4-1_DV_matplotlib_LaborForce_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# (v2) Data Visualization of Labor Force using `matplotlib`\n",
    "\n",
    "The following is a set of Python libraries which power almost all data science tasks. These libraries are as follows:\n",
    " - numpy\n",
    " - pandas\n",
    " - matplotlib\n",
    "\n",
    "After using numpy and pandas for analyzing arrays, series, and dataframes, we turn to matplotlib. Matplotlib is a common Python package for 2d graphics. \n",
    "\n",
    "**Import**\n",
    "\n",
    "The first thing that we have to do is to import it.\n",
    "\n",
    "`matplotlib can` be used in \"interactive\" mode through Jupyter notebooks. To activate this function, simply include the line `%matplotlib inline` in your notebook after importing `pyplot` from `matplotlib`.\n",
    "\n",
    "<hr>\n",
    "\n",
    "An alternate library for plotting with different styling is `seaborn`. But `seaborn` is built on top of `matplotlib` with more visualization styles. \n",
    "\n",
    "The `seaborn` library is the main library used in the Kaggle data visualization course. In addition, note that `pandas` itself has visualization features as well.\n",
    "\n",
    "To know more about these libraries and maximize their samples, you can visit the following:\n",
    "* [`matplotlib` Documentation](https://matplotlib.org/)\n",
    "* [`seaborn` Documentation](https://seaborn.pydata.org/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt # plt is the convention for the alias\n",
    "import seaborn as sns\n",
    "import math as ma\n",
    "import os\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we get into learning some stuff, lets set up the `style` of our plots. Starting from Matplotlib v1.5, you have many different plotting `styles` to chose from. To get all the list of supported ones, run \n",
    "```ipython\n",
    "plt.style.available\n",
    "```\n",
    "\n",
    "Or check out the gallery [here](https://matplotlib.org/3.1.1/gallery/style_sheets/style_sheets_reference.html).\n",
    "\n",
    "To be able to change the `style` that Matplotlib comes with for all of your plots, simply call\n",
    "\n",
    "```ipython\n",
    "plt.style.use('ggplot')\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Solarize_Light2',\n",
       " '_classic_test_patch',\n",
       " '_mpl-gallery',\n",
       " '_mpl-gallery-nogrid',\n",
       " 'bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'tableau-colorblind10']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.style.use('seaborn-colorblind')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Parts of a Plot (in `matplotlib`)\n",
    "Before we begin applying the visualization techniques, we first need to know how charts or **figures** are made in `matplotlib`.\n",
    "\n",
    "### 01. Figures\n",
    "\n",
    "A `Figure` object serves as the '_canvas_' of your plot. You can create as many figures as you need but a `Figure` is mandatory for any plotting to happen.\n",
    "\n",
    "#### Figure Properties\n",
    "- ##### figsize : \n",
    "tuple of integers, width, height in inches.   \n",
    "- ##### dpi : \n",
    "integer, resolution of the figure in dots per inch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 5))\n",
    "\n",
    "# To plot, we just call plot()\n",
    "plt.plot()\n",
    "plt.savefig('blankplot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may also save the figure generated using the function `plt.savefig(<file_name>)`. The default format saved is `png`. There are many other formats supported. To get a list of all the formats, you can use this function: `fig.canvas.get_supported_filetypes()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 02. Axes and a Basic Line Plot\n",
    "\n",
    "We can see from above that `matplotlib` automatically creates the axes for us in a figure. An `Axes` object contains [`Axis`](http://matplotlib.org/api/axis_api.html#matplotlib.axis.Axis) objects (and many other things) and it belongs to just one `Figure`. \n",
    "\n",
    "Lastly, below is a basic line plot of a list of x and y values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values (Note: you may use data series here instead of an invented list!)\n",
    "x = list(range(1,11))\n",
    "y = [i**2 for i in x]\n",
    "\n",
    "#Basic Line Plot\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```ipython\n",
    "fig.add_subplot(numrows numcols fignum)\n",
    "```\n",
    "> `fignum` ranges from 1 to numrows*numcols.\n",
    "\n",
    "> Each figure can have as many axes and subplots as needed. See the [`Figure` class documentation](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.figure.Figure.html) for more details.\n",
    "\n",
    "If you want to place an axes manually, i.e., not on a rectangular grid, use the [`axes()` command](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.axes.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 03. Scaling and Limits\n",
    "\n",
    "By default, `matplotlib` will attempt to determine limits that encompasses all the data provided.  This is the **autoscale** feature. \n",
    "\n",
    "To set the limit we can call `set_xlim([lower_bound, upper_bound])` and `set_ylim([lower_bound, upper_bound])` from the `Axes` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values\n",
    "x = list(range(1,11))\n",
    "y = [i**2 for i in x]\n",
    "\n",
    "# Set the limit to the x and y values\n",
    "ax.set_xlim([1,12])\n",
    "ax.set_ylim([0,120])\n",
    "\n",
    "#Basic Line Plot\n",
    "plt.plot(x, y)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 04. Labels, Legends, and More Lines\n",
    "\n",
    "You can provide a label to your data series that will allow `matplotlib` to automatically build the legend for the plot. `x` and `y` axes may also be labeled using `plt.xlabel()` and `plt.ylabel()`. \n",
    "\n",
    "A title may also be provided to the subplot through the `plt.title()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values\n",
    "x = list(range(1,11))\n",
    "y = [i**2 for i in x]\n",
    "\n",
    "# Set the limit to the x and y values\n",
    "ax.set_xlim([1,12])\n",
    "ax.set_ylim([0,120])\n",
    "\n",
    "#Labels\n",
    "plt.ylabel('Y')\n",
    "plt.xlabel('X')\n",
    "plt.title('Parabolic Function')\n",
    "\n",
    "#Basic Line Plot WITH LEGEND\n",
    "plt.plot(x, y, label='Y=X^2')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* In addition, vertical and horizontal lines may be drawn through the `plt.axvline` and `plt.axhline` functions (may also be stated as attributes of the axes, e.g. '_ax.axhline_').\n",
    "* Such lines are quite useful, for example, when you want to mark certain events part of time series data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ANOTHER EXAMPLE\n",
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values\n",
    "x = list(range(1,50))\n",
    "y = [-i**2+50*i for i in x]\n",
    "\n",
    "#Labels\n",
    "plt.ylabel('altitude')\n",
    "plt.xlabel('time')\n",
    "plt.title('Line Plot')\n",
    "\n",
    "plt.axvline(x=8, linestyle='--', label='event1')\n",
    "ax.axhline(y=max(y), linestyle='--', label='max y')\n",
    "\n",
    "#Plot with Legend\n",
    "plt.plot(x, y, label='Projectile Motion', color='black')\n",
    "ax.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### 05. Customizing the plot design\n",
    "\n",
    "In case you do not like the default styles provided, `matplotlib` also allows you to pass a custom **style sheet** or customize it directly using the `rcParams` object. [Reference](https://matplotlib.org/3.3.1/tutorials/introductory/customizing.html)\n",
    "\n",
    "Below is a sample code snippet which changes the text color of plots.\n",
    "\n",
    "```\n",
    "textColor = '#3c3c3c'\n",
    "highlightColor = '#3b738f'\n",
    "plt.rcParams['text.color'] = textColor\n",
    "plt.rcParams['axes.labelcolor'] = highlightColor\n",
    "plt.rcParams['xtick.color'] = textColor\n",
    "plt.rcParams['ytick.color'] = textColor\n",
    "```\n",
    "\n",
    "This changes the color of the general text and the ticks to <span style=\"color: #3c3c3c; font-weight: bold;\">gray</span> and the labels of the axes to <span style=\"color: #3b738f; font-weight: bold;\">teal</span>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textColor = 'blue'\n",
    "highlightColor = '#3b738f'\n",
    "plt.rcParams['text.color'] = textColor\n",
    "plt.rcParams['axes.labelcolor'] = highlightColor\n",
    "plt.rcParams['xtick.color'] = textColor\n",
    "plt.rcParams['ytick.color'] = textColor\n",
    "\n",
    "plt.rcParams['axes.titlesize'] = 20\n",
    "plt.rcParams['axes.labelsize'] = 16\n",
    "plt.rcParams['xtick.labelsize'] = 14\n",
    "plt.rcParams['ytick.labelsize'] = 14\n",
    "\n",
    "#Alternative Example\n",
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values\n",
    "x = list(range(1,11))\n",
    "y = [(ma.e)**(i) for i in x]\n",
    "\n",
    "#Alternative Way of Labeling Axes\n",
    "ax.set_ylabel('altitude')\n",
    "ax.set_xlabel('time')\n",
    "plt.title('Line Plot')\n",
    "\n",
    "# Set the limit to the x and y values\n",
    "ax.set_xlim([0,7])\n",
    "ax.set_ylim([0,200])\n",
    "\n",
    "#Plot with Legend\n",
    "plt.plot(x, y, label='e^x')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The **lw** parameter in the `plt.plot` command modifies the thickness of the plot curves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another Example: fictional GDP data\n",
