day-2-introduction-to-python-part-II-and-web-scraping/01 - Data Visualization.ipynb

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     "end_time": "2019-07-01T14:06:38.832440Z",
     "start_time": "2019-07-01T14:06:38.819428Z"
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     "execution_count": 1,
     "metadata": {},
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   ],
   "source": [
    "from IPython.core.display import HTML\n",
    "from IPython.lib.display import YouTubeVideo\n",
    "def css_styling():\n",
    "    styles = open(\"../Data/www/styles/custom.css\", \"r\").read()\n",
    "    return HTML(styles)\n",
    "css_styling()"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Synopsis\n",
    "\n",
    "Visualizing your data is key to both understanding its basic prooperties and effectively communicating its results to an outside audience. In this unit we will learn:\n",
    "\n",
    "1. How to import `matplotlib`\n",
    "2. How to make a line plot\n",
    "3. How to make histograms\n",
    "3. How to make a bar chart\n",
    "4. How to make a scatter plot\n",
    "\n",
    "We will also cover some basic principles of making clean and informative visualizations."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization\n",
    "\n",
    "Visualizing your data is a **key** component of data analysis, no matter how big or small your data is.  Our minds are hardwired to process visual information, in fact **one-third** of our brain is dedicated to image processing and **40% of all** nerve fibers connected to the brain come from the retina. Visualization is essentially a high-speed link to your cognitive systems.\n",
    "\n",
    "Let me show you a quick example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traffic accidents\n",
    "\n",
    "Let's look at this image together, it plots the number of accidents per month against the time of day. Then tell me when accidents are likely to occur?\n",
    "\n",
    "<img src='../Data/www/images/visualization_raw_chart.png'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's try something different. This is the same chart, but now I've added a color scale. The individual numbers have a deeper shade of blue the more accidents that occur during that time period.\n",
    "\n",
    "Now tell me, when are accidents most likely to occur? How long did it take you to figure it out?\n",
    "\n",
    "<img src = '../Data/www/images/visualization_heatmap.png'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effective visualizations make a difference\n",
    "\n",
    "Effective visualizations allow us to make accurate decisions quicker. More importantly they help us make **correct** decisions. When you make a visualization, you're actually trying to make a point. The entire point is to persuade your audience of some fact that you know to be true. \n",
    "\n",
    "Whether you accept this statement or not, it is what happens. If you don't construct your visualization in a way that informs your audience and allows them to make a correct decision they can easily reach a different conclusion. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a poor visualization can be disastrous.\n",
    "\n",
    "You might remember or have read about the 1986 Challenger accident. The Challenger was a NASA Space Shuttle that exploded upon take-off killing the entire crew because the O-rings in the rockets cracked and allowed jet fuel to leak into the combustion chamber."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Why did the O-rings crack?**\n",
    "\n",
    "There were rumblings at NASA prior to the Challenger launch that defects in the O-rings occurred at cold temperatures. This data was looked at by a large number of people, but this is how it was presented to senior management (the people actually tasked with making the decision of **whether it was safe or not** to launch the rocket).\n",
    "\n",
    "<img src = '../Data/www/images/challenger_original.png'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predicted temperature for the launch time of the Challenger was 26-29F.  Do you think that was safe based on the data above?\n",
    "\n",
    "**Would you have cancelled the launch based on these data?**\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Really hard to say, no?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use a graph to present the data as a function of launch temperature instead of launch number.\n",
    "\n",
    "<img src='../Data/www/images/challenger_remade.png'></img>\n",
    "\n",
    "Would you have cancelled the launch based on these data?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizations are about making patterns visible!\n",
    "\n",
    "However, by using deceiving methods, they can make non-existent patterns appear real. The book \"How to Lie with Statistics\" provides many examples of deceiving techniques. You should learn about them so that you can recognize them and avoid being dupped by them.\n",
    "\n",
    "<img src = '../Data/www/images/fox_news.png'></img>\n",
    "\n",
    "\n",
    "**Making a good graph is not trivial. It requires thinking and the ability to summarize information.** In fact, it is no different from writing a good essay or writing good code.  You need to know enought to have a point of view.  You need to know how to present that point of view clearly. \n",
    "\n",
    "From here on we will focus on the actual nuts and bolts of crafting a graph in Python, but you should keep these principles in mind. If you are interested more on the concepts and best practices of visualization you should read the books by [Edward Tufte](http://www.amazon.com/s/ref=nb_sb_noss_2?url=search-alias%3Daps&field-keywords=edward+tufte&rh=i%3Aaps%2Ck%3Aedward+tufte), he is considered to be one of the leading experts in this field. If you are looking for practical ways to create interactive visualizations on the web, you should look at the works of [Mike Bostock](http://bost.ocks.org/mike/) and [Scott Murray](http://alignedleft.com) that use [D3](http://d3js.org)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting in Python\n",
    "\n",
    "A number of plotting packages for Python have been released in the last few years. Currently, we like and recommend `matplotlib`. `Matplotlib` was created in 2003 and is the oldest Python plotting library that has remained under active development.\n",
    "\n",
    "However, that doesn't mean that it's always the best for all purposes or that it will remain our recommendation for ever. Among biologists, the `seaborn` library has become popular. If you are able to make your data publicly available, there is a service called `plot.ly` with a Python library.\n",
    "\n",
    "A problem with `matplotlib` is that its documentation is not particularly good. Typically, programmers copy the source code of visualizations they like and modify them. There are also the lots and lots of **stackoverflow** answers concerning `matplotlib`. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Matplotlib` is completely costumizable \n",
    "\n",
    "Unfortunately, the defaults are not particularly good. \n",
    "\n",
    "Let's check what `matplotlib` gives us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:07:00.552028Z",
     "start_time": "2019-07-01T14:06:59.675710Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:07:11.061297Z",
     "start_time": "2019-07-01T14:07:11.057872Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:07:18.570283Z",
     "start_time": "2019-07-01T14:07:18.372265Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10a5940f0>]"
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     "execution_count": 4,
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot([1, 2, 3, 4], [1, 2, 3, 4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**There! That's a plot.** A rather crappy plot, though.\n",
    "\n",
    "To create a costumizable graph, we create a `figure` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-06-23T21:24:32.695507Z",
     "start_time": "2019-06-23T21:24:32.688011Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can actually pass arguments to the figure, like changing its size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:08:58.402175Z",
     "start_time": "2019-07-01T14:08:58.394214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 720x504 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 7))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those dimensions are actually in inches, the first one is the width and the other is the height.\n",
    "\n",
    "Now we have to create something to plot, that something is a set of `axes` inside a `subplot`. `subplot`s let us have multiple graphs inside a single plot. I'll show you it first though."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:09:00.955239Z",
     "start_time": "2019-07-01T14:09:00.938670Z"
    }
   },
   "outputs": [],
   "source": [
    "ax = fig.add_subplot(1, 1, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then plot it (I'm putting all of the commands together here)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:09:32.822833Z",
     "start_time": "2019-07-01T14:09:32.398107Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a3636a0>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 7))\n",
    "ax = fig.add_subplot(3, 1, 1)\n",
    "ax.plot([1, 2, 3, 4], [1, 2, 3, 4], color = 'steelblue', marker = 'o')\n",
    "ax.plot([7, 8 , 9], [7, 8 , 9], color= 'orange', marker='^')\n",
    "ax = fig.add_subplot(3, 1, 2)\n",
    "ax.plot([7, 8 , 9], [7, 8 , 9], color= 'orange', marker='^')\n",
    "ax = fig.add_subplot(3, 1, 3)\n",
    "ax.plot([11, 12, 13], [11, 12, 13], color ='red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we specify subplots the first number is the number of rows of plots. The second number is the number of columns of plots. The third number is the specific plot that you wish to populate. This number goes from 1 to the maximum plot number (num_columns * num_rows). Hopefully this image will make it more clear\n",
    "\n",
    "<img src='../Data/www/images/matplotlib_subplots.png'></img>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now let me demonstrate it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:10:05.594600Z",
