{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lecture 9 (guest) Data Visualization with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seaborn\n",
    "\n",
    "**`Seaborn`** is a data visualization library built on the top of `matplotlib`. It was created by [Micheal Waskon at the Center for Neural Science, New York University](https://joss.theoj.org/papers/10.21105/joss.03021).\n",
    "\n",
    "**`Seaborn`** has all the attributes of the `matplotlib` library (it is a child class), making it considerably easy to plot data using Python.\n",
    "\n",
    "We will learn some of these plots in this class and a few customizations. More about `Seaborn` can be found in [here](https://seaborn.pydata.org).\n",
    "\n",
    "Below you can find a list of functions that we can use to plot data on `Seaborn`.\n",
    "\n",
    "![alt image](https://seaborn.pydata.org/_images/function_overview_8_0.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns # This is how you import seaborn\n",
    "\n",
    "# Datasets\n",
    "\n",
    "## Political and Economic Risk Dataset\n",
    "# Info on investment risks in 62 countries in 1992\n",
    "# courts  : 0 = not independent; 1 = independent\n",
    "# barb2   : Informal Markets Benefits\n",
    "# prsexp2 : 0 = very high expropriation risk; 5 = very low\n",
    "# prscorr2: 0 = very high bribing risk; 5 = very low\n",
    "# gdpw2   : Log of GDP per capita\n",
    "perisk = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/perisk.csv')\n",
    "perisk = perisk.set_index('country')\n",
    "\n",
    "## Tips Dataset\n",
    "# Info about tips in a given pub\n",
    "# totbill : Total Bill\n",
    "# tip     : Tip\n",
    "# sex     : F = female; M = male\n",
    "# smoker  : Yes or No\n",
    "# day     : Weekday\n",
    "# time    : Time of the day\n",
    "# size    : Number of people\n",
    "tips = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/tips.csv')\n",
    "tips = tips.set_index('obs')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here is what we have in these datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>courts</th>\n",
       "      <th>barb2</th>\n",
       "      <th>prsexp2</th>\n",
       "      <th>prscorr2</th>\n",
       "      <th>gdpw2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>0</td>\n",
       "      <td>-0.720775</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9.690170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>1</td>\n",
       "      <td>-6.907755</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10.304840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>1</td>\n",
       "      <td>-4.910337</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10.100940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>0</td>\n",
       "      <td>0.775975</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8.379768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>1</td>\n",
       "      <td>-4.617344</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>10.250120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            courts     barb2  prsexp2  prscorr2      gdpw2\n",
       "country                                                   \n",
       "Argentina        0 -0.720775        1         3   9.690170\n",
       "Australia        1 -6.907755        5         4  10.304840\n",
       "Austria          1 -4.910337        5         4  10.100940\n",
       "Bangladesh       0  0.775975        1         0   8.379768\n",
       "Belgium          1 -4.617344        5         4  10.250120"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "perisk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>totbill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>F</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>F</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     totbill   tip sex smoker  day   time  size\n",
       "obs                                            \n",
       "1      16.99  1.01   F     No  Sun  Night     2\n",
       "2      10.34  1.66   M     No  Sun  Night     3\n",
       "3      21.01  3.50   M     No  Sun  Night     3\n",
       "4      23.68  3.31   M     No  Sun  Night     2\n",
       "5      24.59  3.61   F     No  Sun  Night     4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Data 101\n",
    "\n",
    "The best way to explore the data is to plot it. However, not all plots are suitable for the variables we want to describe. Starting with a single variable, the first question is what type of variable we are talking about?\n",
    "\n",
    "Types of variables:\n",
    "\n",
    "- `Quantitative` variables: represent measurement.\n",
    "    \n",
    "    + `Discrete`: number of children, age in years, etc.\n",
    "    \n",
    "    + `Continuous`: income, height, GDP per capita, etc.\n",
    "\n",
    "- `Categorical` variables: represent discrete variation\n",
    "\n",
    "    + `Binary`: voted for Trump, smokes or not, etc.\n",
    "    \n",
    "    + `Nominal`: species names, a candidate supported in the primaries, etc.\n",
    "    \n",
    "    + `Ordinal`: schooling, grade, risk, etc.\n",
    "\n",
    "For each variable type, there are specific descriptive stats and plots. Below, see an example of the difference between using the `right` and `wrong` descriptive stats for continuous and binary variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    62.000000\n",
       "mean      9.041875\n",
       "std       0.970264\n",
       "min       7.029973\n",