    "textColor = 'black'\n",
    "highlightColor = '#3b738f'\n",
    "plt.rcParams['text.color'] = textColor\n",
    "plt.rcParams['axes.labelcolor'] = highlightColor\n",
    "plt.rcParams['xtick.color'] = textColor\n",
    "plt.rcParams['ytick.color'] = textColor\n",
    "fig = plt.figure(figsize=(6,4), dpi=100)\n",
    "\n",
    "#Axes object with axis\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "#List of Values (Note: no need to take the Y function too literally here, \\\n",
    "#the list Y was made to fluctuate like GDP data)\n",
    "x = list(range(5,60))\n",
    "y = [0.000025*((i-10)*(i-20)*(i-50)*(i-80)) for i in x]\n",
    "\n",
    "#Labels\n",
    "plt.ylabel('y-o-y % change', fontsize=12)\n",
    "plt.xlabel('quarter', fontsize=12)\n",
    "plt.title('GDP Growth', fontsize=14)\n",
    "\n",
    "# Set the limit to the x and y values\n",
    "ax.set_xlim([8,55])\n",
    "ax.set_ylim([-8,8])\n",
    "\n",
    "plt.axvline(x=10, color='red', linestyle='--', label='recession X')\n",
    "ax.axvline(x=20, color='#0000aa', linestyle='--', label='recovery from X begins')\n",
    "ax.axvline(x=50, color='#aa0000', linestyle='--', label='yet another recession')\n",
    "ax.axhline(y=0, color='gray', linestyle='--')\n",
    "\n",
    "#Plot with Legend. lw parameter modifies thickness\n",
    "plt.plot(x, y, label='GDP growth', color='black',lw=4)\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Another Example: Scatter Plot\n",
    "np.random.seed(42)\n",
    "\n",
    "x = np.arange(10)\n",
    "y = np.random.randint(50, 100, size=10)\n",
    "\n",
    "plt.figure(figsize=(6, 4), dpi=100)\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "plt.ylabel('value')\n",
    "plt.xlabel('time')\n",
    "ax = ax.set_ylim(0,1000)\n",
    "plt.title('Scatter Plot')\n",
    "\n",
    "plt.scatter(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Labor Force Exercise\n",
    "\n",
    "Now, let's try working on visualization with a dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New working directory is:  /Users/dino/Desktop/2022-Intersession/ECON197/datasets\n"
     ]
    }
   ],
   "source": [
    "#SET WORKING DIRECTORY\n",
    "absolute_path = '/Users/dino/Desktop/2022-Intersession/ECON197/datasets/'\n",
    "\n",
    "os.chdir(absolute_path)\n",
    "\n",
    "print('New working directory is: ', os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>pufreg</th>\n",
       "      <th>pufprv</th>\n",
       "      <th>pufprrcd</th>\n",
       "      <th>pufhhnum</th>\n",
       "      <th>pufurb2k10</th>\n",
       "      <th>pufpwgtprv</th>\n",
       "      <th>pufsvymo</th>\n",
       "      <th>pufsvyyr</th>\n",
       "      <th>pufpsu</th>\n",
       "      <th>pufrpl</th>\n",
       "      <th>...</th>\n",
       "      <th>pufc33_weeks</th>\n",
       "      <th>pufc34_wynot</th>\n",
       "      <th>pufc35_ltlookw</th>\n",
       "      <th>pufc36_avail</th>\n",
       "      <th>pufc37_willing</th>\n",
       "      <th>pufc38_prevjob</th>\n",
       "      <th>pufc40_pocc</th>\n",
       "      <th>pufc41_wqtr</th>\n",
       "      <th>pufc43_qkb</th>\n",
       "      <th>pufnewempstat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>136.816803</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>241</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>134.119705</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>241</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>135.200302</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>241</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>135.200302</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>241</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>106.370796</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>241</td>\n",
       "      <td>18</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179520</th>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>9804</td>\n",
       "      <td>40792</td>\n",
       "      <td>1</td>\n",
       "      <td>175.981506</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179521</th>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>9804</td>\n",
       "      <td>40793</td>\n",
       "      <td>1</td>\n",
       "      <td>183.608093</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179522</th>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>9804</td>\n",
       "      <td>40793</td>\n",
       "      <td>1</td>\n",
       "      <td>287.060303</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179523</th>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>9804</td>\n",
       "      <td>40793</td>\n",
       "      <td>1</td>\n",
       "      <td>287.060303</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179524</th>\n",
       "      <td>12</td>\n",
       "      <td>98</td>\n",
       "      <td>9804</td>\n",
       "      <td>40793</td>\n",
       "      <td>1</td>\n",
       "      <td>227.555405</td>\n",
       "      <td>4</td>\n",
       "      <td>2017</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179525 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        pufreg  pufprv  pufprrcd  pufhhnum  pufurb2k10  pufpwgtprv  pufsvymo  \\\n",
       "0           14       1       100         1           2  136.816803         4   \n",
       "1           14       1       100         1           2  134.119705         4   \n",
       "2           14       1       100         1           2  135.200302         4   \n",
       "3           14       1       100         1           2  135.200302         4   \n",
       "4           14       1       100         1           2  106.370796         4   \n",
       "...        ...     ...       ...       ...         ...         ...       ...   \n",
       "179520      12      98      9804     40792           1  175.981506         4   \n",
       "179521      12      98      9804     40793           1  183.608093         4   \n",
       "179522      12      98      9804     40793           1  287.060303         4   \n",
       "179523      12      98      9804     40793           1  287.060303         4   \n",
       "179524      12      98      9804     40793           1  227.555405         4   \n",
       "\n",
       "        pufsvyyr  pufpsu  pufrpl  ...  pufc33_weeks  pufc34_wynot  \\\n",
       "0           2017     241      18  ...           NaN           NaN   \n",
       "1           2017     241      18  ...           NaN           7.0   \n",
       "2           2017     241      18  ...           NaN           NaN   \n",
       "3           2017     241      18  ...           NaN           NaN   \n",
       "4           2017     241      18  ...           NaN           8.0   \n",
       "...          ...     ...     ...  ...           ...           ...   \n",
       "179520      2017      41      20  ...           NaN           8.0   \n",
       "179521      2017      41      20  ...           NaN           NaN   \n",
       "179522      2017      41      20  ...           NaN           NaN   \n",
       "179523      2017      41      20  ...           NaN           NaN   \n",
       "179524      2017      41      20  ...           NaN           8.0   \n",
       "\n",
       "        pufc35_ltlookw  pufc36_avail  pufc37_willing  pufc38_prevjob  \\\n",
       "0                  NaN           NaN             NaN             NaN   \n",
       "1                  NaN           NaN             NaN             1.0   \n",
       "2                  NaN           NaN             NaN             NaN   \n",
       "3                  NaN           NaN             NaN             NaN   \n",
       "4                  NaN           NaN             NaN             2.0   \n",
       "...                ...           ...             ...             ...   \n",
       "179520             NaN           NaN             NaN             2.0   \n",
       "179521             NaN           NaN             NaN             NaN   \n",
       "179522             NaN           NaN             NaN             NaN   \n",
       "179523             NaN           NaN             NaN             NaN   \n",
       "179524             NaN           NaN             NaN             2.0   \n",
       "\n",
       "        pufc40_pocc  pufc41_wqtr  pufc43_qkb  pufnewempstat  \n",
       "0               NaN          1.0        25.0            1.0  \n",
       "1              92.0          1.0         1.0            3.0  \n",
       "2               NaN          1.0         1.0            1.0  \n",
       "3               NaN          2.0         NaN            1.0  \n",
       "4               NaN          NaN         NaN            3.0  \n",
       "...             ...          ...         ...            ...  \n",
       "179520          NaN          NaN         NaN            3.0  \n",
       "179521          NaN          1.0        47.0            1.0  \n",
       "179522          NaN          2.0         NaN            1.0  \n",
       "179523          NaN          2.0         NaN            1.0  \n",
       "179524          NaN          NaN         NaN            3.0  \n",
       "\n",
       "[179525 rows x 50 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfs17 = pd.read_stata('lfsapr2017_data.dta')\n",
    "lfs17"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Dictionary\n",
    "\n",
    "We're going to reformat the dataset in terms of the following:\n",
    "\n",
    "**Labor Force**\n",
    "* lf=1 if pufnewempstat>=1 & pufnewempstat<=2 (part of labor force)\n",
    "* lf=0 if pufnewempstat==3 (not part of labor force)\n",