     "start_time": "2019-07-01T14:10:05.302495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a5b6b70>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5GXjZqlDKXsWlldw+J5vMHYd5+Po4br+oi87jEKT0Skr1E3uPnmRcaia7Dp3ghRH9GNqvg92RlI20INSPNuw7RnJaJicrq8kYn8SQrnqcOdhpQSgAvtl6kDvm5hARFsKSOwYT166Z3ZGUD9CCULyZV8Cflqyha+sI0scn0j6qsd2RlI/QgghixhhmfLmNf364mcFdWvHq2AE0Cw+1O5byIVoQQaraYZi2fB1zV+7mpr7n8MzwPoSF6DUO6qe0IILQyYpq7lmYx8cbDjD5ki48cE0cDfQaB1ULLYggc/hEBRMysli15yh/u6kn44bE2h1J+TAtiCCy69AJktOy2Hf0JDNGx3Ntr/Z2R1I+TgsiSKzec5QJGVlUOQzzJg4kIbal3ZGUH9CCCAKfbypk6rxcWkU0Ij0liW5tIuyOpPyEFkSAW5S1m4ffXEdcu0jSUhJpExludyTlR7QgApQxhuc/2coLn27l4u6tmT46nogw/edWZ0d/YgJQZbWD/3lzLYuzCxg2oCP/uKU3oTrjtPoFtCACzInyKqbOy+XLLUXcc3k3/nBVd32rtvrFtCACSFFJOePTs1i/r5i/39ybUQM72R1J+TktiACxreg4yWmZHCyp4LWxCVxxQVu7I6kAoAURAHJ2HWFiRhYNRFgwaRD9YprbHUkFCC0IP/fR+h+4Z0Ee7aPCSU9JIja6qd2RVADRgvBjs7/byV+Xr6d3x+akjkugVUSY3ZFUgLFq8V4RkRdFJF9E1ohIvGfiKqiZjv6pDzbx6Nvruez8Niy4faCWg/IIqxbvvQ44z/kxEJjh/K+yWEWVgz+/sZq3Vu1j1MBOPHZTT11VW3mMVYv3DgVmO9fCWCkizUWkvfN7lUWOlVVyx5wcVmw7xJ+uOZ+pl3bVaxyUR53VMYgzLN7bAdhzyucFzq9pQVjkh+IyktMyyS88zr+G9+XWAR3tjqSCgNsFUcfivbX9GfvZyloiMgmYBNCpk17E464tB0oYl5rJsZOVpCYn6qraymssWbyXmmcMMad83hHYd/ogXd377K3cfohbZ6ygymFYfMdgLQflVe6cxahz8V5gOTDWeTZjEFCsxx/q753V+xg7K5M2kWG8OXUIPc+JsjuSCjJWLd77PnA9kA+UAinWRw0ur3+9nSfe20hibAteG5tA8yaN7I6kgpBVi/ca4E6rQgUzh8PwxHsbSf12B9f1ase/f9eP8FCdjl7ZQ6+k9CFlldX8cfEq3l/7A8lDYnnkhh401OnolY20IHzE0dIKJs3OIXPnYf7n+guYeFFnvcZB2U4LwgcUHCklOS2L3YdKeXFkf27qe47dkZQCtCBst35fMSlpWZysrCZjfBKDu7ayO5JSP9KCsNHXW4uYMjeXyPAQ3rhjCOe3i7Q7klI/oQVhk2W5Bfz5jTV0axNBWkoi7aMa2x1JqZ/RgvAyYwzTv9jGMx9tZnCXVrw6dgDNwkPtjqVUrbQgvKiq2sG05euZ9/1ubup7Ds8M70NYiF7joHyXFoSXnKyo5u4FuXyysZDJl3ThgWviaKDXOCgfpwXhBYeOlzMhI5vVBUf52009GTck1u5ISrlFC8LDdh06wbjUTPYXlzFj9ACu7dXO7khKuU0LwoNW7znK+PQsqo1h/u0DGXBuS7sjKXVWtCA85LNNB7hzXh6tIhqRMT6Jrq0j7I6k1FnTgvCAhZm7+Z+31nFB+0hSkxNpExludySlfhEtCAsZY/j3J1t58dOtXNy9NdNHxxMRprtY+S/96bVIZbWDh5etZUlOAcMHdOTvt/QmVKejV35OC8ICJ8qrmDovly+3FHHPFefxhyvP07dqq4CgBVFPhSVljE/PYuP+Ev5xS29GJuls3SpwaEHUw7ai44xLzeTQ8QpeGzuAy+Pa2h1JKUtpQfxCObsOMyEjm4YiLJw0iL4xze2OpJTltCB+gQ/X/cC9C/NoHxVOxvgkzm3V1O5ISnmEO+tipIpIoYisc3H/pSJSLCKrnB+PWh/Td8z+bidT5uVwQftmLJ0yRMtBBTR3nkGkAy8Ds88w5mtjzA2WJPJRDofh6Y828eqX27nygja8NDKexo30rdoqsLmzLsZXzkV7g1Z5VTV/fmMNb6/ax6iBnXjspp6E6DUOKghYdQxisIispmY9zvuNMestelzbHSurZPLsHL7bfog/XXM+Uy/tqtc4qKBhRUHkAucaY46LyPXAW8B5tQ30t9W99xefJCUti/zC4zz3277cEt/R7khKeVW9nycbY44ZY447b78PhIpItIuxfrO69+YfSrhl+goKjpwkLSVRy0EFpXoXhIi0c64AjogkOR/zUH0f107fbTvEsFdWUO0wLJo8iIvO8+0yU8pT6nyJISILgEuBaBEpAKYBofDjyt7DgCkiUgWcBEY4F/P1S8tX7+P+xavp1KoJ6SmJdGzRxO5IStnGnbMYI+u4/2VqToP6NWMMr3+9gyff30hSbEtmjh1A8yaN7I6llK30Skqg2mF44r0NpH27k+t7t+O53/YjPFSvcVAq6AuirLKaPyxaxQfrfiDlwlge+XUPnY5eKaegLoijpRXcPjubrJ1H+MuvL2DiRV3sjqSUTwnagig4UkpyWha7D5Xy0sj+3Nj3HLsjKeVzgrIg1u8rJjkti7LKajLGJzG4ayu7Iynlk4KuIL7eWsQdc3Jo1jiUpVOG0L1tpN2RlPJZQVUQS3MKeGDpGrq1iSA9JYl2UTodvVJnEhQFYYxh+hfbeOajzQzp2opXxgygWXio3bGU8nkBXxBV1Q4eXb6e+d/vZmi/c3hmWF8ahehbtZVyR0AXRGlFFfcsyOOTjYXccUlX/nzN+XqNg1JnIWAL4tDxcsZnZLOm4CiPDe3J2MGxdkdSyu8EZEHsOnSCcamZ7C8u45XbBnBNz3Z2R1LKLwVcQazac5QJ6VlUG8P82wcy4NyWdkdSym8FVEF8uvEAd83PIzqyEekpSXRtHWF3JKX8WsAUxILM3fzPm2vpeU4Us5ITaBOp1zgoVV9+XxDGGP798RZe/CyfS7q3ZvroeJqG+f3/llI+wa9/kyqrHTy0bC1v5BQwfEBH/n5Lb0J1OnqlLOO3BXG8vIqp83L5aksR915xHr+/8jydjl4pi/llQRSWlDE+PYuN+0t46pbejEjy/Sn0lfJHflcQ24qOMy41k0PHK3h9bAKXxbWxO5JSAcuvCiJ752Emzs6moQgLJw2ib0xzuyMpFdCsWN1bRORFEckXkTUiEm99TPhw3Q+Mfv17WjRpxLKpQ7QclPICdw75pwPXnuH+66hZau88apbVm1H/WD+VsWInU+blcEH7Zrxxx2DObdXU6k0opWphxereQ4HZzsVyVopIcxFpb4zZX99wDofh6Y828eqX27nygra8NLI/jRvpdPRKeYsVFw10APac8nmB82v1NvPr7bz65XZGD+zEK7fFazko5WVWHKSs7eKDWpfeO9vVvUcN7ERU41BGJMboNQ5K2cCKZxAFQMwpn3cE9tU28GxX924WHsrIpE5aDkrZxIqCWA6MdZ7NGAQUW3H8QSllPytW934fuB7IB0qBFE+FVUp5lxWrexvgTssSKaV8hr71USnlkhaEUsolLQillEtaEEopl6TmGKMNGxYpAna5MTQaOOjhOO7QHD+lOXwrA7if41xjTN0XImFjQbhLRLKNMQmaQ3P4ag5fyOCpHPoSQynlkhaEUsolfyiImXYHcNIcP6U5/p8vZAAP5PD5YxBKKfv4wzMIpZRNfKYgfGHuSzcyXCoixSKyyvnxqNUZnNuJEZHPRWSjiKwXkXtrGeON/eFODo/uExEJF5FMEVntzPC3WsaEicgi5774vo4Z0DyZI1lEik7ZFxOtznHKthqKSJ6IvFvLfdbtD2OMT3wAFwPxwDoX918PfEDNBDWDgO9tyHAp8K4X9kV7IN55OxLYAvSwYX+4k8Oj+8T5/xfhvB0KfA8MOm3MVOAV5+0RwCKbciQDL3v658O5rT8C82vb91buD595BmGM+Qo4fIYhP859aYxZCTQXkfZezuAVxpj9xphc5+0SYCM/n8bPG/vDnRwe5fz/O+78NNT5cfqBs6FAhvP2G8AVYvEsQ27m8AoR6Qj8GnjdxRDL9ofPFIQbPDb35Vka7Hya+YGI9PT0xpxPD/tT8xfrVF7dH2fIAR7eJ86n06uAQuBjY4zLfWGMqQKKgVY25AC41fmS7w0Rianlfis8D/wZcLi437L94U8F4fbclx6US81lqn2Bl4C3PLkxEYkAlgK/N8YcO/3uWr7FI/ujjhwe3yfGmGpjTD9qpjNMEpFep0es7dtsyPEOEGuM6QN8wv//FbeMiNwAFBpjcs40rJav/aL94U8F4fbcl55ijDn2f08zjTHvA6EiEu2JbYlIKDW/lPOMMctqGeKV/VFXDm/uE2PMUeALfr5Oy4/7QkRCgCg8+FLRVQ5jzCFjTLnz09eAAR7Y/IXATSKyE1gIXC4ic08bY9n+8KeCsH3uSxFp93+v5UQkiZr9d8gD2xFgFrDRGPOci2Ee3x/u5PD0PhGR1iLS3Hm7MXAlsOm0YcuBcc7bw4DPjPMInTdznHYM6CZqjtlYyhjzkDGmozEmlpoDkJ8ZY247bZhl+8Nn1uYUH5j70o0Mw4ApIlIFnARGWP2D6HQhMAZY63zNC/Aw0OmULN6YC9SdHJ7eJ+2BDBFpSE35LDbGvCsijwHZxpjl1JTYHBHJp+Yv5QgLt382Oe4RkZuAKmeOZA/kqJWn9odeSamUcsmfXmIopbxMC0Ip5ZIWhFLKJS0IpZRLWhBKKZe0IJRSLmlBKKVc0oJQSrmkBaGUckkLQinlkhaEUsolLQillEtaEEopl7QglFIuaUEopVzSglBKuaQFoZRySQtCKeWSFoRSyiUtCKWUS1oQSimXtCCUUi5pQSilXLJt4Zzo6GgTGxtr1+aVClo5OTkHjTGt3RlrW0HExsaSnZ1t1+aVCloissvdsfoSQynlkhaEUsolLQillEtaEEopl+osCBGJEZHPRWSjiKwXkXtrGSMi8qKI5IvIGhGJ90xcpZQ3uXMWowq4zxiTKyKRQI6IfGyM2XDKmOuA85wfA4EZzv8qpfxYnc8gjDH7jTG5ztslwEagw2nDhgKzTY2VQHMRaW95WqVUrb7bdojfL8yjqtph6eOe1TEIEYkF+gPfn3ZXB2DPKZ8X8PMSQUQmiUi2iGQXFRWdXVKlVK2Wr97HuNRM1u87xtGTlZY+ttsFISIRwFLg98aYY6ffXcu3mJ99wZiZxpgEY0xC69ZuXcillHLBGMPMr7Zxz4I8+ndqzht3DCE6IszSbbh1JaWIhFJTDvOMMctqGVIAxJzyeUdgX/3jKaVqU+0wPP7uBtJX7OTXfdrz3G/7EhbS0PLtuHMWQ4BZwEZjzHMuhi0HxjrPZgwCio0x+y3MqZRyKqus5q75uaSv2MnEX3XmpRH9PVIO4N4ziAuBMcBaEVnl/NrDQCcAY8wrwPvA9UA+UAqkWB9VKXW0tIKJGdnk7D7CIzf0YMKvOnt0e3UWhDHmG2o/xnDqGAPcaVUopdTP7TlcSnJaJnuOnOTlkfH8uo/nTxTa9m5OpZT71u0tJiU9i/LKauZOGEhS55Ze2a4WhFI+7qstRUyZm0PzJo2YP3Eg57WN9Nq2tSCU8mFv5BTw4NI1nNc2kvSURNo2C/fq9rUglPJBxhj+83k+z/53C7/qFs2M2+KJDA/1eg4tCKV8TFW1g0eXr2f+97u5pX8Hnrq1D41C7HnjtRaEUj6ktKKKexbk8cnGQqZe2pU/XXM+NZci2UMLQikfcfB4ORMysllbcJTHf9OLMYPOtTuSFoRSvmDnwROMS8vkwLEyXrltAFf3bGd3JEALQinbrdpzlAnpWRhg/u2DiO/Uwu5IP9KCUMpGn248wJ3zc2kTGU7G+CQ6Rze1O9JPaEEoZZP53+/mL2+tpVeHKGaNS6R1pLVv1baCFoRSXmaM4bmPt/DSZ/lcdn5r/jM6niaNfPNX0TdTKRWgKqsdPLh0LUtzCxiRGMMTv+lFSEPfnVxeC0IpLzleXsWUuTl8vfUgf7iyO/dc0c3WaxzcoQWhlBcUHisjJT2LTT+U8M9hffhtQkzd3+QDtCCU8rD8wuOMS80E/k+xAAAXQUlEQVTkSGkFs8YlcOn5beyO5DYtCKU8KHvnYSbOziakQQMWTRpM745Rdkc6K1oQSnnIh+v2c8/CVXRs3piM8UnEtGxid6SzpgWhlAekf7uDv727gf4xzXl9XCItmzayO9IvogWhlIUcDsPTH27i1a+2c3WPtrw4sj/hoZ6ZcdobtCCUskh5VTV/WrKG5av3MXbwuUy7sScNG/j2acy6aEEoZYHik5VMnpPNyu2HeeDaOO64pIvPX+PgDi0Ipepp39GTpKRlsf3gcZ7/XT9+0/9ny9L6LS0Ipeph0w/HSE7N4kR5FekpSVzYLdruSJbSglDqF1qx7SCTZ+fQJKwhi+8YzAXtm9kdyXJaEEr9Am+v2sv9S1YT26op6eOT6NC8sd2RPEILQqmzYIxh5lfb+ccHmxjYuSUzxyQQ1cT709F7izure6eKSKGIrHNxf5SIvCMiq0VkvYjowr0qIFU7DH97ZwP/+GATN/Rpz+wJSQFdDuBGQQDpwLVnuP9OYIMxpi9wKfAvEfHPy8aUcqGsspo75+WSvmInt1/UmRdH9CcsxH8vgHKXO6t7fyUisWcaAkRKzUnfCOAwUGVJOqV8wJETFdw+O5uc3Ud49IYejP9VZ7sjeY0VxyBeBpYD+4BI4HfGGEdtA0VkEjAJoFOnThZsWinP2nO4lHFpmRQcOcl/RsVzfe/2dkfyKivmuroGWAWcA/QDXhaRWs/3GGNmGmMSjDEJrVu3tmDTSnnOur3F3DJjBYeOVzB3wsCgKwewpiBSgGWmRj6wA4iz4HGVss2XW4r43avf0ahhA5ZOGUxS55Z2R7KFFQWxG7gCQETaAucD2y14XKVssSR7DxPSs+jUqinLpg6hW5tIuyPZps5jECKygJqzE9EiUgBMA0IBjDGvAI8D6SKyFhDgAWPMQY8lVspDjDG8/Fk+//p4CxedF8300fFEhgf2acy6uHMWY2Qd9+8DrrYskVI2qKp28Mjb61mQuZtb4jvw1C19aBTiu9PRe4teSamCXmlFFXfNz+OzTYXceVlX7r/6/IB4q7YVtCBUUDt4vJwJ6Vms3VvME7/pxW2DzrU7kk/RglBBa+fBE4xLy+TAsTJeHZPAVT3a2h3J52hBqKCUt/sIEzKyAZh/+yDiO7WwOZFv0oJQQeeTDQe4a0EubSLDyRifROfopnZH8llaECqozPt+F4+8tY5eHaKYNS6R1pFhdkfyaVoQKigYY/jXf7fw8uf5XHZ+a/4zOp4mjfTHvy66h1TAq6x28ODStSzNLWBEYgxP/KYXIQ31Ggd3aEGogHa8vIopc3P4eutB/nBld+65opte43AWtCBUwCo8VkZyWhabD5Twz2F9+G1CjN2R/I4WhApI+YUljEvN4khpBanJiVzSXacX+CW0IFTAydp5mIkZ2YQ2bMDiyYPp1SHK7kh+SwtCBZQP1u7n3kWr6NiiMRkpScS0bGJ3JL+mBaECRtq3O3js3Q3Ed2rB62MTaNFU506uLy0I5fccDsNTH25i5lfbuaZnW14Y0Z/w0MCfcdobtCCUXyuvqub+JWt4Z/U+xg0+l0dv7EnDBnoa0ypaEMpvFZ+sZNLsbL7fcZgHr4tj8sVd9BoHi2lBKL+07+hJktMy2XHwBC+M6MfQfh3sjhSQtCCU39n0wzGSU7M4UV5FRkoSQ7pF2x0pYGlBKL+yIv8gk+fk0DQshMV3DOaC9rUuwaIsogWh/Mbbq/Zy/5LVdI5uSnpKEuc0b2x3pICnBaF8njGGV7/azlMfbGJg55bMHJtAVOPgno7eW7QglE+rdhgee2c9Gd/t4oY+7fnXb/sGxaravkILQvmssspq7l2Yx0frD3D7RZ156LoLaKDXOHiVFoTySUdOVDBxdja5u4/w6A09GP+rznZHCkpaEMrn7Dlcyri0TAqOnOQ/o+KDclVtX6EFoXzKur3FJKdlUVntYO6EgUG7qravqHNiPhFJFZFCEVl3hjGXisgqEVkvIl9aG1EFiy+3FPG7V78jLKQBS6cM1nLwAe7M3JkOXOvqThFpDkwHbjLG9ASGWxNNBZMl2XsYn57Fua2a8ubUIXRrE2l3JIV7q3t/JSKxZxgyClhmjNntHF9oTTQVDIwxvPRZPs99vIWLzotm+uh4IsP1GgdfYcXc392BFiLyhYjkiMhYVwNFZJKIZItIdlFRkQWbVv6sqtrBw2+u5bmPt3BLfAdSkxO1HHyMFQcpQ4ABwBVAY+A7EVlpjNly+kBjzExgJkBCQoKxYNvKT5VWVHHX/Dw+21TIXZd1476ru+tbtX2QFQVRABw0xpwATojIV0Bf4GcFoRTAwePljE/PYt3eYp68uRejB55rdyTlghUvMd4GLhKREBFpAgwENlrwuCoA7Th4glumr2DLgRJmjknQcvBxdT6DEJEFwKVAtIgUANOAUABjzCvGmI0i8iGwBnAArxtjXJ4SVcErb/cRJmRkA7Dg9kH079TC5kSqLu6cxRjpxphngGcsSaQC0scbDnD3glzaNgsnPSWJztFN7Y6k3KBXUiqPm7tyF4++vY7eHaKYlZxIdESY3ZGUm7QglMcYY3j2v5v5z+fbuDyuDS+P6k+TRvoj50/0X0t5REWVgweXrWFZ7l5GJsXw+NBehDS04pi48iYtCGW5krJKps7L5eutB/njVd25+/Jueo2Dn9KCUJY6cKyMlLQsNh8o4Z/D+vDbhBi7I6l60IJQlskvLGFcahZHSitITU7kku6t7Y6k6kkLQlkia+dhJmZkE9qwAYsnD6ZXhyi7IykLaEGoevtg7X7uXbSKji0ak5GSREzLJnZHUhbRglD1kvrNDh5/bwPxnVrw+tgEWjRtZHckZSEtCPWLOByGv7+/kde/2cE1Pdvywoj+hIfqdPSBRgtCnbXyqmruW7yad9fsJ3lILI/c0IOGOh19QNKCUGeluLSSSXOy+X7HYR66Lo5JF3fRaxwCmBaEctu+oydJTstkx8ETvDCiH0P7dbA7kvIwLQjllo37j5GclklpeTUZ45MY0jXa7kjKC7QgVJ1W5B9k8pwcmoaFsGTKYOLaNbM7kvISLQh1Rm+v2sv9S1bTObop6SlJnNO8sd2RlBdpQahaGWN45cvtPP3hJgZ1acmrYxKIaqwzTgcbLQj1M9UOw9/eWc/s73ZxY99zeHZ4H8JC9BqHYKQFoX6irLKaexfm8dH6A0y6uAsPXhtHA73GIWhpQagfHTlRwYSMLPL2HGXajT1IubCz3ZGUzbQgFAB7DpcyLjWTgqMnmT4qnut6t7c7kvIBWhCKtQXFpKRnUVntYN7EgSTG6qraqoYWRJD7YnMhU+fl0qJJIxZOGqiraquf0IIIYouz9vDQm2s5v20k6SmJtGkWbnck5WO0IIKQMYYXPt3K859s5aLzopk+Ol5X1Va10oIIMlXVDv7y1joWZu3hlvgOPH1rH0J1OnrlghZEEDlRXsVd83P5fHMRd13Wjfuu7q5v1VZnVOefDhFJFZFCETnjgrwikigi1SIyzLp4yipFJeWMfG0lX24p4smbe3H/NedrOag6ufPcMh249kwDRKQh8DTwkQWZlMW2Fx3n1hkr2HKghJljEhg98Fy7Iyk/4c7q3l+JSGwdw+4GlgKJFmRSFsrdfYQJ6VmICAsnDaZfTHO7Iyk/Uu+jUyLSAbgZeMWNsZNEJFtEsouKiuq7aVWHjzccYNRrK2nWOJRlU4ZoOaizZsXh6+eBB4wx1XUNNMbMNMYkGGMSWrfWVZc8ac7KXUyek835bSNZOmUIsdFN7Y6k/JAVZzESgIXOA17RwPUiUmWMecuCx1ZnyRjDMx9tZvoX27girg0vjepPk0Z6skr9MvX+yTHG/PiWPxFJB97VcrBHRZWDB5euYVneXkYmdeLxoT0J0WscVD3UWRAisgC4FIgWkQJgGhAKYIyp87iD8o6SskqmzM3lm/yD3HdVd+66vJuexlT15s5ZjJHuPpgxJrleadQvcuBYGclpWWw9UMIzw/owPCHG7kgqQOiLUz+39UAJyWlZHC2tYFZyIpd014O/yjpaEH4sc8dhJmZkERbakEWTB9OrQ5TdkVSA0YLwU++t2c8fFq+iY4vGZKQkEdOyid2RVADSgvBDs77ZwRPvbSC+UwteH5tAi6aN7I6kApQWhB9xOAxPvr+RWd/s4JqebXlhRH/CQ3U6euU5WhB+oqyymvuWrOa9NftJHhLLIzf0oKFOR688TAvCDxSXVnL7nGwydxzmoevimHRxF73GQXmFFoSP23v0JMmpmew8dIIXRvRjaL8OdkdSQUQLwodt2HeMlPRMSsuryRifxJCu0XZHUkFGC8JHfZt/kMlzcogIC2HJlMHEtWtmdyQVhLQgfNBbeXv50xur6RIdQfr4RNpHNbY7kgpSWhA+xBjDjC+38c8PNzOoS0teHZNAVGOdjl7ZRwvCR1Q7DH9dvp45K3dxY99zeHZ4H8JC9BoHZS8tCB9QVlnNPQvy+O+GA0y+uAsPXBtHA73GQfkALQibHT5RwcSMLPL2HOWvN/Yg+cLOdX+TUl6iBWGj3YdKSU7LpODoSaaPiue63u3tjqTUT2hB2GRtQTEp6ZlUVhvmTxxIQmxLuyMp9TNaEDb4fHMhd87LpUWTRiyclES3NhF2R1KqVloQXrYoazcPv7mOuHaRpCUn0qZZuN2RlHJJC8JLjDE8/8lWXvh0KxedF82M2wYQEaa7X/k2/Qn1gspqB395cx2Lsvdwa3xHnrq1N6E6Hb3yA1oQHnaivIo75+fyxeYi7r68G3+8qru+VVv5DS0IDyoqKWd8ehbr9xXz5M29dFVt5Xe0IDxke9FxxqVlUlRSzswxCVzZo63dkZQ6a1oQHpCz6wgTM7IQERZOGqyraiu/pQVhsf+u/4G7F+TRLiqcjJQkXVVb+TUtCAvNWbmLaW+vo3eHKGYlJxIdEWZ3JKXqpc5zbSKSKiKFIrLOxf2jRWSN82OFiPS1PqZvM8bw9IebeOStdVx2fhsWTBqk5aACgjsn49OBa89w/w7gEmNMH+BxYKYFufxGRZWDPy5ezYwvtjEyqROvjhlAk0b6xEwFBndW9/5KRGLPcP+KUz5dCXSsfyz/UFJWyZS5uXyTf5D7r+7OnZd102scVECx+k/dBOADV3eKyCRgEkCnTp0s3rR3HThWxrjUTPILj/Ps8L4MGxA0vaiCiGUFISKXUVMQv3I1xhgzE+dLkISEBGPVtr1t64ESxqVmUnyyktTkRC7u3truSEp5hCUFISJ9gNeB64wxh6x4TF/1/fZD3D47m7DQhiyaPJheHaLsjqSUx9S7IESkE7AMGGOM2VL/SL7r3TX7+OOi1cS0bEx6ShIxLZvYHUkpj6qzIERkAXApEC0iBcA0IBTAGPMK8CjQCpjuPEBXZYxJ8FRgu7z+9XaeeG8jCee24PVxCTRv0sjuSEp5nDtnMUbWcf9EYKJliXyMw2F44r2NpH67g2t7tuP5Ef0ID9Xp6FVw0BP2Z1BWWc19i1fz3tr9JA+J5ZEbetBQp6NXQUQLwoXi0kpun5NN5o7DPHx9HLdf1EWvcVBBRwuiFnuPnmRcaia7Dp3ghRH9GNqvg92RlLKFFsRpNuw7RnJaJicrq8kYn8SQrtF2R1LKNloQp/hm60HumJtDRFgIS+4YTFy7ZnZHUspWWhBOb+YV8Kcla+jaOoL08Ym0j2psdySlbBf0BWGMYcaX2/jnh5sZ1KUlr45JIKpxqN2xlPIJQV0Q1Q7DtOXrmLtyNzf2PYdnh/chLESvcVDq/wRtQZysqOaehXl8vOEAky/uwgPXxtFAr3FQ6ieCsiAOn6hgQkYWq/Yc5a839iD5ws52R1LKJwVdQew+VMq4tEz2HT3JjNHxXNurvd2RlPJZQVUQawqOMj49iyqHYd7EgSTEtrQ7klI+LWgK4vNNhUydl0uriEakpyTRrU2E3ZGU8nlBURCLsnbz8JvriGsXSVpKIm0iw+2OpJRfCOiCMMbw/CdbeeHTrVzcvTXTR8cTERbQ/8tKWSpgf1sqqx38z5trWZxdwLABHfnHLb0JbejOLP9Kqf8TkAVxoryKqfNy+XJLEfdc3o0/XNVd36qt1C8QcAVRVFLO+PQs1u8r5u8392bUQP+eXl8pOwVUQWwrOk5yWiYHSyp4bWwCV1zQ1u5ISvm1gCmInF1HmJiRRQMRFkwaRL+Y5nZHUsrvBURBfLT+B+5ZkEf7qHDSU5KIjW5qdySlAoLfF8Sc73Yybfl6endsTuq4BFrpqtpKWcZvC8LhMPzzo8288uU2rohrw0uj+uuq2kpZzC9/oyqqHPz5jdW8tWofowZ24rGbehKi1zgoZTm/K4hjZZVMmZvDt/mHuP/q7tx5WTe9xkEpD/GrgvihuIzktEzyC4/z7PC+DBvQ0e5ISgU0vymILQdKSE7NpPhkJanJiVzcvbXdkZQKeHW+cBeRVBEpFJF1Lu4XEXlRRPJFZI2IxFsdcuX2Q9w6YwWVDsOiyYO1HJTyEneO7KUD157h/uuA85wfk4AZ9Y/1/95ZvY+xszJpExnGsilD6NUhysqHV0qdQZ0FYYz5Cjh8hiFDgdmmxkqguYhYMo/b4qw93L0gj74xUSydMoSYlk2seFillJusODfYAdhzyucFzq/9jIhMEpFsEckuKiqq84GTOrdkZFIMcyYMpHmTRhZEVUqdDSsKorZzjKa2gcaYmcaYBGNMQuvWdR9HiI1uyj9u6UN4qK5VoZQdrCiIAiDmlM87AvsseFyllM2sKIjlwFjn2YxBQLExZr8Fj6uUslmd10GIyALgUiBaRAqAaUAogDHmFeB94HogHygFUjwVVinlXXUWhDFmZB33G+BOyxIppXyGvsNJKeWSFoRSyiUtCKWUS1JzCMGGDYsUAbvcGBoNHPRwnPrSjPXn6/nA9zO6m+9cY4xbb2iyrSDcJSLZxpgEu3OciWasP1/PB76f0RP59CWGUsolLQillEv+UBAz7Q7gBs1Yf76eD3w/o+X5fP4YhFLKPv7wDEIpZRMtCKWUSz5TEL4w92U984125lojIitEpK8387mT8ZRxiSJSLSLDvJXNud0684nIpSKySkTWi8iX3szn3H5d/85RIvKOiKx2ZvTqmxNFJEZEPheRjc7t31vLGOt+V4wxPvEBXAzEA+tc3H898AE1E9QMAr73sXxDgBbO29d5O587GZ1jGgKfUfMu3GG+lA9oDmwAOjk/b+Nr+xB4GHjaebs1NdMxNvJivvZAvPN2JLAF6HHaGMt+V3zmGYSxce5Ld9SVzxizwhhzxPnpSmomzvEqN/YhwN3AUqDQ84l+yo18o4BlxpjdzvG+mNEAkVKzWlOEc2yVN7IBGGP2G2NynbdLgI38fIpHy35XfKYg3OD23Jc+YAI1De5TRKQDcDPwit1ZXOgOtBCRL0QkR0TG2h2oFi8DF1Aza9pa4F5jjMOOICISC/QHvj/tLst+V/xm4RzOYu5LO4nIZdQUxK/szlKL54EHjDHVPrpcYQgwALgCaAx8JyIrjTFb7I31E9cAq4DLga7AxyLytTHmmDdDiEgENc8Ef1/Lti37XfGngvD5uS9FpA/wOnCdMeaQ3XlqkQAsdJZDNHC9iFQZY96yN9aPCoCDxpgTwAkR+QroS83rbF+RAjxlal7s54vIDiAOyPRWABEJpaYc5hljltUyxLLfFX96ieHTc1+KSCdgGTDGx/7i/cgY09kYE2uMiQXeAKb6UDkAvA1cJCIhItIEGEjNa2xfspuaZziISFvgfGC7tzbuPPYxC9hojHnOxTDLfld85hmEr8996Ua+R4FWwHTnX+gq4+V3/rmR0VZ15TPGbBSRD4E1gAN43RhzxlO23s4IPA6ki8haap7KP2CM8eZbwC8ExgBrRWSV82sPA51OyWjZ74peaq2UcsmfXmIopbxMC0Ip5ZIWhFLKJS0IpZRLWhBKKZe0IJRSLmlBKKVc+l/vsRLjyUNVXAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 288x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [1, 2, 3, 4]\n",