       "25%       8.381027\n",
       "50%       9.185412\n",
       "75%       9.889280\n",
       "max      10.410180\n",
       "Name: gdpw2, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats for a continuous variable (good)\n",
    "perisk['gdpw2'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.096721     1\n",
       "9.661735     1\n",
       "9.375601     1\n",
       "9.414342     1\n",
       "9.178953     1\n",
       "            ..\n",
       "10.127270    1\n",
       "9.690170     1\n",
       "7.029973     1\n",
       "8.548692     1\n",
       "9.891465     1\n",
       "Name: gdpw2, Length: 62, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency table for a continuous variable (bad)\n",
    "perisk['gdpw2'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    62.000000\n",
       "mean      0.451613\n",
       "std       0.501716\n",
       "min       0.000000\n",
       "25%       0.000000\n",
       "50%       0.000000\n",
       "75%       1.000000\n",
       "max       1.000000\n",
       "Name: courts, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Summary stats for a binary variable (bad)\n",
    "perisk['courts'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    34\n",
       "1    28\n",
       "Name: courts, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frequency table for a binary variable (good)\n",
    "perisk['courts'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Plots\n",
    "\n",
    "*Univariate plots* are plots for single variables.\n",
    "\n",
    "### Quantitative Variables: Histograms\n",
    "\n",
    "Starting with numerical variables, one suitable plot is the *histogram*. It breaks the numerical values into brackets and counts how many values are within each bracket.\n",
    "\n",
    "The syntax is:\n",
    "\n",
    "```\n",
    "sns.displot(data = the_data_frame,\n",
    "    x = 'the_variable',\n",
    "    kind = 'hist',\n",
    "    kde = [..True or False..], \n",
    "    rug = [..True or False..],\n",
    "    bins = [..number of bins..], \n",
    "    stat : [..{\"count\", \"density\", \"probability\"}..],\n",
    "    [..among others..])\n",
    "```\n",
    "\n",
    "Let's plot a histogram for the Log of GDP per capita (`gdpw2`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.displot(data = perisk, \n",
    "                x = 'gdpw2',\n",
    "                kind = 'hist',\n",
    "                kde = True,\n",
    "                kde_kws = {'bw_adjust': 0.5})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Customizations\n",
    "\n",
    "We can easily customize the entire plot:\n",
    "\n",
    "1. **Main title**: `plt.title('title here')`\n",
    "\n",
    "2. **X-axis title**: `g.set_xlabels('text')` or `plt.xlabel('text')`\n",
    "\n",
    "3. **Y-axis title**: `g.set_ylabels('text')` or `plt.ylabel('text')`\n",
    "\n",
    "4. **Style**: 'white', 'dark', 'whitegrid', 'darkgrid', and 'ticks'. Usage: `sns.set_style('stylename')`\n",
    "\n",
    "5. Remove the spine: `g.despine(left = True)`\n",
    "\n",
    "6. **Current Palette + display the palette**: `sns.palplot(sns.color_palette())`\n",
    "\n",
    "7. **Which palettes**: `sns.palettes.SEABORN_PALETTES` and to change, use `set_palette('palette')`\n",
    "\n",
    "8. **Save figure**: instead of `plt.show()` use `plt.savefig('figname.png', transparent = False)`.\n",
    "\n",
    "9. **Context**: set the context between 'paper', 'notebook', 'talk', and 'poster'. Use `sns.set_context('context here')`\n",
    "\n",
    "There are even more customization that we can do. Please check the [seaborn documentation](https://seaborn.pydata.org/tutorial/function_overview.html) for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My code here\n",
    "sns.set_context('notebook')\n",
    "g = sns.displot(data = perisk, \n",
    "                x = 'gdpw2', \n",
    "                kind = 'hist', \n",
    "                rug = True, \n",
    "                kde = True,\n",
    "                stat = 'probability')\n",
    "g.despine(left = True)\n",
    "sns.set_style('dark')\n",
    "g.set_xlabels('Log of GDP per capita')\n",
    "plt.title('Histogram with KDE of Log of GDP per capita')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Using the histogram, describe the variables `totbill` and `tip` in the `tips` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical Variables: Countplot\n",
    "\n",
    "Countplots are suitable for displaying categorical variables. \n",
    "\n",
    "The syntax is:\n",
    "\n",
    "```\n",
    "sns.catplot(\n",
    "    data = the_data_frame,\n",
    "    x = 'the_variable', \n",
    "    kind = 'count')\n",
    "```\n",
    "\n",
    "Let's check the risk of expropriation in each of the countries in 1992."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My code here\n",
    "sns.catplot(\n",
    "    data = perisk, \n",
    "    x = 'prsexp2', \n",
    "    kind = 'count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the customizations that we learn apply here as well. We can use them to prettify this plot. \n",
    "\n",
    "However, since the scale is out of order, we can change the order of the x-axis values using the `order` parameter. \n",
    "\n",
    "Even more, for `ordinal` data, it is customary to use a sequential color scheme, i.e., it gets darker as we increase the categories. \n",
    "\n",
    "We can use several palettes:\n",
    "\n",
    "1. `Blues`\n",
    "2. `Greys`\n",
    "3. `PuRd`: Light Purple to Dark Red\n",