    "\n",
    "**Gender and Age**\n",
    "* male=1 and female=0 if pufc04_sex==1\n",
    "* male=0 and female=1 if pufc04_sex==2\n",
    "* age, agesq\n",
    "\n",
    "**Educational Attainment Variables**\n",
    "* hs_under=1 if pufc07_grade<=240 | (pufc07_grade>=410 & pufc07_grade<=520)\n",
    "* hs_grad=1 if pufc07_grade==250 \n",
    "* voc_under=1 if pufc07_grade>=310 & pufc07_grade<=320\n",
    "* voc_grad=1 if pufc07_grade>=601 & pufc07_grade<=689\n",
    "* coll_under=1 if pufc07_grade>=710 & pufc07_grade<=760\n",
    "* coll_grad=1 if pufc07_grade>=801 & pufc07_grade<=940\n",
    "* else: variable=0 if listed condition does not hold\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lf_partof</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>age</th>\n",
       "      <th>hs_under</th>\n",
       "      <th>hs_grad</th>\n",
       "      <th>voc_under</th>\n",
       "      <th>voc_grad</th>\n",
       "      <th>coll_under</th>\n",
       "      <th>coll_grad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179520</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179521</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179522</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179523</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179524</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>179525 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       lf_partof male female  age  hs_under  hs_grad  voc_under  voc_grad  \\\n",
       "0              1    1      0   44         0        1          0         0   \n",
       "1              0    0      1   41         0        0          0         0   \n",
       "2              1    1      0   21         0        1          0         0   \n",
       "3              1    1      0   18         1        0          0         0   \n",
       "4              0    0      1   17         1        0          0         0   \n",
       "...          ...  ...    ...  ...       ...      ...        ...       ...   \n",
       "179520         0    1      0   16         1        0          0         0   \n",
       "179521         1    0      1   47         0        0          0         0   \n",
       "179522         1    1      0   28         0        1          0         0   \n",
       "179523         1    1      0   27         0        1          0         0   \n",
       "179524         0    0      1   16         1        0          0         0   \n",
       "\n",
       "        coll_under  coll_grad  \n",
       "0                0          0  \n",
       "1                1          0  \n",
       "2                0          0  \n",
       "3                0          0  \n",
       "4                0          0  \n",
       "...            ...        ...  \n",
       "179520           0          0  \n",
       "179521           1          0  \n",
       "179522           0          0  \n",
       "179523           0          0  \n",
       "179524           0          0  \n",
       "\n",
       "[179525 rows x 10 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfs17['lf_partof'] = [None for i in range(len(lfs17.index))]\n",
    "lfs17.loc[(lfs17.pufnewempstat >= 1) & (lfs17.pufnewempstat<=2), 'lf_partof'] = 1\n",
    "lfs17.loc[(lfs17.pufnewempstat==3), 'lf_partof'] = 0\n",
    "\n",
    "lfs17['male'] = [None for i in range(len(lfs17.index))]\n",
    "lfs17.loc[(lfs17.pufc04_sex==1), 'male'] = 1\n",
    "lfs17.loc[(lfs17.pufc04_sex==2), 'male'] = 0\n",
    "\n",
    "lfs17['female'] = [None for i in range(len(lfs17.index))]\n",
    "lfs17.loc[(lfs17.pufc04_sex==2), 'female'] = 1\n",
    "lfs17.loc[(lfs17.pufc04_sex==1), 'female'] = 0\n",
    "\n",
    "lfs17['age'] = lfs17.pufc05_age\n",
    "lfs17['agesq'] = [(lfs17.age[i])**2 for i in range(len(lfs17.age))]\n",
    "\n",
    "lfs17['hs_under'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[((lfs17.pufc07_grade<=240) | ((lfs17.pufc07_grade>=410) & (lfs17.pufc07_grade<=520))), 'hs_under'] = 1\n",
    "\n",
    "lfs17['hs_grad'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[(lfs17.pufc07_grade==250), 'hs_grad'] = 1\n",
    "\n",
    "lfs17['voc_under'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[((lfs17.pufc07_grade>=310) & (lfs17.pufc07_grade<=320)), 'voc_under'] = 1\n",
    "\n",
    "lfs17['voc_grad'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[((lfs17.pufc07_grade>=601) & (lfs17.pufc07_grade<=689)), 'voc_grad'] = 1\n",
    "\n",
    "lfs17['coll_under'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[((lfs17.pufc07_grade>=710) & (lfs17.pufc07_grade<=760)), 'coll_under'] = 1\n",
    "\n",
    "lfs17['coll_grad'] = [0 for i in range(len(lfs17.index))]\n",
    "lfs17.loc[((lfs17.pufc07_grade>=801) & (lfs17.pufc07_grade<=940)), 'coll_grad'] = 1\n",
    "\n",
    "lfs17 = lfs17.loc[ :,['lf_partof','male','female',\\\n",
    "                               'age','hs_under','hs_grad','voc_under','voc_grad',\\\n",
    "                              'coll_under','coll_grad']]\n",
    "\n",
    "lfs17\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "lfs17 = lfs17.to_csv('2017-LFS-edited.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's begin exploring the data...\n",
    "\n",
    "* How many rows and columns do we have? \n",
    "* What is the data type of each column? \n",
    "* What is the most common value? Mean? Standard deviation?\n",
    "* Are there any missing values? If yes, how do you want to handle them?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(179525, 10)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many rows & columns?\n",
    "lfs17.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 179525 entries, 0 to 179524\n",
      "Data columns (total 10 columns):\n",
      " #   Column      Non-Null Count   Dtype \n",
      "---  ------      --------------   ----- \n",
      " 0   lf_partof   119195 non-null  object\n",
      " 1   male        179525 non-null  object\n",
      " 2   female      179525 non-null  object\n",
      " 3   age         179525 non-null  int8  \n",
      " 4   hs_under    179525 non-null  int64 \n",
      " 5   hs_grad     179525 non-null  int64 \n",
      " 6   voc_under   179525 non-null  int64 \n",
      " 7   voc_grad    179525 non-null  int64 \n",
      " 8   coll_under  179525 non-null  int64 \n",
      " 9   coll_grad   179525 non-null  int64 \n",
      "dtypes: int64(6), int8(1), object(3)\n",
      "memory usage: 17.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# to get the data types of each column, you can also use df.info()\n",
    "# this also provides you also the non-null counts per column\n",
    "lfs17.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "lf_partof     object\n",
       "male          object\n",
       "female        object\n",
       "age             int8\n",
       "hs_under       int64\n",
       "hs_grad        int64\n",
       "voc_under      int64\n",
       "voc_grad       int64\n",
       "coll_under     int64\n",
       "coll_grad      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or just the data types\n",
    "lfs17.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>age</th>\n",
       "      <th>hs_under</th>\n",
       "      <th>hs_grad</th>\n",
       "      <th>voc_under</th>\n",
       "      <th>voc_grad</th>\n",
       "      <th>coll_under</th>\n",
       "      <th>coll_grad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "      <td>179525.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>28.354736</td>\n",
       "      <td>0.536271</td>\n",
       "      <td>0.160980</td>\n",
       "      <td>0.002891</td>\n",
       "      <td>0.025484</td>\n",
       "      <td>0.091742</td>\n",
       "      <td>0.087921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>20.292405</td>\n",
       "      <td>0.498684</td>\n",
       "      <td>0.367514</td>\n",
       "      <td>0.053690</td>\n",
       "      <td>0.157590</td>\n",
       "      <td>0.288662</td>\n",
       "      <td>0.283181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 age       hs_under        hs_grad      voc_under  \\\n",
       "count  179525.000000  179525.000000  179525.000000  179525.000000   \n",
       "mean       28.354736       0.536271       0.160980       0.002891   \n",
       "std        20.292405       0.498684       0.367514       0.053690   \n",
       "min         0.000000       0.000000       0.000000       0.000000   \n",
       "25%        11.000000       0.000000       0.000000       0.000000   \n",
       "50%        24.000000       1.000000       0.000000       0.000000   \n",
       "75%        43.000000       1.000000       0.000000       0.000000   \n",
       "max        99.000000       1.000000       1.000000       1.000000   \n",
       "\n",
       "            voc_grad     coll_under      coll_grad  \n",
       "count  179525.000000  179525.000000  179525.000000  \n",
       "mean        0.025484       0.091742       0.087921  \n",
       "std         0.157590       0.288662       0.283181  \n",
       "min         0.000000       0.000000       0.000000  \n",
       "25%         0.000000       0.000000       0.000000  \n",
       "50%         0.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000  \n",
       "max         1.000000       1.000000       1.000000  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfs17.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizations\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### How many people are part of the labor force? How many are not?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    72725\n",