    "\n",
    "#Start the figure\n",
    "fig = plt.figure(figsize = (4, 8))\n",
    "\n",
    "#Create the first axis\n",
    "ax1 = fig.add_subplot(2, 1, 1)\n",
    "ax1.plot(data, data)\n",
    "\n",
    "#Create the second axis\n",
    "ax2 = fig.add_subplot(2, 1, 2)\n",
    "ax2.plot(data[:2], data[:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's just import a host of libraries that we will use throughout this exercise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:10:16.171376Z",
     "start_time": "2019-07-01T14:10:16.167489Z"
    }
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import random as rand \n",
    "import matplotlib as mpl\n",
    "import matplotlib.cm as cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And here I will create some fake data that I will use throughout to show some basic plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:10:20.482982Z",
     "start_time": "2019-07-01T14:10:20.476512Z"
    }
   },
   "outputs": [],
   "source": [
    "x = range(5,100,5)\n",
    "y = [i**2 for i in x]\n",
    "z = [100*i for i in x]\n",
    "rv1 = [rand.random() for i in range(1000)]\n",
    "rv2 = [rand.random() for i in range(10000)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the `x` and `y` data as a simple graph like I've already shown you."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:10:26.896223Z",
     "start_time": "2019-07-01T14:10:26.749009Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11a647208>]"
      ]
     },
     "execution_count": 12,
     "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": [
    "#Here, let me create the figure for you\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "ax.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An important part of graphing is labelling axes and providing legends. It's actually pretty simple. \n",
    "\n",
    "Let's add a little bit to our simple graph."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:11:04.548474Z",
     "start_time": "2019-07-01T14:11:04.374358Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11a7ee518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\")\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "ax.set_xlabel(\"x\")\n",
    "ax.set_ylabel(\"f(x)\")\n",
    "\n",
    "# Display legend\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Ok, that's the basics. But how do we make it visually appealing?\n",
    "\n",
    "For that we'll need to start playing with the options. Let's start by changing the font size for the labels and the color of the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:12:17.075875Z",
     "start_time": "2019-07-01T14:12:16.897979Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11a856710>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue')\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"x\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"f(x)\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or rather simply use LaTeX (if you know what it is) to write text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:12:49.951538Z",
     "start_time": "2019-07-01T14:12:49.750218Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10a594518>"
      ]
     },
     "execution_count": 16,
     "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": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue')\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we should really move the legend box, it's blocking the line! And get rid of that silly legend box.\n",
    "\n",
    "Let's make the line **thicker** too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:13:04.155645Z",
     "start_time": "2019-07-01T14:13:03.949644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x119fd9e80>"
      ]
     },
     "execution_count": 17,
     "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": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue', linewidth = 3)\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend(loc='best', frameon=False, fontsize = font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If this was real data, we would maybe want to show the individual datapoints too. To put points on the line we need to add a marker. \n",
    "\n",
    "Matplotlib has a number of different marker styles, but I'll just use a circle since that is the most common.\n",
    "\n",
    "We should also add some text, let says that this is `Figure (A)` of a multi-part figure. We'll add `(A)` to the plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:13:08.528327Z",
     "start_time": "2019-07-01T14:13:08.343336Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(90, 500, '(A)')"
      ]
     },
     "execution_count": 18,
     "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": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue', linewidth = 3, marker = 'o', markersize = 10)\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend(loc='best', frameon=False, fontsize = font_size, markerscale = 1.2)\n",
    "\n",
    "#Adding a label\n",
    "ax.text(90, 500, \"(A)\", fontsize = 1.2 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For a plot like this, we might also not want to have the full frame around the plot. We can remove that by changing the spines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:13:23.716447Z",
     "start_time": "2019-07-01T14:13:23.533526Z"
    }
   },
   "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": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue', linewidth = 3, marker = 'o', markersize = 10)\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend(loc='best', frameon=False, fontsize = font_size, markerscale = 1.2)\n",
    "\n",
    "#Adding a label\n",
    "ax.text(90, 500, \"(A)\", fontsize = 1.2 * font_size)\n",
    "\n",
    "#Turn off the spines\n",
    "for axis in ['bottom','left']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    ax.spines[axis].set_position((\"axes\", -0.1))\n",
    "for axis in ['top','right']:\n",
    "    ax.spines[axis].set_visible(False)\n",
    "\n",
    "    #We'll also need to turn off the ticks on the axes that we turned off\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But you know what's really bugging me now? The y-axis covers a great range, so much that it should be a log instead of linear scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:13:33.933658Z",
     "start_time": "2019-07-01T14:13:33.557337Z"
    }
   },
   "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": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue', linewidth = 3, marker = 'o', markersize = 10)\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend(loc='best', frameon=False, fontsize = font_size, markerscale = 1.2)\n",
    "\n",
    "#Adding a label\n",
    "ax.text(90, 15, \"(A)\", fontsize = 1.2 * font_size)\n",
    "\n",
    "#Turn off the spines\n",
    "for axis in ['bottom','left']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    ax.spines[axis].set_position((\"axes\", -0.02))\n",
    "for axis in ['top','right']:\n",
    "    ax.spines[axis].set_visible(False)\n",
    "\n",
    "    #We'll also need to turn off the ticks on the axes that we turned off\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "#Change the y-scale to log\n",
    "ax.set_yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And of course we can save the figure. To do that we use the `plt` command and just say `savefig()`. The file extension that we give in the filename determines the type of image file. You can choose to save the image as a `jpg`, `png`, `eps`, or `pdf`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:14:13.546562Z",
     "start_time": "2019-07-01T14:14:13.068178Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3nqUpstimkY6INE1jHwfQqkUio/p2ZnT/zpzRqxPJif7nLHXr2EpFAAJopFOb+LohInXweH1M+sPH9Y5QKiS4DI/+z3AGdWtPgttV6zkqAmj2VEhQGyUdkbr5rOWb7Xl89k0ui77dQ8HRUkfvc/I4AGn2NL0mIg1XnlVUnX3+TS7//WY3Bw43vhggJVk/SsQZjXRqiq8bIs1afT3P3C7DqL6d2bC7gN3f1d5+JjnRTWmZt95vCrWfkXIa6YjEs9y8QqbPWlVr5ZnXZ/H6LF+s213ja21aJnHWgHTOPjWDdq2S+fFLi9R+RoJGSUekmWpM5VnL5ATO7JfO2QMzGNyjQ7ViAD0OQIJJ02s1xdcNkWbJ67Nc/vt5jirPkhNczPr5+SQluOs8R5Vn4oCq145X3pHgfVDSkeZpV14hn2bu5D9rdzruDqDKMwkSrekcz1o71xhH90UkKjjpeVZU6uG/63bzSeZOvt6e1+jPUOWZhFNcjXRA02sSOxp62uYNY/uw/UAhC9ftpriWhf4kt4syr0+VZxIuml6rjZKOxIJAe565jGF4n06cP7grXTumcsffFtd7DfU8kyDS9JpIrGpsz7NuHVtx/uCTGD+oC+1btag8rsoziTYa6dQUXzdEotLlv/+Yo6WeBs9LcBmeuGk0/TLaUNd6pSrPJEw0vVYbJR2JZtZaMrcd5Bczlzs6X5VnEkU0vSYSK46WePjP2p3MXbmN7QeOOH6fKs8k1uj/WJEQcVLuvG3/Yeau3MZ/1u6kqLRxRQN62qbEIk2v1RRfN0RCoqFy58uH9+DbXflkbj1Y470pSW5G9e3MovV7KNXTNiV2aE2nNko6EmqBljt369iKi4d2Z/xpXUhNTmwwcU27cgjD+qQFO3yRQCnp1EZJR0Lt2Q+z+Gj1jmqJoi4uYxjVrzOXDO3O6T061KhAU+WZxBAlndoo6UioOS13TnS7ePmOs+nUOiUMUYmEnKOkU/vDzJup8oafIiHj9fkcJRwAj9enhCNxJ66q19TwU0KluMzLJ2t2MGtZjuP3qNxZ4pH+rxdpgsNFZcxduZV3v9xKwdFSx+9TubPEKyUdkVo0tMfmwKFi5izP4cNV22vsr2mVnEBRmbfeQgI94lnilQoJaoqvGyI11Feq7HYZBnZrx9qteXiOSyppbVK4YmRPLhzclazteSp3lnij6rXaKOlIfQLZY9O9UyuuHt2bs0/NIMF9rDZH5c4SZ5R0aqOkI/VpzB6bU05qxzVn9mb4yWm4VKAiooafIo21ICvXUcJpkejmqZtHhyEikeYlrvbpiDTE6R6bxra4ERE/jXREgH0FRbz+342Oz9ceG5HA6DtH4lrB0VLeWryJuSu2Uebw8dDaYyMSOCUdiUtFpR7mLNvCrKU5NabUXAbqW9bRHhuRwCnpSLNT38bOjq1b8OGq7byxcFONDgJ9M9ow5dz+eLy+BvfYqORZJDAqma4pvm5IM1Pfxk5joFVyIvnHJZuuHVK56Zx+nNk/vfLRAtpjI9Jo2qdTGyWd5quxGzs7tW7B5HF9Oe+0LrhdKuQUaSLt05H4MntZDh4HxQBJbhc3n9uPi4Z2JynBHYbIRKSCko40G043drrdLiaN7BWGiETkeJpTkGajyOHGzmKH54lI8CnpSLOwec8hnLY/08ZOkchpFt99xpgzgHuBMuB+a+3eCIckYXK0xMPMLzbw7pdb691bU0EbO0Uiq1kkHaAF8GPgfGAU8G5kw5FQs9ay8Ns9vPDJNxw8XOL4fdrYKRJZzSLpWGsXG2NGA/cBV0U6HgmtXXmF/HneN3y1eX+146f36MC4U07kr59+q42dIlGqWezTMcYMAzKBE4AHrbX31XOu9ulEubo6ClwyrAcLv93DW4s2VeuT1i41mVsnDOCcgRkYY7SxUyQymsfmUGPMCOD31tqzjTEu4HngdKAEmGqt3WSMGQ/cjP8v/ay1dlk911PSiWJ1dRRwGbC2+n8cA1w0tDs3ndOPVi0Swx6riFQT+5tDjTH3A5OBwvJDlwEtrLWjjDEjgSeAS62184H5DVzrVuDWUMYrTZObV8j0Watq7ShwfJFA3xPbcNfEQZx8YpswRSciwRDtJdObgUlVXo8B5gGUj2aGOr2QtfZFa63j8yX8nHYUGNStHU9POVMJRyQGRXXSsdbOxl8GXaE1UFDltdcYE9WjNXHOaUeBzXsP43Y53JQjIlElqpNOLQ7hLxao4LLWOt5eboy5OPghSbA47ShQVKKOAiKxKtaSzmLgBwDlazpZjXmztXZuKIKSpisq9eByOHpRRwGR2BVr373vABOMMUvwV0rcHOF4JAg27i7gsTmrnTXrVEcBkZjW5JJpY0wq0BnoCKQAB4ED1to9TQ8v+FQyHT181jJn2RZeXrAej5MeNkByopsXbj1L+21Eok9oSqaNMW7gIvwtZ84CTqntw4wxBfinwxYC/7LWbmnsZwWb1nSiR96RYv74XiZf5RyoPJaS5GbikG7M/Wq7OgqINFOORzrGmO7AHfj3zXTCYVbDP3KwwGfAS9batwOIM2g00om8FZv28cf3M8kvPPbY6L4ZbXjg8u/RpX2qOgqIxKbgdCQwxnQCpuHfWJlUfjgT/yhmZfmfDwDfAcVAu/J/egDDyv85B3/VmQW+AX4ZqUV9JZ3IKfV4eXlBNnOWHxv0GuCq0b258ey+JLpjra5FRKoIWtI5DKQCW4BXgDettRsbFYkxyfin5P4HuAR/1dzPrbVPNuY6waCkExk7Dhzh/95ZzaY9hyqPtW+VzM8vHcyQXh0jGJmIBEnQ1nR2Ao8Br1tra/YnccBaWwLMBmYbY/oAD3Bs1CTNSG3NOk8+sTXf7sqn1HOs28Dwk9O49+LTaJuaHMFoRSTcnIx0jA1BV9BQXbeBz7wYeB800gmFupp1VpXodnHLef25ZFgPjNNHfYpILGgeXaaDTdNroZGbV8jtLy6stVlnBQM8cs1QRvbtHL7ARCRcHCWdgFZuy4sLRCo5adbpchlWHvfgNRGJL4GWCy0zxpwc1Egkpjlp1un1WeZn7QpTRCISjQJNOj2BxcaYUcEMRmKXmnWKiBOBJp3X8Le9mW+MmdTQycaYicaYrwL8rKBRR4LQOFRUitOaADXrFIlvASUda+2NwKNAC+BtY8zdtZ1njDnbGLMYf8XY4ICjDBJ1mQ6+vflHuXfG0hpP9qyNmnWKSMBbwK21DwP/C3iBJ40xlRs9jTEjjDH/wf8I6VH4K8Ii2v5Ggm/zngLueXkJ2w8ccXR+gtvFpBE9QxyViESzYHSZngDMAloBc/Enson4y+d8+JPNdGvtt00LNThUMh0cX+XsZ/q/vqKo1F8ineh2cfmIHry3Ylu9zTqH9UmLVMgiElrh26dTvlbyHsd+YFeMbH5rrV3f5A8IIiWdpvs0cydPfbC2MrGkJifw66uHcnqPDmrWKRK/Qp90jDE9gIeAHwKJVb60CjjPWpsf8MVDREkncNZa3lq8mRmfZVce69i6Bb+7bjg90k6o550iEgdCujm0pzHmb0A2/nWdRPyPLrgC2Ah8D39JdfdArh8qql4LnNfn49mPvq6WcHqmncAzN5+phCMijgU00jHGlOBvFmqAZfgfVfBZ+dfa469WGw3sAS621ka8XLqCRjqNV1zm5bE5q1m2YW/lscE9OvDwVWeQ2iKxnneKSBwJ3fSaMcYHrAGmWWv/XcvXk/Hv5bkCOApcFy3lyko6dautQ/SYAels3lPA5r2HK887Z2AG915yup5/IyJVhTTpXGWt/ZeD854E7sFfVv1Ta+1zjf6wIFPSqZ2TDtEAV4/uzc3n9sOlDtEiUl10dJk2xtwFPAlgrY34dnQlnZqcdIgGuGHsyUwe1zdMUYlIjAldIUFjWGv/BFwFlIT6syQwjjpEGyg4WhqmiESkuQrLpLy19h3g3HB8ljSekw7RPos6RItIkzWYdIwxvYLxQdba5VWu6TLGdAvGdRtDJdO1U4doEQkXJyOd9caYV4wx/Zr6YcaYRGPMrfj38tzU1Os1VrRU0EWblCRnS23qEC0iTeUk6XwJTAa+McZ8Zoy5zRjTwekHGL9zjDF/BXKBvwCdgMyAIpagG92/4cdHq0O0iARDg7+6WmvHGGMuAf4fMA4YC/zZGLMR+ApYCxwAvgNKgbZAO/wPehuKvztBKv7KhjLgz/gbgOq5xVHgaImHDbsKGjxPHaJFJBgaLJk2xvwQ/6gkE7gQmApcxLFea/VdoKKELgf4B/CytXZ3UwJuKpVMH+Px+nj4nyv5avOx/O8yVHs2jjpEi4hDwdmnU959YLe1tkuVY+2Bc4AzgeHAififJJoM5OEf+WQDi4FF1tqVAfwFQkJJx89ay9MfZDFvzY7KY1PO7cf+Q8XqEC0igQha0vECe621GVWOrQSWWWvvaFKIEaCk4/fGwo288vmGytfa+CkiTRS0zaGHgTbHHRsCTGpsRBIdPs3cWS3hTDjtJG4Ye3IEIxKReOEk6XwLtDDGKMk0A6u3HOCpD9ZWvv5ez47cfdEgjHqpiUgYONl48TowAnjLGPMxsCC0IUmobNl7iN/+66vK7gM9005g2pVD1C1aRMLGyZqOC3geuLX8kMU/d2fxFwxk4n/MQUWF27fW2vo7R0ZIeUeC9yH+1nQOHCrm7pcXc+BQMQAdT2jB01NG06l1SoQjE5FmIrhdpo0xp+Bv3HkmcN5xX656kVJgHceS0BogM1oeXR2PhQSFJWXc98oycvYeAqBlUgJP3DSKXp1bRzgyEWlGQv4Qt/34H9J2OjC4/N8DgRZVTq168R3W2h6N/rAgi7ek4/H6ePitFXyVcwDw77uZft0wzujVKcKRiUgz4yjpBNpMqxjwWmsXAYsqP9E/FdefY4moIhmlAV0D/CwJkLWWP32YVZlwAO65aJASjohETEBJx1rb0hiTUctxH/6ptXXAmxXHjTHp+JOPhNEbCzfx8Zqdla8njz2Z809X7heRyAn5k0OjTXOdXsvNK2T2shwWZOVSVOohMcFFqefYg9kmnH4S9158mkqjRSRUQjq9JlFkxaZ9TJ+1Co/XV1kOXTXh9E5vzT0TtRdHRCJPGzRiXG5eIdNnraKkzFvn0z93HjjCvoKiMEcmIlKTkk6Mm70sB4/XV+85Hp9lzvItYYpIRKRuSjoxbkFWbp0jnApen2V+1q4wRSQiUjclnRhXVOpxdl6Js/NEREJJSSfGpSQ5qwVJSVbNiIhEnpJOjDt3UEaDdYpul2H8oC4NnCUiEnpKOjHuez07NrixKMHtYtKInmGJR0SkPnE151LeZbrZ8Pp8vLFwU51fd7sMCW4X064cosdNi0hUiKukY62d25w2SL775VY2l3eOTnIbzjolg6Ub9lJU4iElOYHxg7owaURPJRwRiRpxlXSak30FRbxa5ZHTN4zrxzVn9o5gRCIiDdOaTox6ft43FJf5n5XXo9MJXDFSazYiEv2UdGLQkuw9LN2wt/L1XRMHkqBHTotIDNBPqhhTVOrh+XnfVL7+/ve6cmrX9hGMSETEOSWdGPPqFxvYf6gYgDYtk5gyvn+EIxIRcU5JJ4Zs3lPAu8u3Vr6+bcIAWqckRS4gEZFGUtKJEV6f5Zl/f42v/KF7g3t24Fx1GRCRGKOkEyM+XLWN7Nx8ABLdLu78/kA9lE1EYo6STgw4eLiYfyzIrnx97Zm9OalDqwhGJCISGCWdGPDXT9ZxtPzRBCe1T+VqbQIVkRilpBPlVmzaxxfrdle+vnPiQJIS3BGMSEQkcEo6Uay4zMtzH31d+Xr8oC4M7tExghGJiDSNkk4Ue2PhRvbkFwHQqkUit04YEOGIRESaRkknSm3dd5hZS3MqX089rz9tU5MjGJGISNMp6UQhn7X86cMsvD7/npxTu7bjgsFdIxyViEjTKelEoU/W7OCbHd8B/gex3fWDQbi0J0dEmoFm8TwdY8x44IdAS2C6tTYzwiE1Sm5eIbOX5bAgK5eiUk+1x09fObIXPdJOiFhsIiLB1CySDv5k80NgMHA+EDNJZ8WmfUyftQqP11c5nVZVvy5tIxCViEhoNIvpNWvtXPyJ5y7glQiH41huXiHTZ62ipMxba8IB+P27a8jNKwxzZCIiodEsko4xpgPwDPCwtXYAHmQSAAAMnElEQVRfpONxavayHDxeX73neLw+5izfEqaIRERCK+qTjjFmhDHm8/I/u4wxLxhjlhpjPjfG9Ck/7SmgC/CYMebKSMXaWAuycusc4VTw+izzs3aFKSIRkdCK6jUdY8z9wGSgYn7pMqCFtXaUMWYk8ARwqbX2RgfXuhW4NWTBBqCo1OPsvBJn54mIRLtoH+lsBiZVeT0GmAdgrV0GDHV6IWvti9Zax+eHQ0qSs5yfkhzVvxuIiDgW1UnHWjsbKKtyqDVQUOW11xgTsz+Rzx2UgdtV//4bt8swXg9rE5FmIqqTTi0OAVU3rbistTE793TFyF4kuOv/T5DgdjFpRM8wRSQiElqxlnQWAz8AKF/TyWrMm40xF4ciqEBltE/lmjqejeN2GZIT3Uy7cggZ7VPDHJmISGjE2tTUO8AEY8wSwAA3N+bN1tq50faI550HjlT+OcFt8HotKckJjB/UhUkjeirhiEizYqytv2S3uTHGWABrbV3ZJ2w3pKjUwzVP/oeSMi8Af546hj4ntgnXx4uIBJOj3+hjbXqtWVn07Z7KhNO9Uyt6p7eOcEQiIqEVV0kn2tZ0qm76HD/oJKJt6k9EJNjiKumU92iLCgcOFbNmywHAPyY9d1BGZAMSEQmDuEo60eSzr3dVLh6d3qMDnVqnRDQeEZFwUNKJkGpTa6dp86eIxIe4SjrRsqazec8htuw7DEBygosx/U+McEQiIuERV0knWtZ05mftrPzzqH7ptFRvNRGJE3GVdKKB1+fjs69zK1+fp6k1EYkjSjphtnrLQfKOlADQLjWZIb06RjgiEZHwUdIJs/lrj02tnTMwA7dL/wlEJH7E1U+8SBcSFJV6WJy9t/K1HlkgIvEmrpJOpAsJ1PZGROJdXCWdSFPbGxGJd0o6YaK2NyIiSjpho7Y3IiJKOmGjtjciInGWdCJVvaa2NyIifnGVdCJVvaa2NyIifnGVdCLB67NqeyMiUk5JJ8TWbDmgtjciIuWUdEKsagHB2Wp7IyJxTj8BQ6io1MOi9XsqX6vtjYjEOyWdEFq8/ljbm24dW9FHbW9EJM4p6YRQ1am1807rorY3IhL34irphHOfzsHD1dvenDNQU2siInGVdMK5T2fB17vwlfe9Oa1HB9LaqO2NiEhcJZ1wmr+2akdpjXJEREBJJyRy9h5re5OU4GLMgPQIRyQiEh2UdEKgagHB6H7ppCYnRjAaEZHooaQTZP62N5paExGpjZJOkK3ZeoCDh/1tb9qmJnFGb7W9ERGpoKQTZFULCM4+VW1vRESq0k/EICou9bC4Stub8047KYLRiIhEn7hKOqHeHLp4/R6K1fZGRKROcZV0Qr05tNojqQep7Y2IyPH0CMsmys0rZPayHOZn7aKo1Ft5/NSu7SIYlYhIdDLW2kjHEFbGGAtgra1rGOL4hqzYtI/ps1bh8frw+qq/LTnRzbQrhzCsT1rgwYqIxA5HUztxNb0WTLl5hUyftYqSMm+NhANQUuZl+qxV5OYVRiA6EZHopKQToNnLcvB4ffWe4/H6mLN8S5giEhGJfko6AVqQlVvrCKcqr89WKy4QEYl3SjoBKir1ODuvxNl5IiLxQEknQClJzgr/UpJVICgiUkFJJ0DnDsrA7aq/WMPtMmr4KSJShZJOgK4Y2YsEd/23L8HtYtKInmGKSETEb9euXXTs2JEtW6oXMi1YsABjDB07dqS0tLTW906dOpV77703ZLEp6QQoo30q064cQnKiu8aIx+0ylft0MtqnRihCEYlX99xzD9deey09e1b/pfeNN94gNTWVgwcP8v7779f63ocffpgXXniBtWvXhiQ2bQ6tqVE3JDevkDnLt/g7EpR4SElOYPygLkwa0VMJR0TC7r///S/jx48nJyeHrl27Vh4vKSkhPT2d6667jjfeeIMxY8bwwQcf1HqNqVOnsmXLFubPn9+Yj3a0OVRJp6b4uiEi0qxcfvnlFBQUsGDBgmrH33nnHSZNmsTbb7/NrFmzmD17Njt37iQ9Pb3GNRYsWMD48ePJzMzktNNOc/rR6khwvFB3mRYRiaQdO3Ywd+5cLrvsshpfe/311zHGMHbsWC6//HK8Xi8zZ86s9Tpjx46lffv2PPfcc0GPMa6STqi7TIuIRNK8efPwer1MnDix2vFDhw7x73//m1GjRtG5c2cmTpxIcnIyM2bMqPU6CQkJXHjhhXz00UdBj1HTayIizYQx5hXgCuAEW+WHuzHmZuAfwH3W2ifKj30ATASGW2tX1HKtXwD/B/Sy1gatn1dcjXTAn2yUcESkmeoFbLU1RxPXl/97TpVjFX++qY5r5ZT/O6j7PuIu6YiINGMdgENVDxhjTgTOBjYA1hjTwxjTA8jEXzh1nTEmuZZrVVynYzADVI8WEZHmw0fNwcS1gBvoC9Q2TdYOuBR4+7jjFdfxEkRxt6ZTn4r1HhERaTwnSxeaXhMRkbDRSEdqZYxZaa0dGuk4ooHuRXW6H9VF0/0wxtwAzAQGW2szjTGPAL8GbrDWvl7He34HPAQ8YK39fZXj7wKnWmtPDmaMGumIiDQfH+Nf1zmr/PX/AAVUr1o73ovl77mp4oAxxgWMAoK+UUdJR0SkmbDW7gfex188gLW2r7W2rbW2qJ73bLPWuq21A6ocHg+kAX8PdoxKOlKXFyMdQBTRvahO96O6aLsffwTONMb0acI1bgQ+tdZmBimmSlrTERFpZowx7wN7rbW3BPDensA6YGxtnQqaHJuSjohI82KM6Yp/8+cwa+3mRr73ZaDAWntPSGJT0olvxphE/D2ZegDJwKP4f8uZgX+38tfAT6y1vgiFGBHGmDTgK2AC4CGO74cx5kHgEiAJeB74gji8H+XfK6/g/17xArcQ5/9vBEJrOnIDcNBaexbwfeA54EngV+XHDP7dynGj/IfLX4GKxde4vR/GmLOB0cCZwDigK/F7P34AJFhrRwO/BX5H/N6LgCnpyL+AaVVee4Az8P82C/6SyfPCHVSE/RF4Acgtfx3P9+MCIAt4B5gLfED83o8NQEJ5OXFroIz4vRcBU9KJc9baI9baw8aYE4BZwK/wT7tWzLseBtpELMAwM8bcBOy31n5c9XC83g/8zR6HAlcBtwOvA644vR9H8E+trQdeAv5EfP+/ERAlHalYdPwMmGmtfQP/RrEKJwD5EQksMqYAE4wxnwODgVfx71eoEG/34yDwsbW21FqbDRRT/QdrPN2Pn+K/F32B0/Gv7yRV+Xo83YuAKenEOWNMZ+AT4BfW2n+UH15dPpcP/nWehZGILRKstWOtteOstWcDa/DvV/goXu8HsAi40PhlAKnA/Di9H9/h390PkAckEsffK4FS9VqcM8Y8A1yDf8qgwt34pw6SgG+BW6y1QW1vHgvKRzu34x/5vUSc3g9jzB+Ac/D/kvoQ/vb4cXc/jDGt8Fd6noj/7/4MsJI4vBdNoaQjIiJho+k1EREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdkShjjPmPMcaW/3NdPee5jTHvVzn38XDGKRII9V4TiTLGmKHAl/ifRLkBOKW2JpLGmBfxPzIZ4DXgRqtvaIlyGumIRBlr7Ur8D9QD6AtMPv4cY8wjHEs4HwNTlHAkFmikIxKFjDF9gW+ABPyPEuhnrS0r/9pU/O30wT8iOtdaWxiRQEUaSSMdkShkrd2A/9ktAD3xP9EUY8xFwAvlxzcAE5VwJJZopCMSpcqf1LkJSAF2AjcAHwItgd3AaGvt1ogFKBIAjXREopS1Nhf/E1wBTgLm4084BcCFSjgSizTSEYlixpi2QA7QrvxQCXCBtfaLyEUlEjiNdESiW1f8pdMV3lbCkVimkY5IlDLGdAOWAhlVDpcAfa212yMTlUjTaKQjEoWMMR3w77/JAHzAq+VfSgZ+G6m4RJpKSUckyhhjWgIfAP3LD90N3Ax8Xf56sjHm1EjEJtJUSjoiUcQYkwC8DYwsP/SYtfY5a60P+HX5MRfw/yIRn0hTaU1HJIoYY14Gbip/OcNae/NxX18JnFH+8kxr7ZIwhifSZBrpiEQJY8xjHEs4H3Kst1pVD1f58/+FOiaRYNNIRyQKGGPu5NhG0Hr7qRljFgOjy19OtNZ+GIYQRYJCSUckwowxVwNv4p952Ii/vc2Bes4/F393AoC1wPfK13xEop6SjoiIhI3WdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGyUdEREJGz+P7MCFFM4h/uOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Change the baseline font\n",
    "mpl.rcParams['font.family'] = 'sans-serif'\n",
    "mpl.rcParams['font.sans-serif'] = ['Arial']\n",
    "\n",
    "#Create the figure\n",
    "fig = plt.figure( figsize = (6, 4) )\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "# We should add a label to our dataset that will go into a legend\n",
    "ax.plot(x, y, label = \"Quadratic\", color = 'steelblue', linewidth = 3, marker = 'o', markersize = 10)\n",
    "\n",
    "# Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "font_size = 15\n",
    "ax.set_xlabel(\"$x$\", fontsize = 2*font_size)\n",
    "ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "\n",
    "# Display legend\n",
    "ax.legend(loc='best', frameon=False, fontsize = font_size, markerscale = 1.2)\n",
    "\n",
    "#Adding a label\n",
    "ax.text(90, 15, \"(A)\", fontsize = 1.2 * font_size)\n",
    "\n",
    "#Turn off the spines\n",
    "for axis in ['bottom','left']:\n",
    "    ax.spines[axis].set_linewidth(2)\n",
    "    ax.spines[axis].set_position((\"axes\", -0.02))\n",
    "for axis in ['top','right']:\n",
    "    ax.spines[axis].set_visible(False)\n",
    "\n",
    "#We'll also need to turn off the ticks on the axes that we turned off\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "\n",
    "#Change the y-scale to log\n",
    "ax.set_yscale('log')\n",
    "\n",
    "#Save the figure\n",
    "plt.savefig('quadratic_logscale_half_frame.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:13:51.217622Z",
     "start_time": "2019-07-01T14:13:51.088479Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "quadratic_logscale_half_frame.png\r\n"
     ]
    }
   ],
   "source": [
    "ls *png"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You see now that the number of options that you can possibly configure to make a graph look like **how you want** is **huge!** There's way too much to go over in this lecture right now and have you retain it, so we're going to stop going over more and more options now. If you want to learn more about the intricacies of `matplotlib` I think that this is a good [tutorial](http://www.labri.fr/perso/nrougier/teaching/matplotlib/).\n",
    "\n",
    "## Sometimes you just want a decent looking graph quickly though\n",
    "\n",
    "I personally can't handle how the basic `matplotlib` graphs look like, even when I am just quickly exploring data. The way that I get around that is by using a relatively new addition to `matplotlib`: `styles`. There are a few (but should be more soon) pre-canned styles that look pretty okay. These are by no means *perfect publishable* figures, but they look decent enough to show a colleague quickly.\n",
    "\n",
    "You can actually change the default style in the entire notebook if you execute\n",
    "\n",
    "    plt.style.use('style_name')\n",
    "    \n",
    "But I don't want to change the style of every plot in the notebook. When you don't want to change the style globally, you can just write a graph like this:\n",
    "\n",
    "    with plt.style.context('style_name'):\n",
    "        #Your graph code here\n",
    "        \n",
    "That `with` statement basically says that all of the code inside that block should use that setting. Once we leave the `with` block the `with` statement is no longer applied.\n",
    "\n",
    "If you've used `R` then you probably know about `ggplot`, you can replicate its style like so:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:14:28.491017Z",
     "start_time": "2019-07-01T14:14:28.320961Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('ggplot'):\n",
    "    fig = plt.figure( figsize = (6, 4) )\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    \n",
    "    # We should add a label to our dataset that will go into a legend\n",
    "    ax.plot(x, y, label = \"Quadratic\")\n",
    "    \n",
    "    # Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "    ax.set_xlabel(\"$x$\", fontsize = 1.6*font_size)\n",
    "    ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "    \n",
    "    # Display legend\n",
    "    ax.legend(loc='best', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And if you're really into Nate Silver's work you can try a style similar to the one used at [fivethirtyeight](http://www.fivethirtyeight.com)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:14:47.206450Z",
     "start_time": "2019-07-01T14:14:46.997813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('fivethirtyeight'):\n",
    "    fig = plt.figure( figsize = (6, 4) )\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    \n",
    "    # We should add a label to our dataset that will go into a legend\n",
    "    ax.plot(x, y, label = \"Quadratic\")\n",
    "    \n",
    "    # Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "    ax.set_xlabel(\"$x$\", fontsize = 1.6*font_size)\n",
    "    ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "    \n",
    "    # Display legend\n",
    "    ax.legend(loc='best', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is also a style called `bmh` that's about halfway between the two previous styles."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:14:53.637235Z",