    "4. `GnBu`: Light Green to Dark Blue\n",
    "\n",
    "Among others. The syntax to create the color scheme is:\n",
    "\n",
    "```\n",
    "sns.set_palette(\n",
    "    sns.color_palette(\"color_scheme\", # If want revert add '_r'\n",
    "                      [..number_of_colors or as_cmap=True..])\n",
    ")\n",
    "```\n",
    "\n",
    "For more about color palettes, please check [here](https://seaborn.pydata.org/tutorial/color_palettes.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My code here\n",
    "sns.set_palette(sns.color_palette(\"Blues\", 6))\n",
    "sns.set_style('white')\n",
    "cat_order = [5, 4, 3, 2, 1]\n",
    "sns.catplot(x = 'prsexp2', \n",
    "            data = perisk, \n",
    "            kind = 'count', \n",
    "            order = cat_order)\n",
    "plt.title('Expropriation Risk in 62 countries in 1992')\n",
    "plt.show()\n",
    "sns.set_palette('colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Do a countplot for the days (`day`) in the `tips` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your answer here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bivariate Plots\n",
    "\n",
    "Univariate plots are excellent. But in reality, most of the exciting questions in science come from combinations of multiple variables (e.g., cause and effect, correlations, relationships, etc). \n",
    "\n",
    "For two variables' plots there are three combinations:\n",
    "\n",
    "- **discrete x discrete**: mosaic plot\n",
    "\n",
    "- **discrete x continuous**: several useful types\n",
    "\n",
    "- **continuous x continuous**: scatterplots\n",
    "\n",
    "### Discrete x Discrete Variables: Mosaicplot\n",
    "\n",
    "The mosaic plot gives an idea of how the ratio of one variable changes when we change another variable. For instance, one empirical question that we can ask about the `perisk` dataset is:\n",
    "\n",
    "**Do countries with independent courts have less corruption than countries without independent courts?**\n",
    "\n",
    "The code to test this idea takes two steps. First, we need to prep the data. Then, we plot the data using the `kind = 'bar'` in the `catplot` function.\n",
    "\n",
    "We need to create a table with cumulative values for the two variables we want to study to prep the data. Here is an example of how to do that:\n",
    "\n",
    "```\n",
    "tab = pd.crosstab(df.v1, df.v2, normalize='index') # 1: Crosstab\n",
    "tab = tab.cumsum(axis = 1).\\     # 2: Cummulative sum\n",
    "      stack().\\                  # 3: Stack the results\n",
    "      reset_index(name = 'dist') # 4: Reset the indexes\n",
    "tab\n",
    "```\n",
    "\n",
    "Then, we need to plot the results using `catplot`:\n",
    "\n",
    "```\n",
    "sns.catplot(data = tab,\n",
    "            x = 'v1', # More variation here\n",
    "            y = 'dist', # Proportions\n",
    "            hue = 'v2', # Less variation here\n",
    "            # Comment hue_order if not displaying well\n",
    "            hue_order = tab.v2.unique()[::-1], \n",
    "            dodge = False,\n",
    "            kind = 'bar')\n",
    "plt.show()\n",
    "```\n",
    "\n",
    "*Full disclosure*: A function exists that builds mosaic plots in one line of code. However, I find the results very ugly. You can Google `mosaic plot in python` and check that yourself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prscorr2</th>\n",
       "      <th>courts</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.722222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.272727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    prscorr2  courts      dist\n",
       "0          0       0  1.000000\n",
       "1          0       1  1.000000\n",
       "2          1       0  1.000000\n",
       "3          1       1  1.000000\n",
       "4          2       0  0.722222\n",
       "5          2       1  1.000000\n",
       "6          3       0  0.272727\n",
       "7          3       1  1.000000\n",
       "8          4       0  0.250000\n",
       "9          4       1  1.000000\n",
       "10         5       0  0.000000\n",
       "11         5       1  1.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Prepping the data\n",
    "tab = pd.crosstab(perisk.prscorr2, perisk.courts, normalize = 'index')\n",
    "tab = tab.cumsum(axis = 1).\\\n",
    "      stack().\\\n",
    "      reset_index(name = 'dist')\n",
    "tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Doing the plot\n",
    "sns.catplot(data = tab,\n",
    "            x = 'prscorr2', # More variation here\n",
    "            y = 'dist',     # Proportions\n",
    "            hue = 'courts', # Less variation here\n",
    "            # Comment here if not displaying well\n",
    "            hue_order = tab.courts.unique()[::-1], \n",
    "            dodge = False,\n",
    "            kind = 'bar',\n",
    "            legend_out = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Do the number of smokers (variable `smoker`) vary by the weekday (`day`)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>totbill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>obs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>F</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>M</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>F</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Night</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     totbill   tip sex smoker  day   time  size\n",