       "0    46470\n",
       "Name: lf_partof, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lfs17['lf_partof'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lfs17['lf_partof'].value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Default `pandas` plot changed to bar chart\n",
    "\n",
    "To change the type of plot associated to `pandas`, we can specify the type of the chart using the `kind` argument.\n",
    "\n",
    "But in its current state, the readers won't know what 0 and 1 represents and what the numbers on the y-axis represent. So we need to label our plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# here we specify the figure size as well to make it follow an aspect ratio we like\n",
    "# the colors can also be set together with any other options for making the chart look \"pretty\"\n",
    "textColor = 'black'\n",
    "highlightColor = '#000000'\n",
    "\n",
    "plt.rcParams['text.color'] = textColor\n",
    "plt.rcParams['axes.labelcolor'] = highlightColor\n",
    "plt.rcParams['xtick.color'] = textColor\n",
    "plt.rcParams['ytick.color'] = textColor\n",
    "\n",
    "ax = lfs17['lf_partof'].value_counts().plot(figsize=(8, 6), kind='bar', color=['#68abb8', '#999999'], width=0.5)\n",
    "\n",
    "ax.set_xticklabels(['Part of Labor Force', 'Not Part'], rotation=0)\n",
    "ax.set_title('Labor Force Survey 2017')\n",
    "ax.set_ylabel('Adult Population');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the distribution of the age of the passengers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lfs17['age'].hist(bins=20, edgecolor='w')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms\n",
    "\n",
    "To generate the distribution of a specific column in a `pandas` `DataFrame`, you can simply select the column with a numerical value and call `hist()`. You can adjust the bin size and colors according to your preference.\n",
    "\n",
    "For bin size, you may want to review the rule of thumb taught in the descriptive statistics module in Econ 116 (computing for the **class frequency**) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lfs17['age'].hist(bins=10, edgecolor='w', color='#c8586c',  figsize=(10, 5))\n",
    "\n",
    "# Here you can override the colors of set using rcParams by simply passing a value for the arguments.\n",
    "ax.set_title('Distribution of Adult Population According to Age',color='#c8586c', fontweight='bold')\n",
    "ax.set_xlabel('Age', color='#c8586c')\n",
    "ax.set_ylabel('Population', color='#c8586c');\n",
    "\n",
    "# To remove the spines of the chart manually, you can choose to set the visibility to False\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(True)\n",
    "ax.xaxis.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the peak is somewhere around 18 to 20."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "To better visualize the \"average\" age, let's try adding the `mean` and `median` of the age distribution using \n",
    "\n",
    "```ipython\n",
    "ax.axvline(x=value)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S6UD8QADwsRVqj7dq5nkW42MsJITNlS/VMdbY9/pHiftDqN8XtKnPcku1dB8lYxmcuJ88JkqpVMdjUvK4ui8Mso2VPSYMstlMPvcJbXsv4sfjv6ifgCY1WRGlmsF2Ep2F/xY/ZL3g0Uw+12xn6zDIbhUG2f6ZfG5pJp/7Wyafuy6Tz30PP+qroCu+xrAg/iGMN/e0p63CILuumjoMsttS/4w7/lo/it0eVOhXEwbZHjQ/j1v8jKrY1xYf7FGvhix67uNj6xfjB9S0l7RiadEAl5gJsdHM8X3YlVg/njDI9qPpjt4fxW7vGvqpHAiD7OY03Im6Jer92ERNcR3hFerXyhwXdcgvOIn6J9fxfR3XUZ+ZhiRHvf8uk88tjBLBA2g6EWzL90vyvUieICe7JjT23rVZ1N93fLMF64vHF+/Cs0sYZI9OFg79tD+FGSWeja0aF3X4j5e1MMgWBlGU6hhrTPK3Z91nMPpsHlfk9tpiFvVPdoJEDWyx31lxzf1elPx4DP3UQcV08ehF3fv9Mn5Km4Jjo+O0oKn34onY7c3wn+kr4n/Alfj+hGFTAalmsHR2iDqi98TX4sUnnQZfBX9yC7d1NHBJGGQfx39BvIc/u0xOZBk/Y4ln/YNCPy/US/gzs6nJealK6P4wyP4WP/fceOofU1Nit5MH4pNhkH0UP6JrG5r2DnWTLp8cBtmV+ClRFrVgbrVb8bVchR+660M/v9g7+CkqRsTKXtnI4JFS6fBYQj+n4bjYorn4wTBJW+JHa4LvL/ZF/Ki+qfgRiYWaqcmhn9biffxozKaaKULqTli2AZ4Lg+xsfA33Jo0+qmnJs9s7os7Ztfg+Oq+2crtNyuRza6MO2T+PFm0DPBMG2T/hz9zjye0i1u+HFNcRn5mGJPtv3R4G2Tx+WpmTm3ls/H3fPQyy1+LnMQP4VaKPUz2ZfO4/YZB9GDg4WnRC6Kf2eBr/A3p4rPjfiuis3xrjqF/zErJ+0yb4ASmFmryjopPzxfiRzEdRNzr47jDI/hk/tcfG+EEC86kbHPEz6kbsbwA8HgbZu/EJwGD8Z+ER4MwSH2MNuQ//O1SoAfxRNLBsDv59KWUf5SZl8rkF0ev6crRoLPD36Pjeg7aNbG7y96KdjsdkwvYX/ACSpHHARrHHTMnkc7VhkL0RP60M+P3wdBhkp+F/E5pK0q/AH2td8Pv1hTDI3oP/bPbCT2W0H/43Z3+a6KqgZLB0RtH4mcGTwImZ4i4WvwF+5zU2f9aT1D9L/SPwI+q+pOJfHDMoTX+QpNfwB9wZDay7LZPP/Sl2/0/4H6NCH8oh1E3tMI36CUvS3dRNGTCIuibMl4gmUm1MJp/7OAyyR+A/nIW5pJJnguBHEV7SwPKSSSmW5OW2zs/kc/lkoTDIDsd/URRqBE8F/prJ594Ng2wWP52MRds6LSqzCn8cNtbB+lp8klGo3dgh+qvFX82goVn6m/NX/CXPCiNBj6CudvIN/KS17eUy/Bd1YY7PHaO/uA+BI6PBNQ35L/79aO/PzHoy+dyzYZB9gLof2u2pGyAzHf/aGhs4cjd1CWMX/KTpBbdQ17+5MSfip6nZJbr/xegv7gX83HjtKf6jXQsck8nn1ms+C4PsCdQNluqJr7X/VSafmxX6+fJuwJ8gGf7H+MjYw9cNqsrkc38Kg+y5+M9zV/znJ94CAHVN0FCaY6xBmXxueejnDP0zfioSi8Xi8L8TNcVss42y+N/M4dH9mtjz30/9pCxei9iclvxelOx4jGrq4+VeyeRzDU4RFfrZAwojh/cMg+wO0YwgP8a3/BXi2Y26qW8afS8y+dzMMMieDvwav08H4vs7F03NxKW1Fv+l+B6+Sv4G4IBMPje6yESwMMv+g/iztk/wX1wf4udiOhcYG685yuRzL+BrFEMaPiNpD+/iz+Juwp8Nr8Kf8Z5FIsmJRqsdgE90PsR3LP4X/gem0Y4VkR8Bv8D/2DfUMbpJmXxuJj4J+Tn+DH5JtJ35+MTsqEw+d3w71wqmFUu8yeMDGpn9Ppo+46HYoi9FZ8tk8rmb8F9GT+KP70+om4T6ERqRyedm4/f5DPwxuQSfdOyLnz+raJl8biF+2pN/UH++zXYXjfwcj6/F/TP+c74a30frP/gf8h0z+VxTzUrv4/sBT4ke316fmcYcg69NmBfF/gY+UfkS9eeEqyeTzz2A/xF7nvUHBTQrk88VXvd38JPrfog/7hfjm7rOwE+L0V4tGIVJjuMnIH9tKBGM/CGKrWBdEpnJ527Hd/+5Cr/fl+Dfy/n447veqNxMPnc5fvTsFHxyvwz/Hs7Df+bvj5UtxTHWqIy/rNy+1F1Heim+UuFQYqOLO0Imn3sXf0z8Fv+5WIlP2L5DXe1owWJartnfixIfj0dSv6XjhibKJtd9LYpnKb4G7yr8cbEKPwflj/BXuomr915E38874+dtfAm/T1fiawefwvcL/gL1K4/WY861tg+uiIg0J2zmUoki1SjqI9ijoenNwiB7DXXXhnbAZpl8rj1HGKcuDLIbNtTdIgyy38P3+yvYK5PPNdn/rzXUTCwiIiIdrQd+mrQ78QNt3sM3c46j/nx9+UpPBCMvhkH2SXxt3jv4KWdqqN/l68n2SARByaCIiIikox9N93Gb0cz6SrIRvq/oSY2sf4H6l1AsKSWDIiIi0tFW4y+usB9+3swB+CbhBcBz+L6yd2fyuWrpy3YZfnDXSHwfxC74ft7PA/fgZwVJXqigZNRnUERERKSKaTSxiIiISBVTM3ErHXLIIe7BBx9MOwzp5OacdV69+5+98rKSbfu68T8A4Du3XVqybYqISKfV6DW8lQy20qJFi5ovJJKiLXfZPu0QRESkE1AyKFKhxp11ctohiIhIJ6A+gyIiIiJVTMmgSIW6+bs/4+bv/iztMEREpMypmVikQi39qEMv3SsiIp2UagZFREREqphqBkVERKTF1q5dyzvvvMPSpUvTDkUi3bp1Y/DgwfTt27dVj1cyKCIiIi22aNEizIztttuOLl3UwJg25xzLly9n3rx5AK1KCJUMilSoEXt9Pu0QRKQCffTRR2y55ZZKBMuEmdGrVy8233xz3n33XSWDIlLn4G8FaYcgIhWotraWbt26pR2GJGy44YasXr26VY9VWi8iIiJFMWv0ymaSkrbsEyWDIhVq8uk/ZvLpP047DBERKXNqJpZmudo1WNf0D5VyiaOzWL1iVdohiIhIJ6BfVmmWdd2AZdMnpx0GvcZOTDsEERGRiqNmYhEREZEqpmRQREREKl5NTQ3f/OY3OfvssxkwYACDBg3immuuYeXKlXz729+mX79+DB8+nKlTp657zLx58zjuuOPo378//fv35/DDD+e1115bt37OnDkcccQRDB06lI022ojddtuNadOm1XveLbfckp/+9KdMnDiRvn37MmzYMC6//PIOe90toWRQpELtULMHO9TskXYYIiJl4/bbb6dPnz6EYcgPfvADzjzzTI488khGjBjBrFmzmDBhAl//+td59913WbZsGfvvvz89e/bk0UcfZebMmWy66aYcdNBBLFu2DIAlS5Zw6KGHMn36dJ5//nmOPvpovvzlL/Pyyy/Xe96rrrqKnXbaieeee47zzjuPc889l5kzZ6bxFjTInHNpx9ApjRo1ys2aNSvtMDqM+gy2jzlnnVfv/mevvCylSEREWmb27NmMHDmy3rLrxv9gvXK7HDKa0cePY9XyFUyZOGm99XsedSB7HjWWJYs/5pYzLllv/ReOO4xdD9uXxe+9z+3n/bLeuu/cdmnRcdfU1LBy5cp1SZhzjsGDB7P33ntz3333AbB69Wo22mgj7rjjDj755BMuueQSXn311XXTttTW1jJ48GCuv/56vvKVrzT4PHvttRfjxo3jggsuAHzN4N57700+n19XZtttt2XChAnrypRKQ/smptG5ZzSARERERKrCzjvvvO62mTF48GB22mmndcu6detG//79WbhwIS+99BJz586lT58+9baxbNky5syZA8DSpUu56KKLmDZtGu+99x6rV69mxYoV9Z4n+bwAm222GQsXLiz1y2s1JYMiFapwpt6aM2gRkWI09T3TfcOeTa7v3X/jJtf333RQyb7HkldOMbMGl61du5a1a9eyyy67cOedd663nQEDBgBwzjnn8OCDD3LFFVew7bbb0qtXL8aPH8+qVfWn9mrsOcqFkkERERGRhN122418Ps/AgQPp169fg2WeeOIJxo8fz9FHHw3AihUrmDNnDiNGjOjASNtOA0hEREREEk444QSGDBnCEUccwaOPPsrcuXN57LHHOPvss9eNKB4xYgT33nsvzz33HC+88AInnngiK1asSDny4ikZFBEREUno1asXjz32GFtvvTXHHnss22+/PRMmTGDx4sX0798fgCuvvJLBgwczZswYDj30UPbaay/GjBmTcuTFUzOxiIiIVLwZM2ast+zFF19cb9n8+fPX3R4yZAg333xzo9vcYosteOSRR+otO+ecc+rdf+ONN1oUS5qUDIpUqF0OGZ12CCIi0gkoGRSpUKOPH5d2CCIi0gmoz6BIhVq1fAWrlne+jswiItKxVDMoUqEKM/5rnkEREWmKagZFREREqpiSQREREZEqpmRQREREpIopGRQRERGpYhpAIlKh9jzqwLRDEBGRTkA1gyIVas+jxrLnUWPTDkNEpKpcccUVbLnlluvuT5o0iR133DG9gFpAyaBIhVqy+GOWLP447TBERKraOeecw6OPPpp2GE1SM7FIhbrljEsAzTMoIpKm3r1707t377TDaFKH1gya2Q/N7Bkz+8TM3jezv5jZjokyt5iZS/w9nSjTw8xyZrbIzJaa2X1mNixRpr+ZTTWzj6O/qWbWL1FmeBTD0mhb15pZ93Z7A0RERCQVNTU1fPOb3+Tss89mwIABDBo0iGuuuYaVK1fy7W9/m379+jF8+HCmTp267jHz5s3juOOOo3///vTv35/DDz+c1157rd52f/GLXzB06FB69+7N+PHjWbJkSb31yWbiZ555hoMPPpiBAwfSt29fRo8ezcyZM+s9xsyYMmUKxx57LBtttBFbb701v/vd79rhXfE6umawBvg18AxgwMXAI2b2Oefch7FyjwAnxe6vSmznauAIIAA+AK4EppnZ7s652qjMHcBw4FDAATcCU4H/AzCzrsD90ePHAJsAt0ZxZdv+UkVERCrbnLPOS/X5P3vlZUWVv/322znrrLMIw5D77ruPM888kwcffJBDDjmEWbNmceutt/L1r3+dAw88kH79+rH//vuzzz778Oijj9K9e3euuOIKDjroIGbPnk2vXr34/e9/zwUXXEAul2P//ffn7rvv5rLLLmPAgAGNxvDpp59y0kkncc0112BmXHfddRx22GG89tprDBw4cF25iy++mEsvvZRLLrmEm266iVNPPZUxY8awxRZbtPr9akyH1gw6577onLvZOfeic+4FfMI3CPhCouhK59z82N+6RNHMNga+BnzfOTfdOfdctJ2dgYOiMiOBQ4DTnXNPOedmAhOBcWa2XbSpg4EdgJOcc88556YD5wKnmVnf9noPREREJB077LADkyZNYtttt+Wss85i4MCBdOvWjTPOOINtttmGH//4xzjneOqpp7jzzjtxznHzzTez8847s/322zN58mSWLFnCtGnTALj66quZMGECEydOZMSIEZx//vnsueeeTcZwwAEHcNJJJzFy5Ei23357crkcPXv25MEHH6xX7qSTTuLEE09km2224Sc/+QkbbLABjz/+eLu8L2kPIOkTxbA4sXy0mS00s1fN7AYzGxxbtzvQDXi4sMA59zYwG9gnWrQ3sAR4Kva4J4GliTKzo8cWPAT0iJ5DREREKsjOO++87raZMXjwYHbaaad1y7p160b//v1ZuHAhzz77LHPnzqVPnz7r+v1tvPHGLF68mDlz5gAwe/Zs9t5773rPkbyftHDhwnXJ48Ybb0yfPn1YuHAhb731VqOxbrDBBgwaNIiFCxe2+rU3Je0BJNcA/wbijeUPAn8E5gJbAj8F/h41Aa8EhgK1wKLEthZE64j+v++cc4WVzjlnZgsTZRYktrEo2vZQRDq5Lxx3WNohiIiUlW7dutW7b2YNLlu7di1r165ll1124c4771xvO001AzdnwoQJLFiwgKuuuoott9ySHj16cOCBB7JqVf0ecY3F1R5SSwbN7EpgNDA61s8P51z8XX/BzJ4F3gQOxyeJjW4S3zdw3aZaWabR5WZ2OnA6wJAhQ5gxY0YT4VSWtSs2STsEulTg+/2ZxP2SHlO92mGbIlL1Nt54Yz799FMABl90QaqxFOJoidraWlatWlXvMWvXrmXlypX1ljnnWLFiBZ/73OfI5/P06NGDfv36NfjcI0aM4PHHH+fYY49dt/zJJ5/EObdumytXrmTt2rXr7j/xxBP84he/YN999wV8TeF77723XhzLly9fL65kmaQVK1Y0+p1fU1PT6ONSSQbN7CrgOGB/59zrTZV1zr1rZu8A20aL5gNdgYHA+7Gig4HHYmUGm5kVagfNzPD9ExfEyiT7Kg6Mtp2sMSzEMgWYAjBq1CjX1BtbKsumT27352hOr7ETyyOOmmPSDqHk5tz313r3S3lMLX7Pfzz6bzqoZNsUEZk9ezZ9+vRJO4yide3ale7du9eLvUuXLvTo0aPeMjOjZ8+enHrqqVx33XWceOKJXHzxxQwfPpy3336bP//5z3zjG99Y1+9w/Pjx7LPPPtTU1HDPPfcwa9YsBgwYsG6bPXr0oEuXLuvujxgxgnvuuYf99tuPpUuXcu6559K9e/f14thwww3XiytZJqlnz57suuuuRb83Hd5n0MyuAY4HDnDOvdyC8gOBzYH3okXPAquBsbEyw4CR1PURnAn0xvcLLNgb2ChRZmRiSpqxwMroOUQ6tdvP+yW3n/fLtMMQEemUevXqxWOPPcbWW2/Nsccey/bbb8+ECRNYvHgx/fv3B+CrX/0qkyZN4vzzz2fXXXflhRde4Kyzzmpyu7/97W9ZsmQJu+++O8cddxynnnpqvSuWpMFi3era/8nMfoUf+Xsk8N/YqiXOuSVm1huYBPwBn/xtCVyCb00b6Zz7NNrO9cCXgAnUTS3TH1g3tYyZ/RUYBpyGbx6eArzhnItPLfNvfO3i2dRNLfNH51yzU8uMGjXKzZo1q3VvRBHKokauXGoGx05MO4SSS07LUOw0CU25bvwPAE06LSKlNXv2bEaOHJl2GNKAZvaNNbaio2sGv4UfQfw3fLJX+DsnWl8L7AT8GXgVn5y9AuxdSAQj38P3H7wLP0p4CfB/8b6HwAnA8/hRxw9Ft9fNXRiVPRxYFm3jrmib5yAiIiJSJTq0z6BzrtGsNFq/HPhiC7azAj8xdKM1eNHchCc2s523gHHNPZ+IiIhIpUp7nkERERERSVHa8wyKSDupOeWotEMQEZFOQMmgSIXacf9M2iGISIVyzuFnbJNy0ZYBwWomFqlQC+e+w8K576QdhohUmK5du7J69eq0w5CE5cuXr3fVkpZSMihSoX5/4XX8/sLr0g5DRCpMv379WLBgQbtdGk2K45xj2bJlzJs3j8GDB7dqG2omFhERkRYbOHAg77zzDq+88kraoUikW7duDBkyhL59+7bq8UoGRUREpMW6dOnC8OHD0w5DSkjNxCIiIiJVTMmgiIiISBVTM7FIhRr7ja+mHYKIiHQCSgZFKtR2++yadggiItIJqJlYpELNmz2HebPnpB2GiIiUOSWDIhXq3ktu4N5Lbkg7DBERKXNKBkVERESqmJJBERERkSqmZFBERESkiikZFBEREalimlpGpEId/r3xaYcgIiKdgJJBkQq11a6fSzsEERHpBNRMLFKh5v7rv8z913/TDkNERMqckkGRCnX/Vbdx/1W3pR2GiIiUOSWD0mm42jVph1AWMYiIiJSS+gxKp2FdN2DZ9MmpxtBr7MRUn19ERKTUVDMoIiIiUsWUDIqIiIhUMTUTi1Soo354WtohiIhIJ6BkUKRCbT7ys2mHICIinYCaiUUq1CtP/YtXnvpX2mGIiEiZU82gSBFc7Rqsa/ofm5bEMf03dwGw3T67dkRIIiLSSaX/qybSibT39DYt3bamuBERkVJRM7GIiIhIFVMyKCIiIlLFlAyKiIiIVDH1GRSpUF+56DtphyAiIp2AkkGRCjV4q2FphyAiIp1AhzYTm9kPzewZM/vEzN43s7+Y2Y6JMmZmk8zsXTNbbmYzzGyHRJkeZpYzs0VmttTM7jOzYYky/c1sqpl9HP1NNbN+iTLDoxiWRtu61sy6t9sbINKBXvxHyIv/CNMOQ0REylxH9xmsAX4N7AMcAKwBHjGzAbEy5wJnA1lgD2AhMN3M+sTKXA0cDQTAGKAvMM3MusbK3AHsBhwKHBLdnlpYGZW9H+gTbSMAjgF+WZJXKpKyGTffy4yb7007DBERKXMd2kzsnPti/L6ZnQR8DHwB+IuZGXAmcKlz7g9RmQn4hPB4YLKZbQx8DTjFOTc9tp03gYOAh8xsJD4BHO2ceyoqMxF43My2c869AhwM7ABs4Zx7OypzLnCjmZ3vnPukHd8KERERkbKQ9mjiPlEMi6P7WwFDgYcLBZxzy4HH8LWJALsD3RJl3gZmx8rsDSwBnoo915PA0kSZ2YVEMPIQ0CN6DhEREZGKl3YyeA3wb2BmdH9o9H9BotyC2LqhQC2wqJky7zvnXGFldHthokzyeRZF2x6KiIiISBVIbTSxmV0JjMY35dYmVrtk8QaWrbfJRJmGyrekTKPLzex04HSAIUOGMGPGjGZCaru1KzZp9+doTpcZMxRHO8WwBa/Xu/90C7fdpQXH3kcffQTQIcepiIiUt5qamkbXpZIMmtlVwHHA/s65+K/h/Oj/UCDefDuYulq8+UBXYCDwfqLMY7Eyg83MCrWDUX/EQYntfCER2sBo28kaQwCcc1OAKQCjRo1yTb2xpdKe18FtqV41xyiOdorhvcT9vXp+0OI4mvP57fwg/P6bDio2LBERqSId3kxsZtfgB4Mc4Jx7ObF6Lj5JGxsr3xM/2rfQ/+9ZYHWizDBgZKzMTKA3vl9gwd7ARokyIxNT0owFVkbPIdKp9d90kBJBERFpVofWDJrZr4CTgCOBxWZW6Ju3xDm3xDnnzOxq4Hwzexl4FbgAPxjkDgDn3MdmdhNwuZktBD4ArgT+AzwSlZltZg/iRx+fhm8engxMi0YSgx+A8hJwm5mdDWwCXA7coJHEUgn+9YCvKN/1sH1TjkRERMpZRzcTfyv6/7fE8ouASdHtXwAbAr8C+gMhcLBz7tNY+e/h5yi8Kyr7N2B8ou/hCcC11I06vg9Yd30u51ytmR2On/fwSWA5PuE8p/UvT6R8PHnnA4CSQRERaVpHzzNoLSjj8InhpCbKrMBPSp1tosyHwInNPNdbwLjmYhIRERGpVGlPLSMiIiIiKVIyKCIiIlLFlAyKiIiIVLHUJp0WkfZ18jU/TDsEERHpBIpKBsMg2x34IRAAw/HX8Y1zmXxOCaZIGejdf+O0QxARkU6g2MTtcuDbwF+BP+InaBaRMvTPe6cDsOdRY5spKSIi1azYZPAY4MJMPvez9ghGRErnn/f66TyVDIqISFOKHUDSG38ZNxERERGpAMUmg38BdDkDERERkQpRbDNxDrgtDLJrgQeAD5MFMvnc66UITERERETaX7HJYKGJeBJwYSNlurY6GhERERHpUMUmg6cCrj0CEZHSOn3ypLRDEBGRTqCoZDCTz93STnGISIl137Bn2iGIiEgn0KoJosMga8DngAHAB8DsTD6nGkORMvLEHdMAGH38uJQjERGRclb0tYnDIPt14D3gP8AM4AXg3TDIfq20oYlIW/z7wSf494NPpB2GiIiUuaKSwTDIngBMwSeApwKHRf9fAKaEQTYoeYQiIiIi0m6KbSY+F7g9k8+dlFh+axhkpwLnAfmSRCYiIiIi7a7YZuLtgN81su530XoRERER6SSKTQY/BYY1sm5YtF5EREREOolim4n/Cvw8DLKvZvK5xwsLwyC7N/DTaL2IlIHv3HZp2iGIiEgn0Jo+g3sBM8IgOw8/qngovlbwf9F6EREREekkimomzuRz84FdgDPwl6b7BHgayAK7ZvK5BaUOUERa5x+//QP/+O0f0g5DRETKXNGTTmfyuWXAddGfiJSpl2Y8A8D+px6dciQiIlLOip50WkREREQqR7M1g2GQfR04KpPPPR8G2blAU5edc5l87rMli05ERERE2lVLmokfxfcNLNzWNYhFREREKkSzyWAmnzsldvvkdo1GREqmW8/uaYcgIiKdQFEDSMIg+2Pgxkw+924D6zYFTsvkcxeXKjgRab2JU/RRFBGR5hU7gORCGr8CyWbRehERERHpJIpNBq2Jdf2BlW2IRURK6OFf53n41/m0wxARkTLXktHENcABsUUTwyA7LlFsQ+Bw4KXShSYibfHq088DcPC3gpQjERGRctaSPoP7ARdEtx1wSgNlVgH/Bb5borhEpBNwtWuwrkXPXV+xcYiIdEYtGU18EXARQBhk1wJ7ZfK5f7Z3YCJS/qzrBiybPjntMOg1dmLaIYiIdFpFnUpn8jldsURERESkgrS6XSUMsoOBnsnlmXzurTZFJCIlsVG/PmmHICIinUCx8wx2AX4KTAT6NVKsa1PbMLN9gXOA3fHT0ZzinLsltv4WYELyqZ1ze8XK9ACuAAL84JW/Ad9yzr0TK9MfuBb4UrToPiDrnPsoVmY48Cv8AJnlwB3AOc65VU29BpHO4JRrz087BBER6QSKbfY9E/g28Ev8NDM/xyeHc4E5wGkt2EZv4EXgDHwC1pBHgE1jf4cl1l8NHI1PBscAfYFpZhZPRO8AdgMOBQ6Jbk8trIzK3g/0ibYRAMdEr02krLnaNWmHICIiFaLYZuJTgIvxydhPgXsz+dxzYZD9KfAwMLy5DTjnHgAegHW1gA1Z6Zyb39AKM9sY+Bq+RnF6tOwk4E3gIOAhMxuJTwBHO+eeispMBB43s+2cc68ABwM7AFs4596OypwL3Ghm5zvnPkk+t0i5aMnAjYfu+y8AX/zS59otDg3cEBHp/IqtGdwamJXJ52qBNfgmWjL53Gp8gnhqieIabWYLzexVM7vBzAbH1u0OdMMnnwBEydxsYJ9o0d7AEuCp2OOeBJYmyswuJIKRh4Ae0XOIdGpvv7GYt99YnHYYIiJS5opNBj+mbtDIu8B2sXUbAANKENODwHjgQOBsYE/g71E/QYChQC2wKPG4BdG6Qpn3nXOusDK6vTBRZkFiG4uibQ9FREREpAoU20z8L+Bz+Bq0h4CLwiC7HF9L+DPgubYG5Jy7M3b3BTN7Ft8EfDjwxyYeavhJsddtqpVlGl1uZqcDpwMMGTKEGTNmNBFOaaxdsUm7P0dzusyYoTjaKYYteL3e/adbuO2WxPHJ2m5FbbM1ymGfFOIQEZHG1dTUNLqu2GTwanxTMcCF+EEZt0f33wS+U+T2muWce9fM3gG2jRbNx49YHgi8Hys6GHgsVmawmVmhdtDMDBhEXW3gfOALiacbGG07WWNYiGUKMAVg1KhRrqk3tlTKYkLfmmMURzvF8F7i/l49PyhZHC92WV3UNlujHPZJIQ4REWmdopqJM/nc9Ew+Nzm6PR/fhDsC2AUYkcnn/lPqAM1sILA5db+bzwKrgbGxMsOAkdT1EZyJH7W8d2xTewMbJcqMjB5bMBZYGT2HSKfWt19P+vZbbypQERGRetp0Mc9MPueA/xXzGDPrDWwT3e0CDDezXYAPo79JwB/wyd+WwCX4vn73AjjnPjazm4DLzWwh8AFwJfAf/JQ0OOdmm9mDwGQzOw3fPDwZmBaNJAY/AOUl4DYzOxvYBLgcuEEjiaUSfGW8xkGJiEjzmk0GwyC7bzEbzORzjzVTZBTwj9j9wrWPbwW+CeyEH0DSD58Q/gP4inPu09hjvofvp3gXdZNOj3fO1cbKnICfdLow6vg+Ys3YzrlaMzsc+DV+pPG6Sadb+FJFREREOr2W1AzOoPGBFnGFwRlNXoHEOTcjKtuYLzb3RM65FUA2+muszIfAic1s5y1gXHPPJ9IZ3f+HFwE4/OgdU45ERETKWUuSwf3bPQoRKbn35n2cdggdxtWuwbq2qddLRcUhIlKMZr+1Mvncox0RiIhIa7XkiiwdQVdkEZHOqNhJp0VERESkghTVnhEG2b83U8Rl8rkD2xCPiIiIiHSgYju3dGH9wSSb4C9L9z7waimCEpG222Rw77RDEBGRTqCoZDCTz9U0tDwMsp8F/gT8vO0hiUgpHHXc59MOQUREOoGS9BnM5HNzgEvxkzaLiIiISCdRygEk7+MvTSciZeDeO5/n3jufTzsMEREpcyWZECsMsgOAs4A5pdieiLTdBwuXpB2CiIh0AsWOJp7L+gNIugNDottHlyIoEREREekYxdYMPsr6yeAK4E3g7qjvoIiIiIh0EsWOJj65neIQERERkRS0aQBJGGQHlSoQESmtTTffmE033zjtMEREpMwVPYAkDLL7ARcDewLdwyC7CgiBH2fyucdKHJ+ItNLhR++YdggiItIJFFUzGAbZY4G/A4Pxcwp+F7gCP4Dk72GQPabkEYqIiIhIuym2ZvBi4H7gyEw+t7awMAyyFwL3AT8B7ildeCLSWr+/7VkAvjJ+95QjERGRclZsn8GtgOvjiSBAdP/XwJYliktE2uiTj1bwyUcr0g5DRETKXLHJ4GtAY4NGBgH/a1s4IiIiItKRik0GzwcuCoPsHvGFYZDNAJOAH5YoLhERERHpAMX2Gfw+0BN4OgyybwML8INHPhPdPjcMsudGZV0mn9uvZJGKiIiISMkVmwzWAi9HfwVzoz8RKSOf2bJ/2iGIiEgnUOwVSGraKQ4RKbEvfulzaYcgIiKdQJuuQCIiIiIinVtrrkCyKXA2sB8wAPgAmAFcmcnn5pc0OhFptTtuegaA47+2RzMlRUSkmhV7BZIRwL/xVx5ZAvwTWAqcAfw7DLLbljpAEWmdZUtXsWzpqrTDEBGRMldszeBlwCdAJpPPvVFYGAbZLYCHo/VfLll0IiIiItKuiu0zuD/wo3giCJDJ597EzzO4f2nCEhEREZGOUGwy2B34tJF1n0brRURERKSTKDYZ/DeQDYNsvceFQdaAb0XrRaQMbD1iIFuPGJh2GCIiUuaK7TN4MTANmB0G2buA94ChwLHAtsDhpQ1PRFrrgEO2SzsEERHpBIpNBmcB/4dPCs8HDHDAs8C4TD73cGnDExEREZH21GwyGAbZrsCPgDOBPvhL0k0DjsAng4sz+dyydoxRRFrhluufBuDkb+6VciTVw9WuwboWPX1rxcUgIp1LS74xvgH8GD+x9DPA1sBRwMeZfO6U9gtNRNpizeratEOoOtZ1A5ZNn5xqDL3GTkz1+UWk82lJMngacEMmn1v3DRMG2YnAdWGQnZjJ5zSrrYiIiEgn1ZLRxFsDdyeW3QV0BbYoeUQiIiIi0mFakgz2xl91JK4w12CfYp/QzPY1s/vMbJ6ZOTM7ObHezGySmb1rZsvNbIaZ7ZAo08PMcma2yMyWRtsblijT38ymmtnH0d9UM+uXKDPczP4SbWORmV1rZporUURERKpGS3sZbx4G2a1j97vGln8UL5jJ515vZlu9gReB26K/pHOBs4GTgVfw/RWnm9l2zrlCEno1fgBLAHwAXAlMM7PdnXOFjlJ3AMOBQ/Ejnm8EpuJHQ2NmXYH7o8ePATYBbsUPisk28xpEyt52OwxJOwQREekEWpoM3tPI8j81sKxrA8vWcc49ADwAYGa3xNeZmeFHLV/qnPtDtGwCsBA4HphsZhsDXwNOcc5Nj8qcBLwJHAQ8ZGYjgUOA0c65p6IyE4HHo6TyFeBgYAdgC+fc21GZc4Ebzex851yyNlSkUxlz4DZphyApKJfRxOUSh4g0ryWf1I4cMbwVfhLrdfMVOueWm9ljwD7AZGB3oFuizNtmNjsq8xCwN7AEeCq27SeBpVGZV6IyswuJYOQhoEf0HP8o9YsTEWlv5TCiGTSqWaQzaTYZzORzt3ZEIJGh0f8FieULgM1jZWqBRQ2UGRor875zzhVWOuecmS1MlEk+z6Jo20MR6eRuvPZJAL7+3S+kHImIiJSzcq3Dd4n71sCypGSZhsq3pEyjy83sdOB0gCFDhjBjxoxmQmq7tSs2affnaE6XGTMURzvFsAX1u9g+3cJttySOT9Z2K2qbrVEO+0RxlF8MhThEpHzU1NQ0uq7cksH50f+hQLz5djB1tXjz8f0SBwLvJ8o8Fisz2MysUDsY9UcclNhOsspkYLTtZI0hAM65KcAUgFGjRrmm3thSKYvmnppjFEc7xfBe4v5ePT8oWRwvdlld1DZboxz2ieIovxgKcYhI59CSqWU60lx8kja2sMDMeuJH+xb6/z0LrE6UGQaMjJWZiR+1vHds23sDGyXKjExMSTMWWBk9h4iIiEjF6/CaQTPrDRSGOXYBhpvZLsCHzrm3zOxq4Hwzexl4FbgAPxjkDgDn3MdmdhNwedQHsDC1zH+AR6Iys83sQfzo49PwzcOTgWnRSGLwA1BeAm4zs7PxU8tcDtygkcQiIiJSLdJoJh5F/ZG6F0V/t+LnFvwFsCHwK6A/EAIHx+YYBPgesAZ/JZQNgb8B42NzDAKcAFxL3ajj+4DvFFY652rN7HDg1/iRxsvxCec5pXiRImnbcdfN0g5BREQ6gQ5PBp1zM/A1dY2td8Ck6K+xMivwE0M3Ojm0c+5D4MRmYnkLGNdUGZHOaq8xW6UdgoiIdALl1mdQREpk1ao1rFq1Ju0wRESkzCkZFKlQt/0m5LbfhGmHISIiZU7JoIiIiEgVUzIoIiIiUsWUDIqIiIhUMSWDIiIiIlWs3C5HJyIlslvmM2mHICIinYCSQZEKtVtmeNohiIhIJ6BmYpEKtXTJSpYuWZl2GCIiUuaUDIpUqPxvZ5H/7ay0wxARkTKnZFBERESkiikZFBEREaliSgZFREREqpiSQREREZEqpqllRCrUnqO3TDsEERHpBJQMilSonXfbPO0QRESkE1AzsUiF+mjxcj5avDztMEREpMwpGRSpUPdMfY57pj6XdhgiIlLmlAyKiIiIVDElgyIiIiJVTMmgiIiISBVTMigiIiJSxTS1jEiF+sL+n007BKlirnYN1jX9n5hyiUOknOkTIlKhRu40NO0QpIpZ1w1YNn1y2mHQa+zEtEMQKXtqJhapUO8vWML7C5akHYaIiJQ5JYMiFerPdz3Pn+96Pu0wRESkzCkZFBEREaliSgZFREREqpiSQREREZEqpmRQREREpIppahmRClXzxRFphyAiIp2AkkGRCrXNdoPSDkFERDoBNROLVKh33/mYd9/5OO0wRESkzCkZFKlQD/zxRR7444tphyEiImVOyaCIiIhIFSu7ZNDMJpmZS/zNj623qMy7ZrbczGaY2Q6JbfQws5yZLTKzpWZ2n5kNS5Tpb2ZTzezj6G+qmfXroJcpIiIiUhbKLhmMvAJsGvvbKbbuXOBsIAvsASwEpptZn1iZq4GjgQAYA/QFpplZ11iZO4DdgEOBQ6LbU9vhtYiIiIiUrXIdTbzGOTc/udDMDDgTuNQ594do2QR8Qng8MNnMNga+BpzinJselTkJeBM4CHjIzEbiE8DRzrmnojITgcfNbDvn3Cvt/QJFREREykG51gxubWbzzGyumd1pZltHy7cChgIPFwo655YDjwH7RIt2B7olyrwNzI6V2RtYAjwVe84ngaWxMiKd2thxIxk7bmTaYYiISJkrx5rBEDgZeBkYDFwAPBX1CxwalVmQeMwCYPPo9lCgFljUQJmhsTLvO+dcYaVzzpnZwlgZkU5ti60HpB2CiIh0AmWXDDrn/hq/b2ZPA68DE4CnC8USD7MGliUlyzRUvsntmNnpwOkAQ4YMYcaMGc08ZdutXbFJuz9Hc7rMmKE42imGLXi93v2nW7jtlsTx4Rv+nGnAlkNaF1yJ4ugIiqO8Yii3OEQEampqGl1XdslgknNuiZm9BGwL/ClaPBR4O1ZsMHW1hfOBrsBA4P1EmcdiZQabmRVqB6P+iINYv9YxHssUYArAqFGjXFNvbKksmz653Z+jOb