     "start_time": "2019-07-01T14:14:53.449904Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z53y6kIIi5FmDQSQLM8nDbHAe73h6ufOlHURimo8eX5WRgz0eLSkoQp41GESyMJM8zPrz2N5+gJtf2EFfVLN4ZjmfnT85Iwd7PFpSUAT5+ek/9WiySBZmkodZfn4+e3xBbnh+O/5QlDOnTuBLZ06RYhInBUVQUpJ93RuHI1mYSR5mfTlOrn9+G13BCKdMLuH6sxsyduTg0ZCCImhvb7e6CbYhWZhJHgd1BcLcumIXrb3GMPS3LppGfq78CB1M0hC43W6rm2AbkoWZ5GHoH4becyDGNHfmD0M/WlJQhHQNHUSyMJM8jGJyw/Pb2NYeoLowl28tyfxh6EdLUhEEg0Grm2AbkoVZtudhnJlsY4v3ADXF+Vx7kisrhqEfLSkoQp41GESyMMvmPA6Eotz05+1sbj1AdbGD+y5oxC2d3hKSS15CnjUYRLIwy9Y8DoSi3PSX7Wxq9ceLyTHUlhRkbR4jJQVF4HRmz3wNRyJZmGVjHoFwlJv/sp13WvxUFTm47/xjmBifICsb8zgaUlAELpfL6ibYhmRhlm15BMLGmcnbLX4qi4wzk4mlB2dbzLY8jpatC4pS6gal1Gql1JtKqWVKqUal1GtKqVeVUj9SSuXEt7tNKbVGKfW6Uuq0+LIhtxWH6+zstLoJtiFZmGVTHsaZyQ7e9vipLDTOTCaVmqfuzaY8RsO2P2SVUmcBC4AzgIVAPfAgcLPW+kOAAi5RSp0cXz8fuBz4QfwlDtt2XHcgjVRUVFjdBNuQLMyyJY9AOMqtL+zgLU8vFYXGDfjJZYfPA58teYyWbQsKsBh4C3ga+CPwLHAKsCq+/nlgEXAm8II27ALylFJVw2wrhtDT02N1E2xDsjDLhjyCkRi3vrCDjft7KS/M4/4LGplcNvS9kmzIYyzs3G24EmgALgSmAc8AOVprHV/fA5QBpcDg8SH6l6shtjVpbW1l2bJl5OXlEY1GWbp0KcuXL8fj8VBUVERubi7d3d1UVVXR0dGB1pqqqipaWlooLi4GoLe3l5qaGrxeL0opysvL8Xq9lJaWEo1G8fv91NbW4vF4cDgclJWV0dbWRllZGaFQiEAgMLA+Pz+fkpIS2tvbcbvdBAIBgsHgwHqn04nL5aKzs5OKigp6enoIhUID610uF/n5+fh8PiorK/H5fITD4YH1w+1TKBSiubk5o/ZptJ+T1+tFa51R+zSWzykajdLc3JxR+zT4c8pxFHDXyt1s6YgwoSCHa08qotKpaG5uHnKf2traKC8vt/U+jcfnNBx18GeuvSil7gG8WusH4l9vBI7RWhfGv74EOBfYCji11vfGl6+PL9+gta4bvK3W+urB77F69Wo9e/bs8dol2+rr66Og4PDT+2wkWZhlch59kRi3vrCd9ft6KXflcd8Fx1A/IXEvrkzO42isW7fuzaampnmHLrfzJa/XgI8owySgCFgRv7cCsAR4FfgbsFgplaOUmoJxFtMGrB9iWzEE6Vt/kGRhlql59MUvc63f14vblce9IygmkLl5JIttL3lprZ9VSn0YWINR+JYDO4GfKKXygc3Ak1rrqFLqVWD1oO0AvnLotuO9D+lCukIeJFmYZWIefZEYt724g/X7epjgzOO+849hygiKCWRmHslk24ICoLX+2hCLFw6x3e3A7Ycs2zrUtuJwMonSQZKFWablEYrEuOOlHazbGy8mFzQyxT3yhxUzLY9ks/MlLzFOfD6f1U2wDcnCLJPyCEVi3P7SDtbu6aHMmce9FzTS4D66M45MyiMVpKAIKisrrW6CbUgWZpmSRyga484VOw8Wk/MbmXqUxQQyJ49UkYIi5LeuQSQLs0zIIxSJcddLO1mzu5vSglzuPb+RaeWjuxeSCXmkkq3voYjxEQ6HrW6CbUgWZumeRyAc5fYXjd5cRjE5ZtTFBNI/j1STgiKyes6LQ0kWZumcR09fhFv+soNNrX7crjzuWTL6M5N+6ZzHeJBLXkL61g8iWZilax6dB8Jc99y2gflMHrxwbGcm/dI1j/EiZyiCoqIiq5tgG5KFWTrm0dob4vrnt7HH10ddWQH3LGmkujg53X3TMY/xJAVFkJuba3UTbEOyMEu3PPb4gnz9T9vw+sPMqHDxzY/MwO1K3hzw6ZbHeJNLXoLu7m6rm2AbkoVZOuWxoz3Al//4Hl5/mDnVRdx3fmNSiwmkVx5WGPMZilKqCKjBGB3YhTHyb5vWWi42pomqqiqrm2AbkoVZuuSxudXPTX/eTm8oysmTS7ht0TRcjuSfTaRLHlY56oKilMrFGFL+POBDwByMCawO3c6HMXDjq8BvtdY7x9ZUkSodHR0UFhZa3QxbkCzM0iGP9Xt7uO3FHQQjMRY0lHHjOVPJz03NxZd0yMNKIy4oSqkG4GrgSqCKIYrIISYAFwDnA99USr0C/ERr/ZtRtlWkiF2nMLCCZGFm9zxWN/u4e8VOwjHNokY3X/lwA7k5R/rRNHp2z8NqRywo8dkPbwE+C/R3ldiIcfaxNv7vNqATCALu+J+pwKnxP2cDTcA5SqmbgZu01n9M5o6I0ZPT+IMkCzM75/Hytg7uXdVMTMNFx1ayfEEdOSp1xQTsnYcdjOQMZQfGXCQ7gZ8Dv9Rav5dge2/8z1bgBQClVAHGZbIrgIuB3yulrtNaPziGtoskaWlpoaGhwepm2IJkYWbXPJ7d3Mb//G03Grj8xBr+fd5EVIqLCdg3D7sYSUHZA3wLeEJrHR3Nm2it+4DfAb9TSjUC13PwbEdY7EjTemYTycLMjnn8emMLP31jHwDLTp3EZSfWjNt72zEPOxlJQZmjk3jhUGu9DbhKjcevE0KIjKG15tG1+/nVxhYUcPWCOi6aI5eg7OSIXSGSWUzG43XF0evt7bW6CbYhWZjZJY+Y1vxg9R5+tbGFHAVfO6vBkmJilzzsalTPoSilqrTW3mQ3Rlijpmb8LhnYnWRhZoc8ojHNA39t5qVtnThyFDc1TWVBwwRL2mKHPOxstJ21/66UOiapLRGW8Xrld4N+koWZ1Xn0RWLctWInL23rxJmXw92LZ1hWTMD6POxutAVlGvA3pdTpyWyMsIbczjpIsjCzMg9fMMLX/7SN15t9FOfncs+SRuZOLrGsPSDHx5GMtqD8AmOolRVKqaVH2lgpdYFS6s1RvpdIsfLycqubYBuShZlVeezr7uNLz2xlU6ufyiIHD1x4DHNqrB/pV46PxEZVULTWnwTuBpzAb5RS1wy1nVLqLKXU34BngJNG3UqRUnIaf5BkYWZFHlta/VzzzFb2dvcxvdzJ9y6emZS5TJJBjo/ERj3gjdb6VmAZEAUeVEoNPKSolJqvlHoJWAGcDmhAhlyxqdLSUqubYBuShdl457G62cd1z72HLxjh5MklPHDhTCqL7PPImhwfiY1ptGGt9aNKqT3Ak8A1SqnpGEXqAoyxvmLAr4G7tNabx9pYkRrR6KieV81IkoXZeObxzCYvP1y9h5iGxTPLuebMKeSlcFyu0ZDjI7ExD8mptX4R+ARGAbkIo5ho4FfA8VrrK6SY2Jvf77e6CbYhWZiNRx4xrXl4zV6+/7pRTK48uZYvf8h+xQTk+DiSMZ2hKKWmAjcCn+pfFP97PfCfWuuusby+GB+1tbVWN8E2JAuzVOcRisS476/NrNrRRa6Caz80hfNmVqT0PcdCjo/ERnWGopSappR6GHgX4z6KA3gF+CjwHjAXo1uxjKKWBjwemQutn2Rhlso8uoMRbvjzdlbt6KLQkcNdi2fYupiAHB9HMtozlC3x71XA3zGGo38FQCm1CqNX1wJgtVLqIq21dBm2MYcjudOkpjPJwixVeXh6+rjpz9vZ7eujotDB3YunM6PC/hNXyfGR2GjvoTgw5kG5SGu9oL+YAGitOzDmPvkdUAusUkpdNOaWipQpKyuzugm2IVmYpSKPrW0HuOaZrez29THV7eS7F89Mi2ICcnwcyWgLymVa65O11s8NtVJr3ae1/lfgv4FC4Cml1NWjbaRIrba2NqubYBuShVmy81iz28dXn32PzkCEuZOK+c5FM6kutk+34COR4yOx0T7Y+NsRbvdl4EsYl8b+ezTvJVJPfus6SLIwS2Yez21p49YXjLnfFx1Tzt2LZ1CUn5u01x8PcnwkNqZeXiOhtf6eUmo3xnAtwoZCoZDVTbANycIsGXlorfnZ2v38cmMLAB+fW8snT65Ny3Gx5PhILOUFBUBr/bRS6pzxeC9x9AKBgNVNsA3JwmyseYSjMR746y5e3t5JjoJrzqhnyezKJLVu/MnxkdgRC4pSarrWesdY30hr/Y9Br5kD1Gmtd431dcXYSd/6gyQLs7Hk0R2McNeKnWzc34vLkcPN50zj1Pr0HrpEjo/ERnIPZYtS6udKqVljfTOllEMp9VmMZ1U+PdbXE8khfesPkizMRpvHzo4AV//hXTbu76XclccDFxyT9sUE5Pg4kpFc8loDXAl8XCn1KsaQKk9qrdtH8gbxuePPAi4HlgLlgB+j27Gwgfz89Ollk2qShdlo8nh1Zxf3rWomGIkxs7KQ286dRpWNBngcCzk+EjtiQdFan6mUuhj4JrAQ+DDwA6XUe8CbwD+BNqATCAETADfGJFzzMJ6aL8Lo6RUGfoAxWOQRx4FWSlXH3+NcIAL8DGOcsLeB5VrrmFLqNozxwyLAl7TWa5RSjUNtO4I8slJJibWTFtmJZGF2NHnEtObxdR6eWG/8Ft/U6OZLZ06hIG/MQwbahhwfiY3kHsqnMM4mPgB8BLgKuBCYFf/zb4m+Pf73DuAR4FGt9f6RNEwp5QAeAvrvgj0I3Ky1XqmU+jFwiVKqGaPIzQfqMR6mPHWobYGnR/K+2ai9vZ3i4mKrm2ELkoXZSPPwh6Lcu7KZ1bt85Ci46rTJfPT4qrTsyZWIHB+JjeSS16PAfq31ZOB54HmlVDlwNnAGcBowEWMGxwKgA+OM5V3gb8BrWuu1o2jb/cCPgRviX58CrIr/+3ngvPh7vKC11sAupVSeUqpqmG2loAzD7XZb3QTbkCzMRpLHXl8ft7+4g+auICUFudx49lROqUv/+yVDkeMjsZEUFM3BM41+LwB/11qn5Ol3pdSnAa/W+i9Kqf6CouKFA6AHKANKgcH3cvqXD7XtYVpbW1m2bBl5eXlEo1GWLl3K8uXL8Xg8FBUVkZubS3d3N1VVVXR0dKC1pqqqipaWloHfUnp7e6mpqcHr9aKUory8HK/XS2lpKdFoFL/fT21tLR6PB4fDQVlZGW1tbZSVlREKhQgEAgPr8/PzKSkpob29HbfbTSAQIBgMDqx3Op24XC46OzupqKigp6eHUCg0sN7lcpGfn4/P56OyshKfz0c4HB5YP9w+KaXo7OzMqH0a7ee0Z88eJkyYkFH7NJbPKS8vj87OzmH3aV+smG+v2s2BiKau1MEXPlDIbHcu+/bts+0+jeVz6urqYsaMGRm1T6P5nIb92X3w5+6wP9y7AIfWumjQshjg0VpPSvjNo6SU+itGIdMYUwdvBU7WWufF11+CcV9lK+DUWt8bX74+vnyD1rpu8LZDFb/Vq1fr2bNnp2IX0kpzczMNDTIwNEgWhxouD601v3urlYff2EdMw+kNZXx9YQOFafbk+9GS48Owbt26N5uamuYdunwkd8s2A06l1NLkN2toWusPa60Xaq3PAjYAn8S41HZWfJMlwKsYl9QWK6VylFJTgBytdRuwfohtxTCkb/1BkoXZUHn0RWLcu6qZ/11jFJNPzK3ltkXTMr6YgBwfRzKSgvIExiWvXyml/qiUujbFbRrOV4A7lFKrgXyMrstvYhSL1Rg35JcPt60F7U0b0rf+IMnC7NA8vP4QX3n2PVZs68SZl8OtTdP45CkTycmwm+/DkeMjsZHcQ/khcDzwWYzuuefHl9copVoweoBtiP+9EdistU7axMvxs5R+C4dYfztw+yHLtg61rRia0+m0ugm2IVmYDc7jHU8vd67YSWcgQm1JPnecO51p5S4LWzf+5PhIbCTPocSAzyulvgf8K0bPrkUYZy1V8X83DfqWkFJqEwcLzAZgo0wHbF8uV3b9UEhEsjDrz+P5LW38z+t7iMQ0J00q5uZzplHqHJehAG1Fjo/ERnxEaK03AXfAwE15L8aUvydi3Dg/EeNMxonxMOM84jwDAAAYI0lEQVRcjJvqxL9nt9Z6arIaLpKns7OT0tLM7OZ5tCQLM297B4+97eOZTcY8IJceX8VnT5tMbk52XOI6lBwfiY32V4wgENVavwa81r8wPujjbA4Wmf5CU43x4KGwoYoKe8/jPZ4ki4O6AmG+/88g73gDOHIU15xZb/s531NNjo/ERlVQtNaFSqnDugzHL49tiv/5Zf9ypVQtRmERNtTT0yNP/8ZJFoZNLX6+8fJOvP4w5YV53LZoOsdWFx35GzOcHB+JjfoiqNZ631Fs6wGke4RNyaRBB2V7FjGtefKtVh59Yx9RDdPLcvnG+bOpKHJY3TRbyPbj40iy766aOIz0rT8om7PwBSPct6qZNbu7AfiXD1RzxQcqKC6UYtIvm4+PkcicYUDFqEnf+oOyNYu3Pb184ektrNndTUlBLneeN53Pzp9Mu7fF6qbZSrYeHyMlZyhCukIOkm1ZxLTmN/9s4Wdr9xPTMKe6iBvPmUp1sTHvR7blcSSSR2JSUIRMGjRINmXRFQhz76pm1u7pAeBjJ1Tz6XmTyBvUJTib8hgJySMxueQl8Pl8VjfBNrIli3/u7+ULT7/L2j09lBbkcvfi6Vx12mRTMYHsyWOkJI/E5AxFUFlZaXUTbCPTs4hpza82tPDYOuMS1/E1RdxwztRhp+jN9DyOluSRmBQUgc/no6hInjGAzM6i80CYb69qZt1e4xLX5SfW8KlTJiZ86j2T8xgNySMxKSiCcDhsdRNsI1Oz2LCvh3teeZ+OQIQyZx5fP6uBeSOYVTFT8xgtySMxKShC+tYPkmlZRGOaX27w8Iv1HmIaPlBbzA1nN1A5zCWuQ2VaHmMleSQmN+WF9K0fJJOy6DgQ5oY/b+OxdR60hitOquHe8xtHXEwgs/JIBskjMTlDEXJNeJBMyWLd3m6+vbKZzkCECc48rj+7gZMnH/0ouZmSR7JIHolJQRHk5mb+1K0jle5ZBCMxfrpmL3+IDzd/4sRirj97KhWjHD4l3fNINskjMSkogu7ubtxut9XNsIV0zmJTi5/7VjWzt7uPXAWfOHkil59YM6a5S9I5j1SQPBKTgiKoqqqyugm2kY5ZhKIxHl/n4bf/bCGmYarbydcWNtBYWTjm107HPFJJ8khMCoqgo6ODwsKx//DJBOmWxfb2A9y7spmdnUFyFFx2QjVXnjKR/Nzk9LdJtzxSTfJITAqKQGt95I2yRLpkEY1pfrWxhV+s209Uw6TSAq5bOIXjapI7+VO65DFeJI/EpKAIOY0fJB2y2NUV5L5VzbzrPQDAJXMq+Y9TJ+FyJP+GcTrkMZ4kj8TkORRBS4vMedHPzlnEtOapt1v5z6e38K73AFVFDu5ZMoPlC+pTUkzA3nlYQfJITM5QhMyRPYhds/D09HH/ql3809MLwHnHlPOF0+soyk9tN1a75mEVySMxKShC2JjWmj+/286P/7GXQDjGBGce135oCqc3lFndNCEOIwVF0NvbS0VFhdXNsAU7ZdHuD/Od13YNzPH+oWkT+OIZ9ZQ5x++/rZ3ysAPJIzEpKIKamhqrm2Abdsnile2dfP/13fT0RSkpyOXqBXWcNd2NUqN/SHE07JKHXUgeiclNeYHX67W6CbZhdRZef4g7X9rBt155n56+KKfWlfK/S4/l7Bnl415MwPo87EbySEzOUIQlP6jsyqosojHN79/x8ti6/QTCMVyOHD43fzJLZlVY+vnIsWEmeSQmBUVQXl5udRNsw4osNrf6+e5ru9nREQDgzKllfP6DdVQXj3yY+VSRY8NM8khMCorA6/XS0NBgdTNsYTyz6OmL8Ogb+3luSxsaqCnOZ/mCOj44xT49uOTYMJM8EpOCIigtPfp5MjLVeGShtebl7Z089Pe9dAUj5Cr4lxNq+PjcWpx59rqtKceGmeSRmBQUQTQatboJtpHqLHZ3Bfmf13ezYZ/xgOLxtUV88Yx6prpdKX3f0ZJjw0zySEwKisDv91NZWWl1M2whVVmEIjF+tbGFX29sIRzTlBbk8tn5kzn3GGt6b42UHBtmkkdiUlAEtbW1VjfBNlKRxdo93Xz/9T3s6+4D4CMzK7jqtEmUjuMDiqMlx4aZ5JGY/Y9okXIej0duNMYlM4v2A2Ee+vseVu7oAqDB7eSaM+o5vjZ9xoOSY8NM8kjMlgVFKeUAHgGmAgXA3cAm4GeABt4GlmutY0qp24ALgAjwJa31GqVU41DbjvNupA2HY3TzjWeiZGQRjWme29LGI2/s40A4RkGu4sqTJ7L0A9XkjWE6XivIsWEmeSRmy4ICfAJo11pfqZSqANYDG4CbtdYrlVI/Bi5RSjUDC4H5QD3wO+BU4MFDtwWetmJH0kFZmX26qVptrFlsafXz/df3sLXNmKtkfn0pyxfUUVtSkIzmjTs5Nswkj8TsWlB+Czw56OsIcAqwKv7188B5wLvAC9qYRm2XUipPKVU1zLZSUIbR1tZGUVGR1c2whdFmsb+nj0ff2DdweauyyMHy0+tY0FBm65vuRyLHhpnkkZgtC4rWuhdAKVWCUVhuBu7XB+ff7AHKgFKgfdC39i9XQ2x7mNbWVpYtW0ZeXh7RaJSlS5eyfPlyPB4PRUVF5Obm0t3dTVVVFR0dHWitqaqqoqWlZWBehN7eXmpqavB6vSilKC8vx+v1UlpaSjQaxe/3U1tbi8fjweFwUFZWRltbG2VlZYRCIQKBwMD6/Px8SkpKaG9vx+12EwgECAaDA+udTicul4vOzk4qKiro6ekhFAoNrHe5XOTn5+Pz+aisrMTn8xEOhwfWD7dPLpeL5ubmjNqn0X5OwWCQffv2jXif8gpL+c1bXl7c6SeiwZEDi6cXc9kHqgj2+vD78yzfp7F8ToWFhTQ3N9vuc7Lq2AsGg/T19WXUPo3mcxqOsuscyUqpeoyzih9qrR9RSu3RWtfF110CnAtsBZxa63vjy9fHl284dFut9dWHvsfq1av17Nmzx2eHbKy1tZXq6mqrm2ELI80iFI3xzKY2frnBQ09fFAU0Nbr59LxJthgyJVnk2DCTPAzr1q17s6mpad6hy215hqKUqgFeAK7WWq+IL16vlDpLa70SWAK8AmwD7lVK3Q/UATla6zal1FDbimEEAgGrm2AbR8pCa82qHV08snYfnp4QAHMnFfOZ0ybTWFk4Hk0cV3JsmEkeidmyoAA3Am7gFqXULfFl1wDfU0rlA5uBJ7XWUaXUq8BqjKH4l8e3/Qrwk8Hbjmvr04z0rT8oURZveXr533/s5V2vccO9we3kM6dN4tS60rS+T5KIHBtmkkditiwoWutrMArIoRYOse3twO2HLNs61LZiaNK3/qChstjdFeSnb+zj9WYfAOWuPD51ykTOm1lBbpp1Az5acmyYSR6J2bKgiPGVn5851/zHanAWnYEwv1jn4bktbcQ0OPNy+NcTqvmXD1TjcuRa2MrxI8eGmeSRmBQUQUlJidVNsI2SkhKCkRhPv93Krze2cCAcI0fB+bMruPLkiVQUZteDbXJsmEkeiUlBEbS3tx+xO2A2iMY0f3xrH3/cGaLtQBgwHkxcdtok244GnGpybJhJHolJQRG43W6rm2CpaEzzyvZOfrnBw26fMYBjY4WLz8yfzNxJ2f0babYfG4eSPBKTgiIIBAJZOXFQOBrjpfc6+NXGFvbHuwBXunL5j9PqOKfRTU6G9tw6Gtl6bAxH8khMCoogGAxa3YRxFYrE+PPWdn7zzxZae41LW5NLC/i3k2qY4ehhxjSZN7xfth0bRyJ5JCYFRWRN3/pgJMZzm9v47VstdByIANAwwcm/nVTDwulucnMUfX1yfXywbDk2RkrySEwKisj4vvUHQlGe2ezld2958QWNQjKjwsUVJ9VyxtQy06WtTM/iaEkeZpJHYlJQBE6n0+ompERvX4Tfv+Pl6Xe89PQZc4HPqirk43NrmV8/9NPtmZrFaEkeZpJHYlJQBC5XZnWJ9QUjPPVWK3/Y5OVA2JhX7fiaIq6YW8spk0sSDpOSaVmMleRhJnkkJgVF0NnZmRE9VzoOhHnyrVae3dxGMGIUkrmTivn43FpOmDiy7r+ZkkWySB5mkkdiUlAEFRUVVjdhTLa3H+CZTW2s2NZBKGpMx3BqXSkfn1vLnJqjmwwp3bNINsnDTPJITAqKoKenJ+2e/o3ENK/t7OKZTV7ebvEPLF/QUMYVJ9Uys2p0Q8mnYxapJHmYSR6JSUERhEIhq5swYu0HwvxpSxvPbWkb6Ppb6MjhvJkVXHRsJfUTxnbTNJ2yGA+Sh5nkkZgUFGH7vvVaaza1+PnDJi+v7uwiflWLhglOLp5TSVNjOYX5yRn91+5ZjDfJw0zySEwKirBt3/pgJMYr2zt5ZpOX7e3GTHk5Cs6cWsbFc6o4cWJx0ie2smsWVpE8zCSPxKSgCNt1hdzf3ccfN7fxl63tA8+PlDnzOH9WBRccW5nSOdvtloXVJA8zySMxKSjCFpMGxbRm3d4entnk5R+7uolf1WJWVSGXzKniw9MmkJ+Xk/J22CELO5E8zCSPxKSgCHw+HxMmTLDkvT09fbyyvZMX3+tgT3zoeEeOYuEMNxcfW8ns6qPr9jtWVmZhR5KHmeSRmBQUQWVl5bi+X3cwwl93drFiWwfvDOryW1Xk4MJjK1kyq4IJLmtmRhzvLOxO8jCTPBKTgiLw+XwUFaX2TKAvEuPvu3ys2NbB2j09RGLGRa2CXMWCqRM4Z4abeXWl5OZYOwfJeGSRTiQPM8kjMSkognA4nJLXjcY0G/f38PK2Tl57v2tgXK0cBfPqSjhnRjlnTC3D5UhOl99kSFUW6UryMJM8EpOCIpLat15rzfb2ACu2dfDKjs6Bhw/BuMF+zgw3C6e7KS+05pLWkchzBmaSh5nkkZgUFJGUvvX7e/p4ZVsnL2/vZFfXwVntJpbk09RYztkz3GN+in08yHMGZpKHmeSRmBQUMaprwlpr3u8MsmZ3N6ubfWxqPXhzvcyZx8LpE2hqLGd2VWHSHz5MJbk+biZ5mEkeiUlBEeTmjuweRjASY8O+Htbs7mbNbt/AfOxgvrl+Sl0peRbfXB+tkWaRLSQPM8kjMSkogu7ubtxu95DrPD19rNndzT92dbNxf8/A8PAAE5x5nFpfymn1pZxaV5q08bSslCiLbCR5mEkeiUlBEVRVVQ38OxLTvOPp5R+7u3ljdzfNg+6HAMysLOS0eBGZWVVomo89EwzOQkgeh5I8EpOCInh/fxt7okWs2d3Nm3t78IeiA+sKHTmcPLmU+VOMsxC79s5Klo6ODgoLRzeXSiaSPMwkj8SkoGShNn+ITS1+3mn187anl21tATTtA+vrywqYP6WM0+pLOa6mCEdu6sfQsgut9ZE3yiKSh5nkkZgUlAwXjWl2dASMAtLSy6ZWv+lmOhhjZ504qZjT6suYX1/KxNICi1prPbmkYSZ5mEkeiUlByTA9fRE2t/rjBcTPu94DBCMx0zaFjhzm1BQxp7qIOTVFFPd1MHP6VGsabDMtLS3ynMEgkoeZ5JGYFJQ0prVmb3ffQPHY1OI/7CY6wKTSgoECclxNEVMmOE1jZrW3y3AS/WS+cDPJw0zySEwKShqIaY23N8yuriDNXUF2dQbZ1WX86R10Ax3AkauYWVnIcTXG2cex1UW4LRq5VwiRXaSg2Eg0ptnf02cUjs4gu+MFZHdX32GXrfqVu/KYU1PMnBrj7GNGhYv8o7yJ3tvbS0VFRTJ2Ie1JFmaSh5nkkZgUlHEWjWm6ghE6DoTZ120Uj/4zjj2+PsKxoXuRuF15TJngpMHtZMqEg3/crrwxD21SU1Mzpu/PJJKFmeRhJnkklrEFRSmVA/wQOBHoA67SWm9L1fuFojE6D0ToCIRpPxCmY+CPsaz/665ghGFqBgDVxY6BYtEQ/7t+gpNSZ+o+Kq/XS319fcpeP51IFmaSh5nkkVjGFhTg/wFOrfXpSqkPAg8Al4z1RaMxzSNv7BtUJIyC0dMXPfI3x5U586gozKO6ON8oGm4nDRNc1E8osGRukHQavDHVJAszycNM8kgskwvKmcCfAbTWf1dKzUvGi+YoeGZzG32H3NPIVeB2OSgvdFBemGf8Hf+6YtAyt8thu4ETy8vLrW6CbUgWZpKHmeSRWCYXlFLAN+jrqFIqT2s9MONTa2sry5YtIy8vj2g0ytKlS1m+fDkej4eioiJyc3Pp7u6mqqqKjo4OtNZUVVXx0UYnpYVOJjhzKYj10VhXQ7C7g9ycHMrLy/F6vZSWlhCNRvH7/dTW1uLxeAiHHfRRxt62NsrKygiFQgQCgYH1+fn5lJSU0N7ejtvtJhAIEAwGB9Y7nU5cLhednZ1UVFTQ09NDKBQaWO9yucjPz8fn81FZWYnP5yMcDg+sH26fQqEQeXl5A10ie3t7qampwev1opQatE+lh+2Tw+GgrKyMNpvtU1VVFS0tLUe9T9u2baOysjKj9mksn1M0GiU3Nzej9mksn1NbWxuzZs3KqH0azec0HJWpQwkopR4E/q61/k386z1a67rB26xevVrPnj3bkvbZSWdnp4ygGidZmEkeZpKHYd26dW82NTUddtUnkwdp+htwPkD8Hspb1jbHvqLRkd//yXSShZnkYSZ5JJbJBeVpIKiUeh34DnCtxe2xLb/ff+SNsoRkYSZ5mEkeiWXsPRStdQz4vNXtSAe1tbVWN8E2JAszycNM8kgsk89QxAh5PB6rm2AbkoWZ5GEmeSQmBUXw+9//3uom2IZkYSZ5mEkeiUlBETz11FNWN8E2JAszycNM8khMCoogEokceaMsIVmYSR5mkkdiGfscykisWLHCCzRb3Q6rdXR0VJaXl7dZ3Q47kCzMJA8zyWNAQ1NT02HTV2Z1QRFCCJE8cslLCCFEUkhBEUIIkRQZ+2CjGJpSygE8AkwFCoC7gU3AzwANvA0sjz8YmhWUUtXAm8C5QITszuIG4GIgH2M+oVVkYR7x/yc/x/h/EgU+Q5YfGyMhZyjZ5xNAu9b6Q8AS4PvAg8DN8WWKJMwbky7iPzgeAgLxRdmcxVnAAuAMYCFQT/bmcT6Qp7VeANwJfIPszWLEpKBkn98Ctwz6OgKcgvGbKMDzwKLxbpSF7gd+DOyLf53NWSzGGET1aeCPwLNkbx5bgbz4zK+lQJjszWLEpKBkGa11r9a6RylVAjwJ3IzR26+/u18PUGZZA8eRUurTgFdr/ZfBi7Mxi7hKYB7wrxjj4D0B5GRpHr0Yl7u2AD8Bvkd2HxsjIgUlCyml6oFXgMe11v8HDL4OXAJ0WdKw8fcfwLlKqZXAScBjQPWg9dmUBUA78BetdUhr/S4QxPxDM5vyuBYji5nAiRj3U/IHrc+mLEZMCkqWUUrVAC8AX9daPxJfvD5+/RyM+yqvWtG28aa1/rDWeqHW+ixgA/BJ4PlszCLuNeAjyjAJKAJWZGkenRyc8bUDcJCl/0+OhjzYmGWUUt8FLsM4le93DcYpfT6wGfiM1jqrZhKKn6V8HuNs7SdkaRZKqXuBszF+2bwR2EkW5qGUKsboDTkRY9+/C6wlC7M4GlJQhBBCJIVc8hJCCJEUUlCEEEIkhRQUIYQQSSEFRQghRFJIQRFCCJEUUlCEEEIkhRQUIYQQSSEFRQghRFJIQRFCCJEUUlCEsJhS6mNKKa2UCiulZgyzzWPxbXbGx2MTwnakoAhhvd9iDE6ZB9x06Eql1J3AlRiDFC7RWreMb/OEGBkZy0sIG1BKXYgxqVUEmKW13hFf/u8YgxT2AYu01q9Z10ohEpMzFCFsQGv9LPB3jLOUGwGUUudiTE+sgSulmAi7kzMUIWxCKXUOsALjLOWjwOMY089+RWv9oJVtE2IkpKAIYSNKqZcx5iPp9z2t9TVWtUeIoyGXvISwl+8P+vcfMaaiFSItSEERwiaUUhXAtwYtytFax6xqjxBHSwqKEDaglHICfwBmAusxpiK+QCl1uqUNE+IoSEERwmJKKYVxA/4M4F1gEfCb+OpvWNUuIY6W3JQXwmJKqQcx7pW0AqdrrXcopY4F3sb4pa9Ja/2ylW0UYiTkDEUICymlvohRTALARf0PNGqtNyNnKSLNyBmKEBZRSl0KPBn/8qNa698fsn4O8BbGL34XxR9+FMK25AxFCAsopT4IPIHxf/DLhxYTAK31JoxxvgDuit9rEcK25AxFCCFEUsgZihBCiKSQgiKEECIppKAIIYRICikoQgghkkIKihBCiKSQgiKEECIppKAIIYRICikoQgghkkIKihBCiKSQgiKEECIppKAIIYRIiv8PUH+tIo29nF8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.style.context('bmh'):\n",
    "    fig = plt.figure( figsize = (6, 4) )\n",
    "    ax = fig.add_subplot(1,1,1)\n",
    "    \n",
    "    # We should add a label to our dataset that will go into a legend\n",
    "    ax.plot(x, y, label = \"Quadratic\")\n",
    "    \n",
    "    # Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "    ax.set_xlabel(\"$x$\", fontsize = 1.6*font_size)\n",
    "    ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "    \n",
    "    # Display legend\n",
    "    ax.legend(loc='best', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, something to be aware of is that grey backgrounds typically don't print out very well unless you have a nice color printer!\n",
    "So typically we'll need to change the axis background color to white before we print. We can do that when we create the axes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:14:57.958604Z",
     "start_time": "2019-07-01T14:14:57.770278Z"
    }
   },
   "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": [
    "with plt.style.context('bmh'):\n",
    "    fig = plt.figure( figsize = (6, 4) )\n",
    "    ax = fig.add_subplot(1,1,1, facecolor = 'orange')\n",
    "\n",
    "    # We should add a label to our dataset that will go into a legend\n",
    "    ax.plot(x, y, label = \"Quadratic\", linewidth = 3)\n",
    "    \n",
    "    # Now we can label the axes. Always label your axes! Who knows what is in the graph otherwise\n",
    "    ax.set_xlabel(\"$x$\", fontsize = 1.6*font_size)\n",
    "    ax.set_ylabel(\"$f(x)$\", fontsize = 1.6*font_size)\n",
    "    \n",
    "    # Display legend\n",
    "    ax.legend(loc = 'best', frameon=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Matplotlib` has much more than just line plots\n",
    "\n",
    "`Matplotlib` enables us to make `scatter`, `bar`, `histogram`, `heatmaps`, `box plots`, and `violin` plots. \n",
    "\n",
    "You can see some simple examples of all of these types at the Matplotlib Gallery [page](http://matplotlib.org/gallery.html).\n",
    "\n",
    "One of the most used types of plots is a [histogram](https://en.wikipedia.org/wiki/Histogram). This is the type of plot you use when you want to examine the distribution of values in a dataset.\n",
    "\n",
    "When would you want to look at a distribution? Well what about when you have a bunch of GPAs for the class students? It turns out that we have that GPA data!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:02.810980Z",
     "start_time": "2019-07-01T14:15:02.801784Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3.27, 3.966, 3.25, 3.44, 3.62, 3.7, 3.7, 3.964, 3.46, 3.754]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def read_gpa_data(filepath):\n",
    "    '''\n",
    "    Reads in the gpa data file of undergraduate students\n",
    "    input:\n",
    "        filepath - str, filename\n",
    "    output:\n",
    "        gpas - list, float gpas\n",
    "    '''\n",
    "    data = [l.strip().split(',') for l in open(filepath).readlines()]\n",
    "    gpas = []\n",
    "    for gpa_line in data:\n",
    "        try:\n",
    "            fgpa = float(gpa_line[0])\n",
    "            if fgpa < 4.0:\n",
    "                gpas.append(fgpa)\n",
    "        except:\n",
    "            pass\n",
    "    return gpas\n",
    "        \n",
    "gpas = read_gpa_data('../Data/Day3-Visualization-and-Functions/gpa_data.csv')\n",
    "gpas[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now it doesn't make any sense at all to look at this as a line, we'll want to see how the GPA of our incoming freshman class is distributed. For that we use a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:09.206273Z",
     "start_time": "2019-07-01T14:15:09.005446Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4.5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.hist(gpas, color = 'steelblue')\n",
    "ax.set_xlabel('GPA', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('Count', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You just hand the numeric data directly to `matplotlib` and it will figure out how to bin it for you! That isn't always correct for your problem though. Sometimes you'll need to change the number of bins and you can do that by specifying a value to the `bins` keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:12.451791Z",
     "start_time": "2019-07-01T14:15:12.240711Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4.5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.hist(gpas, bins = 20, color = 'steelblue')\n",
    "ax.set_xlabel('GPA', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('Count', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also give it a custom list of bin sizes that you want it to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:16.299401Z",
     "start_time": "2019-07-01T14:15:16.107844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Count')"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4.5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.hist(gpas, bins = [0, 2, 3, 3.2, 3.4, 3.5, 3.6, 3.7, 3.8, 4], color = 'steelblue')\n",
    "ax.set_xlabel('GPA', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('Count', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You've noticed that what we've shown is just the count though. Many times people will want to look at the `probability density function (pdf)` or `probability mass function (pmf)`. When you plot the `pdf` or `pmf` it allows you to actually calculate the probability that a certain value on your x-axis will occur based on your data.\n",
    "\n",
    "To make one of these plots we just need to give the `normed` parameter a `True` value when we create the histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:19.461597Z",
     "start_time": "2019-07-01T14:15:19.220284Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/lgaalves/miniconda3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'pdf')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x324 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4.5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "ax.hist(gpas, bins = 20, normed = True, color = 'steelblue')\n",
    "ax.set_xlabel('GPA', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('pdf', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bar charts\n",
    "Let's say that you had some data about the different spending habits of 18-24 and 25-29 year olds at different locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:28.068768Z",
     "start_time": "2019-07-01T14:15:28.063543Z"
    }
   },
   "outputs": [],
   "source": [
    "locations = ['bar', 'grocery store', 'rent', 'clothing', 'plastic cups']\n",
    "young_spending = [100, 5, 80, 5, 60]\n",
    "old_spending = [10, 100, 75, 55, 10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And you wanted to create a bar chart to show the difference between these two groups. We can do that!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:29.944575Z",
     "start_time": "2019-07-01T14:15:29.773721Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Spending (\\\\$)')"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G3Nl3kHGwFMbrWGDfrNcPJun9Dq+uVNUngfv7ztG1qrq3qvYnWQVsAzb3nalrVfVAko8Cf8ngv8FUSPIGYG9VfbbvLONiKYzXvLOztTQlOQG4Ebimqq7tO08fqur1wEnA5Uke1XeejryRwTyr7cAzgKuTPL7fSEdmav4vtiM3Mbi++nFnZ0+PJI8DbgD+oKo+33eeriX5TeAJVfUe4MfAAeDBflN1o6pOO7g8LIZzq+q7/SU6cpbCeDk7ezpdADwGuDDJwbGFjVW1JAYeR/C3wEeSfAFYAfxhVd3XcyYtkpPXJEmNYwqSpMZSkCQ1loIkqbEUJEmNpSBJaiwFSVJjKUiSGktBktT8H1A4iaktexUNAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize = (6, 4))\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "#For a bar chart we need to create a dummy set of x-data.\n",
    "#It's easiest to just use the `range()` function on the number of groups\n",
    "num_categories = len(locations)\n",
    "x_positions = range(num_categories)\n",
    "\n",
    "#Now we can add the 18-24yo\n",
    "ax.bar(x_positions, young_spending, label='18-24yo')\n",
    "\n",
    "#Then the 25-29yo\n",
    "ax.bar(x_positions, old_spending, label='25-29yo')\n",
    "\n",
    "#Label it\n",
    "ax.set_ylabel('Spending (\\$)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Woops!** The bars are right on top of each other!\n",
    "\n",
    "You'll need to **offset** each of the bars. If you also set the **width** of the bars to the **offset**, you know that the bars won't overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:15:50.050942Z",
     "start_time": "2019-07-01T14:15:49.845031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.39999999999999997, 1.4000000000000001, 2.4, 3.4, 4.3999999999999995]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x11b6a72b0>"
      ]
     },
     "execution_count": 35,
     "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": [
    "fig = plt.figure( figsize = (6, 4))\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "#For a bar chart we need to create a dummy set of x-data.\n",
    "#It's easiest to just use the `range()` function on the number of groups\n",
    "num_categories = len(locations)\n",
    "x_positions = range(num_categories)\n",
    "offset = 0.35\n",
    "\n",
    "#Now we can add the 18-24yo\n",
    "ax.bar(x_positions, young_spending, offset, label='18-24yo', color='steelblue')\n",
    "\n",
    "\n",
    "old_xpos = [x_pos + offset +.05 for x_pos in x_positions]\n",
    "print(old_xpos)\n",
    "#Then the 25-29yo\n",
    "ax.bar(old_xpos, old_spending, offset, label='25-29yo', color='limegreen')\n",
    "\n",
    "#Label it\n",
    "ax.set_ylabel('Spending (\\$)')\n",
    "plt.legend(loc = 'best')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I really want to label the x-axis ticks too. We can do that by changing the positions of the ticklabels to include our offset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:16:03.633642Z",