       "obs                                            \n",
       "1      16.99  1.01   F     No  Sun  Night     2\n",
       "2      10.34  1.66   M     No  Sun  Night     3\n",
       "3      21.01  3.50   M     No  Sun  Night     3\n",
       "4      23.68  3.31   M     No  Sun  Night     2\n",
       "5      24.59  3.61   F     No  Sun  Night     4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Your answers here\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Discrete x Continuous Variables: Boxplots, Swarmplots, Violinplots\n",
    "\n",
    "Suppose we want to test whether the data distribution varies based on a categorical variable. For example:\n",
    "\n",
    "**Do you think that having an independent judiciary affects the GDP per capita of a country?**\n",
    "\n",
    "We can check if this hypothesis makes sense by looking into the distribution of GDP per capita and segmenting it by the type of judicial institution.\n",
    "\n",
    "The syntax for building these plots is almost the same as making a single boxplot. The difference is that you add the categorical variable to one of the axes:\n",
    "\n",
    "```\n",
    "sns.catplot(\n",
    "    data = data_set, \n",
    "    x = 'categorical_variable',\n",
    "    y = 'continuous_variable',\n",
    "    kind = 'box') # Or 'violin', 'swarm', 'boxen', 'bar'..\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My code here\n",
    "sns.catplot(x = 'courts', \n",
    "            y = 'gdpw2', \n",
    "            data = perisk, \n",
    "            kind = 'box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Are the tips from smokers higher than tips from non-smokers? (the idea is that smokers would compensate non-smokers for the externality caused) Check that in the `tips` dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your answers here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continuous x Continuous Variables: Scatterplots and Regplots\n",
    "\n",
    "To plot two continuous variables, one against the other, we can use two functions. First, we can use the `relplot` function if we want to explore the relationship without fitting any trend line. The syntax is the following:\n",
    "\n",
    "```\n",
    "sns.relplot(data = data_set,\n",
    "            x = 'independent_axis_continuous_variable',\n",
    "            y = 'dependent_axis_continuous_variable',\n",
    "            hue = 'optional_categorical_to_color',\n",
    "            kind = 'scatter')\n",
    "```\n",
    "\n",
    "And an excellent version of it, with distribution plots on the top and the left, can be built using the `jointplot` function:\n",
    "\n",
    "```\n",
    "sns.jointplot(data = data_set,\n",
    "              x = 'independent_axis_continuous_variable',\n",
    "              y = 'dependent_axis_continuous_variable',\n",
    "              hue = 'optional_categorical_to_color',\n",
    "              kind = 'scatter') # Or 'scatter', 'kde', 'hist', 'hex', 'reg', 'resid'\n",
    "```\n",
    "\n",
    "If you want to add a trend line, it is better to use `lmplot` (instead of 'reg' in the plot above). The syntax is the following:\n",
    "\n",
    "```\n",
    "sns.lmplot(data = data_set,\n",
    "    x = \"total_bill\", \n",
    "    y = \"tip\", \n",
    "    hue = \"smoker\",\n",
    "    logistic = ..False or True.., # Logistic fit for discrete y\n",
    "    order = ..polynomial order.., # Polynomial degree\n",
    "    lowess = ..False or True..,   # Lowess fit\n",
    "    ci = ..None..)                # Remove conf. int.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g32wiN1MrFbinSzT6Ng9ChdmOmEAtArQqqWN5PblchgGtQvBZbC+YbHYE/+8XAao/Fg2RRGL1Oqkj0HXo1Aro1AqpY3gUnjojIiJRsWiIiEhULBoiIhIVi4aIiETFoiEiIlGxaIiISFSS3N68evVqbNq0CWq1GiNHjsTMmTNrvL5y5Ups2rQJAQGXp1m/++67MXnyZCmiEhFRPbm9aNLS0rBt2zZs2rQJPj4+mD17Nnbt2oXk5GTnezIyMvDqq6+ia9eu7o5HREQNzO2nzk6cOIF+/frBz88PCoUC/fv3x9dff13jPRkZGVi1ahVSUlKwZMkSmM2cPZWIqKlye9G0b98e+/btQ2lpKcxmM/bs2YPCwkLn61VVVWjbti3mzp2LLVu2oLy8HG+99Za7YxIRUQNxe9H06dMHqampuO+++/DQQw8hKSkJKtXVeZ58fX3xzjvvoFWrVlAqlXjggQewd+9ed8ckgq0iG9ay3yHYrVJHIWrS3F40lZWVSE5OxrZt27BmzRqo1WrExl6dujwnJwcbN250bguCAKWSU7KR+9jNFSjPeB8X13ZH9n/ao2jv07CVX5A6FlGT5faiyc7OxqxZs2Cz2VBRUYGNGzdixIgRzte1Wi2WL1+OrKwsCIKAtWvXYujQoe6OSV7CanPgRF4Ffjxfgvz/LUhmyTuEoq8fgcNUDDhsqDj6NipO/FfipERNl9sPFRITE5GcnIw777wTdrsdU6dORVJSEqZPn47HHnsMHTt2xJIlSzBz5kxYrVZ069YN06ZNc3dM8gJlRgtW7s/Ei7vOwOYQ0DbMD+unJCEqJ83lvZUn/gP/zo9A6RNaYzyzuBq/XCyD0WpH+wh/dIoKdFd8oiZDJgjXrvPXtGVnZ2Pw4MHYvXs3YmJipI5DjdyeswUYsuqHGmPjOkTg/e4nUfz1QzXGfeKSETZ6A+QqH+fYr4WVSHnvIE4XVAEAtEo5vn64D/q2CBY/PFETwpkByGv9XlztMvbFyXw4DL2gDE50jsmUPtD3WlCjZABg3+/FzpIBAJPNgb99dQZGi+va80TejFfZyWtFB/q4jPVspocisCUix34Oc8ERCLZqqEPaQx3aweW9F0qMLmOnCytRbbXDpwkunGUtPQd7dR4UvpFQBTaXOg55EBYNea2kmEBM79UM7/x4+Y6yEJ0aK1LaXV5WWdsMyoBmN9z/tuucInugZzOE+DatJYAFwYHqc5+jYOc0CJYKyDV6GEasga75MKmjNVkVZhsOZZXiyMUyxOh90KuZHs2CvHdFVV6jIa9WbrLiZH4lyow2tDbo0CLYt9b7Vppt2Hg0B898fhJlJitm9W2OJwe0lPwDxW6phK30VwCAUt8KCrX/Dd9vKT6NnI96QrBdPUKTqQMQfe9BqPQtRc3qqVYdyMTMTcec233jgrDh/u6IDNBKmEo6PKIhrxagVaFXs6Bb2tdPo8TUHs2Q3MYAi11ATKAWSoW0lz2t5RdQ/P2zqD67CQCgazUOIbe/AmVA3J/uY6/MqVEyACBYymGrymHR3ILM4mo8+8XJGmNp50tw9FK51xYNbwYgqqeoQB80D9ZJXjIAUP37dmfJAED1b1tQde6LG+6j8A0H5KoaYzKlDxQ+YaJk9HRmmwMVZpvLeOV1xryF9P8yiKjBVP++zXXst6033EcV1Aahg98C5P87wSFXIXToO1AFxYsR0eM10/vgro6RNcZ0agXaht/4FKYn46kzIg/iE30HTJm7ao7FDrrhPjK5En6J90ITngRb1SUofaOgCk6ATMbfQ2+Fj1qBf4xsi8gADT7+OQftwv3wjxFt0Y5FQ0SeQBc/BlVnN8CS/zMAQGXoAl3r1JvuJ1OooA7tcN3buKnu4kN98eqdHTBvUGv4a5Tw03j3R613//REHkYd1BrhYz+DtegUAEAVnAilb7jEqbyTQi7z2ov/f8SiIfIwSl04lDqWCzUeLBoiiQl2C8z5v8BafAJydSA04Uk3fViUqClh0RBJrPr8V8j/LBXA5WenVYbOiEjZeMNnX4iaEt5WQiQhu7EIxXufxpWSAQBrwRGY836SLhRRA2PREElIsBlhr8pxGXdYyiVIQyQOFg2RhBS+EfDr8EDNQZkcqpB20gQiEgGv0RBJSCZXIrDr45BBjorj70PhF42QASugMXSROhpRg2HREN2Ckmor9mcW4eszBWgT5o8eMYFIyyzBb0VVGNE2DLc1D7683EAtqAKbI3jAywhMevJ/c4yFiJyeyL1YNCQ6QXDAkv8LzHmHIVP6QBPRA+prVrBsagRBwL8PXcDT2044x1qG6JDSLhwr92di5f5MvDOhEx7sVfu7xmRyJZT+XNqCPBOLxkMJDjvMeekw5x6ETKWDNrI31BKd9zfnpOHSpmGAwwoAkGtDEDn+qyY73cn5EiMW7zxdY+xcUTX0PlePYOZvP4WRbcP5ZDgRWDQey5SzH7mbhwOOy1OTX/5w/xrq0PZuzeGwmVF66BVnyQCAw1SE6vNNt2jsDgFmm8Nl3HHNGoLVFjtsDo9ZU5CoXnjXmQdy2EwoO/Sys2SAyx/uxgu73Z5FcNhgr3S9fddelev2LA2lmd4HD/eueVosQFvzd7an72iFmEAezRABLBqPJDissFVechm3V+e7PYtC7Qv/zjNdxnXNh7s9S0NRKeV4ZlA8XhndFh0i/DG5WzS2TO2BrBIjOkcFYOW4DpjROw4ymUzqqESNAk+deSCF2h8BXWajaHfND3ifuKGS5NG1SoFgq0bZ4f+DXO2PoL4vQhPZS5IsDSVW74On74jHw33i4KNUQKmQo3+LYJhtDvheMyW84LDBUnQCttJzUOgMUIW0h0Krly44kQRYNB5K1zIFsJtQlv4aZFc+3CN6SpJFqQtDYNdH4ZtwD2RyJRTaYElyiMFfc/UGAKVC7rKcc/Xv25H/+d2AYL/8/s6zENTnBZYNeRUWjYdS+oYhoMts6NrcA7lcCXkj+GBT6rxrDXprRTYKv37EWTIAUHHkLfi2GQ+f6H4SJiNyLxaNh1PqQqWO4LUEcxkcxgKXcYcE18qIpMSbAYhEovCLhOqPU8nI5FDqW0mSh0gqLBoikSi0wTAMXQ2VoTOAy88yhY38GOpgTpjpTnZLJRxWo9QxvBpPnREBKKwy45eL5civNKN1qC86RQZAo1LU++tqwrog8q6dsFVchEKrh9I/tgHSUm3YzWUwZu5A2eH/g0zlC33PedDG3AG5UiN1NK/DoiGvV1xlwVOfncCa9GwAgEwGfHhvV0zq2jBzjym0wR51p11TYczchYLt9zm38z5NQcT4r+ATc7uEqbwTT52R1zuaW+4sGQAQBGDO5gycL6mWMBXVh8NuRvkv/89lvOrXrRKkIRYNeb2SaqvrmNGKCpPtOu+mpkAGOeSaIJdxhUbv/jAkTdGsXr0aw4YNQ0pKCt5++22X10+ePInU1FQMGzYMCxYsgM3Gf/AknvhQX6gUNaeL6RMXhFi9j0SJqL5kChUCk54EZFc/4mRKHXQtR0uYynu5vWjS0tKwbds2bNq0CZ9++imOHDmCXbt21XjP3LlzsWjRIuzcuROCIGD9+vXujklepH24P754oBeaBV0ulkHxIVg1oRMCfWq3cBk1TtrofoicsAf6XgsQdNtSRN79DTTh3aSO5ZXcfjPAiRMn0K9fP/j5+QEA+vfvj6+//hrJyckAgIsXL8JkMqFLly4AgNTUVLzxxhu499573R2VPFyZ0YK086X45tdCtArxxWfTekCrUiDSXwP/Wq6OSY2XTK6ENqovtFF9pY7i9dx+RNO+fXvs27cPpaWlMJvN2LNnDwoLC52v5+fnw2AwOLcNBgPy8vLcHZO8wIc/XcSod3/Eim9/w8xNRzHhv+nQKOQsGaIG5vYjmj59+iA1NRX33Xcf9Ho9+vTpgyNHjjhfdzgcNaZXFwSB061Tg8sqMWLB9lM1xs4WVuGXnHLEBeskSkXkmdx+RFNZWYnk5GRs27YNa9asgVqtRmzs1YfYIiIiUFBwdX6owsJChIV512SMJD6rwwGj1e4ybra5jhFR/bi9aLKzszFr1izYbDZUVFRg48aNGDFihPP16OhoaDQapKenAwC2bt2KAQMGuDsmebhYvQ8e6dO8xpifRoEOkQHSBCLyYG4/dZaYmIjk5GTceeedsNvtmDp1KpKSkjB9+nQ89thj6NixI1asWIGFCxeisrIS7du3x5QpU9wdkzycSiHHU7e3RFSAFh8cykL7CD/MHRiPduH+Ukcj8jgyQRAEqUM0lOzsbAwePBi7d+9GTEzDTB9Cnq/MaIWPSg618vpzm1lsdhRWWaD3UUOnrv/8Z0TehnOdkde70fMyJ/Iq8PKeX/H5iTz0bKbH34Ynonus3n3hiDwAi4boT5RUWzD1k59xOKsMALDzdAHSs8vw4+P90CLYV+J0RE0H5zoj+hPniqudJXNFYZUFZ/KrJEpE1DTdtGg2b96MlStX4tSpms8crFq1SrRQRI2BTqWAUu76DJefhtdpiOrihkWzfPlybN68GUVFRZgyZQq2bdvmfG3Hjh2ihyOSUnyoLxYOaV1jbEKnSN6ZRlRHN7xGs3fvXmzevBlqtRoTJkzAgw8+CIPBgN69e8ODblYjui6VQo5H+7VE77ggnMyrRIsQHXrE6hGkU0sdjahJuWHRyOVyqNWX/1G1a9cOr7zyCp566imsW7eO08KQVwjSqZCcEIbkBM5OQXSrbnjqLC4uDv/85z9RVFQE4PJMyw899BCmTZuG4uJitwQkIqKm7YZF8+KLLyI7Oxs//fSTc2zatGmYOnUqKioqRA9HRERN3w2LJjg4GMuXL4dWq62xyuXkyZPx448/ih6OqLZMVjsqzVyJlagxqtVzNO+//z4GDBiAZ599Fnv27IHFYoFKxTU7SHo2uwN7zhbizvcPov+b+/GfQxdQVGWWOlaDc1gqYbywByUHlqA8431YSs5KHYmo1mo911llZSX27t2LPXv2ID09HUlJSfi///s/sfPVCec68z4HMovR/839cFzzt/i9uztjWs9m0oUSQcWJD1G46wHntjKwJSJSd0AV2Fy6UES1VOuZAYqKilBaWgqj0QiLxYLS0lIRYxHVzt5zRTVKBgCWf/sbSo1WaQKJwFZ5CcX75tUcKzsHS8Ev0gQiqqNazXU2aNAgWK1WjB49Gvfeey9ee+01aDQasbMR3ZSvyvUp/UCt8rpP9DdVgsMKwVzmMu6wVkuQhqjuanVEc88996B58+b45ptv8NVXX+G7777jXWfUKNzeKgSB2pq/Ly0a2gZ+Gs+ZL1bpF42AznNqjMkUGqhDO9QYyyqtxvZT+dh+Mg9ZpSwhajzqtB5NVVUV9u7di5UrV+L8+fM4fvy4mNnqjNdovNORnHJ8dSYfxdVWDEsIQ+9memiuc6TTlFnLs1B5+iNUZvwbKn089D2fgyaqr/PB6ZN5FUh570ecKzYCAFoG+2Dbg73QltPlUB1t2LABFosFkydPbrCvWatf+7KysvDdd9/h+++/x7Fjx9CjRw/Mnj27wUIQ1UfnqAB0jvLsJZhVAbEI6vEsAjpOh0zpA7nSp8brG47kOEsGAM4VG7HxaA6eH5rg7qjUxKWnp6N169Y3f2Md1Kpo7r77bgwaNAj33HMP3njjDee0NETkXgpt8HXH92e6ztSx73fO3uHJNm7ciH//+9+Qy+UICgrCyy+/jO+++w5r1qyBXC5HaGgonn/+ebRo0QLz5s1D69at8eCDDwJAje1BgwahU6dOOH36NP76179iz5492L9/P7RaLXr37o0FCxbAYrFAEASMHz/+lo50alU0+/fvR1lZGQ4fPozvv/8ePXv2hL8/D8mJGou7O0fhqzOFLmPkmU6dOoUVK1Zgy5YtiIyMxAcffICpU6fC4XBg3bp1CA4OxubNmzF79mx88cUXN/16rVu3xuuvvw4A2L17N1q3bo3Jkydj/vz5GDRoEGbMmIGCggL84x//wKRJkyCX120ps1q9e8+ePUhOTsZ//vMfvPvuuxg6dCh++OGHOn0jIhLP8MRwzOzTHHIZIJcBj/SJw/DEcKljkUgOHDiAfv36ITIyEgAwdepUDB48GCNHjkRw8OWj3tTUVOTl5SE7O/umX6979+7XHR86dCjeffddzJkzB7t27cLChQvrXDJALY9oXn31VXz44YdISLh8vvf48eNYuHAhtmzZUudvSEQNLzpQi1fHtMOcfs0BAC1DdNAoPeuGCLpKoVDUmEHfZDIhKysLLVu2rPE+QRBgs9kgk8lqLO1itdZ8zkyn0133+wwcOBA7d+5EWloaDhw4gDfffBObN29GREREnfLWqpq0Wq2zZACgffv2XCaAqJHRKBVoG+6PtuH+LBkP16tXLxw4cAD5+fkAgE8++QR79+7Fl19+6ZxZf9OmTdDr9YiLi0NQUBAyMjIAAHl5eTh48OCffm2FQuGc2/Kpp57Cl19+iVGjRmHx4sXw8/PDhQsX6py3Vkc0AwYMwOrVq/GXv/wFCoUCn376KVq3bo2ysjIIggC9Xl/nb0zkLUqqLbA7BIT68SFnahgJCQmYO3cuHnroIQCAwWDAV199ha+//hr3338/HA4HgoODsWrVKsjlctx33314+umnMWzYMMTExKB3795/+rUHDBiAZcuWAQBmzZqFBQsWYN26dVAoFBgyZAh69OhR57y1eo6mffv2sNvt1/8CMhlOnjxZ528sBj5HQ41JldmGHafzsWjnaVRb7Hh2YDzGd45EqC8Lh7xLrY5oGtuDmURNwYHzJZjw33Tn9qzNx6BTKzCle6yEqYjc74ZF8+mnn95w57FjxzZgFCLPsu1ErsvYyv2/Y0LnKPh42MwFRDdyw6LZsWMHAKCgoADnzp1D7969oVQq8eOPP6Jt27YsGvJYRosNZwurYHUIiA/xRaBP3ddfigzQuozFBGqh5I005GVuWDT/+te/AAAzZszAa6+9hmbNLq/xkZOTg+eff178dEQSuFRuwpJdp7H6xwsQBGBQfCjeHt8JrUN96/R1RiaG45Vvri5ZoFLI8NcBraBS1v05BKKmrFbXaC5duuQsGQCIiopCbq7raQEiT7D3tyKs+uHqLZx7fi3Efw9lYcnwhDrd1t8pKgDfz+6LA+dLYLY50KuZHt2i9SIkJmrcalU0BoMBb7zxBsaNGwfg8j3bsbGecUGz0mzD6YJKmKwOtDH4wsBbUL3e9eYI+/R4Lp4Z1Ar+mrqdQmsfEYD2EZ494SfRzdTqGH7ZsmU4c+YMxo4di3HjxiEnJwcvvfSS2NlEd6nchEe3HEOP179H/zf3Y8i/DuBELtfZ8XZJMYEuYwNbhcBX5Tlr3BDdim3btmHkyJFITk7G2rVra71frYpm4sSJOHnyJAIDAxEQEIBjx45hypQp+Otf/+p8MrUp2v97Mf5z+Oo8QMdyK/Dm/kzY7A4JU5HUBsWHYlDrEOd2qxAdHu4TB7kHrdpJVFd5eXl47bXX8NFHH+HTTz/