1qjlEc7RTDe4n7e/X8oGRx3PhwCMBh3/1Ca0IrWRwdQXGUVwzlFMeGY47Euqb/U+dq15RFHCINKfsj08x6AtsD/wDm4hO5scAzsfVjgO9HD3kWWB2VuSMqMwwYSV0fwZlAb3zfwcKyvYGNqN+PUEREOjHrukFZJKW9xk5MOwSRRpVdMmhmVwB/Ad7C1+b9CJ+k3Rr167saON/MXgZexfcpXEKU+DnnPjazm4DLoz6AHwBXAv8BHonKzDazB/Gjj0/DNw9PBqZpJLGIiIhUk7JLBoFhQJ66Zt6ngb2cc29G638BbAj8CuiPH3BysHPu09g2vgesAe6Kyv4NGO+cq42VOQG4lrpRx/cB32mPFyQiIiJSrsouGXTOHdfMegdMiv4aK7MCPyl1tokyHwIntipIERERkQpRdsmgiJTGYV/eMe0QRESkE1AyKFKhNhu2cdohiIhIJ1CuVyARkTb63yvv879X3m++oIiIVDXVDIpUqBkPvQrANtsNSjkSEREpZ6oZFBEREaliSgZFREREqpiSQREREZEqpmRQREREpIppAIlIhTriq59POwQREekElAyKVKhBQ3qnHYKIiHQCaiYWqVCzX5jP7Bfmpx2GiIiUOdUMilSoJ/8xB4CROw1NORIRESlnqhkUERERqWJKBkVERESqmJJBERERkSqmZFBERKSdudo1aYcAlE8cUl40gESkQh1z0m5phyAiEeu6AcumT047DHqNnZh2CFKGlAyKVKh+/TdMOwQREekE1EwsUqH+89w8/vPcvLTDEBGRMqeaQZEK9c8n3gBg5902TzcQEREpa6oZFBEREaliSgZFREREqpiSQREREZEqpmRQREREpIppAIlIhQpOHZV2CCIi0gkoGRSpUBv17pF2CCIi0gmomVikQj0XvsVz4VtphyEiImVOyaBIhXoufJvnwrfTDkNERMqckkERERGRKqZkUEREpEq42jVphwCUTxziaQCJiIhIlbCuG7Bs+uS0w6DX2IlphyAxqhkUERERqWKqGRSpUOO/kUk7BBER6QSUDIpUqO7d9fEWEZHmqZlYpEI9/fhcnn58btphiIhImVMyKFKhXvzXu7z4r3fTDkNEZD3lMJq4HGIoF2pHAszsW8D3gU2Bl4AznXOPpxuViIhIZSqHUc0a0Vyn6msGzeyrwDXAz4FdgaeAv5rZ8FQDExEREekAVZ8MAmcBtzjnbnDOzXbOZYH3gG+mHJeIiIhIu6vqZNDMugO7Aw8nVj0M7NPxEYmIiEhHKJc+g+UQhznn0o4hNWa2GTAP2M8591hs+Y+BE5xz2yXKnw6cHt3dDnilnUMcCCxq5+eQ4mm/lC/tm/Kk/VK+tG/KU3vsl0XOuUMaWqEBJF4yI7YGluGcmwJM6ZCIADOb5Zwb1VHPJy2j/VK+tG/Kk/ZL+dK+KU8dvV+qupkYn3XXAkMTywcDCzo+HBEREZGOVdXJoHNuFfAsMDaxaix+VLGIiIhIRVMzMVwJTDWzfwJPAt8ANgN+k2pUXoc1SUtRtF/Kl/ZNedJ+KV/aN+WpQ/dLVQ8gKYgmnT4XP+n0i8D34gNKRERERCqVkkERERGRKlbVfQZFREREqp2SwTJkZt8ys7lmtsLMnjWzMWnHVG3M7Idm9oyZfWJm75vZX8xsx0QZM7NJZvaumS03sxlmtkNaMVcjM/t/ZubM7LrYMu2XlJjZpmZ2a/SZWWFm/zWz/WLrtW86mJl1NbOfxH5T5prZT81sg1gZ7ZcOYGb7mtl9ZjYv+t46ObG+2f1gZj3MLGdmi8xsabS9YW2NTclgmdG1kstGDfBr/JVoDgDWAI+Y2YBYmXOBs4EssAewEJhuZn06NtTqZGZ7AacB/0ms0n5JgZn1ww/CM+BwYCR+HyyMFdO+6XjnAd8GvgtsD5wR3f9hrIz2S8fojR+XcAawvIH1LdkPVwNHAwEwBugLTDOzrm2KzDmnvzL6A0LghsSy14BL0o6tmv+iD3Et8H/RfcNfw/r8WJkNgU+BiWnHW+l/wMbAHHyiPgO4Tvsl9X3yc+DJJtZr36SzX6YBtyaW3QpM035Jdb8sAU6O3W92P0Tfe6vwV0grlPkMsBb4YlviUc1gGdG1kstaH3xN+uLo/lb4ycrX7Svn3HLgMbSvOsIU4B7n3N8Ty7Vf0nMkEJrZXWa20Mz+bWbfMTOL1mvfpOMJYH8z2x7AzD6HP4l6IFqv/VIeWrIfdge6Jcq8DcymjftK8wyWl4FAV9a/+skC4KCOD0dirgH+DcyM7heuWtPQvtq8g2KqSmZ2GrANcFIDq7Vf0rM18C3gKuBSYBcgF627Du2btFyGP5n9r5nV4n/3f+ac+3W0XvulPLRkPwzFt1Alr1m8gPWvpFYUJYPlqUXXSpaOYWZXAqOB0c652sRq7asOZGbb4Zsjxzh/BaHGaL90vC7ALOdcoS/av8xsW3z/tOti5bRvOtZXgfHA8cBL+CT9GjOb65y7KVZO+6U8tGY/tHlfqZm4vOhayWXGzK7Cd9Q9wDn3emzV/Oi/9lXH2htfg/6ima0xszXAfsC3otsfROW0Xzree8B/E8tmA4XBb/rMpONy4Arn3J3OuRecc1PxV94qJO3aL+WhJfthPr71cGATZVpFyWAZcbpWclkxs2vwZ9MHOOdeTqyei/9gjo2V74kf3aV91X7+BOyEr90o/M0C7oxuv4r2S1qeBLZLLBsBvBnd1mcmHb3wlQxxtdT9/mu/lIeW7IdngdWJMsPwI/fbtK/UTFx+yvlayVXDzH6F75N2JLDYzApna0ucc0ucc87MrgbON7OX8UnIBfgRYnekEHJVcM59BHwUX2ZmS4EPnXMvRvevRvslDVcBT5nZ+cBd+Kmxvgv8PwB9ZlLzF+AHZjYX30y8K3AWcBtov3QkM+uN7+8MPhkfbma74L+/3mpuPzjnPjazm4DLzWwhviXkSvz0Wo+0Kbi0h1frr8Eh598C3gBW4s8E9k07pmr7w/e/aOhvUqyMAZPwzWMrgEeBHdOOvdr+iE0to/2S+r44HHg+et9fxSeDpn2T6j7pg5+b7k383Hav4/vd9tR+6fB9UdPI78otLd0PQE/8wKwPgGX4ZP8zbY1N1yYWERERqWLqMygiIiJSxZQMioiIiFQxJYMiIiIiVUzJoIiIiEgVUzIoIiIiUsWUDIqIiIhUMU06LSLSjsIgeyPwNeCqTD53VtrxiIgkaZ5BEZF2EgbZDfGXmOoLLAQ2z+Rza9KNSkSkPjUTi4i0n6PwieAD+IvJH5JuOCIi61MzsYhI+5kALAZOxl8ObDwwLV4gDLIBcCGwJfAacD7+2rFk8rmaWLmBwE+ALwED8Re2vzKTz01p35cgIpVONYMiIu0gDLKbAQcBd2XyufeBPwFfCoNs/1iZscDtwMvA0cAV+OvIjkhsqy/wJP7av5Oi/38Brg+DbLadX4qIVDjVDIqItI+T8Cfct0X3bwUC4KvAb6JlFwH/BY7K5HMOIAyyLwDPAq/GtnUGsAWwUyafey1a9kgYZPsBF4ZB9nr1RRSR1lLNoIhI+xgPvJbJ52ZG9x8B3o2WEwbZrsAo4A+FRBAgk889h28CjjsECIG5YZDdoPAHPARsAnyuXV+JiFQ01QyKiJRYGGT3wCdol0W1dwV/BL4TBtkRwMdAN/wo46QFifuDgW2A1Y085SZtClhEqpqSQRGR0psQ/T8v+ksajx80shqf6CUNAd6K3f8AnzSe0cjzvdK6MEVElAyKiJRUGGS7A8fhm3V/0ECRq/D9CX8EzAKODoPspFifwd2BraifDD4IZIG3MvlcQzWJIiKtpmRQRKS0xuGbbc/O5HMzkivDIDsZuB6owdcOPgzcGwbZKfgpYybhJ6peG3vYVfiBJ4+HQfYqfE3gRsD2wJhMPndE+7wUEakGGkAiIlJaE4BPgbsbWZ8HlgMTMvncdOAEYCRwL75J+Wx8Mvhx4QGZfO5jYB/85NXn4QeO/BY4AvhHu7wKEakauhydiEgZCYPsMOB/wM8y+dxP0o5HRCqfmolFRFISXbv4Svy0M4uArYFzgWXAjSmGJiJVRMmgiEh6aoGhwHX4foZLgceBYzP53HtpBiYi1UPNxCIiIiJVTANIRERERKqYkkERERGRKqZkUERERKSKKRkUERERqWJKBkVERESqmJJBERERkSr2/wFDXkDhZkk4LgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = lfs17['age'].hist(bins=15, edgecolor='w', color='#f5ba98',  figsize=(10, 5))\n",
    "\n",
    "# Here you can override the colors of set using rcParams by simply passing a value for the arguments.\n",
    "ax.set_title('Distribution of Adult Population According to Age', color='#c8586c', fontweight='bold')\n",
    "ax.set_xlabel('Age', color='#c8586c')\n",
    "ax.set_ylabel('Population', color='#c8586c');\n",
    "\n",
    "# To remove the spines of the chart manually, you can choose to set the visibility to False\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.grid(False)\n",
    "\n",
    "ax.axvline(x=lfs17['age'].mean(), color='#70284a', linestyle='--', label='mean')\n",
    "ax.axvline(x=lfs17['age'].median(), color='#dc7176', linewidth=4, label='median')\n",
    "ax.legend(fontsize=14);\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is basically the same plot but wuth some \"threshold\" or marker set for the mean and the median.\n",
    "\n",
    "**Review of Econ 116**: What is the skewness of the distribution if **median < mean?** \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplots ###"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m\n",
       "\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnotch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0msym\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvert\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwhis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpositions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpatch_artist\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbootstrap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0musermedians\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconf_intervals\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmeanline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshowmeans\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshowcaps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshowbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mshowfliers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mboxprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mflierprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmedianprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmeanprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcapprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwhiskerprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmanage_ticks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mautorange\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Draw a box and whisker plot.\n",
       "\n",
       "The box extends from the first quartile (Q1) to the third\n",
       "quartile (Q3) of the data, with a line at the median.  The\n",
       "whiskers extend from the box by 1.5x the inter-quartile range\n",
       "(IQR).  Flier points are those past the end of the whiskers.\n",
       "See https://en.wikipedia.org/wiki/Box_plot for reference.\n",
       "\n",
       ".. code-block:: none\n",
       "\n",
       "          Q1-1.5IQR   Q1   median  Q3   Q3+1.5IQR\n",
       "                       |-----:-----|\n",
       "       o      |--------|     :     |--------|    o  o\n",
       "                       |-----:-----|\n",
       "     flier             <----------->            fliers\n",
       "                            IQR\n",
       "\n",
       "\n",
       "Parameters\n",
       "----------\n",
       "x : Array or a sequence of vectors.\n",
       "    The input data.  If a 2D array, a boxplot is drawn for each column\n",
       "    in *x*.  If a sequence of 1D arrays, a boxplot is drawn for each\n",
       "    array in *x*.\n",
       "\n",
       "notch : bool, default: False\n",
       "    Whether to draw a notched boxplot (`True`), or a rectangular\n",
       "    boxplot (`False`).  The notches represent the confidence interval\n",
       "    (CI) around the median.  The documentation for *bootstrap*\n",
       "    describes how the locations of the notches are computed by\n",
       "    default, but their locations may also be overridden by setting the\n",
       "    *conf_intervals* parameter.\n",
       "\n",
       "    .. note::\n",
       "\n",
       "        In cases where the values of the CI are less than the\n",
       "        lower quartile or greater than the upper quartile, the\n",
       "        notches will extend beyond the box, giving it a\n",
       "        distinctive \"flipped\" appearance. This is expected\n",
       "        behavior and consistent with other statistical\n",
       "        visualization packages.\n",
       "\n",
       "sym : str, optional\n",
       "    The default symbol for flier points.  An empty string ('') hides\n",
       "    the fliers.  If `None`, then the fliers default to 'b+'.  More\n",
       "    control is provided by the *flierprops* parameter.\n",
       "\n",
       "vert : bool, default: True\n",
       "    If `True`, draws vertical boxes.\n",
       "    If `False`, draw horizontal boxes.\n",
       "\n",
       "whis : float or (float, float), default: 1.5\n",
       "    The position of the whiskers.\n",
       "\n",
       "    If a float, the lower whisker is at the lowest datum above\n",
       "    ``Q1 - whis*(Q3-Q1)``, and the upper whisker at the highest datum\n",