     "start_time": "2019-07-01T14:16:03.360420Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, 'Bar'),\n",
       " Text(0, 0, 'Grocery store'),\n",
       " Text(0, 0, 'Rent'),\n",
       " Text(0, 0, 'Clothing'),\n",
       " Text(0, 0, 'Plastic cups')]"
      ]
     },
     "execution_count": 36,
     "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": [
    "fig = plt.figure( figsize = (6, 4))\n",
    "ax = plt.subplot(1, 1, 1)\n",
    "\n",
    "#For a bar chart we need to create a dummy set of x-data.\n",
    "#It's easiest to just use the `range()` function on the number of groups\n",
    "num_categories = len(locations)\n",
    "x_positions = range(num_categories)\n",
    "offset = 0.35\n",
    "\n",
    "#Now we can add the 18-24yo\n",
    "ax.bar(x_positions, young_spending, offset, label='18-24yo', color='steelblue')\n",
    "\n",
    "#Then the 25-29yo\n",
    "ax.bar([x_pos + offset for x_pos in x_positions], old_spending, offset, label='25-29yo', color='limegreen')\n",
    "\n",
    "#Label it\n",
    "ax.set_ylabel('Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "plt.legend(loc = 'best')\n",
    "\n",
    "#Axis labels\n",
    "ax.set_xticks([x_pos + offset for x_pos in x_positions])\n",
    "ax.set_xticklabels( [label.capitalize() for label in locations], rotation=90, fontsize = 1.3 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Maybe we want to look at this spending data as a scatter plot though. We'll have one axis be 18-24yo spending and the other axis be 25-29yo spending."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:16:17.036803Z",
     "start_time": "2019-07-01T14:16:16.880693Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '25-29yo Spending (\\\\$)')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "ax.scatter(young_spending, old_spending, color='orange')\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Those points are a bit small.... Let's make them bigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:16:23.463341Z",
     "start_time": "2019-07-01T14:16:23.300863Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '25-29yo Spending (\\\\$)')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "ax.scatter(young_spending, old_spending, s=50, color='orange')\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why doesn't scatter take a `size` attribute and use `s` instead?\n",
    "\n",
    "Well, in a scatterplot we typically would use the `size` to encode a third variable. This means that `s` will actually take in a list of sizes and apply a size individually to a datapoint.\n",
    "\n",
    "Let's say that for each one of these categories, I had data that represented how big that market sector was in millions of dollars per year. We could show that with the size."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:16:57.319742Z",
     "start_time": "2019-07-01T14:16:57.158847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '25-29yo Spending (\\\\$)')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Gjb+AbQPqX8+m1Q5QXfvEsu/DmxHasnwR+BB4G1gKLAuqavwXsA1wAk159TMGmvFPWGfZ+C/J6VBziVYOKvrbTQQ2tpTL7dNzoBlt2hMXptIOa0Xri9cJulQYFvgjIj4apHNHgeulPRt0ohk/8qWmVkzGbQUbPEb2PSn8OxF8MKl8Nb9sHBmanMLaYSy6ICR68Dyu6TS7QjXJpaiiMr/+UuaDIwFVogo3ahPUhvwMvBmRKxXdS7rZPz48TFp0qTezoZZ85v7BsyfnoLtgOEwbOV+fZFM0uSIKGts8WpHGVsHeLirgAsQEYskvQBsUOVxzKwvGrJsulnFqv1pegNYS1KXteOSRgHrAa9UeRwzs5ZSbdC9CRgJnC2pvVgCSUOBC0gX1q6u8jhmZi2l2uqF7wMfBg4HtpJ0KfAoMAtYAng/cAiwEvAicFrtWTUza37VDmI+RdJewEXAWsBJRZIJuB84JCJerz6LZmato9qSLhFxp6R1gI8CuwFrAksCbwFPkKoUroqIRfXIqJlZK6g66AJExDxSve0F9cmOmVlr678N68zMekHVJd2s48N+wFaklgztpHrcYiIijq72WGZmraKqoCtpJGn6ns1yi7rZJAAHXTPr96ot6Z4IjCcF05tJF87m1CtTZmatqtqg+xFSwN0/ItzxwcysTNVeSFsJeMoB18ysMtUG3TeBjnpmxMysP6g26N4IrClpXP2yYmbW+qoNut8ijbNwoaTl65gfM7OWVu2FtAOAq4BPAM9LehB4CZhfIn1ExEFVHsvMrGVUG3RPp3OyyYGk9rqblU7uiSnNzKD6oHsqDqRmZhWrdmjHU+qcDzOzfsED3piZNVDNQVfS3pJ+I+kmSfdmy0ZJ+q6kFWrPoplZ66hllLFxwGXAprlFdNbzrk5qVvZFSQdExI015DH/mN8E9gEGAb8BbgXOy477MHCcB003s76sqpJuwShjLwK/AKbkJZkFPAWMAK6TVPMU7JImAFsD2wA7ACsDZwAnR8R2pKC/b63HMTPrSdVWL3wVWA24HFg7Ir5E3jTrEfE4aer1S0ml0q/VmE+AXYGHgCuBa4HrSEH/1mz9DcDOdTiOmVmPqaVzxDzg2GzKnsVERIekTwF7ATtWeZx8ywKrZPtbFbgGaIuIXJXGDNIcbWZmfVa1Jd1VgUci4q2uEkXEO6Sxdper8jj5pgP/iIj5EfEEMJf3BtkRwNuFG0k6VtIkSZOmTZtWh2yYmVWv2qA7D1i6zLRLkOp4a3U7sJuS9wHDgZuyul6A3YHbCjeKiN9FxPiIGD969Og6ZMPMrHrVVi88BGwjaaOIeLBUIkmbkKZmXywYVioirpO0PXAP6cfiOOBZ4PeSBgGPAX+r9ThmZj2p2qB7LrAtcJGk/SPiqcIEktYE/kpqzlWXKdoj4utFFu9Qj32bmTVCtUH3z6Qpe/YAHpR0P6lEi6SzgHWBCdn+bwX+VHNOzcxaQFV1ulmLgf2BX2X72BJYhtRW9nOkplttpBLu3hHhWSbMzKihR1pEzAc+L+lHpItY6wEj6ewYcUNEPFuXXJqZtYiqg25ORLxCquM1M7Nu1Bx0JQ0HdgHWILWbfQN4FLglIhbWun8zs1ZSy4A3w4FTgONJXX0LvSPpe8BZHoTGzCypKuhKGgxcD+QGmnmGNMrXDFK97kbAONK0PpsCh9Uhr2ZmTa/aku7xwPakySgPi4iJhQkk7QycDxwq6YaIuLjqXPaGua/Df0+A+e/ARqfAqA17O0dm1gKq7Qb8CWARsEexgAuQjaG7D6kk/Jkqj9N7btkNnr0Apl4J/9oOFszs7RyZWQuoNuiuBjwcEQ91lSgiJgGPAM1XTHznUYiFQMCihTDnpd7OkZm1gGqD7rukAWfKIWB+lcfpPSvuBe3DoH0IDF0ellitt3NkZi2g2jrdq4BPSdovIq4slUjSFqROE83XjnebS+C5i2DBDFj149A2sLdzZGYtoNqgewKwFXChpK8Df4yIufkJJO0F/I40EtiJNeWyN7QNgNU+0du5MLMWU23QvYTUPGwoaX60H0t6FHgLGAasQ+d4u7OAhyUV7iMioh6Dm5uZNY1qg+5ueY9FCrTjS6RdIrsViiLLzMxaWrVBtx5znpmZ9TtVBd2IuLX7VGZmVqjaJmNmZlaFioKupFGStpO0WAlZ0m6SrpD0kKSJkr4paVT9smpm1vzKql6Q1Ab8GPgsMBhYHXg+b/2pwEm5p9n9dsCxknaPiMfrlmMzsyZWbkn3fODLpCZi80itFQCQ9EHgZFKwfQ34EnAoqQPFKsCV2Wy9Zmb9XrdBV9KOpCA6CzgKGBURj+Ul+UF2vxDYNSLOiohLIuIAUrBeCziyvtk2M2tO5ZR0DyO1qf1SRJwXEQtyKySNA7bI1l9eZACcXAn4wLrk1sysyZUTdLcllXKLjZ+Q30lisTEYImIqaYDz9arKnZlZiykn6C4PPJdNu15oQt7jm0psP53OLsFmZv1aOUG3DZhTYt0OpKqFhyNieok0S5OGgjQz6/fKCbqvAGMLF0raGMgNWPOvYhtKGk0a8PyVajNoZtZKygm6/wbGSNq+YPkReY9Ljan76ewY/648a2ZmraecoPv77P4SSXtKWk7SkaR5zwJ4KCLuKNwoG0/3xCzNJfXKsJlZM+u2R1pE/EfSz0mdHq7JWyVSXe9R+eklfRHYm3SRTcDFxYKymVl/VFaPtIj4CqkL8AukQCrgHmCniLivIPlnSUM/CrgMd4wwM/ufsod2jIhzgHOyQWwWRMSsEkmvJo3P8FeXcM3M3qvi8XQj4u1u1n+t+uyYmbU2j6drZtZADrpmZg3koGtm1kAOumZmDeSga2bWQA66ZmYN5KBrZtZAFbfTLSRpHWBP0rQ8I4CZwNPAPyPiv7Xu38yslVQddCUNA84GPkZn1+CcAH4k6VLgUxHh8XTNzKgy6EpqJw1+s2O26HbgPlIpd0lgM2BL4KPAssAuNefUzKwFVFvSPRLYiTQAzoeLVSNI2gy4AthJ0uERcX712TQzaw3VXkj7BKkK4YBS9bYRMRn4CKna4Ygqj2Nm1lKqDbobAk9mgbWkiLgXeBLYoMrjmJm1lGqD7mBS/W05ZgBLVHkcM7OWUm3QfR5YPxtbtyRJS5FKuS9WeZzC/Y2R9KKkdSStIel2SbdJOluS2xybWZ9XbaC6HhgClAx22fKzgUFZ+ppIGgj8ls7p4M8ATo6I7Uj1xvvWegwzs55WbdD9KfAWqUnYZEnHSdpa0oaStpF0PKkJ2YFZup/WIa+nA+cAL2fPNwNuzR7fAOxch2OYmfWoqpqMRcSr2Wy/1wIbA78okkzANGC/iHil+iyCpCOAaRHxD0nfzC2OiMgezyC1DzYz69Oq7pEWEXdJWgP4HLAH7+0G/ASpSuHsiJheh3weBYSknYH3A+cDY/LWjwCKTiMk6VjgWICxY8fWIStmZtVTZ2GxOUiaCHyaVGXxs4iYKOkc4JaI+GtX244fPz4mTZrUgFyaWX8iaXJEjC8nbVV1upKelHSypFWr2b5OvgJ8V9JdpIt1f+vFvJiZlaXa6oU1gO+Sgt4dwAXAZd3NFFwPETEh7+kOPX08M7N6qrb1wg7AH0n1qNuSWhW8IukySftIqnnISDOzVlRV0I2I2yLiWGB5YH/gymzVAdnjVyX9StKW9cmmmVlrqKkXV0QsiIirIuIjwHKkVgITSa0JPgvcIekpSd+StFrNuTUza3J16zobEe9GxB8i4oPAaOAEYDawGnAK8JSkmyS555iZ9Vt1rXuVtD5wEPBhYH06Z5O4C1iBNOj5BEk3Ah+JiBn1PL6ZWV9XjznSVgEOyW65IRwFTAEuBM6PiClZ2h2BP5C67P4aOLzW45uZNZNqp+sZTRp34VDStDyQAu07wGWkQHt74XYRcYukw0jT++xTVY7NzJpYtSXdl4B2UqBdCPyT1DX36oiY1822uXEYuktnZtZyqg26A4D/kgLtxRHxegXbLiC1bHioymObmTWtaoPuRhHxcDUbRsRUUmcKM7N+p9qg+3Q2wthSpHEPFgDvAq9ExDv1ypyZWaspO+hKGgR8HvgYqZVCqRkjXgZuAc6JiDvrkUkzs1ZRVucISWsBjwI/Jg1anruIlrvNBd4EFgErAh8HbpN0jSRPSmlmluk26GaTS/6T1LPsVtIUPO8HNgc+ATwMBGmg8WHAeNIIZK8BewE3SRrcE5k3M2s25ZR0vwiMBU6NiJ0i4vKIeDAiJkfEBaSS779IHSHGRMR9EfFdYG3SOAzjs32YmfV75QTdA0il1u8WW5nNU3YCaZCbY/OWzyB1nlhAqgc2M+v3ygm6qwIv5k0CWcwz2f1m+Qsj4lVSXfCa1WXPzKy1lBN03wXWlTSyizQbdLFuFDC/olyZmbWocoLuRNIFsnOLzQghaSDwM9LFtLsL1h0MjAPuqzWjZmatoJyg+31SSXU/4D5Jn5K0uaStsunNHwEmkJqM/QZA0haSLiZdXAvg5z2ReTOzZtNt54iIeETSIaTJJzcgC6x5BEwH9omI6dmybYGDScH6qxFxdf2ybGbWvMrqHBERVwEbksbAfYo0Qtg84HHgdGDDiLgrb5N7gK8B60fEr+qaYzOzJlZ2N+CIeA74XJlpbyeNmWtmZnnqNkeamZl1r5IBb5YHjgE2zbZ7lDSW7gPdbPcLUjXDB2vJqJlZKygr6EraG7gIGE7nZJN7AF+VdD5wfETMKrH5JsDWtWbUzKwVdBt0Ja0H/BUYQrpA9ndS8N0TWI80ueRmknaNiFdK7sjMzMqq0z2BFHDPAbaKiFMj4huk1gyfJbViWB+4VdIKPZZTM7MWUE7Q3RGYBXw5f/yFSM4BPgTMAFYHbpS0TI/k1MysBZQTdMcAT0XE3GIrs+ZhewKzgXWA6yUNq18WzcxaRzlBdybQZbVBRNwBHESaOWJz4IpsTAYzM8tTTtD9LzBG0r5dJYqIvwOfJrVu2AW4zDNGmJm9VzlB93ekQHqepOO7ulgWEX8kXXgTsDeptcPy9ciomVkr6DboRsRfSaOFLQmcBUyV9Kku0v8E+FL2dAPSBTYzM6P8AW8OB44jzRAhYGo36c8CdgVeqDWDZmatpJIBb84Gzpb0PuCdMtLfKGlN4CPANtVn0cysdZQddHMi4uUK0i4ELsluZmb9Xl1HGZN0qaRnuk9pZtY/1XtoxxVIc6KZmVkRHk/XzKyBHHTNzBrIQdfMrIEcdM3MGqjiJmPduAF4us77NDNrGXUNuhHxw3ruz8ys1dQl6EragjTAzSrAfOAx4PJs2nYzM8uUM0faC8DdEXFgkXXDgD+RuvpC56SVAZwm6TfAV7KeaWZm/V45Jd2VgOdKrLsM2I0UZG8kjb0r4P3AB4HjSaXfD9eSyWxA9HNJHS8GA98nTQF/Xnbsh4HjImJRLccxM+tpVVcvSNoH2B2YDuwREfcWrN8YuAbYW9LhEXF+Dfn8ODA9Ig7L5mC7nxTgT46IiZLOAfYFrqzhGGZmPa6WJmP7k0qZXykMuAAR8QCp2kHAUTUcB1KJ+lt5zxcCmwG3Zs9vAHau8RhmZj2ulqC7UnZ/TakEWTB+AtiohuMQETMjYoakEcDfgJMB5c1OPIM0yPpiJB0raZKkSdOmTaslG2ZmNasl6L6V3c/rJl0HMLSG4wAgaWXgFuCCiLiYNAlmzgjg7WLbRcTvImJ8RIwfPXp0rdkwM6tJuUG3vciym7P7rUttJGkcsBY1ziAhaTngn8A3IuLcbPH9kiZkj3cHbqvlGGZmjVDuhbQtJb1NajHwSHZ7HpgF/ETSdhExO3+DrGR6KSlgX1tjPk8ElgK+JSlXt/sF4BeSBpHaBf+txmOYmfU4dVaLlkggXUuaYHKVvMWFG50bEcdk6QcBVwM7AEOA14GNIuL1emW6WuPHj49Jkyb1djbMrMVImhwR48tJ221JNyL2znY6Atgwu22U3W8AjALezEs/X9IHs30/AxzQFwKumVlfUMnElDOAO7Pb/2TVCIWdEn5Gakd7ZUTMrzWTZmatouaxFyLixSLLvlnrfs3MWlHFTcYkLZn1Cis3/RhJYys9jplZKyo76Eo6Ppvp903gdUmvSvqZpO4av14OTKklk2ZmraKsoCvpAuAsYFVSt17LR+ACAAAV6klEQVQBY4AvAg9L6q4LrrpZb2bWL3QbdCV9HPgYMJM0atg4YH3gJOAdYDTwd0mH9lw2zcxaQzkX0j5Japd7WETkj7PwmKQ/AVcBWwB/lrQwIi7tgXyambWEcqoXNgZeKwi4AETEq8BOpNG+2oELJO1a3yyambWOcoLuUOC1Uiuz7r97AfcCA4G/ZdP3mJlZgXKC7ivAWpKGlEoQEbOAPUmtFIaT6njXr08WzcxaRzl1urcAnwDOBD5TKlFEvCHpQ8DtwPLATZI+Wpdcmpn1lFkvwItXwusT4e0HoWMutA2GJdeDMTvAyvvBiDXqdrhyBrxZmzQ9zmDgPuAK4KqIeKxE+o1IwzCOIY2lOwcYHhHFhodsKA94Y2b/8/YjMPkLMO2O9HzR3MXTtA0GCZbaDDb7OSxTfEybSga86bZ6ISKeAA4iNRnbjDQpZMmdR8SDwHbAk6SLa0uUkxEzs4aIRfDw9+Afm8NrN6dgWyzgAiyal0q+b9wBN24P930VFtU2uXlZnSMi4lpgTdI8ZTeRpuDpKv1TwCbAj0htec3Met+iDrj9YHjkNOiYw+Kj1HahYw48dTZM3AM6qh/Hq+xuwBHxekT8ICI+FBH3lJF+TkScBCxHasdrZta7Jn8RXr4eOmZ3n7aYjtkw7Xa46/Cqs1DLHGlliYj5ETG5p49jZtal126FKedWH3BzOubAS9fCi1dVtXmPB10zs14Xi+DOj9cecHM6ZsPdR1VVzeCga2at75V/wYKiE4ZXb9FCePGKijdz0DWz1vfkr2DhzPruc+EMeOLnFW/moGvWkzrmw73HwRXLw9Wrw9SreztH/dP0//TMft96IFVdVMBB16wn3fclmPInmPsazJoCdxwC0+7q7Vz1L/PfggXv9sy+1Q4zn61oEwdds570wmVZe9BMxxx4yaXdhpr3JrQN6pl9tw2A+W92ny5/k57JiZkBMGD4e59rIAwY2Tt56a/UTkWdICoS2f7L56Br1pM2PQPah6XHGgiDl4I1jundPPU3Q5evqQdZlzrmwrCVK9qk5inYzawLK+8HO/0Lpl4FA5eENY6FId3N5Wp11T4Ehq8MM3tgftwBIyp+Px10zXra6K3TzXrP+/aEp86BWFC/faoNlu9uTt7FuXrBzFrfWp+DtjqPLts2FNb9SuWb1TcXZmZ90Mg1YcyOqV69HtQOozaAZTaveFMHXTPrH7Y8N9Xv1kPbYNjm4uo2rU8OzMz6uKHLwzZ/gfahte2nfRhs8VtYYrWqNnfQNbP+Y8U9YasLs2Z8qnz79qGw2S9h1Y9XnQW3XjCz/mXs/jBybbj9QJj9Aiyc1f02A4bD4NGw7WUl50krl0u6Ztb/jFof9ngQtvgdjNo41dEOGAnKlUPb0/P2oTBi7VS63evxmgMuuKRrZv1V2wAYd2i6zXkF3pwMbz+cBihvHwoj14WlN0sdK+qo2ynYW4mkacDzvZ2POlsWeKO3M1FnrXhO0Jrn1YrnBJWf1yoRUVbXtH4VdFuRpEkRUft/nj6kFc8JWvO8WvGcoGfPy3W6ZmYN5KBrZtZADrrN73e9nYEe0IrnBK15Xq14TtCD5+U6XTOzBnJJ18ysgRx0m4ikgZIukHSbpHsk7SNpDUm3Z8vOltSU76mkMZJelLROC53TNyXdJWmypKOb/byyz9/Fku7MzqGp3ytJH5A0MXtc9DwkfSf7rt0paYt6HLdpXiAD4OPA9IjYDtgd+BVwBnBytkzAvr2Yv6pIGgj8FsjN4NgK5zQB2BrYBtgBWJnmP689gAERsTVwKvADmvScJH0d+AOQG3ZssfOQtCnpvfsAcDDw63oc20G3uVwGfCvv+UJgM+DW7PkNQOVD2fe+04FzgJez561wTrsCDwFXAtcC19H85/UkMCArBY4EFtC85/QMsH/e82LnsS3wz0heIJ17zXMtOeg2kYiYGREzJI0A/gacTLoYmrsaOgNYstcyWAVJRwDTIuIf+Yub+ZwyywLjgQOBTwMXAW1Nfl4zgXHA48DvgV/QpO9VRFxO+tHIKXYeI4F38tLU5fwcdJuMpJWBW4ALIuJiYFHe6hHA272SseodBeyS1a29HzgfGJO3vhnPCWA68I+ImB8RTwBzee8XthnP60ukc1oL2Bj4MzAob30znlNOse/Ru9njwuU1cdBtIpKWA/4JfCMizs0W35/VH0Kq572tN/JWrYjYPiJ2iIgJwH+Bw4EbmvmcMrcDuyl5HzAcuKnJz+stOkt+bwIDafLPX55i53EHsKukNkljSf9Uah5nwqOMNZcTgaWAb0nK1e1+AfiFpEHAY6Rqh2b3FeD3zXxOEXGdpO2Be0iFm+OAZ2nu8zoTOFfSbaQS7onAJJr7nHIW+8xFREd2rnfR+R7WzJ0jzMwayNULZmYN5KBrZtZADrpmZg3koGtm1kAOumZmDeSga2Z9nqQJWe/Fpud2umbWZ0kaBkwAjgfWkvQGcEdEvNWrGauBS7oNkPVouTv7wHSVrl3S8ZImSZqV3e6T9EVJVf9ASvqQpGslvS5pvqRXJV0haesK9rFalp8+3bBb0vqSfinpMUnvZnl+VtIlkvbr7fz1BEkbSIrC90bSedny03srb+WQNF7SAklHFyz/JPAScD2pl9jqpMGDnpf05SL7OURSh6TtGpHvqkWEbz18A34IBPBGF2naSR+oyG7PAE+Q+oQH8A9gYBXH/n7ePt8C7id15QygA/hMGfsQcHNuP739enaRzy+RBjHJnetkUtfid/Jeg5uAJXo7r3U+7w2KvTfAedny03s7j13kfSDwCGlEtra85R/Ne8+uJo3S9iRwSd7yzxbsS8C92XdnaG+fW8