FunXr8Ouvv9Zq31r9ijZkyBBUVVVh8uTJkMvl2LhxI6qqqpCQkIBFixY5bxpoatIvui6Pu+N0PspMCQjx5VII3iouWIdPJifhZH4lLHYH2ob5ISrQ5+Y7Eknso/RszN9+ClmlRsTqffCPEYm4N6lhHl5PS0tD7969nTPBDBs2DDt27MCcOXNuvCNqWTSHDx/G5s2bndsLFy7E+PHj8dJLL2HTpk11Drx161asXr0awOXpDp599tkar69cuRKbNm1CQMDlc9t33313g672dkW3aNdTJMltQl2WBibvE+qnQX9er6Mm5KP0bMzYeBTV1suzuFwoNWLGxqMA0CBlk5+fD4PB4NwOCwvD0aNHa7VvrT5Rq6qqUFlZCT8/PwBAZWUlTCbTLUQFjEYjli5dih07diAgIACTJk1CWloa+vbt63xPRkYGXn31VXTt2vWWvkdt3dY8CPd2jcZHP18EALQN98Oj/VpCqeDtp0TUtMzffspZMldUW+2Yv/1UgxSNw+GocdelIAi1vguzVkVz11134e6778bw4cMhCAJ27dqFCRMmYM2aNS7TUt+M3W6Hw+GA0WiETqeDzWaDRlPzN8eMjAysWrUKFy9eRI8ePfDss8+6vKchRAX64O27OuLx/i1hstnRxuCLcH/Xh+yIiBq7rFJjncbrKiIiAocPH3ZuFxQUICwsrFb71upX9xkzZuC5555DRUUFTCYTnn/+eUydOhVdu3bF0qVL6xTWz88Pjz/+OEaMGIHbb78d0dHR6Natm/P1qqoqtG3bFnPnzsWWLVtQXl6Ot956q07foy78tSr0aKZH/5YhLBkiarJi9de/jvhn43XVt29fHDhwAMXFxTAajdi1axcGDBhQq31rNXtzQzp16hTmzZuH9957D/7+/nj66afRqVMn53TXf3TixAnMnz//pvOuAZy9mYi81x+v0QCATqXA6vGdGuyGgG3btmHVqlWwWq0YP348pk+fXqv93H4xYt++fejTpw9CQkKgVquRmppaYxGenJwcbNy40bktCAKUSl6cJyK6kXuTYrB6fCc00/tABqCZ3qdBSwYAUlJS8Pnnn2Pnzp21LhmgltdoGlJiYiKWL1+O6upq+Pj4YM+ePejYsaPzda1Wi+XLl6NXr16IiYnB2rVrMXToUHfHJCJqcu5NimnQYmkobj+i6devH0aNGoXU1FTceeedsNlsmDFjBqZPn45jx44hODgYS5YswcyZM503H0ybNs3dMYmIqIG4/RqNmHiNhoio8eHFD6J6EgQB2WUmyAFEN9AdPkSehEVDHiuzuBon8iqgksvRPsIfUYENf/t6foUZ7x28gJf2nIVCJsMLyQn4S1IMpzBqIFabA/mVZgRolfDX1n3xOWocWDTkkY7mlGP4Oz8gt8IMAOgSFYANU7qjVR0XL7uZ7afzsWD7Kef2k58dR6zeB6mdIhv0+3ij0/mVeOWbX7H52CV0jgrAslFt0TsuWOpYdAs41wp5HIdDwNsHMp0lAwC/5JRj15mCBv8+7x+84DK+7khOg34fb1RhtmL25qP496EslJls+O5cMYat/hGn8yuljka3gEVDHsdks+NAZonL+M/Xma27PuRyGTpE+LuMJxoa9qjJG2UWG7Hn16IaYxX/W6SQpFNZWYnRo0cjOzv75m++BouGPI5OrcTErlEu48ltDNd5d/082LNZjdm+DX5qjO/s+r2pbnxUcvioFC7jfmqe7b+RilMf48J78fj9dQ0uvBePilMfN9jXPnLkCCZNmoTMzMw678uiIY90T5co3Ns1CjIZoFLI8OzAeAxoGXLzHeuoW4weaY/2w0eTu2HdfUn4fvZt6BjJpZvrq2WwL5aOSKwxNiIxDB0jXY8g6bKKUx+j6OuZsFdcACDAXnEBRV/PbLCyWb9+PRYvXlzriTSvxedoyGMZLXacK66CUi5HyxAdVFz+oUkpN1lxOKsMJ/IqEKv3QfdYPaJFuHPQU1x4L/5/JVOTwr8Zmj1Yu5Uwa2PQoEH473//W6fPWB6HksfyUSvQPoJHF01VgFaFQa1DMah1qNRRmgR7RVadxt2Jv+IREXkAhX9sncbdiUVDROQBgm77G2RKXY0xmVKHoNv+JlGiq1g0REQewD9xEkKGvA2FfzMAMij8myFkyNvwT5wkdTReoyEi8hT+iZNEL5Y9e/bUeR8e0RARkah4REP1ZjeVwlb+O2QKLVT6eMgUnPyQiK5i0VC9WIpPo/CrGTBfOgDIlQjs/gwCuz4KhU/DPxxJRE0TT53RLRPsVpT/9M/LJQMADhvKDv4D5rzD0gYjokaFRUO3zG4qRvW5bS7jloKjEqQhosaKRUO3TK4JgDqip8u4Sh8vQRoiaqxYNHTL5EofBPVaCLnP1VmRdS1ToIl0LR8i8l68GYDqRRPeFVET02AtOQ25SgdVcDsofLgKIhFdxaKhelMFxkEVGCd1DCJqpHjqjIiIRMUjGqo3i82OrFIT1EoZYvW6m+/QiAgOO8wFv8BamAGZyhea8CSoAltIHYvIo7BoqF5+L6rG378+g/+mZyNAo8TylHa4p3MUfDVN46+WKXsvcreMAgQ7AECpj0fE2G1Q6VtJnIzIc/DUGd0yh0PA6h8y8e9DWbA7BJQYrXho/REczCqVOlqt2M3lKN7/vLNkAMBW+itMuQclTEXkeVg0dMsKqsz4z+Fsl/HDTaRoBJvxuqsPOowFEqQh8lwsGrplfmolEgx+LuMx+qaxrrtCFwb/jg+6jGvCkiRIQ+S5WDR0y3w1SvxtRAK0yqt/jbpEBaB3XJCEqWpPJpPBr8MDCEx6GjKVH5QBzRE2aj3U4SwaooYkEwRBkDpEQ8nOzsbgwYOxe/duxMTESB3Haxy7VI4TeRXwVSvQOSoQsXofqSPViSA4YK/MARRaKHWhUsch8jhN49YgatQ6RgagY2SA1DFumUwmh9Kfv5gQiYWnzoiISFQsGiIiEpUkRbN161aMGjUKo0aNwssvv+zy+smTJ5Gamophw4ZhwYIFsNlsEqQkIqKG4PaiMRqNWLp0KdasWYOtW7fi8OHDSEtLq/GeuXPnYtGiRdi5cycEQcD69evdHZOIiBqI24vGbrfD4XDAaDTCZrPBZrNBo9E4X7948SJMJhO6dOkCAEhNTcWOHTvcHZOIiBqI2+868/Pzw+OPP44RI0bAx8cHPXr0QLdu3Zyv5+fnw2C4upCWwWBAXl6eu2MS1Vp2qRFnC6ugUynQNtwPAVqV1JFuqqDSDLlMhhBftdRRyAu4/Yjm1KlT2LRpE7755ht8//33kMvleO+995yvOxwOyGQy57YgCDW2iRqTX3LK0Of/7cPgfx1An/+3D49uOYbcCpPUsf5UUZUFqw9kosc/v0fvN77HRz9lo8JklToWeTi3F82+ffvQp08fhISEQK1WIzU1FQcPXp3EMCIiAgUFV+eaKiwsRFhYmLtjEt2UyWrD3786g4tlV4tlTfpFHLxQKl2om/jqTAEe2XQMF0qM+K2oGn/56Gfs+71E6ljk4dxeNImJiUhLS0N1dTUEQcCePXvQsWNH5+vR0dHQaDRIT08HcPkOtQEDBrg7JtFNlRpt+O5cscv4mYJKCdLcnM3uwNtpmS7j649cdH8Y8ipuL5p+/fph1KhRSE1NxZ133gmbzYYZM2Zg+vTpOHbsGABgxYoVeOmllzB8+HBUV1djypQp7o5JdFPBOhWGJRhcxtuF+0uQ5uYUchniglynB2pqUwZR08O5zojq4XhuBSZ+mI7juRWQyYAn+rfEc4PjEeqrufnOEvjhfAkGvp0Gs80BAAjQKvHNzL7oGh0ocTLyZJzrjKge2kf4Y88jffBbURV8VAokGHyhVTXef1a944KQ9mg/HLxQAqVchl7NgtChCc9TR01D4/0XQdREGPw0MPg1ziOY6+kaHcgjGHIrznVGRESiYtEQEZGoWDRERCQqFg0REYmKRUNERKJi0RARkahYNEREJCoWDRERiYpFQ0REomLREBGRqDgFDRF5vGqLDb/klOPXwipE+GvQNSoQBv+mM21QU8eiISKPJggCPvr5ImZsOOocm9wtGv8c2wHBOi5l7Q48dUZEHu334mr89bPjNcbW/nQRx3MrJErkfXhEQ0RNyq+FVdhzthAn8spxR3wo+rcIRsgN1v+pMNtQaba7jJcYrWLGpGuwaIioybhYasRd/zmEY5cuH428sS8TfxuWgOcGt4ZcLrvuPnF6H/SIDcShrDLnmFYpR+tQX7dkJp46I6Im5FhuhbNkrli6+yzOFVf96T56nRrv39MFIxIvL7vdNswPX07vhbaNdMltT8QjGiISVbnJiozcCpQYrYgP8UVCmN8tf60rS1Bfy2J3wOa48Yr07SMCsH5Kd+RVmBGoVSHElzcBuBOLhohEU1RlwaIdp/D2gfMAAJ1agc8f7Ik7WoXe0tdrH+GPYJ0KxdVXr6/clxSDFsG6m+7rq1aiZQg/8qTAU2dEJJpfcsqcJQMA1RY7Zm48isJK8y19vfhQX+ya0QdTkmLQLtwPS0ckYnFyAjRKRUNFJhGw3olINLkVroVyuqAKpSYbQv1u7YHJbjGBePfuzjBa7fDXquobkdyARUNEorneKa3+LYIR5le/ayRKhRz+Cp6QaSr4J0VEoukSFYB/3dUROvXlU1vtw/3wxrgOCOCRiFfhEQ1RLRzJKcNXZwpQarRhWIIBvZrpoeZ1gZvSqZWY3jsOA+NDUW62IU7vc8unzKjpYtEQ3cSRnHLc/lYayk02AMBLe87iy4d6YVhCmMTJmgaZTIbWhlu/pZmaPp46I7qJb84WOEsGAAQB+PvXZ1Btsd1gL/cpqjJj3S8XMeKdH/DEpxlIzy6VOhJRDTyiIbqJCrNroZQabTd9SNBdPv45B499mgEA2Hm6AB8czsL+Of3QPoJPvlPjwCMaopsY1NqAP06j9fTtrRrFBe1LZSb87eszNcbKTTb8klP2J3sQuR+LhugmejbTY8eM3hgUH4JOkQH4YGIXjG4XLnUsAIDs+vNI4k+GiSTBU2dEN6FSyDGktQG3xQXDLjjgp5H+SOaKiAAtFg9tgzlbMpxjgVolukQHSpiKqCYWDVEt+agVABrfLc0Tu0YjzE+D/x7OQmKYHyZ1i0E7zkxMjQiLhqiJC9apMb5zFMZ3jpI6CtF18RoNERGJyu1HNBs2bMCHH37o3M7OzsaYMWOwaNEi59jKlSuxadMmBAQEAADuvvtuTJ482d1RidyiymzDjxdKkZZZjKhALfq3COYDjuRR3F40EyZMwIQJEwAAZ8+exezZszFnzpwa78nIyMCrr76Krl27ujsekdttPZ6Lv3z0s3O7VYgOu2b0RosQLjVMnkHSU2cvvPACnnzySQQHB9cYz8jIwKpVq5CSkoIlS5bAbL61tSuIGrvcchOe3naixthvRdX46SKfgyHPIVnRpKWlwWQyYcSIETXGq6qq0LZtW8ydOxdbtmxBeXk53nrrLYlSEonLYnegzOQ684DR6rpkMVFTJVnRfPLJJ5g2bZrLuK+vL9555x20atUKSqUSDzzwAPbu3StBQiLxRQf64NHbmtcYUyvk6BjJ25PJc0hSNBaLBYcOHcKgQYNcXsvJycHGjRud24IgQKnkXdjkmRRyGWbf1gJLhyeiebAPBrcOwVcP90anyACpoxE1GEk+wU+fPo3mzZtDp3NdfU+r1WL58uXo1asXYmJisHbtWgwdOlSClETuERvkg+eGtMb0Ps2gUymdi4QReQpJjmiysrIQERFRY2z69Ok4duwYgoODsWTJEsycORPDhw+HIAjXPcVG5GlCfTUsGfJIMkEQGsdc5w0gOzsbgwcPxu7duxETEyN1HCIiAmcGICIikbFoiIhIVCwaIiISFYuGiIhExaIhIiJR8UlIqreLZUacLaiCTq1AYpgfArSNZwVKIpIei4bq5UhOGcb8+xAulBgBAPclRePlUe0QEaCVOBkRNRY8dUa3zGyz4x+7f3WWDACsSb+IgxdKpQtFRI0Oi4ZuWUm1FXt/K3QZP5lfIUEaImqsWDR0y4J1KgxLCHMZbx/BCSGJ6CoWDd0ytVKBZwa2QvuIy1Pay2TAY/2ao1esXtpgRNSo8GYAqpf2EQHY80gf/Fp4+a6zNqG+8FHzrxURXcVPBKo3g58GBj+N1DGIqJHiqTMiIhIVi4aIiETFoiEiIlGxaIiISFQsGiIiEhWLhoiIRMWiISIiUbFoiIhIVCwaIiISFYuGiIhExaIhIiJRsWiIiEhUnFSTqBaMFhtOFVSh3GRFyxAdYvU6qSNRA7DY7DiWW4HM4mpEBGjRKcIf/lqV1LE8DouG6CaKqy1Y/s2veOXb3yAIQGSABlun9UR3rrvTpAmCgPVHLuH+T36GIFwemz+4NZ4bFA9fDT8aGxJPnRHdxE/ZZXj5m9+cH0aXys146rPjKDdZpQ1G9fJbUTVmbT7q/HMFgH/sPovjeVyKvKGxaIhuIqvU6DK2P7MYxdUsmqas1GhFpdnuMl5QaZEgjWdj0RDdRLMgH5exfi2CEaLjufymLFavRYvgmn+2GqUcLUN4/a2hsWiIbiIpOhDzB7eGTHZ5OzpQi1fvbM+Lxk1cuL8Wn9yXhLZhfgAuX3vbMrUHEgx+EifzPDJBuPYMZdOWnZ2NwYMHY/fu3YiJiZE6DnkQk9WG0wVVKDfZ0DJYh2i961FOfZmtduzPLMa7P16AUiHHgz1j0bd5MFQK/j4opsJKMy5VmBGiUyEqsOH/XIl3nRHVilalROeoQFG/x77MYgxd9YNze+1P2fh2Zl/0bxki6vf1dqF+GoT6aaSO4dHcXjQbNmzAhx9+6NzOzs7GmDFjsGjRIufYyZMnsWDBAlRVVaF79+548cUXoVSyE8lzCYKAN/dn/mEM+DA9m0VDTZ7bj8knTJiArVu3YuvWrVixYgVCQkIwZ86cGu+ZO3cuFi1ahJ07d16+1339enfHJGoUHJ5zZpu8mKQnf1944QU8+eSTCA4Odo5dvHgRJpMJXbp0AQCkpqZix44dEiUkcg+ZTIbZtzX/wxhwX1KsNIGIGpBk56PS0tJgMpkwYsSIGuP5+fkwGAzObYPBgLy8PHfHI3K7fs2D8fUjffDuD+ehUsjxQM9m6NM8SOpYRPUmWdF88sknmDZtmsu4w+GA7Mp9pLh87vrabSJPpVEpMCg+FIPiQ6WOQtSgJDl1ZrFYcOjQIQwaNMjltYiICBQUFDi3CwsLERYW5s54RETUgCQpmtOnT6N58+bQ6VyfwI2OjoZGo0F6ejoAYOvWrRgwYIC7IxIRUQORpGiysrIQERFRY2z69Ok4duwYAGDFihV46aWXMHz4cFRXV2PKlClSxCQiogbAmQGIiEhUnNuCiIhExaIhIiJRsWiIiEhULBoiIhIVi4aIiETlUVMi2+2Xl2XNzc2VOAkRUf1FRER4xMz1Tf8nuMaVGQUmT54scRIiovrzlEc1POo5GpPJhIyMDBgMBigUCqnjEBHVi6cc0XhU0RARUePDmwGIiEhULBoiIhIVi4aIiETFoiEiIlGxaIiISFQsGiIiEhWLhoiIRMWikUh+fj5mzJiBsWPHYuLEicjOzpY6klucOHECHTp0kDqG6NLT0zF+/HiMGTMG999/Py5evCh1JNFs27YNI0eORHJyMtauXSt1HNGtXLkSo0aNwqhRo/DKK69IHadpEEgS999/v/DRRx8JgiAIH330kfD4449LG8gNqqurhYkTJwpt2rSROoroBg4cKJw8eVIQBEHYsGGD8Mgjj0icSBy5ubnCwIEDhZKSEqGqqkpISUkRzp49K3Us0ezfv1+45557BLPZLFgsFmHKlCnCrl27pI7V6PGIRgLFxcU4deoUJk6cCAC466678MQTT0gbyg2WLVuG+++/X+oYorNYLHj88ceRmJgIAEhISMClS5ckTiWOtLQ09O7dG3q9HjqdDsOGDcOOHTukjiUag8GAefPmQa1WQ6VSoVWrVsjJyZE6VqPHopFAVlYWoqKisGzZMtx111147LHHoFKppI4lqt27d8NkMmH48OFSRxGdWq3GmDFjAAAOhwMrV67EkCFDJE4ljvz8fBgMBud2WFgY8vLyJEwkrtatW6NLly4AgMzMTGzfvh233367tKGagKY/W1sjt337drz00ks1xuLi4nDixAk8+uijeO6557BhwwbMmzcPa9askShlw7nez9uyZUtUVlbigw8+kCaUiP7s5/3ggw9gsVgwb9482Gw2PPzwwxIlFJfD4YBMJnNuC4JQY9tTnT17Fg8//DCeeeYZNG/eXOo4jR4n1ZTAhQsXMG7cOKSnpwMAjEYjevfujSNHjkicTBwbNmzAqlWr4OvrCwA4deoUEhMTsXbtWvj5+UmcThxVVVWYOXMm9Ho9VqxYAbVaLXUkUWzZsgWHDx/G0qVLAQBvvvkmBEHAnDlzJE4mnvT0dDz22GOYP38+Ro0aJXWcpkHaS0Tea+TIkcK3334rCIIgfPHFF8KkSZMkTuQ+3nAzwMyZM4WFCxcKdrtd6iiiunIzQFFRkVBdXS3ceeedwpEjR6SOJZqcnByhV69eQlpamtRRmhQe0Ujk3LlzWLx4MUpKSuDn54dly5Z5zSF4QkICTp8+LXUM0Zw4cQLjxo1DfHy8cy2RsLAwvPPOOxInE8e2bduwatUqWK1WjB8/HtOnT5c6kmj+/ve/Y9OmTWjWrJlzbOLEiZg0aZKEqRo/Fg0REYmKd50REZGoWDRERCQqFg0REYmKRUNERKJi0RARkahYNOS1fvzxR4wePbpeXyMhIQHFxcXXfe3999/HqFGjcOedd2Lq1Km4cOFCvb4XUVPFoiESQVpaGjZu3Ih169bhs88+w9ChQ/Hcc89JHYtIEpzrjLxadXU1HnvsMZw/fx4BAQFYsmQJAGDJkiWoqqpCQUEBEhMT8frrr0Oj0aBDhw4YPHgwTp06hRUrVgAAXn/9dRw7dgwOhwNPPPEEBg4ciNDQULzwwgvOKXY6duyId999V7Kfk0hKLBryapcuXcKKFSvQrVs3rFu3Ds888wy6d++OsWPHYsyYMbBarUhNTcW3336LYcOGwWq1YuDAgfjnP//p/BoxMTFYsmQJzpw5g/vuuw/bt29HmzZtnK9bLBasWLHCK2auJroenjojr5aQkIBu3boBAMaNG4eMjAzMmjULwcHBeOedd/DCCy8gPz8f1dXVzn26d+9e42tcmX6kTZs2aNWqFX7++Wfna8XFxXjggQeg0+nw5JNPuuEnImp8eERDXk0ur/m7lkwmw4IFCyAIAkaMGIE77rgDly5dwrUzNel0uj/9Gg6Hwzm/2alTpzBr1iwMGTIEzz77LBQKhYg/CVHjxSMa8mqnT5/GyZMnAQDr1q1DUlIS0tLSMHv2bIwcORIAcOTIEdjt9j/9Glu2bAEAHD9+HBcuXEDnzp2Rm5uL+++/H7NmzcL8+fNZMuTVeERDXq1ly5ZYuXIlsrKyEBISgmXLluHbb7/F7NmzodPp4Ofnhx49etzw1uSsrCyMHTsWMpkMr776KvR6PRYtWgSj0Yg1a9Y4F7RTq9XYsGGDu340okaDszcTEZGoeOqMiIhExaIhIiJRsWiIiEhULBoiIhIVi4aIiETFoiEiIlGxaIiISFQsGiIiEtX/B3qDEOr6e1T9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# My code here\n",
    "sns.relplot(data = perisk,\n",
    "            x = 'barb2',\n",
    "            y = 'gdpw2',\n",
    "            hue = 'courts',\n",
    "            kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Or maybe you want to see it in two different plots\n",
    "sns.relplot(data = perisk,\n",
    "            x = 'barb2',\n",
    "            y = 'gdpw2',\n",
    "            col = 'courts',\n",
    "            kind = 'scatter')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(data = perisk,\n",
    "              x = \"barb2\", \n",
    "              y = \"gdpw2\",\n",
    "             hue = 'courts')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 405.725x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.lmplot(data = perisk,\n",
    "           x = \"barb2\", \n",
    "           y = \"gdpw2\", \n",
    "           hue = \"courts\")\n",
    "g.despine(left = True, bottom = True)\n",
    "plt.xlim(-7, 3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Are the tips related with total bill in the `tips` dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Your answers here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Great job!!!**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extras\n",
    "\n",
    "Excellent job learning `seaborn`! It is an easy-to-use yet powerful package to generate lovely plots.\n",
    "\n",
