Visualization With Seaborn.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Visualization with Seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
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   "source": [
    "Matplotlib has proven to be an incredibly useful and popular visualization tool, but even avid users will admit it often leaves much to be desired.\n",
    "There are several valid complaints about Matplotlib that often come up:\n",
    "\n",
    "- Prior to version 2.0, Matplotlib's defaults are not exactly the best choices. It was based off of MATLAB circa 1999, and this often shows.\n",
    "- Matplotlib's API is relatively low level. Doing sophisticated statistical visualization is possible, but often requires a *lot* of boilerplate code.\n",
    "- Matplotlib predated Pandas by more than a decade, and thus is not designed for use with Pandas ``DataFrame``s. In order to visualize data from a Pandas ``DataFrame``, you must extract each ``Series`` and often concatenate them together into the right format. It would be nicer to have a plotting library that can intelligently use the ``DataFrame`` labels in a plot.\n",
    "\n",
    "An answer to these problems is [Seaborn](http://seaborn.pydata.org/). Seaborn provides an API on top of Matplotlib that offers sane choices for plot style and color defaults, defines simple high-level functions for common statistical plot types, and integrates with the functionality provided by Pandas ``DataFrame``s.\n",
    "\n",
    "To be fair, the Matplotlib team is addressing this: it has recently added the ``plt.style`` tools discussed in [Customizing Matplotlib: Configurations and Style Sheets](04.11-Settings-and-Stylesheets.ipynb), and is starting to handle Pandas data more seamlessly.\n",
    "The 2.0 release of the library will include a new default stylesheet that will improve on the current status quo.\n",
    "But for all the reasons just discussed, Seaborn remains an extremely useful addon."
   ]
  },
  {
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   "source": [
    "## Seaborn Versus Matplotlib\n",
    "\n",
    "Here is an example of a simple random-walk plot in Matplotlib, using its classic plot formatting and colors.\n",
    "We start with the typical imports:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {},
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('classic')\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5It3L41IJy4S"
   },
   "source": [
    "Now we create some random walk data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wQQpeapOJy4T"
   },
   "outputs": [],
   "source": [
    "# Create some data\n",
    "rng = np.random.RandomState(0)\n",
    "x = np.linspace(0, 10, 500)\n",
    "y = np.cumsum(rng.randn(500, 6), 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o64ZM0RhJy4W"
   },
   "source": [
    "And do a simple plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 273
    },
    "colab_type": "code",
    "id": "2LfL_LEPJy4W",
    "outputId": "33aa351f-41dc-4dd3-ec19-d6b2f4e74d22"
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the data with Matplotlib defaults\n",
    "plt.plot(x, y)\n",
    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "z5DNQox7Jy4Z"
   },
   "source": [
    "Although the result contains all the information we'd like it to convey, it does so in a way that is not all that aesthetically pleasing, and even looks a bit old-fashioned in the context of 21st-century data visualization.\n",
    "\n",
    "Now let's take a look at how it works with Seaborn.\n",
    "As we will see, Seaborn has many of its own high-level plotting routines, but it can also overwrite Matplotlib's default parameters and in turn get even simple Matplotlib scripts to produce vastly superior output.\n",
    "We can set the style by calling Seaborn's ``set()`` method.\n",
    "By convention, Seaborn is imported as ``sns``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "jKVefpprJy4a"
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sns because of it's inventor => Samuel Norman \"Sams\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-rMyikMpJy4d"
   },
   "source": [
    "Now let's rerun the same two lines as before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Dh0h0ETfJy4d",
    "outputId": "8b6149de-788b-4d6e-ce34-99aeb65ef2b4"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# same plotting code as above!\n",
    "plt.plot(x, y)\n",
    "plt.legend('ABCDEF', ncol=2, loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ampEUE47Jy4g"
   },
   "source": [