lz7u0MtPIt+xB8J+9D0lXQ/VyW5l1gx7zlW5MGTwngpAqPv3u23QLg03nLBwDfztYtBDbtZj+fzjuH6O3XtZtznUUasq+tYP1HgFezNJf3dn7rfO6lgu4KwDrA6N7OYxd5/1qW933ylgl4Plv+02zZKcDE7PGp2brpwOCC/e2crTult8+t5Dn3dgZa9QYsD1yVH6y6Cbr3ZGlOKLLuuGzdkxXm4V/ZdmeWWH9Ztv5PXexjZVJJcXYfD7q3ZPn7fBdpdsx7L9br7TzX8dyLBt2+fiONuvYW8HDB8nWy81kELJktyw+6SwDzgXnA5kX2e3f249snf2xcp9sDJH2I9FdoX1Lp6ptlbLZidv9QkXWTs/uxFeShjTQIM5QehOS67H7TLnb1W9K4ot8p99i9JHcO95ZKEBG3AFOyp5v3eI6sO8cBo0gzOORbKrufGxHvFKwjImYCawDDIqLY+/1HYBjwxTrmtW4cdHvGeqRf4wtIpZD/lLHN1Oz+/UXWrZ/dv1BBHtqAA0hVAw+XSDM8uy96kU7SJ0h/2y8kjaZfLM0ykuZlF2zGl0iziqRF2YWtYXnLR0k6RdKDkmZLmpnNJ/Y1SUPLO83/yQ1IvUc36XYD1gQuzcvHuCz/j0saLOkHkp6XNEfSE5K+J2lUqR1K2lfSPyRNlzRX0jOSzpK0QpG0R2TH+pWkZbP7F7LX8EWl+bkW2y7bdnlJZ0qakuXtMUmfp8T3uNiFtKzpVUi6TtJwST+U9HSW71clXShprRL7W0rSqdnrNEfS1Oyi5dKSJmb7nVDqdSrYVxtwLKlE+5eC1bnvwlBJmxTbPiJeiIiOEru/lHS94milqaD6lt4uarfijVQP+/685xPovnrhGDrrdHfIW74J8Eq27gt1zuc/sv1eVmTdcqQ6s9dJsyCU/AtLmpImgJ+VOM6JFFRjkAJfrt5uIemC10N0Xjh8AFi+gnP5K51/Sc8jzU3WVua247Jtn8p7TaYC99F5Ye4RYIWC7QT8js4qi5dJQx3OzJ5PA8YXbHNEtu4K0o/oIlLp++G8/TwHjCrYbq0sfZCqeu7N+1z8u9h7Q5ELaXmfxVtJF1UDeDF7vDB7/iawWsG+3kca8jBIf+0nk4aqzL1uj2SPJ5T5mm+Rpb+/xPq7s/UPA2uTV71Q5v7vyrbfqd7f75q/d72dgf5wo4ygm6U7KftCLQKezj7kHdmXuKKLaGXkabe8L/mHi6y/PFt3SPa8q6C7X16gWizQ5X0hd8qeDyRN+RLARGClvLRrkQJuAP+u4HxWB97IO6dc8Lia1KqhZB0unUE3stf7eDqHPV0VeJAiF+CAL2fLXwI+mLd8OGkSwyAFyhF5647IO9YT5F3EJP1Yz8rWfS1vuYA76WzFsnS2vI00DmzuhyoK8ncepYNukIJ2fr7XoTOQ/7pgX9dly+8GVs5bvj959f2UH3RPyNL/psT6jUmzNOTek1eyz8yWxT5jRbY/Pdv2ez3xna7pu9fbGegPN8oPugfmBaj828vktT6oQ37WJpXCciUeFazPNde5Lm9ZV0F3EJ0Bb0LBuk3oLE21ZcsOy5a9SnahpGCblegsLe5ewXmtSV6pr8jtEeCgItuNy0tzRpH1a9BZClw7WzYk75x3LJGf/1DwD4X3Bt0PFNnmD9m6K/KWbZ8te4uCEnC2/s/F3hu6D7rFXouTs3X3FXkPZ1JQ2s/WH5+3zwmF60u8Njdk6Y/rIs1qpH8wHQXv46tdbZdteyQV/nA36uY63T5C0ndIdVHLkqZ7HkG6yPAxUp3r2ZLOqMNx1ia1VV2W9OH9WGSf0mz9MsAvSZPwfaacfUbEfNKXA+DQgtUfy+4viojcPFR75i0rdqFkKunvN8Be5eQh2+6piNieNLPrD0l/9fPnvloPuETS+ZJUYje/KLLfp0kl8vy8bwMsA7wW6QJdMZdk97sXWfdmRNxdZPkT2f3IvGW7ZffXR0SxObr+WOL4XQmK19MXO/7e2f11EfFKiePPq/D447L7KaUSRMSUiDiIVLXxF9KPzpukqq9fSepqSvSnC47TZzjo9gGS1iG1m11E+qv/10gz/74TafLJXUglrS9J2riG42xOmvtpRVJ97a5ZgMv3C9LEkCdExIsV7P787P6A3MWL7GLJIdnyC/PS5i7U3N/F/u4rSFu2iLgvIk6KiM2BpYEPA+eSJoeEVNIu9oMyPSKeK7Hb3MXINbL79bL7JSTdXuxGulAE6Z9FoZdKHGdOdt+et2zN7P7REts8WGJ5V2ZGxLtlHn/dro4TEXPoDNblyk1lvtiPbpH9v0ZqDfQgqQnjD7JVn1WaEqmY3LnVPGV6vTno9g37kd6LiRFxV+HKiHgAuCZ7eiCApKO6+LIvRtIepNLaaOA10l/iBwvS7EUqqd4BnF3JCWSltidIQS5XMtuJVEq5PyLyW1DkZlid0cUuZxakrUr2w3V1RBxNqrN8PFt1bJHkXfXnz+U1N6NvriQ4nFTqLXZbt2CbfPO7yXp+STy3/awSaXM97ipRyfGX6eb40PV7WUyuNcjsSjaKiNkRcTLwf9mig0skzeV1iKTBFeatRzno9g2rZPePd5HmyYK0Yyn9ZX8PSYeSLigNI/2d2yYiirUH/kh2vw2wKNefP+vT/7/0ecsnFGx/QXZ/UHZ/SMHynFxAHUlpuUAzs4s0ufx8OWvadWZX6SLieTrbTK9ZJMmwIstycnnNjZ+R+1JfFxHq5rZ0d+fQjdyPwRIl1g/hvUGy3nLn2tUPYKU/jrl/HYv9IElaU9Ixkg4qXJfn1ux+tRLrc0E96P4HpqEcdPuG3F+hou0zM7lg+y5ARJxS6kuev5Gk/Ul//QeQWgVsExHPlDjGk6RSbrFbflVAblnhX8MLSB/yPZVmVd2TdBGksB1m7q9o0TaYmc2y+6e7SJMjUjXEPlmVRldey+6nF1m3QhftcXPVOrm/+E9l9+sWSZsyldr/bilpTDd56k7u9SrWhrvLPNRJ7pw3LLYyK0kW+xHryuvZ/TJF1h1Caor39S62zwXrUiXl3H5fz79m0Rc46PYNE7P7XSWtWLgy+9Lumj29tXB9KZLWBy4i1c/dQ7qy/Gqp9BHxw4jYttgNODwvXW75/QXbv5DlbxSpmdZywL+KHPP67P5QScVKOiuRevMB/LOMU72cVOe9GulKelcO7GK/Iu888/KzNrAdqc49V83zb1IpfHVJO5c41h9J7UV/1k2eunN1dr+HpPcVWX9Ejfuv5PjFfkAOASrtzJL7IVns8056zQA2kbRR4UpJ7XRe3CvVAzG33ydLrO81Drp9ww2kxubDgGuzLzmQSkukzgdLk5o8XVFk+1J+R/rr+Sqwd4kr3/WWu6D2rey+sGoBUkuHJ0hB+aosyAKQ9Ya6nvRa3EXnF76k7OJXrmrhzKw32Hu6TGe9v35M+jF4BzitxO5+KGnPvO3WIL3mbcDZuav3ETEj75gX5QdeSUOzqo6dSCX9n3d3Dt2c32TSCHTDgCsLXq9PAp+tZf9lHP8eUvvgJYAr8gO/pF2o7vxyLTe2LLLuRtKFSwEX5r+XSj0Vf0/qpTkL+FOJ/ef2e2cVeetZvd1mrT/cKK9H2iqkv6xBKlE9SqrjzbVRnAKsXsExt6SzXeNU4PYubov1SCuyv7IGVSHV7eUa+M8g9Y8vlm4dOnukLaB4j7SVKzhfkYJgfpvOZ0lf7vzX8WVg+4Jtx+Vtk+sZ9jTv7aV1M3mdHLLtBpCa+eW2fY7UTC1/KMmjC7Y5Ils+qcR55Nq8TixYvnz2+uTqKCfR2UPtmmLvDV230y36WSQ10QvguYLl76OzB1quR1ru8/oAqclYANuW+X5tlXuPSqxfj8625HOz9+3d7DOVy8NHuth/7n0s2oa6N2+9noH+cOvug56XbiSp6dgDpLqq2dmH51SKNIrvZl9fzfvid3d7roz9lT2SFXBxlva8btItBXwvO8fZpB5IdwNfAIZU+VpvTOqNdB+p/nZ+9uW9k1RHWKxzwbi812IFUvB+mfTjcX8WCIuOZUwK9geRqiumk35AXiOVjrcrkv4Iqgi62bolge+S/iXMyYLgKaQfuh4Nutm6pbPX5llSkH0ROCPLVy7oblbm+yRSQSKANUukWRE4izQ+bu79eZP0I7NFF/tekfTj/RJldgVv5C3X1dGsbiRNBHYgdTG9uZez062sCufZ7OmISKNYWZmyv/y5C1orRUSpNsiF232BVDXxw4g4qZu0p5L+oUwoY78nktryfiMiflJOXhrJdbpWV5JWI3VbfY40xq01OUm7ZyOR/aZEkly77GnlBtzM70n/Qg4vo9XJom7W5zuC9K/pnAq2aRgHXauZpOUkrSXp/aSLZAJ+Gf4b1SruJ1XBfErSezojSNqCNLgPVBjkImI28FPSWBsHdpP2lDJLuXuSmq+dFcV73PU6Vy9YzSTtDvw9b9FjwCYRUWl//F7h6oXuSTqZVP8OacSvl0jjd4zLlt1A6sJeUUcEpQlX7yX17FsvIhbWkMc20g/EINLQqn3y8+eSrtXDk6Qv4mxS8P1QX/3AW3Ui4vukevorSRfxNiQ1YZtIGtFrr0oDbrbfhcBRpCE0j+4meXcOJV3wPbovf/5c0jUzayCXdM3MGshB18ysgRx0zcwayEHXzKyBHHTNzBrIQdfMrIH+H0aDKDHq2NkWAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "market_size = [500, 400, 800, 10, 20]\n",
    "\n",
    "#Figure code\n",
    "fig = plt.figure(figsize = (5,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "ax.scatter(young_spending, old_spending, s=market_size, color='orange')\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Alternatively, we could show the market size through color instead of the marker size.\n",
    "\n",
    "To do that we'll need to use a `colormap`. Why is that? Well we want to show, on a continuous scale, how different the market sizes are of these sectors. For us to make a list of colors and map that to an a numerical range would be a lot of work.\n",
    "\n",
    "Fortunately matplotlib has a whole host of colormaps that go from a minimum value to a maximum value. There are **three** types of color maps to use:\n",
    "\n",
    "* **Sequential** - This is when you have a value that is **only** growing or decreasing. This means, *ideally*, that its minimum is logically zero and the range over which you evaluate it is continuous. Think of something like the GDP of countries: some countries have a very low GDP, others have a high GDP, but everyone has a positive GDP and a country can have any GDP value greater than zero.\n",
    "* **Diverging** - This is when you are plotting something that has **both** positive and negative values. This typically occurs when you are comparing **new** situations to some baseline case. So, in biology, it is the up- and down-regulation of genes in a new condition, or in economics the change in employment by geography over time (think like retail workers now versus 1980).\n",
    "* **Qualitative** - This scheme is for when you have **groups** of data that are not continuous. This is like age groups or preferences (do you like cheeseburgers or pizza?). These scales aren't typically represented as continuous, so we just want a set of colors that maximizes the visual difference as much as possible.\n",
    "\n",
    "These are the color schemes that `matplotlib` has available:\n",
    "\n",
    "### Sequential\n",
    "<img src='http://matplotlib.org/_images/colormaps_reference_00.png'></img>\n",
    "<img src='http://matplotlib.org/_images/colormaps_reference_01.png'></img>\n",
    "\n",
    "### Diverging\n",
    "<img src='http://matplotlib.org/_images/colormaps_reference_02.png'></img>\n",
    "\n",
    "### Qualitative\n",
    "<img src='http://matplotlib.org/_images/colormaps_reference_03.png'></img>\n",
    "\n",
    "Not all of these color schemes are good choices though. Don't ever, **ever** use the `jet` colorscheme. I haven't even shown it to you so you can try it, but never use it!\n",
    "\n",
    "So let's actually add color to our example instead of size. Our market sizes are all positive, continuous values so we should use a sequential color scheme. I like the `Purple Blue` color scheme typically, so let's use that.\n",
    "\n",
    "We'll need to get our color scheme (which is called a colormap in `matplotlib`) and we'll need to tell the `scatter` function what `colormap` (`cmap` for short) it is that we'll be using. Instead of specifying a color, we'll give it the values to use with the colormap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:17:30.934555Z",
     "start_time": "2019-07-01T14:17:30.776690Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '25-29yo Spending (\\\\$)')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We get our colormap here\n",
    "cm = plt.cm.get_cmap('PuBu')\n",
    "\n",
    "#Figure code\n",
    "fig = plt.figure(figsize = (5,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "ax.scatter(young_spending, old_spending, s=100, c=market_size, cmap=cm)\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Great! But when you show a plot like this your audience needs to know what the color corresponds to. To make that happen we add a `colorbar`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:19:36.137445Z",
     "start_time": "2019-07-01T14:19:35.880372Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We get our colormap here\n",
    "cm = plt.cm.get_cmap('PuBu')\n",
    "\n",
    "#Figure code\n",
    "fig = plt.figure(figsize = (15,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "#The scatter function returns itself, we put it into a variable and then feed it into the colorbar function\n",
    "sc = ax.scatter(young_spending, old_spending, s=100, c=market_size, cmap=cm)\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "\n",
    "#Adding a colorbar\n",
    "cbar = plt.colorbar(sc)\n",
    "\n",
    "#You need to add a label to the color bar too\n",
    "cbar.set_label('Market Size (\\$Millions)', fontsize = 1.2 * font_size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But I would really prefer it if the colorbar could start at `0`. Just like having a bar chart bar start at zero, it's just a good practice. We can add in the value maximum and mininum parameters for the colors in the scatter function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:18:39.652499Z",
     "start_time": "2019-07-01T14:18:39.377402Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bar 100 10\n",
      "grocery store 5 100\n",
      "rent 80 75\n",
      "clothing 5 55\n",
      "plastic cups 60 10\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#We get our colormap here\n",
    "cm = plt.cm.get_cmap('PuBu')\n",
    "\n",
    "#Figure code\n",
    "fig = plt.figure(figsize = (5,5))\n",
    "ax = fig.add_subplot(1, 1, 1)\n",
    "\n",
    "#We add in the x-data points and then the y-data points\n",
    "#The scatter function returns itself, we put it into a variable and then feed it into the colorbar function\n",
    "sc = ax.scatter(young_spending, old_spending, s=100, c=market_size, cmap=cm, vmin=0, vmax=1000)\n",
    "\n",
    "#Labels....\n",
    "ax.set_xlabel('18-24yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "ax.set_ylabel('25-29yo Spending (\\$)', fontsize = 1.6 * font_size)\n",
    "\n",
    "#loop to label data points\n",
    "offset = 3\n",
    "for index in range(len(locations)):\n",
    "    print( locations[index], young_spending[index], old_spending[index] )\n",
    "    plt.text(young_spending[index] + offset, old_spending[index] + offset, \\\n",
    "             locations[index].capitalize())\n",
    "    \n",
    "#plt.text(60, 60, 'Grocery', fontsize = 32)\n",
    "\n",
    "#Adding a colorbar\n",
    "cbar = plt.colorbar(sc)\n",
    "\n",
    "#You need to add a label to the color bar too\n",
    "cbar.set_label('Market Size (\\$Millions)', fontsize = 1.2 * font_size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-07-01T14:18:41.914943Z",
     "start_time": "2019-07-01T14:18:41.908569Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bar 100 10\n",
      "grocery store 5 100\n",
      "rent 80 75\n",
      "clothing 5 55\n",
      "plastic cups 60 10\n"
     ]
    }
   ],
   "source": [
    "for index in range(len(locations)):\n",
    "    print( locations[index], young_spending[index], old_spending[index] )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! You've covered a lot of `matplotlib` techniques. There's always more to learn, especially in this library, but this should be more than enough to get started!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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