    "Next, you should take a look at the following packages to keep developing your skills:\n",
    "\n",
    "- [`plotnine`](https://plotnine.readthedocs.io/en/stable/index.html#): Implements the ggplot *grammar of graphs* in python\n",
    "\n",
    "- [`cartopy`](https://github.com/SciTools/cartopy): Package to make maps in python.\n",
    "\n",
    "- [`plotly`](https://plotly.com): Builds interactive graphs in python (and other languages). Check also the [`dash`](https://dash.plotly.com/introduction) for plotly in python.\n",
    "\n",
    "Now, try the extra exercises below to sharpen your learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        polInf collegeDegree female   age homeOwn govt     length  id\n",
      "0  Fairly High           Yes     No  49.0     Yes   No  58.400002   1\n",
      "1      Average            No    Yes  35.0     Yes   No  46.150002   2\n",
      "2    Very High            No    Yes  57.0     Yes   No  89.519997   3\n",
      "3      Average            No     No  63.0     Yes   No  92.629997   4\n",
      "4  Fairly High           Yes    Yes  40.0     Yes   No  58.849998   4\n",
      "   Murder  Assault  UrbanPop  Rape\n",
      "0    13.2      236        58  21.2\n",
      "1    10.0      263        48  44.5\n",
      "2     8.1      294        80  31.0\n",
      "3     8.8      190        50  19.5\n",
      "4     9.0      276        91  40.6\n"
     ]
    }
   ],
   "source": [
    "## Extra Datasets\n",
    "\n",
    "## Political Information Dataset\n",
    "# ANES 2000 Political Information based on interviews\n",
    "# polInf          : Political Information\n",
    "# collegeDegree   : College Degree\n",
    "# female          : Female\n",
    "# age             : Age in years\n",
    "# homeOwn         : Own house\n",
    "# others...\n",
    "polinf = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/pinf.csv')\n",
    "pinf_order = ['Very Low', 'Fairly Low', 'Average', 'Fairly High', 'Very High']\n",
    "polinf['polInf'] = pd.Categorical(polinf.polInf,\n",
    "                                  ordered=True,\n",
    "                                  categories=pinf_order)\n",
    "\n",
    "## US Crime data in the 1970's\n",
    "# Data on violent crime in the US\n",
    "# Muder: number of murders in the state\n",
    "# Assault: number of assaults in the state\n",
    "# others...\n",
    "usarrests = pd.read_csv('https://raw.githubusercontent.com/umbertomig/seabornClass/main/data/usarrests.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercises\n",
    "\n",
    "1. (Univariate) In the `polinf` dataset, make a count plot of the variable `polInf`. Imagine you want to use this for a talk, so adjust the context. Change the x-axis label and title to appropriate descriptions of the data. (Hint: to rotate the axis tick labels, use `plt.xticks(rotation=number_degree_of_your_choice)`)\n",
    "\n",
    "2. (Univariate) In the `polinf` dataset, make a histogram of the variable `age`. (Hint: set the context back to `notebook` before starting)\n",
    "\n",
    "3. (Bivariate) Do you think political information varies with a college degree? Check that using the `polinf` dataset!\n",
    "\n",
    "4. (Bivariate) Do you think political information varies with age? Check that using the `polinf` dataset!\n",
    "\n",
    "5. (Bivariate) Do you think there is a correlation between `Murder` and `Assault`? Check that using the `usarrests` dataset!\n",
    "\n",
    "6. (Challenge: Multivariate) There are four continuous indicators in the `usarrests` dataset: `Murder`, `Assault`, `UrbanPop`, and `Rape`. Do you think you can build a scatterplot matrix? The documentation is in [here](https://seaborn.pydata.org/examples/scatterplot_matrix.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Your answers here\n",
    "\n",
    "# 1.\n",
    "sns.set_context('talk')\n",
    "g = sns.catplot(data = polinf,\n",
    "           x = 'polInf',\n",
    "           kind = 'count')\n",
    "plt.xlabel('Political Information')\n",
    "plt.xticks(rotation=45)\n",
    "plt.title('Political Information ANES 2000 Survey')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.\n",
    "sns.set_context('notebook')\n",
    "sns.displot(data = polinf,\n",
    "           x = 'age',\n",
    "           kind = 'hist',\n",
    "           rug = True,\n",
    "           kde = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 446.3x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 3.\n",
    "tab = pd.crosstab(polinf.polInf, \n",
    "                  polinf.collegeDegree, \n",
    "                  normalize = 'index')\n",
    "tab = tab.cumsum(axis = 1).stack().reset_index(name = 'dist')\n",
    "sns.catplot(data = tab,\n",
    "            x = 'polInf',\n",
    "            y = 'dist',\n",
    "            hue = 'collegeDegree',\n",
    "            hue_order = tab.collegeDegree.unique()[::-1], \n",
    "            dodge = False,\n",
    "            kind = 'bar',\n",
    "            legend_out = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4.\n",
    "sns.catplot(data = polinf,\n",
    "           x = 'polInf',\n",
    "           y = 'age',\n",
    "           kind = 'box')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 5.\n",
    "sns.jointplot(data = usarrests,\n",
    "             x = 'Assault',\n",
    "             y = 'Murder',\n",
    "             kind = 'reg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 6.\n",
    "sns.pairplot(data = usarrests)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}