       "    below ``Q3 + whis*(Q3-Q1)``, where Q1 and Q3 are the first and\n",
       "    third quartiles.  The default value of ``whis = 1.5`` corresponds\n",
       "    to Tukey's original definition of boxplots.\n",
       "\n",
       "    If a pair of floats, they indicate the percentiles at which to\n",
       "    draw the whiskers (e.g., (5, 95)).  In particular, setting this to\n",
       "    (0, 100) results in whiskers covering the whole range of the data.\n",
       "\n",
       "    In the edge case where ``Q1 == Q3``, *whis* is automatically set\n",
       "    to (0, 100) (cover the whole range of the data) if *autorange* is\n",
       "    True.\n",
       "\n",
       "    Beyond the whiskers, data are considered outliers and are plotted\n",
       "    as individual points.\n",
       "\n",
       "bootstrap : int, optional\n",
       "    Specifies whether to bootstrap the confidence intervals\n",
       "    around the median for notched boxplots. If *bootstrap* is\n",
       "    None, no bootstrapping is performed, and notches are\n",
       "    calculated using a Gaussian-based asymptotic approximation\n",
       "    (see McGill, R., Tukey, J.W., and Larsen, W.A., 1978, and\n",
       "    Kendall and Stuart, 1967). Otherwise, bootstrap specifies\n",
       "    the number of times to bootstrap the median to determine its\n",
       "    95% confidence intervals. Values between 1000 and 10000 are\n",
       "    recommended.\n",
       "\n",
       "usermedians : 1D array-like, optional\n",
       "    A 1D array-like of length ``len(x)``.  Each entry that is not\n",
       "    `None` forces the value of the median for the corresponding\n",
       "    dataset.  For entries that are `None`, the medians are computed\n",
       "    by Matplotlib as normal.\n",
       "\n",
       "conf_intervals : array-like, optional\n",
       "    A 2D array-like of shape ``(len(x), 2)``.  Each entry that is not\n",
       "    None forces the location of the corresponding notch (which is\n",
       "    only drawn if *notch* is `True`).  For entries that are `None`,\n",
       "    the notches are computed by the method specified by the other\n",
       "    parameters (e.g., *bootstrap*).\n",
       "\n",
       "positions : array-like, optional\n",
       "    The positions of the boxes. The ticks and limits are\n",
       "    automatically set to match the positions. Defaults to\n",
       "    ``range(1, N+1)`` where N is the number of boxes to be drawn.\n",
       "\n",
       "widths : float or array-like\n",
       "    The widths of the boxes.  The default is 0.5, or ``0.15*(distance\n",
       "    between extreme positions)``, if that is smaller.\n",
       "\n",
       "patch_artist : bool, default: False\n",
       "    If `False` produces boxes with the Line2D artist. Otherwise,\n",
       "    boxes are drawn with Patch artists.\n",
       "\n",
       "labels : sequence, optional\n",
       "    Labels for each dataset (one per dataset).\n",
       "\n",
       "manage_ticks : bool, default: True\n",
       "    If True, the tick locations and labels will be adjusted to match\n",
       "    the boxplot positions.\n",
       "\n",
       "autorange : bool, default: False\n",
       "    When `True` and the data are distributed such that the 25th and\n",
       "    75th percentiles are equal, *whis* is set to (0, 100) such\n",
       "    that the whisker ends are at the minimum and maximum of the data.\n",
       "\n",
       "meanline : bool, default: False\n",
       "    If `True` (and *showmeans* is `True`), will try to render the\n",
       "    mean as a line spanning the full width of the box according to\n",
       "    *meanprops* (see below).  Not recommended if *shownotches* is also\n",
       "    True.  Otherwise, means will be shown as points.\n",
       "\n",
       "zorder : float, default: ``Line2D.zorder = 2``\n",
       "    The zorder of the boxplot.\n",
       "\n",
       "Returns\n",
       "-------\n",
       "dict\n",
       "  A dictionary mapping each component of the boxplot to a list\n",
       "  of the `.Line2D` instances created. That dictionary has the\n",
       "  following keys (assuming vertical boxplots):\n",
       "\n",
       "  - ``boxes``: the main body of the boxplot showing the\n",
       "    quartiles and the median's confidence intervals if\n",
       "    enabled.\n",
       "\n",
       "  - ``medians``: horizontal lines at the median of each box.\n",
       "\n",
       "  - ``whiskers``: the vertical lines extending to the most\n",
       "    extreme, non-outlier data points.\n",
       "\n",
       "  - ``caps``: the horizontal lines at the ends of the\n",
       "    whiskers.\n",
       "\n",
       "  - ``fliers``: points representing data that extend beyond\n",
       "    the whiskers (fliers).\n",
       "\n",
       "  - ``means``: points or lines representing the means.\n",
       "\n",
       "Other Parameters\n",
       "----------------\n",
       "showcaps : bool, default: True\n",
       "    Show the caps on the ends of whiskers.\n",
       "showbox : bool, default: True\n",
       "    Show the central box.\n",
       "showfliers : bool, default: True\n",
       "    Show the outliers beyond the caps.\n",
       "showmeans : bool, default: False\n",
       "    Show the arithmetic means.\n",
       "capprops : dict, default: None\n",
       "    The style of the caps.\n",
       "boxprops : dict, default: None\n",
       "    The style of the box.\n",
       "whiskerprops : dict, default: None\n",
       "    The style of the whiskers.\n",
       "flierprops : dict, default: None\n",
       "    The style of the fliers.\n",
       "medianprops : dict, default: None\n",
       "    The style of the median.\n",
       "meanprops : dict, default: None\n",
       "    The style of the mean.\n",
       "data : indexable object, optional\n",
       "    If given, all parameters also accept a string ``s``, which is\n",
       "    interpreted as ``data[s]`` (unless this raises an exception).\n",
       "\n",
       "See Also\n",
       "--------\n",
       "violinplot : Draw an estimate of the probability density function.\n",
       "\u001b[0;31mFile:\u001b[0m      ~/Library/jupyterlab-desktop/jlab_server/lib/python3.8/site-packages/matplotlib/pyplot.py\n",
       "\u001b[0;31mType:\u001b[0m      function\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Alternative option: BOX PLOT\n",
    "plt.boxplot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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BUn+X/3Fq7/NnAa8spZwZEW8CXg/8rNmuL6N+hg6gnr0c0bx/lLJj/byUUlZHxKuBdwPnt7adJdSe5ncfdnqjKKX8KiLeQL3s+sO+eR9M7R/zwxHnPXDf2SznS4APAOdFxCepQf9Aauekn1N/Yjado6hn0f9epv5d+4nUdfds6sHghog4itox7osR0fsd7t2b5f0K9baQva/yKKX8ISL+keZ2pxHxderl8A3A7Zt670rNk/EYZ3qPcKRUWo/rqWcPq4ATqJd+NrujS+k4yqOeyb6qadBLqTuiq6gfoufSuvsK9TdrH6L+brf3k5Hlrbp1Hq0245czxdkJNeS/QD1y+it1J/3wAdNa0ixv7843F1I3oKXtevW952XUyzLXN2VWD2qb1pHa86i9G//aPFYBz+9q56naoGv5h1jf96Vu1FdRj4IvbdbBbTrKLmMOz3BnWKfeMu8NvJyNd5r6LfUmAJv9fKp53+OaNl9HPYM8ldpBZsptqDWNHag/cbqIjXdQ+wB1RzBofd+XunO8jrrz+NuRPVPfaWombbLZdjDsOqQePPyl2ZZ3m6bs7aiXNK+k7zNNvelD785GvbsufQ54cMc0hirLGD8vfWUOpV5e7u2f/p0Z3mlqwPSnWq9Pp17ta8/7QuoB+7CfmSn3nU2Zh1FvPrGmaeOLqAejS4aY/iI2HiDefZqyO1DPpm+g7+dY1CuHZ1HvNPUnNt5p6vhmuvfsmNbSZvwvmzb6I/UzfjLw+GHbZ5hHNDOUNEBzh5tnUG/AsnqytZFmLyJuRu25e0Ep5f7TlV/oIuIcahjfvJTyl0nVY152mpIkzV5E3CIitm0NW0y9xL0d9Qx/ixARO3T9Fj7q/35+ALXz4cTCFub3z4IkSbPzJOCNEfE16iX0Xaj9Ru5M7QPw/slVbexuT/2e/CzqpezF1BtiHEC9/PzyyVWtMnAlact1LvXuSwex8UYPF1P7BbyjDHczkIXiCupdBx9I/ScaN6V2+Ps48JZSyq8mWDcAv8OVJCmD3+FKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIsnsuJ77bbbmXp0qVzOQtJkuaNVatWXV1KuUXXuDkN3KVLl7Jy5cq5nIUkSfNGRFwyaJyXlCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUqweNIV2NLtsssurFmzZtLVGL+PvRye+e5J12LB2XnnnbnmmmsmXQ1JE2DgzrE1a9ZQSpl0NcYuPv6KLXK55lpETLoKkibES8qSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKsKAC19viSdKWY2vbpy+owJUkaaEycCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIMFbgRcVBEfDEifhsRJSKOmON6SZK2EhEx8ceiRXN//jnsHHYELgReDKydu+pIkrYm/XebesUrXrHZuDPOOGOTYaeeeuomr1esWLHJ61WrVm3y+jvf+c4mr7/3ve9t9nq77bajlDLnoRullNHeEPFn4AWllOXTld1///3LypUrZ1i1znkzan0nbSHWeRjx8VdQjnzXpKux4Gyp24M0E/1h2/+56A3vDWuXG/drgO23355169bN+vMZEatKKft3jRt7nEfEsyNiZUSsvOqqq8Y9+Ylfdhj1IbVNepv04WO+PHraZ7Zdjj766E1ev/71r9/k9Yc//OFNXp944okjjW+fKc8Fz3Dn2EKs8zA8w52ZLXV7kGaiP3Q9w5UkKUFE8MpXvnKTMFy0aBFf/epXNyl32mmnbfL67LPP3uT1eeedt8nrc845Z5PX55577mave2HbP++54BnuHFuIdR6GZ7gzs6VuD9JM9D4Pcx10w9Zlw4YN45jOwDPcxbOeuiRJs7C1HIQOFbgRsSNwx+blIuD2EXFP4JpSyqVzVDdJkrYYw36Huz9wfvPYHji2+fuNc1QvSZK2KEOd4ZZSVgCTv8guSdICZS9lSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUoIFFbhby91IJGlrsLXt0xdU4EqStFAZuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlWDzpCmwNImLSVRi/j718y1yuObbzzjtPugqSJsTAnWNb9L1Cj3zXpGsgSQuGl5QlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUpg4EqSlMDAlSQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJYhSytxNPOIq4JIxTnI34OoxTm9rZTuOh+04HrbjeNiO4zHbdtyzlHKLrhFzGrjjFhErSyn7T7oeC53tOB6243jYjuNhO47HXLajl5QlSUpg4EqSlGChBe5HJl2BLYTtOB6243jYjuNhO47HnLXjgvoOV5KkhWqhneFKkrQgGbiSJCUwcCVJSrBgAjcinhcRF0fEuohYFREHTrpO81VEvCYifhARf4yIqyLi9IjYr1UmIuKYiPhdRKyNiBURcbdJ1XkhiIjXRkSJiOP7htmOQ4iIW0fESc32uC4ifhoRD+wbbztOIyK2iYg39e0HL46IN0fE4r4ytmOHiDgoIr4YEb9tPsNHtMZP224RcdOIeH9EXB0Rf2mmt8co9VgQgRsRhwDHAW8F7gV8F/hKRNx+ohWbv5YBHwQeADwYuBH4WkTs0lfmVcDLgRcC9wWuBM6KiJ1yq7owRMT9gGcBP2qNsh2nERFLgHOAAB4N3JXaXlf2FbMdp3c08HzgRcA+wIub16/pK2M7dtsRuJDaZms7xg/Tbu8DngQcChwI3Az4UkRsM3QtSinz/gGcC5zQGvZL4G2TrttCeDQb23rgsc3rAH4PvK6vzPbAn4DnTLq+8+0B3Bz4FfXgZQVwvO04Uvu9FThnivG243Dt+CXgpNawk4Av2Y4jteOfgSP6Xk/bbs0+4AbgsL4ytwM2AA8fdt7z/gw3Im4C3Ac4szXqTOoZnKa3E/Vqxprm9V7A7vS1aSllLfAtbNMuHwE+XUr5Rmu47TicxwPnRsRpEXFlRFwQES+IiGjG247D+Q7woIjYByAi9qUeBH65GW87zsww7XYfYNtWmcuAnzFC2y6evsjE7QZsA1zRGn4F8ND86ixIxwEXAP/ZvN69ee5q09sm1WlBiIhnAXcEnt4x2nYczt7A84D3Am8H7gm8vxl3PLbjsN5BPXj+aUSsp+6/31JK+WAz3nacmWHabXfqVcL2PzW4ou/901oIgdvTvkNHdAxTS0S8BzgAOKCUsr412jadQkTchXo59MBSyg1TFLUdp7YIWFlK6X3XeH5E3In6/ePxfeVsx6kdAhwOPBX4CfXA5biIuLiU8tG+crbjzMyk3UZq23l/SZl6RLGezY8ibsnmRyTqExHvpX7B/+BSyq/7Rl3ePNumU7s/9QrLhRFxY0TcCDwQeF7z9x+acrbj1H4P/LQ17GdAr9Oj2+Nw/gV4Vynl1FLKj0spJwPvYWOnKdtxZoZpt8upV1p3m6LMtOZ94DZnFquAg1ujDqb2VlaHiDiOeiT84FLKz1ujL6ZuQAf3ld+O2vPONt3o88DfUc8keo+VwKnN37/AdhzGOcBdWsPuzMb/le32OJwdqCcf/dazcT9uO87MMO22CvjvVpk9qD3uh2/bSfcYG7JX2SHUHmJHNQt4HLWn2Z6Trtt8fAAfAP5I7VCxe99jx74yRzdlngjsRw2R3wE7Tbr+8/lBXy9l23HoNrtvs7N6HfX78CcD1wHPtx1HasflwG+oP61aCjwBuAp4t+04bdvtyMaD5r8Cb2j+vv2w7QZ8CPgtte/QvYBvUvvGbDN0PSbdECM02POA1cD11KONgyZdp/n6oH6n0PU4pq9MAMdQL/etA84G9pt03ef7oyNwbcfh2u3RwA+bNvoF9bekYTuO1IY7UX8Legn1t6S/pvYx2M52nLbtlg3YJy4ftt2A7aid/f7QhPbpwO1GqYf/LUiSpATz/jtcSZK2BAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq40j0XEHSPi5Ii4OCLWRsSvI+JDEbFzR9kXR8TqiFgXEd+PiAc0r5e3yu0VEZ+IiKsi4vrmH8I/IW2hpK3UQvp/uNLW6DbUG9a/BFhD/WfurwW+TP33gQBExFHU++x+FPgUcAfgFGBJ/8Qi4nbAucCVwEupN78/BPhMRDy+lPLFuVwYaWvmvZSlBSQiFgP3A74N3LuUcn5ELKLe0P7HpZRH9ZV9IvAZ4KRSyhHNsI8CjwP2KaX8oa/sWcAtSin3zFoWaWvjJWVpHouIm0TEayPi5xGxlvpv7r7djO79j9k9msenWm//AnBja9gjqGfH10XE4t4D+Cpwj4i42ZwsiCQvKUvz3NuAFwJvpP6j6z9Rw/Wz1H8XBnDr5vnK/jeWUtZHxNWt6d0SOLx5dNmV+n9BJY2ZgSvNb08B/q2U8ubegIjYsVXm983zLfsHRsQ2wG6tsn+gniG/Y8D8fjfzqkqaioErzW87UC8j9zuy9fo3zePJwMf7hj+ezT/jZ1A7W/2klLJ2fNWUNB0DV5rfzgCeERE/Bi4Cngg8oL9AKWVDRBwLnBARJ1K/y90beDVwHbChr/gbgO8D34qI44HVwM7AfsDepZRnzu3iSFsvA1ea314IBPCW5vWXgUOpofk3pZQTm0vNLwWeBlwIHAacTg3dXrlLI2J/4BjgrcAtqJeZLwROmssFkbZ2/ixI2kJFxH2pwXx4KeXkSddH2toZuNIWICL2Ap5P7RD1R+Cu1Btk3ADsV0r56wSrJwkvKUtbirXU72EPp34nuwb4GvBqw1aaHzzDlSQpgXeakiQpgYErSVICA1eSpAQGriRJCQxcSZISGLiSJCUwcCVJSmDgSpKUwMCVJCmBgStJUgIDV5KkBAauJEkJDFxJkhIYuJIkJTBwJUlKYOBKkpTAwJUkKYGBK0lSAgNXkqQEBq4kSQkMXEmSEhi4kiQlMHAlSUrw/wE9ZgI2o87ivgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = fig.gca()\n",
    "\n",
    "#MATPLOTLIB\n",
    "plt.boxplot(x=lfs17.age, vert=False)\n",
    "\n",
    "#SEABORN\n",
    "#sns.boxplot(data=lfs17.age, orient='h', fliersize=10, linewidth=1.5, notch=True, saturation=0.5, ax=ax)\n",
    "\n",
    "#Axis properties, usable with either the matplotlib or seaborn version of the boxplot\n",
    "ax.set_xlabel('age\\n', fontsize=16)\n",
    "ax.set_title('Distribution of Population According to Age \\n', fontsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Multivariable Exploratory Analysis\n",
    "\n",
    "The previous questions were concerned with describing one variable at a time. Let's try to visualize the data with more than one variable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What is the distribution of the age of those who are part of the labor force, and those who are not? \n",
    "\n",
    "From the comparative visualization of the two distributions, can you spot a difference?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "'''\n",
    "#BOX PLOT 2 Example. Note: This more complicated example which breaks down labor force participation \\\n",
    "according to age is easier to model with a boxplot using SEABORN\n",
    "'''\n",
    "\n",
    "fig = plt.figure(figsize=(8,6))\n",
    "ax = fig.gca()\n",
    "\n",
    "sns.boxplot(x=lfs17.age, y=lfs17.lf_partof, orient='h', fliersize=10, linewidth=1.5, notch=True, \\\n",
    "            saturation=0.5, ax=ax)\n",
    "\n",
    "ax.set_xlabel('age\\n', fontsize=16)\n",
    "ax.set_ylabel('Labor Force (LF)', fontsize=16)\n",
    "ax.set_yticklabels(['Part of LF', 'Not Part of LF'], fontsize=14)\n",
    "ax.set_title('Distribution of Population According to Age \\n', fontsize=22)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#BACK TO HISTOGRAMS and BAR CHARTS. Let's first divide our dataset according to the following:\n",
    "lf_partof = lfs17.loc[lfs17['lf_partof'] == 1]\n",
    "lf_notpartof = lfs17.loc[lfs17['lf_partof'] == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes_object = lf_partof['age'].hist(bins=15, alpha=0.8, color='#68abb8', label='lf_partof', figsize=(10, 5))\n",
    "\n",
    "# To make overlapping plots, you can simply reuse the same `Axes` object.\n",
    "# We can also adjust the opacity or the alpha channel by specifying a value less than 1.\n",
    "lf_notpartof['age'].hist(ax=axes_object, bins=15, alpha=0.5, color='#999999', label='lf_notpartof')\n",
    "\n",
    "axes_object.set_title('Distribution of Population according to Age and Labor Force Participation', fontsize=20)\n",
    "axes_object.set_xlabel('age', fontsize=16)\n",
    "axes_object.set_ylabel('population', fontsize=16)\n",
    "axes_object.legend(fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overlapping the histograms doesn't really give a good view."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we create a figure to hold 2 side by side (juxtaposed) plots\n",
    "fig = plt.figure(figsize=(15, 6))\n",
    "\n",
    "# adding a subplot manually, we need to specify the row, columns, index of plot\n",
    "ax = fig.add_subplot(1,2,1)\n",
    "\n",
    "lf_partof['age'].hist(ax=ax, bins=15, edgecolor='w', color='#68abb8')\n",
    "ax.set_title('Age Distribution of Adults Part of Labor Force', fontsize=18)\n",
    "ax.set_xlabel('Age', fontsize=14)\n",
    "ax.set_ylabel('Population', fontsize=14)\n",
    "ax.set_xlim([10, 90]) # setting the same limit for both plots\n",
    "ax.set_ylim([0, 16000]) # setting the same limit for both plots\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.xaxis.grid(False)\n",
    "\n",
    "# adding a subplot manually, we need to specify the row, columns, index of plot\n",
    "ax2 = fig.add_subplot(1,2,2)\n",
    "\n",
    "lf_notpartof['age'].hist(ax=ax2, bins=15, edgecolor='w', color='#999999')\n",
    "ax2.set_title('Age Distribution of Adults Not Part of Labor Force', fontsize=18)\n",
    "ax2.set_xlabel('Age', fontsize=14)\n",
    "ax2.set_ylabel('Population', fontsize=14)\n",
    "ax2.set_xlim([10, 90]) # setting the same limit for both plots\n",
    "ax2.set_ylim([0, 16000]) # setting the same limit for both plots\n",
    "ax2.spines['right'].set_visible(False)\n",
    "ax2.spines['top'].set_visible(False)\n",
    "ax2.xaxis.grid(False)\n",
    "\n",
    "plt.tight_layout() # this adjusts and makes the figures fit in the frame \"tightly\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>male</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lf_partof</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>32240</td>\n",
       "      <td>14230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>27325</td>\n",
       "      <td>45400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "male           0      1\n",
       "lf_partof              \n",
       "0          32240  14230\n",
       "1          27325  45400"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# What about the gender of the labor force? \n",
    "\n",
    "bygender1 = lfs17.groupby('lf_partof')['male'].value_counts().unstack()\n",
    "bygender1\n",
    "\n",
    "#OR\n",
    "\n",
    "#bygender2 = lfs17.groupby('lf_partof')['female'].value_counts().unstack()\n",
    "#print(bygender2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "male = bygender1[1]\n",
    "female = bygender1[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Stacked bar chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(8, 5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "ax.bar(male.index, male, color='#009392', width=0.5, label='Male')\n",
    "\n",
    "# To stack them on top of one another, we can use the argument bottom\n",
    "ax.bar(female.index, female, bottom=male, color='#d0587e', width=0.5, label='Female')\n",
    "\n",
    "ax.set_xticks([0, 1])\n",
    "ax.set_xticklabels(['Not Part of LF', 'Part of LF'], fontsize=14)\n",
    "ax.set_title('Labor Force by LF Participation and Gender', fontsize=18)\n",
    "ax.set_xlabel('Labor Force Participation', fontsize=14)\n",
    "ax.set_ylabel('Population', fontsize=14)\n",
    "ax.legend(fontsize=14);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "\n",
    "An alternative way to plot the bar charts is directly through `pandas`.\n",
    "\n",
    "We'll use the Labor Force Participation and HS Education Attainment as the keys for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>hs_under</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lf_partof</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21806</td>\n",
       "      <td>24664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41380</td>\n",
       "      <td>31345</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "hs_under       0      1\n",
       "lf_partof              \n",
       "0          21806  24664\n",
       "1          41380  31345"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hs_under = lfs17.groupby('lf_partof')['hs_under'].value_counts().unstack()\n",
    "hs_under"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = hs_under.plot(kind='bar', figsize=(8, 5), color=['#6cc08b','#217a79'])\n",
    "\n",
    "ax.set_title('Labor Force Participation by HS Education', fontsize=20)\n",
    "ax.set_xticklabels(['Not Part of LF', 'Part of LF'], rotation=0, fontsize=15)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('No. of Passengers', fontsize=15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}