\n",
    "In-class assignment:\n",
    "\n",
    "1. Using the same type of plot, plot the 5 least popular films."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try to plot the top 10 genres.\n",
    "\n",
    "#### Application - Data Manipulation with Plotting\n",
    "We can split the each observation on '|', and collect the genres in the form of a list. In what follows, we do so using functional programming."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dat.genres.apply(lambda x: x.split('|'))[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then use a for-loop to loop over all observations/entries, and another for-loop to loop over the genres, and use a dictionary to store the counts for each genre.\n",
    "\n",
    "The complexity associated with the following chunk of code is NM, where N represents the number of observations, and M represents the most number of genre each film could have. As it turns out, N = 1,000,000, and M = 5 => MN = 5,000,000. Python takes about 1 second to compute this operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "genre_counts = {}\n",
    "for genres in dat.genres.apply(lambda x: x.split('|')):\n",
    "    for genre in genres:\n",
    "        try: genre_counts[genre] += 1\n",
    "        except: genre_counts[genre] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "genre_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then sort by the values in the `genre_counts` dictionary to get the top 10 genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import operator\n",
    "\n",
    "sorted_genre_counts = sorted(genre_counts.items(), key=operator.itemgetter(1), reverse=True)\n",
    "sorted_genre_df = pd.DataFrame(sorted_genre_counts[:10], columns=['Genre', 'Counts'])\n",
    "sorted_genre_df.set_index('Genre', inplace=True)\n",
    "\n",
    "sorted_genre_df.plot.bar(figsize=(20, 12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preliminary Regular Expressions\n",
    "\n",
    "In this lesson, we will also be dealing with a not-so-gentle introduction to regular expressions (a gentler introduction will be done next lesson). This is just to give you a flavour as to why regex can do, as a motivation.\n",
    "\n",
    "Suppose we are interested in the number of films which are produced and shown for each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "dat.title.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Noting that the year is given in parentheses (round brackets), we can make use of this specific pattern to obtain the year of production. \n",
    "\n",
    "### Preliminary Text Analysis; Regular Expressions\n",
    "Typically, to conduct preliminary text analysis, we import the following libraries:\n",
    "\n",
    "1. The regular expressions library, `re`\n",
    "2. The string library, `string`\n",
    "\n",
    "Regular expressions are a sequence of symbols and characters expressing a string or pattern to be searched for within a longer piece of text. In our case, we are interested in searching for the pattern \"(YYYY)\" from the Title column.\n",
    "To do so, we can write a function that does that, and apply it to the column. \n",
    "\n",
    "We will be using the function, `re.findall`, which takes 2 arguments, the pattern to search for and the string. The function returns a list, containing all the terms that fit the pattern provided in the string. Understanding that our pattern is likely to be unique, we use `[0]` to tell Python to return the first element of the list returned. What if the pattern is not unique instead? It turns out that we can loop over the list, to find the exact string that matches the patern we are trying to find out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import string\n",
    "\n",
    "def year_from_title(text):\n",
    "    pattern = '\\(([0-9]+)\\)'\n",
    "    return re.findall(pattern, text)[0]\n",
    "\n",
    "dat.title.apply(lambda x: int(year_from_title(x))).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat['Year'] = dat.title.apply(lambda x: int(year_from_title(x)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 12))\n",
    "sns.distplot(dat.Year, kde=False)\n",
    "plt.show()"
   ]
  }
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