    "Ah, much better!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-TgtGs0vJy4g"
   },
   "source": [
    "## Exploring Seaborn Plots\n",
    "\n",
    "The main idea of Seaborn is that it provides high-level commands to create a variety of plot types useful for statistical data exploration, and even some statistical model fitting.\n",
    "\n",
    "Let's take a look at a few of the datasets and plot types available in Seaborn. Note that all of the following *could* be done using raw Matplotlib commands (this is, in fact, what Seaborn does under the hood) but the Seaborn API is much more convenient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KMN-5k2FJy4h"
   },
   "source": [
    "### Histograms, KDE, and densities\n",
    "\n",
    "Often in statistical data visualization, all you want is to plot histograms and joint distributions of variables.\n",
    "We have seen that this is relatively straightforward in Matplotlib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vx6AjIGMJy4h",
    "outputId": "c1d8a3a5-75a7-41ce-e185-30875c74e1c7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\furqa\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  \"\"\"\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.random.multivariate_normal([0, 0], [[5, 2], [2, 2]], size=2000)\n",
    "data = pd.DataFrame(data, columns=['x', 'y'])\n",
    "\n",
    "for col in 'xy':\n",
    "    plt.hist(data[col], normed=True, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZN460bgvJy4k"
   },
   "source": [
    "Rather than a histogram, we can get a smooth estimate of the distribution using a kernel density estimation, which Seaborn does with ``sns.kdeplot``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Glj5WbuoJy4k",
    "outputId": "f37dbe2c-de77-4154-996d-38e16c10e190"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in 'xy':\n",
    "    sns.kdeplot(data[col], shade=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wdZyWxOWJy4n"
   },
   "source": [
    "Histograms and KDE can be combined using ``distplot``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7lDYsc2cJy4n",
    "outputId": "34ce0074-f391-4f1c-c664-55aafc879b43"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(data['x'])\n",
    "sns.distplot(data['y']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "w8_He8o7Jy4p"
   },
   "source": [
    "If we pass the full two-dimensional dataset to ``kdeplot``, we will get a two-dimensional visualization of the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6-ky27G8Jy4q",
    "outputId": "f8bcdcd5-605f-437e-9261-22b359c1b7e7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\furqa\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:679: UserWarning: Passing a 2D dataset for a bivariate plot is deprecated in favor of kdeplot(x, y), and it will cause an error in future versions. Please update your code.\n",
      "  warnings.warn(warn_msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "M-ROMGvhJy4t"
   },
   "source": [
    "We can see the joint distribution and the marginal distributions together using ``sns.jointplot``.\n",
    "For this plot, we'll set the style to a white background:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "4HeVYgtfJy4u",
    "outputId": "235fd7a0-893d-4d7e-d6d1-db1422d85a8a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"x\", \"y\", data, kind='kde');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-hEZ2VIIJy4w"
   },
   "source": [
    "There are other parameters that can be passed to ``jointplot``—for example, we can use a hexagonally based histogram instead:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DJlm4IrtJy4w",
    "outputId": "d62928f3-3580-4168-c7ee-54a85da96bcb"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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zh27F4DTvnmXik10ljHSuknOfxGi/+GQTIO3TJ2BbxsAxm8TocEMiRJxYpFR4/tWd3KW1nh8jFjI3uVQqLUbtnld4nYW6i5WFMjgfX/3mnKFcsrC1Hhwoqm4SmRj5Ba4MiVCIhcJCxYZxB3lBWb5UHmEkYBr8ruce7UcpFG7WR7EAZ4BzC4nBxGygPSGCuMfoXJjD8TZ+WPpBEBkkQgRBEMTMIBEi7itKKfSDGNvtIHcD/yBtxImAH8S55qJCSux2A3T6YaEDwpmlCkpO/gZ9MthzmNyGY3GcXaqi7OYv+/SDGOs7XqERarPmFFb9nIZlcMzXncLvEicCrW448JC7VZRSaPdC7Ex5dmGUIIonm61mbVzf6CKIJpcokFLBD2OEBfcd0MX+eI7jA4N2gzjK0XJZnlCWK7Sx4w3+O055Q7RgStwXlFLwggS7XS0KSgF9P4brmGjWnIG1y7Q2tLebGAhLlMiB6aVhcAgp0e5F6HkxFPRg1OpFaFRt1Mr2INoN0B5w83UXzZoLL4yxuuXBD0cHRiEVRLbpPrSh71iG7rfFwaBFJIgS3Nzqw9tX/8ULEvhhgs2Wj8VGCUvN0tieSbVkoeJaSITEbjccVP2chmVyzFVtuI4JBqDiWhBSYbcbwA9H20iEQteP0fNjlEsW5qr2gfZulFJo9UKsbnmQUkIqvV9Wck3MVcefXSIUEpEgiEYNSzv9CKtbfSRCt7HbDVEr21hZKMO1TUiptIANCVwW7LC/6iljgG1x2BYfqVjLgJFIvaPMcJ7Qfo5T3hCJEHHPiWKBjV1/ID4ZCtojLQgTLM65KDlm7p6FkAqeH2FS4FgiJHq+RBQL+KEYCTRQ6f/aXS1MKwuVEb+0zLus4lp4+GwDq9s9bLfDiX0IYx2e/dDZOhxLl+Teb8b50JkGNnY9bOz6I5/NvvdW28d2x8djDzQHg/NwP2xuYGmuhK4XodUrfttdmnMH4jPcD84ZFhsl9IMYO53x76KgXwD6foyV+VLh5r0XxLiy2h2Iz3AbXpDADxIsNd20RPnos8tC2rtehM1WACH2taG0MHW9CEtzJZQmVI5V2Ct5Xi3bQ99z71qmwVEt6UjDbGZEe19HBxIh4p7jT3ClHkZB2/8XDRzJvgFs8nVEfhIpANvU+SOTyESg28+P/AK00NiWMXEZaNBGwVKJUnp2UDQD4ZxNnQllpSV4zj3jnE2tYaRnE8UzhnY/Kly+U8BEARrGD0Xh8l12T4ooiujLrs05ic9RhPaECGKYuzKGTW8k33PhFroxteb3QRo5PpAAHU1IhAiCIIiZQSJEEMPclWCqu1AO4i704q40coSjy4ijAYkQcUcopXR1z4LBSkc2Fa8OZeG4eWG9UzxBD2QcmoUNy5zNJSElyq5V+F3CWEBK5LYhpUJ1StRSFAsIoXJDtpVScAtCrQG9RxYLWRiiblu82NRToXi/RymUXauwH4wBUSxRpHi2xdMgjvw2dEXavCJ+OipSKRSG2xNHEwpMIG4J7QDNBiaZQZRACC0ApTRvIzuerdGXHBNnl6ro9EN0vRhQe0MWT6PKwkgPzK5jYv8edJSVzc7pj1TaODS/EqlCnEh0ogQ73QDzdRen5svgjIFzBiElEqFw5WYbG7s+OGdwbR08sH+fIU4kXriyg2bdwcp8BZzrNnSZbYVWN0IUS1TLFsJw1KB0EGIeCfzTc2tYapbw4OkGTIPBMPjABDWMdInwetmCH41u6kulEMUCrU6Aa+tdLDRcPHimAdvkgxB1pYDrG13c3OxDAWhUbFTK9iCKjgGwLJ6Gxo+LnVT6AXW9CJ1+hFrFTnN/9vrBmI5Ka9ac1EB1ssLYFketXMZio4TNto+tXV9Xl02L+hmc7eVIjUXXpWH4sUQQC7R6EaolC3NVB0j9+I4zD51rYGWlOfFYlje0n5JrHrnQbRIh4pZQSr9lZ+KTIYRCz49hGAxl1xobkvRg46JRcdDqhfCCJB3o9yxtMl8ygzM4tgkpJcJYTHxBllJBKoUwErllrIWQA6EcbmO7HWCnE6BZc9CoOLix1cPmUEi1lDqniXNt+rk/MksB2OmE2OmEaNYcLDdL6PkJekOeagbnKJe0oWjPixAnYpDLkrG562Nz18fSXAkPn2sgEVpgMjhnqLg6f6bdjxBEAu1uMJJHs90OsN0OsNBwcW65hu22j5tb/ZHr7HZDtPsRlpsl1MoO5mp2rh9eGAsEYYJOfy8cnjOGkmPBsRXiWOdozVW1cEwKBuCMwTQYnJHoPYaV+QqW5krY2PXQ7kVoVHRy7aQ2EiEhhEKw7/l3vRg9L0atYg+czo8rRXlCeRzF/CESIeKWEFLBC/LDmIXQy115IcicM9Qr9kiuzq1eA9BC0PMnZ9xnhHF+aLBSegB/9WYn9/NSKsRCFoYH73ZDCKlyB0OD67Dt/Qmsw2y2fCzPl3PvCU8z/7daXu4WTSZGhd8lkViacwu/z1bLz51RcqZfMKql4pDskmvmPn+DcyzPlVF2ipf5dMmMnJktdJ5To2KfmBLpxxnaEyIIgiBmBokQQRAEMTNIhIhbwuAM1bKV6/VmmXyqcSSf0oa2YbFQdic7AkSxwCs32vjhlR1stfyxSDWlFHY6AZ5/dQeXb7TH/OCGqZXtXNcAxzYwX3dRca2JS2VK6YJ0Nzd7aHWDiRFzWVRXpWQVRu+9eHUXN7d6E6PVoljADxJUXCt3mcs2OWplO92nGT+eBYfkGX4qpbDd9rHbDeEF8cTvYho6iKBWsQu9/rwgRt+PIGROhVnOUJvy/JebJSzNuRPP4ZzRUtwxgvaEiFtCVwnlKDkMrg0EkS5nbZk8tW+ZnrnO03jd/W2YBofrGOBp/R2uFKrlPYNKP0zw2loX6zveIMJqbbuP9R0Py/MlzNdctHsRLt9sp6HUeiB9+XoLFdfCykJlxO06s9kpOSZc2xh4lDm2gWbNhWnwwaBdMbQxqI7Ck7qSaCx0aW/oSLKuF6FWtlGvOJCpQGV2RabBUXEt7RC9L6gD0GI1CDKou1ieL0NKhd1uiDCtYGoYHGWXpW1rHzvb4nDsPf84x9a2QlGiTVdd28SDZ+qYq7kTS3xngr2+46XBHtn+UTR4prbF0ag6KLt719n/7Ebb1CamPS8ePFNjqKBfVl+p5DC4DhCGyZ4R7dDz1383EUTa/VtIhUbFRrVsUfnuYwSJEHFLDBtIZgN4yRk9dittFbWR/dk0OMIowj8/vz4QnwyZ/sP6toeXr7UGg+gwKnV9vrHZxcPn5ibOrvSgaGK+7sA0xr3hWBbxZRnY7ewFAGRXyvrU9aJUyMZ/tRhjOpTdMdHzxgMvsja2OwG6fjwxgo0xBoMxlN30Oeh/HDvHsUycnq/gzFI198VAKYUXru6OGYtmxIlE2QVOL1TG2hh+domIcgMmtOu5zjfa34csXNx1TLg5z58BKNkG3IXyIOKSxOd4QSJE3BF3Y0DYn1c06bgOf85f5pNKR9UV5TLapoFBfYccjKHZzyR0JBxyzVSVwvTyCFPyLZVCYfQgcLD7Xnatwu8iFabWdCqq2ZP1Y1r+6KSy5uNtFD9/BhSecxwpyhPKY8qtPpSQCBGHggMNLtna1x1d6A4/f5eY9lUOSTfvGwd5/idJgIDbzxM6ahxB3SQIgiCOCyRCxH1hWpnlA5Vhvhu+YXejiWnLaXehG+SQNs5d+RkiDh0kQsRtk5ljxonINcqUaURZmFrW7D9He79pa53MimdSGwsNN93cHu+HjtgDlptl7cOWs4fRS6uJ5pmPAkASy1yV0YX1FLp+lO4/jZ832LvIaV9IlTpCJLn94AywTO1dN+mrZCasQZjkGnoyAB0vTEPEJ9xTpQClUC3lOxcwBvT9CEkyuY2BeS2Q+/wB5JqsHhTtUygH93zSz5BSSrt1FBm2EocS2hMibonhX/AwFoPqnQyAbRtwLGMQwRbFAqtb/YGnWsU1cXqxoqO+0sG150Vo92JIpTf8a2Ubjcqe95WQCrvdAH4o8NDZBrwgxupWH16a+8OgxefCSg2ObSARElfXOrj0WhtSSQynqgip8OpqB65t4PRiBZWhjXsrDQfevwmfGbXGicS/vLiBV2+2oVRWyruOZt3V1jzQUV5zqSloZpqa+dbJVHyurHawtqPtdyyT4/RCGY2KrY1QGVCvOnjwTAPVknbzbvcjrG71tQCmgRetXoieF+uwbc7QqNqolCwd2gxtGtqsuXBsA30/DZO2jdT+R3+vdlpW2zD4mEEpg+5bs+bAdUz0g3jEoFYBIwanUmFwXdc2ALD0xUBXfx32B7zVn7PhcHQAqbmsiWHf1eH7jLQfWdg6cPL2ko4aJELELZENyNE+V+vM/Tn7r+fHI4aeANAPErx8vY1a2UKj6qDvj77JKwV00sGxWrKQCAk/HL1O2bXw8Lk5BGGMMBZYWaiMlIY2DY6Hz87h4kod//PZVbT746W2g0jg1ZsdlF0Tr39wHo6VHwF2da2D6xs9XFntjEyQ/DDBc6/uoOSY+Jm3PoBKyRpxpGaMwbYMWCbH9Y0e1rY9rO+Oer/FicRr6z1YJsfP/sQ5LDcrqAzNTBhjmKs6aFRsvHKjjRsbPXRT8Rl+HjudEO1ehNddmEO1ZMOxR5NvEyHR8+Xg/J4fj/Rj2KCUQaV5U6NDQ2ZQC2hB7fnxSISgAhDGEmEssVB34Ni3Jz4ZUSyRCDmWvCtTX0HOGSyDI0rGDW6jWCKKI5Rdc6JLOHG4mJkI/emf/im+/OUvAwB++qd/Gp/85Cdn1RXiFshmOEXsdEadnvfjBckgYTHvGt0JeTTDlF0Lp+bL+cahBke1bE0UoYwwFoUCBEw3OfXDBI2qUxheHMZikGA7iTiROLdcyx0wGWOwTQP9IMltQ0hV6P4AAFEiC41UOdNOBkZBnK8WtGLjWMvkhY4KB2GSAA0jpUIoi38Oi5ZdicPDTETo29/+Nr71rW/hS1/6Ehhj+OhHP4qvfvWreM973jOL7hAEQRw6bidPaFKdocNeY2gmIrS0tIRPfepTsG19Yx5++GHcvHlzFl0hboNpOS4H2RietkrDCxJCs2sIKWEa+W/+096EVVogznXyfw2yvZii2VIYFbdxkPvhBQnqlfEiehlCyqnfJ9u4z59h3vlzAfSM6TBUOGXseFcfv508oUkc9hpDMxGhRx99dPDnK1eu4Mtf/jL+8i//chZdIW4R02CoVeyxzWBALyu1emFukTkAKLsmmjUHBmeIhUQQihGxyTbaqyULUulKpcN7S4PqpGGCVi+Caxtpdc89MQojgZeutbA2ofLkMFIC//zDddQrNh4800B9KCDCDxM88/1V/NNza5CpZ1ktDSAY9NVgKDsWvvrP19Co2nj9xXkszu0NGomQuLnZw/qOh3rF0nsmqQ/cfv7Pr7yIZs3BO958BudPVQdCkgiJ77+yje88v44k0RVbGxV7pCZQFkSwsesPTEZLzl7NH6WULlYX5S9vmQZDs+qg5JpQarInnJWapM7XtWi2euFIzR/DYJir6Gdxp+4GZddM9xlHK7rqvu4FWmS+gsMCnQXJ3I1+EPeemQYmvPTSS/i1X/s1fPKTn8TFixdn2RXigGS/0HrdX4tR14uw2w3hF+xZZOKjo8CyiDQDVmpQGsUS9YqNcmnPnNJgekCdq9nY7YRo9cI0LHmv3SASWNv24NgGyo6JK2sdvLbeA1Sxhc8wnX6E77+8hWrZwumFCr7/yha++/wGFNTAaLTTj9HuR6hXbO2sXbIGpcwBoN2L8J3n11EtWXj8QhNBmODGZm+olLUuGe5YHGGsB85hFICdbogvP3MVtbKFt73hFNr9CN99YQNSqsFg3/N1ZdFq2cJSw8XCXAmObQzuaSIUttrBQMxNrq+Xdysy0XKdPXPSPVNXLUYAxsxpy66JkmvCD5I0kMRGuWTeNWPRga+cbcKxMQjxz8Rn2FewWuK6CmuUwDL54IWExOdoMDMR+t73voePf/zj+PSnP41f+IVfmFU3iNtkbxBguLnVLxzwqyULzbozZhyq/8pgmgbm66WJRpt65qFzf/JETkGL0b+9tIk4kbe1RCOVQqcf4R+/dx1esOd+PXwcQDobsScuz4m0DPdzl7dhW3xiPxhjsEw+JkIZiZDY7Yb4yhyeAQgAACAASURBVP/3GhhnE81YkX7fMzmlGTIX676fTDRSHeb0YmWicAyLEfKOQ4tR5kx+Lwb9PTGaLCzZ3w0Dg8hCEp+jxUxEaHV1Fb/+67+Op59+Gk8++eQsukDcJQ5mYMkKvdD0IFg8eEilpm5GTTMwza5VdEoi1JgAjXz+AAMc58V7FSrNiSrsKyve0+KMQU1xYy2+6+k9R/F3mvZ979eAf1j6Qdx9ZiJCf/7nf44wDPEHf/AHg3/75V/+ZXz4wx+eRXcIgiCIGTETEfrMZz6Dz3zmM7O4NEEQBHGIIMeEE0wWsnuvlzIO6uVVWFMGmOrqycEwOfZs6BpT+jDtVqhsWbDoHIlCV8aDLGFOW32USk1dblNTCxcVH9b9uD8/I8Q4t5MnNIn9uUOHLW+IROgEkg0sQmjzUdsajTi6pTakwumFMrbaQWqWOX7eVttHHAssNnV1zOHN9MzAdLPlo17RGf/D+xTaaBOF+zQqjYQ7tVDGVsufGAadebmJNMJq/3eVqR2RYxvww2RMBLLTbcuAFArcZGMikZ0jpMr9xcpyk4IwGdz3YQzOwDnDg2fq8IMEN7b6gFIjEYGMASZnWNvuY6lZhmGwkaCPNN4Djm3ANhmiZLL5aCIVrq51sTJfHutLdt87/Wjg6Tdt3464u9ytPKH9HLa8IRKhE8Sw+ATRXgRYlMiR3Asgf7AZFp8g1G3YloEzixUEYYLdbogodV0OY4EoFYQg9LHZCrA452KpWU6TURX8UAzCj/3QT803HV0FFbrU9eauN5KPkvYEqRE0/DBBIiQMznBqvowoFtjthggiMRAfP9rzGAtjCdvkcB1daTVOJF650cbmrj96iVRlGAPmqg5OLVRGfNn2C5BrGbCt8YRTpfScJAwFdrrBIP8mCykuOZZ2/zY4Hj8/h/MrtYF1zuMXmrj02i6ub/SglI4Sm0vzomKhcHOrj5JjoFlzYZkcjEHnNA1F8LlSIYgFolgODFmzZwdooamWLJxe3PPh6/QjdLy9st2dNDydxIi425AInSDiRCKMxcSoq8zkslq2C0tLJ2Kv7MJ+XMfEacfEy9d24YfjsxGpFDZ2fXR6IU4tVCeuBkWxxPqOjyQRCGIxyNPZj5SAF8QTZ0i2ZeDUfBlX17roePHEpa8okfDDBNfWurn+ckoBS/MlLDZKhZ5sFVeXXcgbmLtehG4/RjzBCy1OJOIkxM+97QIWGqWxe19yTLz50SUsz5exutmDOcFfzg8F/LCPtzy2OLGkN+cMZceEkhF2u5PvWc+P8dK1FuYb7sCtexipgFYvQhAJLM2VDuSsQBAHgeoJnTDuhqnjVDscFG83yCnLa4A2F80ToIxpbRzE1qXj5RucAjoZskiAsnOKZgY6b6e4pk6z5haKv2MZU/sxaXlvGHYAu50oHnelHuY42+QQs4FEiCAIgpgZJEInCAb91l5Ekkw3yixqI6twWUScSGy1/dzrZJv3RRU540TqKqk5r+ZKKex0AnhBfkmISYEU+9vwghidfph7ThTFePnVmwijKdfJvwyUUnj5Wgt+QZkFISUMI3+WwxmDYxmFy2TTvi8w/fkzIHWluP0pkQ4CEVQBlQBAe0InCtPkME0+Vq1ymCBKEETaG861x2vtmAaDaYybXCqlsNsNsbbdzxWhJDU49YIEjDNcvt7GhdM1nJqvpFU/VWqMGUAKvZmfBUxkhp1xomvz7Ha1MLAOUK+mG/FMt3F9o4fvvbCJfhBDSYVyuuledq1BPzZ2PWy1/Ilh0JlDdxQl6PsBbm50UClZuHhmDo2qC0CLzwsvXcGlV66BM0D9E/DG113Em1//MBzHGtzL3W6IKMeiRymVFgGU+H+/dx2MAW94aAH/4YnlQV91kEWAMBJ6X8cGvDAZBGpwxnB+pYbHL8zB5DowIYolwnjPXDaKBVq9cKxA4CSCSCCIxFilWc50UIS2HEoQxntLhAcNUpBSjfzMMKQRfLfQBnH8YOqIvY5cv34d7373u/G1r30N586dm3V3jiw6rHm8yuZ+dBSZOXGQyEKjb2x2cX2jBynVxPILQkrsdrT47D9spKHhF1Zqg3DtyZ5r2iS01QsBNR6ZxqAH2++/sg0vGBdYznR+hG0aaPVCMIaR0t/Z/pEQAmEUT+xHtsEvwjZevXoTDEAi9gb2rKzEj77xEZw5vZJGCY5/l0x8gkiOlawwuP4yP/roEh46W0M8wXw0u++LcyU8en4OBmMjrtrZr3Tfj7G63UcwIUjkoDiWgfm6kxapY2MzrcxfrqiCqX7pGXflHrSBtI0pe14nhWyM+z/+8m+wsnLmrrfvWMbUFZH93MvcIpoJnVAYYwOH5yKmbXQDCldWu4Vt9LwY/ZylJh1coJffiq7V6UdodcPJBqapKH3n+Y3cgU4qoO8n6CMZfGZ/GwAQhPlLfFIqbO+0sb1xfeJSUiZIhuUijIurgmZlFfaLdhZsYRpsrIRBBmMMjs3xxMX5ifcse2HY7R5s9lOE6xhp6HdeyP70Jd44FrnPBdDPzrjDSqzHkXuVJ3Q73MvcInryxFTuy1LJAS5xN7pRoHMHuoaCAi8ofw2gsDy2buMARf2m/GYysPu2p3I/nj8txp1cSIQIgiCImUEiRBAEQcwMEqGTTLqcU7SsI9WU41LBLAgdBor3lQC9RCaFKlyS4YxNDHoYoJBa5hScIkVxraD0//uL7w1jcANCiNzrcMbQ8/zCe2akBqZF37fnxYUh6kkaVVEU5n7Qzeeifmj3hCnJyVN+Rg6ynCfV9OsQxxMSoROIUgpSKrR6ETr9SJd/zhkEoligH+gBcfi4lNqDbLPlo1K2B55jw8ONTPN98oISONN7IwuNEupVe68s83AbUiGIBNZ3fXS9OM112etH5oV2c7unferSQ8PjnpICURjg6uUf4tJz/4xeZwdS7m3YZ+daFsfyfBX1qqvLkA+Lp1IQIkG3s5v2MZOs7LvoKL9qvYGez7DdCsbuWXYu4wz1igXb4mPfF9ARiVdWO/jh1V34YQIhR8VIKYWuF+Nr372Oq2sdCLGX25M9234Qo5vjBsFY5odn48xiBbWyNYgwHD6n5JhYbpZRLeVbOWXCXyQ0tmWgUrKK28CtGegSxweKjjtBZANVqxei58WDSLMgEggjAcc24Eww4BRCh3IbBoNjmTA4S/NsgkEkmeuYcGwjDT3W4bg73RDhhByZrPmFRglLzdLgjd0yMWjDjxJEscTNrT663l4iaM9PYHCmc4dSJ+krq92x6CulACkFkjjCzWuvoNPaGhy7fOlZOG4ZZ88/gmp9HrbJYVt7OTHVsoNKyUbfj9DqehCJQHt3A35/LwpQDqudUqjWG1hcOg3bcQAA/SBBP0hQdk3MN7Qlz7DEc85Qck04dporFMs0N4sPwq232wG22wEWGi4ePTcHy9JlwYej5n5weQeXrrXw2ANzuLBShxfosOxJUXGZu/Z+g9Nm3UW96qDTD9Htx3AdE2cWK4Ny2QBQNW0kQg6MTx3LgDPBjTwP0+ColkfbsC0OxzYLZ54nmbtVyuFuMFwO4m6Ha5MInSD8MMF2J5icuwItRpbJkbe6JoTCtudjs+Xn5PIwuI6JaxvdwtBg1zbx4JnaIK9mUhsvXW9htzv5TV5IhZ1OiEuv7RZm99987RJ2ttYnHgsDD1cvP4+ffPI/wcjpR7XsYGdzDWtra7nXUErhsSfelBsx5wUJTCNEs+5OPJ6Jkevk11LabgdQqoVzy9WJ50SxxA8u76DVDUfyhfbj2AYW59yJ0XsGZ2jWXJxbrk58LsCekBTVfZrG3WjjpHCYQrSHudvh2rQcR8yEwzIA3Y1+HJY2DnKNadc5SD+OyvcljgYkQgRBEMTMIBE6AqjU9sQP49zlpzAW2Gr58IJ4YoBB5ofmFphc6uqqxf3Q7eefwxlwfqWOU/PliVFXDMDKQglLcyVYOUtHO60evvlPP8ClV16DEJOX9TgHzi5VUHImrygnSYJEmbDdOvLiv5SUuPTiD9Bp7048HkUhNtdfQ9jfhZI57gXcwObmJnw/PyIuiAR2O8FYgEFGrWzh8fNzmK87E49zBlTL1sRjGUJIXN/sodULJ/aDAWhUbZQdM/cZC6mw0w5TKyeKVCPuD7QndIgZGFzGewNxFEewzXRDlzOEscBuJ9R1YKD3IAyDoVlzBgN0GItBgIBtcdgWR5TItPJoui9h89y6OEop9LxYD3A5feWMwXWMdM0fWGi4uHi6jiurncGG5tmlKp642IRpchicoVKyEIQJWr0IcSKx3erhv3393/C9565ASQnOOZ774St4/esewiMPnhvZu7FMA/ONEuZqLrwwxs2Nvq6wmiRot9vodrtgzIJbbcIuNxD7bYR+F5nrHGNahDbXV7G9uYFqrYaLDz2GRqOJKApx5ZUf4trVl7XBqVIIgw7cUgOWWwPjBhg3YDslgHH0+334ngfLttBszsN13ZH7GCcSSSLR9WLUyhbqFRuGwVEv23j4XAOVkgXOgKVmCWEk8MqNDnY6ATgDFuZKODVfHkTf7UcIiSDaMzTt9mOsGQwrixU0Kjr4YL7uDrWho9XiRH9OpbWd/CFTVD9M0OoyzNUcVNzJvoEEcbcgETqkRLGAH04ObY4SiTAO4YUCSTJqcKmgq59utQLYFh+bKWQDim1y2CaHlCqdAU0eaIIoweauX+gzpw0sRwXMYAwGBx4+18AjDzRQcSwYBhvJXcmMK13HxP/++W/gv3/7h5BKDfJjRBpG/dwPX8HVa6t49zvfOhZEwDlDtWTjkQcsfP/F13D9+kYasKYG94UxDqfShGFX4HfWATVqLCqlQKfdwg+e/R64kvD6nTR3aXQWFgUdhGEP8yuPghsWhmdYUimEYYSN9XXUG3U0m03tz6f2vO0Anf/jhwl+7icvoFFzRsKWDcZQdjle/2ATXpAgSmSu+GSz0v1lz6VSiBKF6+s9iMUy/uOPngVnbCxXyzI5TMPCxq4/5nOnkM2KAni+gaVmiYSIuGeQCB1SorjYeFIqTDeFnGo+isIaNYCeWU0rxlpU8dPgHE46+5o0kDGmM26+9swPESeTv3OcCMzP1QsHQs4ZbqxuDxym96MUIEUMBoW8uyaFQOB3IKWceI6UEpZTBucm8pb4pFKolCsA2MR+SAU0Sjbq1fzcG4Pre1XkQaertRYlGSucP1XLTVjNBLLIaFUBsG8hDJsgbgcSIeKOuV/httOucJAuMM6AwqJ7B4kOKy5ffpB7MW3LhYZ94jDlCQ0znDOUcSe5QyRCBEEQh5DDmic0iTvJHaLoOOKOuV/LNdPitQ4S0DX9nOmNTGtD3XYJuaPJ3Yiko2i8kwuJ0CHFmlLki6HY8FNX/byzX2ypFOwp/ZBSIYy0v1leaHicSMjU02wSUSzwxMNnc10HOGPY2e0gSQRkTpizEAILzSryRUSBMY4kjgCVVywOYNxM/zx5/0okAaSUYDnXMThHv+8BavLzYQzoBzGiWOSGbAPT9+oYy6rS5nyeM6zt9AsNSHWkXLHpaxiJtGJuTqG/tBrunRiQZvt4ef6FxPGGluMOKY6tSyZnPmwZSikkQsKPxss+A3v+YHNVG9WyDSggjJOxKp2GwVBKw7wToRBEyUAkZBrO1e5HuSaY2lg0wc2tPrwgQb1i46GzDZRd7QWmAEABmy0Pm60ADDqv59xyFZwBnHNEscB2O8DffvMyQmMeD1xwsLWxCs/3dIg2Y4PBbbfdxd9+5X/gkQcfwOtf9yBMwwDnHEIIeH6If/v+i7i5tgXGeBo6baSbRBJSCOxc/wGu/fAbSEIPcyuPYOnCj8IwTYCleVNpFJvllGFYDmQSIomjgRgppWCYNrhpo9+6AdOuwK00wbgue60jDDmazSaq1epezXG190yggLJrolF18N0XNrA45+Lhsw1tlZQKsC6FLRAVBJ2Y6bObq9rwQ4FWN0QidcRfVi790QcauHi6jp4XwbaNgcEssCewjDGszJd1HlM3TM1hR68VRALXN3uol23UKzaQfS0FxEKXbQ9jAcfiaNYcWJaxZ9Q6ZYacma1mIeY89QQciaCkoIhjD4nQIYZzhrJrQUqFrhchigWCWObOKGyTo+yaqFfsvV9eBpQcC46lEMYJpIQ2/xz6RbdMBtOwkAiF3W4Az4/R9ScnpUaxzlta3/HgDbljd/oR/u3SJuoVGw+eqcMPE2y2gpG+vrbew43NPuYbDizO8d//+Rpevt4eHC+VK3jg4iPwfQ83XruMJElLcafHhZB48eWrePnyNTx88SzOnlnGCy9fxeranjmpUhJh0AdjHHHQRb+1ihsvfhNx0Bucs7t6CbtrL2Nu5VGsPPxW7A8D4NwAt8vgpgMRBQBjMCwHnO8N5EnURy/qw7QrmFs8i1qthmp11Nstu3+myWGZHHM1Z2SGu9UKsNUKsNhw8dj5OcSJyhUfns589j+7smui5BgIIl1C+/RiGRdW6oOBXEHPZqJIwE1D6YdhjOkw+dQ4diMNxx9GpS8kHS9CvWzDsQ20e9FI/loYS6zt+LAtjuVmqdCUVEoJKfXL0fBsXUoFL0jSn3syNj0pkAgdAbKyAl6BKSgALM+XcsN6OWcoOflZ94wxWCbDdjsoDP0OY4HX1rq5dXk6/Qiv3Gjn9kNIhZdea+OZ76/mXqdUKsMtldDrdiceF1Li0uVruHT5Wm4/lZJYe/nb2L7xQs4JEp2Ny1h56Mf1rGkCnBvgbiX3GoAWo9OnTxe+sZccI9fAFAC22gFOe0lh3SXT0C8Yk8iE5C2Pz+cu4yrokP6845lxrGGw3GXcTIzQz+0molgn5jp2/tCilF6SzENKPUMypiwFE8cDesoEQRDEzKCZEEEQxCHksOYJTSIREl0vuq0wbRKhI0LZMeEsGGj1wom1ehyLo+fFaWE0c2xpJ4r3Np/nqg7K+zzBdLXOCCXHhGns+Yrtp+SYePSBOazveNjthiPHlFKIYu2R5ljZHsjoUlenF+A7338NN9Y6qJRLKJfcsX4EYQTDqaFqOPC6bUixb+mGMVi2C8ZMCBFBxKP9AIBqrYEn//P/Br+7jX/9H/8XdtZeHb1fpRre+OQHcP6Jt2N3axUba9dGqq0CgO1WUZ1bASDR3V1HHI6uQ7mujbe/9U14y5su4PpGDy9da40tMTq2gVql+BfTtQ3c2OyhUrLQSH3lhjE4g2PnL1owBizU3all1rOBwrXHbZYyskCFVi8cW5YzDYa5qjPYE+r540tqFVcH1BQlMHPOUCvbEwNm9HV0oMZJrzl0lPKEgNvPFSIROiJwzmAYHIuNEoRU2O2G8MMEjsXh2nsDfZxIxEk0EKNYSLTSCqfZkLLdCbDbzUxODfT8GO1eBKX03oBtGbBMPmJymWEaHDC0GenphQpWt/vY6QSIYokg3ahWCvBDAT/0UHJ1FJcXxPjuc9dx5caujopSQN/z0O97qFTKKLkOwihCr++lgw+HabmoNR3IJILXbUHIBLZdAuMmGNe2M5bhwjQdJEkIEYeo1ho4tXIGpmWBMY5KfR7/yy9+Eq3Na/jXf/yv6He28CNvez8uvuEd2oeOGVg+cx6Lpx7A1vo1bKxfg2mVUJs/DcOwAKYH/7nlC5BJhM7OKkyW4O0/8Sb8+JtfN4jSe+hcAw+e0Yatl15rpcahTu5gD2jxcWz9MiAV0PNj9LwY1bIWI9syUHKMQcTbfnRpdBenmuUD1QraezYJgggTxcgwOMquLrTnBwlavRAAQ7Nmw3VMMOifkWbNwVzVRisVo0rJxFzV0fuXjBWGWrPUSNW1TTh2GjgRi/Rn1hh8FwrXPhmQCB0RsoEiC1JYqDvo+flvi3Ei0fU8BNH4m6ZSgFAKW+1At43x7JpMjJRSCCaU6M76cWaxgptbfUTJaHhv9kcvSHDl+g6evXRzID4ZWeRc3/PQ7fUHIdn7+2FYDkr1ecShP2IKmn2XbGZ07oELqYP18KyBwTBtLJx+CO/6xU+CMQ4dVT08Q+PgBrB85jzMchNRGA7EZ68fHIbl4vyDj+Gp97wRhsFH8po4Y4DB8FAacr3TCXJFgTGgXnHGjmffqefHqDgm5uvj5wxf73UXmxPNSQ9CJkamYY/lCWV+fmXXRMk1J4Zc62tqMZqvO1DZPZhwbh7ZdVzbGLxIsVtsgzj6kAgdVbII7IJfVHHAZNWis/KSFDN0OYn8aDoA6PmRTprMu8ZwflJuJxU457nh6UoBtu3sE6BhGAzTSh228y7CIYUcE6BhatUSwFhuYi1jurxGodnq1PwZoFwqLqFgGOy2BWiYoq6w9GZNM45VSt1ROHU26yHROZlQdBxxopg6zNE4OAKV+ybuNSRCBEEQxMwgESIKOUx7w3djo3ra8uJBrnE/3trvxn2/W8aiFCBA3EtoT+iIwhmDbfJcmxelFAyDIxaTXRaygSURcmJZb5WaUmYRcrZpjO0/SCkhlXYE8IIktwLoQrOKmxsdeEE0NqAZhi7O06yX0OoGgz4NwxhgWTYYFOI40j5vw8ehq6OuvvYyVs5dhGGYYHw0NFwkEUKvi8DroLF4DoZpYPQdTEEIAeHtIOEuTMMC9rVhGhztro9OP0Sz5uaK0cpCBdfWtavE/j0sxjDww8sNXADQ7kWDst/7z+MsNY6NxVjlXGCvqmwUCxhcF8fL2zsSQsI0xx0jsjaSWEAxBsvQRqe0bHb/OEp5QsBenaFbrS1EInREYYyh5FpwUhfrTIy0wamCn7of7ycTgSANiwX0oJeZXKr0nJ4X4/LNNnqezgWplCycWawMjDClUnjpWguXb7YHARDDIceZe/Z2O0AYCZw7vQg/iLC104YfxsjGxPOn53Hx7AJsy4AQEq+t7uLyta2BCHLGYVoWDMMAYxUkcQzf6yEKQygoKCnhddbR3rwCKWJcvfRPWDn3KC489hZwbmgvOa+DZ7/9f2Pt6nMAgOrcMt74tvdh8exjOthBKWxev4SXn3sGod8DGMPc0oM49eCPwrQcGKYByzTw2MVlrCzW0fUS+GF/Yr4VANTKNp64OI9WL8TaVh+JUIPyDq5twrbGq5Vmfyu5JhpVG7ZpoOvFMA2GsqOvwbl++Tg1X8Z83R0Tlkw4wtSQNMtZcm0DzZoD0+SDAAInfd6TXj4yAdvthoM8HsfSbeiQbhKj+8FRyxPKuNV8IRKhIw7ne2K0ueshEvklE+JEIEnk2Oxp2ORytxtipxOMJSL2/RgvXWvBNBikVLi63h2LvgsigSASUEr/OdwX2l1ybTxwZglJEsPgCudWmiPJrIbB8eC5BZw/3cR3n7sGPxSp+OwNeKZlodZowuu1sXr1eXit1ZFkViUlVl97EWvXX0Kj3kSvvTEQn4xeawPPfOXPUZ1bxoUn3oH165e0+AwaUWhtXEZr41X85E+/Dw8/8jqcWqyN9CMROsTd7Okw9TFRYQzNmou5qoPVbQ99Py7MGapXLFRK1lhybyIUOl6MpTkXS3MlzDfc3Ei0di+CFyZjCbNBJLC67WG+7mCx4Q5eFCa20Y/gh+NJpGEssLbjoVqy0Kw5B6piSxAHgUTomMA5Q5RI5OgPgNR+v8CcVAFY2+4XlhHoejE2W36u0AFasIpqGZVLDk5PGLgzDIOjWikhFkFuG4ZhwGvdhBTJxONKStx89d/hdXdy2+i1N3H10vcK3uoVHCPEylI9t41pNZsYY6iUrML7Dui8oaJwa4NzPfspGP27flz4XBKhYFnFod/Tnt2070EQt8rMAhP+7u/+Du9973vxsz/7s/gv/+W/zKobBEEQxAyZiQitr6/j6aefxuc//3n8zd/8Db7whS/g5ZdfnkVXDg1FFTCz43nlEzKmLZFk6/3TzilCSjUWODDpnKJ2ssJ8xf0oPJy2P6X6qGmDG8WT/VK5VnhcKoYkyS+hoZRCtxdMvW/TckrDSEy/91OOG1MucpAVtDtNfiWIW2Umy3Hf/va38ba3vQ1zc3MAgJ/7uZ/DV77yFfzGb/zGLLozUxIhEYQJhNRZ565jjESrSanQ8SJ0+trbrVqy0KjaI9UnszaqJQtSAUGYIB5aUlFKR1Lt36MZxg8TrG73Rz43jJQKq9t9XLnZQSIUKiVdIXS4Pk2cSLS7IfpBAsb0ZvakTfg4Ubix0UfZNTFXc2APVf0MY4H17T6EZKiWXURxPAigyL5Ld3cdG9dfhGmXAUjEoQc1ZD7KTRtzSxfhPPZTUFJg6+q/YOPKv43sHZ176EfwE//pQ6g0FtHZ3cC/f+fr2N64PjhuWA6Wz78Ru3ET//idl3DhzDwunlsY7NkopbC508FzL15Dp+djYa6Cn/rxR3D+zPzY962XLdTKFvpejFY/GlkyC8IEu90QV252UHJMPHS2joXGXuQdZ8BCo4TFuRJ6fgLL5CjZ45GKAHB6oTzwFRwuOMgYMF93sbJQniqGK/NlSKmw2wvR90eXOqslC3O1cZsfgrgTporQb/7mb+LDH/4w3v72t9+1i25sbGBpaWnw9+XlZTz77LN3rf2jQJJI+EMltQH9pusFiQ6/tjj8MEHHi0dCknt+jL4fo1KyUC2biGM5mCExxmAw7fmlFOAFMXpBciDx6edUUpVSYXWrjyurXSi1NxvzggSen6BcMlF2LfT9GH64N2gptVeFdViMhm1z/CCBHyYoOSbKromdToDWkDM3YwyubcO2FMIowvbGDWxcvwQpYggh0qmfAcutAkpCiASNhfNwa0s6Mg4AuIFTD70VSxfegs2r/wKbx/gP7/xfUao0YFo6gmdu4RR+6j2/iG57G9//l2/CLC9gbvlhGIYBBQYhFa6u7uDKzR2cP91EtWzj0is30PeCwYxua7eH//aP30e96uKn3/oYzp1ujpTRZgBqFRuVsr5Xa9settsBoljs3Y8wwQtXdmFbHI8+MIfHzs9heb4Mhj17Hm1QKyeKEWMMpsGw0HDRrCm0uiFcx8Sp+fKBLX4YYzAMhvmai2ZVixED0+KDO7cJIoj9kIlSGAAAIABJREFUTBWh97znPfjc5z6H3/3d38Uv/dIv4UMf+tBgBnO7SCnH7PtPUsinELK4sqRSusJpXoVL6MqUBp8cKpsN9tNmP0JKvHytVbhEd3Wti+vrvbGloOyvfT9B308merJl+iqHnu+YyanSgra21QcmtKHS79NrbWD1ynNj5RbSbwwwA0sPvAGmpc00R5phBrhp4JE3vwsXz5/T7tn7Pm+YFuYWTuGBJ96JKIqhwEbayCIBr1zfQuD1J96zOBHYbvULl804YzA5x+pWf6LoC6nghwIrCxWcmq/kDvpJIsGcnIqwjIEbDGeXqrBt47Z83TKD0oW0IuxJ+v08LByVPCHT4IPUDUCnGdzS56ed8P73vx/vf//78corr+Cv//qv8dRTT+HHfuzH8Cu/8it405vedOs9BrCysoLvfve7g79vbm5ieXn5tto6ihwk/3zanshBhoQpW0gYbKsUnCeEnLoXMWgrB3aA3qrB/yYjhS5/LQu2krhhFN5bzs3csuOa1KG7oL9CSl1GouDmllyr2FhWShg8v4w2ALjO5CW3oa5OhTN2R8aiAInPLDkqeUJveXwZy/Pl2/78gQITpJS4evUqrly5AiEEFhYW8Du/8zv4kz/5k9u66Nvf/nY888wz2NnZge/7+Id/+Ae8853vvK22CIIgiKPL1JnQ008/jS9+8Yt44IEH8JGPfAR//Md/DMuy4Hke3vWud+HjH//4LV/01KlT+MQnPoFf/dVfRRzHeOqpp257VkUQBEEcXaaK0M7ODv7sz/4Mr3vd60b+vVwu44/+6I9u+8Lve9/78L73ve+2P3+kOYgh5LRw6wNcZtpCCjtAVzhnE/d77g35YdeMMR2MUICUEgYvCt3OrIDy7wxjxSuU2qeu+GZk4dZF5a2ntRHH8o73ShWKPeoI4jAwdTnu937v98YEKOOnfuqn7nqHjjOZI/G0fB8AKDsmbDP/8SgF9IMEQsqx/JLMd037lOW34dgG3vDgPColc+L+g5QKtYouNc3YeDE2XaLZwHzdgWMbY6G7mYC5jgHH4mAYz5cRSYz2zgZe/NevobV1A0IkGJEAJSGSGF53G5HXhlJy7DpKCiRhH69892/Q2bichmLvtZH12/MjrG+1IMR4Tg5jAOfAw+eXsdCsgKfBHYPjyJ6fHPnMMIbBYVkmfvDKJtZ3vNx8qUbVxo89voSKa8I0RhuxTI6SYyAuyE0CdE7QtB+jINLRieSETRxmyLbnPpANAJkj9UHGA84Zyq4JV+ry2pOsdBKh0PUSmAZDydFRUApAEO6dX3IsOHZqcpr6gZkGg2ubMAyOSglo1l20exEu32yjn1q/tHohuv0ICkCj5qBWsdHp63wlKC1gczUtPoAOPw4igXY31BF5LM2PqdgDgXMdna8UhAJCCPTa2/j373wdW+vXAADXL/8AtbklPPbm/4ha8zSgFG68+ixefeG7SKLUwqe/C9utwS43AAAi9rF17Tn4nQ0AQGvtEtzaIi6+8WdQW3wQjBswTAu2aYFxjk7PR6fno14rY3m+DsMwwLk2FrVMBsYYapVlnD0V48b6LrZ3dbSblIk2TR16eNkfGWMwTQOnTy2hOVcHYwwvXm3hys0uHjxTw1KzDM4ZbJPBsU0YnKHiWji7VMHatofnLu8giARsi+NtP7KCJy7OwzAmvzyYBkPJNnKP7yfzBMwMarP+EsRhgUToPhDGYiQf5FbIxIhHCYJocmhYJkYGZxNnWZyxgRgppcYixBhjmKs5+LHHlvB337ysE2Mn9GOu5qBesSGkHDPaBPSsyF0oI44FDGO8fIDO+zHR293A//zG/4PtjRtjbXRbm/jeN76ISn0eod9HEodj50RBF35vG157DWG/NXY86G7hhW//V7zhnb+KhXNPTCz53el66PcDvPl1D8BMxWfkuzgWHj6/DJHcwM31ncL1yEceOo9yaby0QxgLvHC1BamAxx5ojAkHYwynFytYWSjDsU0sNcu5rgeWweDYxkiS8kHJDGqFUCjfYvgsQdxr6CfyPnGnqyEHeXudtszHtQd/4TWELLb24ZzpJM6Ck2y7+DgAdNtbhcf9XguyKB4bCpHfKb6ITCYK0DCGMS5AI8f59A2xSQI00g2pCsOtGWNYTmdLRedMs+UhjhdHJU8oqyNURFEIN4kQQRDEIeSo5AkdhP/85MXcY1Te+z7AUzuVIrJiYXmYht6wzkNIhd1OMOKztp+uF2Gr5eduUseJRGuoGNokONdVNvOQUmFrpwcviHLP6fU6EMnkEgxAukz1wCNozOcnMHPTRn3pQTA++T1KKYWdtZewu3qp8Dr9IL/8gVIKvW4HSZxvUGqaBmyr+NlxzuCHSW4bnLPpbdyFSRBNpIjDCM2E7gOWyWGZHDINMhh2kHaGNoyBPb+1bLwyOIPr6M1spTAwqMx82oSU2GoF2NzV02EFYK7q4NR8+f9v79yD5KruO/895777OW/NjN4PQFhgHBxsY5NgxSy2DKzCay2HitlKuQxUYojLxiGuTagKTsCJK7jKVFiXy2U2BLyJ4gcxFRLbMska490ExYYY8ZaQNNKMZjTvft3n2T9O355+3Ns90sx0j0a/T1Vw1N1zzrn3zpxvn3N+v++vYgw6X3Bw5MQs5gtOxRts61AW/d0WGGNwvQAHXz2F535xEp4fyLODOoPS8GxK4XKyNMtGqaHoBYHAxNQ8Rk5NQwQCQgDZjIUNg91ImNKjbXZmCv/xf5/FsSOvR261McawYevFuPTdH4SmGwCA6dOjeOnfn8XM1Ck5DkVDumcYmpUBEwKD26/A5PFfYvLEqxCBnOg9pwC3MIs3//17eOuF7yM7sBk7P3AbeoYvqlxLOpWEaRiYnrMxPWcjk9SRKQdRCCEwPj6OV145hEKhKEO/FQ2a1QVFk1tvmqpg88YBDA30VrbRHDeoFPUDpOHn+oEkDF2RBfA4R1fagGUo5UqpDP1ZE33diUpouOMGKNU9f8tQoPDm24bN4JzBPMvzJIJYaZg4x2I3R0ZG8KEPfQgHDhzAhg0bOj2cM0YIUSnLEE7w9T56gPQh45xXzgGqPxOWU/jl4UmMns7L1+qeImNA0lQxX3CRKzgN7ytcilGh6OGFV8cRBKJmBRT2lknpWD+QqnH2rr+eIyOTODIyCQFRU21VTqwMliZw6sgLGDn6FoQIagQozJkZ3nQh3vW+a6DrBriiVfcA3/NwevwEXn3552CqCc54zbkVQwDf93Hslwdw+uiLgAgaPOYUzUDP4AX44Mf+BzJdvZVIwso4GJN5NW4Oh998FaVSsaGEA2MMiqpj1yXvxPqhAflsIspjc8Yw0JOAoTVa7zAm7/2l23sx1JeSYes86vkH8vxtKeLDANNQK+JDUXHnBuEc98d/8b/Q2z/Y6eEsC82242gl1GbClQjn0UmE4Wth9FnUZzhncO0Ao6fzsbkiQgDj08XYrTU/ECg5Hn720lhkIEL4WlTJ6XreODoROwYBgbffehkTR1+P3I4Kt8J+9deug6pqDe+H5qIBFCh6MrImkgAHVzimT7xSU66hGt+1kcx0I5WWIdT1bYT+eK+9+jLsUjHmegSymRTWD/XHhkgzxrCuNwFTj9smBCxDxVBfMjLQYOH5x5cCXyyGpsZ+eSCI1QKtzztEq4lBOmHHf0YAYMuwyd9yflqW+av1Ynsx3bTOjWlV9I+hUX7ODPlMFhGF2KKNlga1yyQcJEDEaodEiCAIgugYtB23SlmU59c5dZq3DKyl611L10KsCOdKnlA19bWFFvUzKzQWYgmEZyfNkhw5bz2Phf5ncVs/8lydgUPEni25brOEUTlWTVXgB0FsqLOmJ9DMFlTTNJSKeaQzXbHj0HUDfiDKW1mNH1IUDt3KgkHAsaPPdEqFOYgmyaucAZpuwvechqCEEM+TCbBxV8OYDCowmiTsun4Axle+mKMvyMD0XOZczBM6m9pCtB23iggj52ZzNl47No1jp+ZhO16NE4IfBHBcHyOnckhbKoyyQWn1NMMAWIaCLUNpbBnKwNSVmhyRsJ+ZnIPh/iRSSa3BoFThDP1dJi7e0oN1PZY0IK1rww8CnJoqoLc7i6RlVcKOFz4TwHeKMlx73XYYVlpO4OWPaJoGTdNx+Xvej22b+pBNG+AMDWP1PB+BksDwll1IpLLlNuSHVIVDURRs2bwFn/6Tx3Htf/s0EqkuGObCH6+qm0j1DGPDrv+Cyel52I4jJ+fy+2GQW3fWxEevvRrv+dXLYVkmNG3hO5qmqcikk3jfuy/BxoE0kpYGVnXfpQGqrGZ66Y5ebFyXhq7yhnuqqRwbB1KYL7hwyk7ZKxWg6rg+8iUXnt9ocksQqwVaCa0SgkBgLm9jbLJQMR91XGkYmknoGOyVti5vj85hfKpQKXttGSoMTRqD2m4Ay5DGono5os3QgdRGDbmii+On5mG7PmbmbeQKLgTkxNmdNpFJGpjL28gVPPRmTFy8tQddaaMyvsFeFSXHw+kZmRA7PlXA6ZmiXLkwjmQyAcsyUSyWMJfLwXdLmJsahefIVYlmJKANbINrF+DkxuHaRbz7Pe/H5e/5AMyyYFiQRqjzeQdTsyV4foCZuSJsV+ZE6WYCAxsuglMqYHbyOAq5OWzevAXbd+yAYcixvnf3zXj3r/1X/Py5p/HDb/9PKEYSF191Gwa2/AoYYwiCANMzc1AUjmw6BcPQ0ZM1MdCTqEQBbt+2BVu3bMLbR4/jFy/+JxSF4/3v/RXs2LqpIn59XRa6UgZmcjYKJRfD/Sns3NyNhLkQ4Zctm76OTcpy4FuGshgo52YBQNHxUXJ9GFrZZXwFViy+L5AvulAUBktXy87mtDIiVg8kQquElw9Pxm6vzRUcTMwWUbKjXQY4l2I00KNHRmYxxpBO6MgmDbz45umaZNkQpSxG77+0p5LkWo+py/IS//nm6cjQb86lGI0eewWF/HxkG5qRwLqhX8G1H7oamtYYks0ZQzZlYOTULKbnSpFt6GYCw5svxgWbeiO3K1VVwxUfvBG926/CxFQusg3fDzA3n8Pud1wAHlHym3OObVs34x0XbYOpR98PVeXo67Jw6fYNseH22ZSB/u4ELCM+ZLvk+JUV7Urh+zJRmgxMidUGbccRBEEQHYNEiCAIgugYJEJLQAgB1/ORL7pwvaUd/m4cTCGT0CPfYwzoSRsY7kvG+n/pmoKEqVasgKLGqmkc29ZnYreX8gUb//rCYbx2ZCIy0s0PAoxN5psanALAwNAW9PQPR589MIZEqhsnJgoV/7t6Zqan8PK//wijh18sV1ptvBbHcfHWsXHk8tFbdprKcdmFg7j84uHYe6JpGo6NzSNXiDZbZQwtjWeH+pLYPJhGOhHl9iC3Ofu6THSl9dhIx8WUaDA0BWZE9dozgbzjiNUIbRCfBUJI77aS7VfsXjw/KBtFypLNZ3r425UykU4Y8Dwfo5MFzOUdMAZkEtJYM2yuJ2NiJmfj1GQBrh9A1xR0p01oGgdngFABUwdKjlcRRrtc5tnUFAz2JDDQncDkbBFHTs6h5PjIF2wcH5vGfK6EQAiMnJrFv/1yBFdcsh4Xbu6DAHD05BxeOzZTI7T14d/hvzNdvchkuzG0fivGTh7B1MQoBICe3kEMrt8KRVUxm3Mwn3eQSmgY7E3BMlVMT03i+Z/8CG++/gpEEIBzBScP/xzD29+FgY27wBUVvu/DcaRoFIoCM/NFpCwDG4a6kU6a0FSOod4ksikDjAGbh7tw2YVDeOmNMbz0+hhcz4dh6MikklA4l+MouDB1BUN9SSQtrWL4qTexzhnuS+LynQNImRpUlSOdNOB6PkZP5zFfcGV0YbeFvqxVuTfphI5cwcVc3oEfiHKF2+bmpIamVKrXAvLLxplU6A1/xtCV5TG/INrGassTWkwOkHUWZ45kYHqGBIGMNgqa3LZw4jjbKKQgECjaLhwvkGHAESaZQSAwm3cAxiKDEYSQ45yaK0XmtEghFfjf//yfOHU6F3k9msph6Dqy2TQYZAXXaurbjcydEQH8ciAEV3hDkTlW/s+bLz6L40cOQQRBg8O2qmrQrDS2XnYtOFcjV5ycM7z/XZuxc0t/5IojCALkCi7+z89PyFVelCcfY9gynMaGgVRTUfgv791UEZ96/EBI122+4D5eczuEQCAE5vNSSOP6URVeCSKIev5CCOSKblMhUhVWidajiLhzh9VqYHo2OUCLgVZCZ4gfBE0FCFi6+SQvl22I8yBjZd//qKiu6s+UHLmVFZ1UyeB6HsYnowUIkEmXpqnUOGNX02gkGtURhxKzHVb5GQEce+vlBufrEM9zkU72xCaqAlK4t67vbpLcy5ErutJvL+bxBUKgr8tq+uy60waSMQIEyK01zuKfP2MMCFonkOpNQrZD77lWXx815ey/CBFEu6BN4vOdFnPUaprClsNrtaW56KJG0toolSCIxUEiRBAEQXQMEqE62nVEtmqO4loMY5WMEkDrsSxmrK3u++Kut/lKZ9U8W4I4ByARqkIe+CLWzyusmtmKZuHa4cF0UA4uiPtMq3Daeo+1KCxDrfE3a+yDoTtjxV4TZwxO6LMW0xdDeA4S/X74XlglNO4zw5sugKZpkVtZqqrCzs+AoUkbCsfxsdmm53XphA6ANQ2VnpotxT47xoC5vN3UrNUvV80NgnhPON7kfoWEv0Nxv4dA/DOptOGvrDcdQSwHFJiAhWizkuPLUGsGGHpjzo3jylDnOBiTE399NctwEhAACkUPM3kbvi9gGSq604acqDmriGAYXt0MacWjwfMFirYX6T6dtHSYhoq5vINcORorFL/JuRJePjyF3p5uWFYCp6dmYTsegrLQCiGgaioUVUOuUCrfD7UqaAJIWBqGepNIWBryRRdjp/MolFwEYiH3ZeO6FNYPpMAYcHIij6Nj84CQk7XCGTJJHe/Y2oPrrroLIyMjePrp7+PNN9+C53lQFAUCDNsvvhwX7HoPVN3E7Fwek9PzAORkrygcCVPDhVsGoOs6Rk8X0JMxyjk1C/d0aq6IU1MFDPcnkCu4mM055S8Ecqy6pmDb+gx6s2aDEJbjQNDfncDmwQxOTRWRMOWz42XT1rDk+thkHjM5B5rCMdBjoTttVsQibJcxhlRCL4f5Rz871wvg+Q4MTWmwUQrTA1ppi+cHmMs7MHSlJrSWzqyI1cR5HaIdCAHfF3BcP9JPLRQjIaRBaNydUjiDoSuxpZRtx4PjBZjNOTWO2CFSjHS4XtBSfKIIw63zpejkT0BO+sfGpPnpK29PY77QWAa7ULQxOj4NxnnsqiSTNJBKGhjsTdaYdS604eL0TBHZlI71A6mGFZ0fBBg9XcB83sEFm7rQm220qj9x4gT+7tv/ACORxQW73gvdMGveD4TA/HwBjutg64Ze9HYlG8aqKgxpS4Pt+hifLjSElwshUCh58LwA6wdSkeIDAElLRdrSsWkwU5OvE5IwVZi6gqm5EmZzjUmvqsKxaTCFpBl9P8Ocs0KTZ8cA6LoCzhhsx28ZnRmHZajLUjacWFnCOe6xb30Pg4PDbeu3VR6QZarl3YRl7nfZWzyXEECh1DgZhwQCsVn91SSt6AkmpOj4kRNU5X3bg66evbsxYwyayppU7JFC6XoBfvH66YpLdz0Jy0AymYgU5BDOGbYMZWLDwxOWhl29ViVPpnEcHJsH003v2fr16/Ghj96K6Tk7egyMobc7heGBVOxWoucLnDydj125hivJjevSTe97f1cCG9elY98vlDyMns7Hvu/5AebzLpIRgh2OQ7p3x/+eCQC2E78CXyyuF8S6RxCrj3bXE1qpPKBW0G8kQRAE0TFIhAiCIIiOcV5vxzEmi6g5TnTAgapwmIZSCRaodw5gDE33UMOzGgYgbakoOn6k9U3UWUM10ivMhxACpqFCq/Om8/wAJ8ZzGJnII53UMdiTiKwJtGldGr/14Yvws1+O4fVj05FnXAM9KTiuj5n5YsO2nKmr6OuyMF9wZG0hrTYjnwHoyRgY7E3A9QJMz9sNZ1yFkou3T85hNu9gx8YubB3KQKk6NwqCAK+8NYafHXwTmqphaLAfiUTjloQfCIyMzSOT0pFJGg0Rb/1dJjYP9sN2fbx8eApTdbWJLEPFZRf0YctQBmOn8xiZyDU8m3U9CVywIQtV5eWglehnlx5IIl/0MJt3GqLmUpaG3mztmVYU6aQOzw1Qchs94RTOYBoqGJPbclHnhqE/nBfjK8fL55YEsdo4z0WIVSYSQ1cqRp+h+HC2MNknTU16ujkegkDURC1FeXvVG5wqCkfSZAiEQNH24fuiHLXEI9sApPjI/hZeK5S8ShQeg8CJiTxGJvKAkAf2M/M2ZnM2simjQYxMQ4VpALvfvQEfeOcQnn9pFK8enV64H5D2NqbBsU5XYTseZuaLUBWOof4ULEOrRMeVHA8lx4Ohy4P53oxZqf7KGZOHnOV7Ol0e05GTczIEGtJy5vWj03j96Awu3NyFTetSeOPtU3j+54fheT4c10fJdpE/cgxJy8LgYD+SCaty7iWE/N+5nIx0y5bFaF2Pha3DGWgqh8I5LEPFlZcMYr7g4peHJ1G0Pbzrgj5sGc5Wxrp+II3h/hROns5jZHwevVkLu7b2VIJN5POvenbl51/97NIJDamEJiPv8g4ShoqhviR0TVmUSzZnDJrGoWk63LIYcSbFJ/x5WUmX1RjUVhucyvMlDk2VQS6244ExaaqrKAttEMRq4rwWoZDwDzMUo+rXqj+jKKzmgDnuD7poR4dYM8agMIakyWpei6Jge3Dc6AABIYC5vCMFpCw+9e/PzNvQVY51PYmGPuRExfFr7xrGkZNzcDy/MqlXj8s0NGxM6rBMtSEAIOzSdjxctqMXuqY0fIaXy4+PTRZw8NWJSrh0iFxZCLx+dBo//Ml/QgQ+XK92RRoEAvP5Atj4aWzdvKHBeDS89rmci/fuGkRfl1kTFBE+t2xKx1WXDSGdMKTvXlU7chXFsL4/hQs3dkFVWM3qrNJOk2cXfqFJJzQMdFvQVCU2HymOsL1QjGL7KX8JsYzo9wFUxCiqDYJYTZAIVRHmlTT7g13MZ+IMP6vbaEXQog3fF5ECVI3cwonvS0Bu5TV3YuZNE3SFkFuSzfrJFVyIQMRG7nm+gOu6TZMqZch4/DgDIZBN6bFReay8OmMs/v5zzqCqLLaNsJ1mSNHjZyxA9W0s9fdwMW0QxGqARKiOxfzR0h82sdIsx+8h/Z6e26xEPaFmuUBnUwtoOSARIgiCWIWsRJ5Qp3KBmkEh2ivBMnwBXZQZ5zK4jy6HYcaq+cZ9Tnl/EAQBkAitCJautjSXjCP0sfN9eUgfd+Zj6ByDPYmmRqbj0wXpKxdhFeT5smrrul75rah+vAyhbZECy1Ai+whfevP4DDw/utifEAKbBlPo67Yio8SUsrHprgs3lv3pGrcKNFWBpgikExo4axxrGOX25sgsfD/aPDb8GbXJWY0QAnN5J/ZaQpq1AQBeEwNbgiBqoe24FUBVOdKKNKgs2o05G1GEkWOzeRtjk4VKdJ0ZmpyWAwQYA0xNga5xdKUMbBlK4/ipHE6ezjdEuJUcH28cn0HS0jDcl4SucQgB+H6AX7wxgTdHZhEEAtmUBtvxUXKCith0pQ1sHc4ilZDRgPmii9HTORSqrifs68TpPEanCljfl8SmwTQ4l5FifiBDmgUYrrx0CDPzNl45UpWzw4AdG7qwbX0Wmspx9RXb8MvXT+L//uJwWTgFerJJXPWrF2BDeW88X3Rx5OQcpudK5XriDOv7k9i4LgVNVTAykUfSqjUXVTlHd5WpqR8IlGwPbjk6T4gFc1oZeehWDEoVLsP0OZPPIvQHlIa3tVGQmsph6uqSghII4nyDRGiFCD3BVIWjYEujzGaMns5jNu80hHaXbA+jtoeBLgvdWRN6nQGlpirYtj6LjevS+H8vj0UKXr7o4o3jMxXTzsMnZ2tWR6ycj2LoAj0ZCz0ZsyI+IUlLw46N3ZiYLmB0Mt/QTxAIHB/P4cTpPC7d3guF8waz1q60gSvfOYTZnI2pORsbBlI1XmaqouBdF2/EJRcO47XDp5BNWxXxqR7HJdt7kS+6mJm3MdCTaPBDyxc95IsedqzPoDtjVsQnROEMSUuD5wWYmC3B9RrNaQslD4WSh+G+BLIpo8GclnOGRDl3zPX8swrJJgiCRGjFYUxuN7WyQZ0sJ3HG4QeiqQOypsqw4Gah3TM5GyfGc7G1cBhjGOyNdlsIMXWZM+THLO+C8urHMuIn5GzKQDZlxL6vKgp2XdDcPThpaUha0aagIYoqk1Xj4JzBaVKaA5AmtlFbhNVtGDr9GRHE2dKRM6GDBw/illtuwd69e3H77bfjxIkTnRgGQRAE0WE68hXu3nvvxV/91V9h586d+Pu//3t88YtfxKOPPtqJoSyZsFJqtcVP1GdataEqrHxGEU14HtQMTeXw/Phv9pxLB4NcMb58hecHTVdcDICucRTt+H7k9hdiz8LC869m21dBIJomli4GUa5wGtePrJSLyKJyIa26X8zzJ4izYTnzhML8oE7lAjWj7SNyHAf33HMPdu7cCQC46KKL8Dd/8zftHsaSWfCHk5Uxpc9XbWG7IBCw3Wb2O9LgtOh4GOxLwfcFpudLNTWMVEXayawv185xIwwqZQCEjx0buuD5smjcTG6hFg9nDH1ZE7u29gAARiZy+Plr4zWF7ZTyGcfETAmcM3QldaQStTV/dI1juD+J4f4kZnI23joxW1OMrTdj4gOXDWHDQBpBIHB0bL7m/EgIWUDQdnyI8rVJX7OFBbkfBDUmnaE32plM8Apn6E4bKDk+RiZyyCR0ZJJ6RYyEEHDLFWlTCb3iCVjtdKEqDAM9CfRkzEjngXp/wKjnTxBLYTnzhFZjflBI20VI13Xs3bsXgHRMfuSRR3DNNde0exhnTb34hATlQ3/OGAxdHsrHiQ9QNietaoMxaRnTm7UQBAJCcGQSAAAfgUlEQVRzeRt9WQvD/bJqaDiBVhtUzheciqFm2IamKtgwkMJQXwJjkwVoKkd/twWGhTY2rktjfX8KJydy+I/XxoHye9XiOZOzMZN30J3U0ZM1YRm1QtCTMZFNGeUggxJ+decAhvtTlXBphTNsHc5gy2AaR0bn8PbYXKUwW3jbPF8gV3ShKgy6qsD1GyvLOp6MWjM1BXoLMQrFJ2Eu2BWFPntzBQfphIaEoaFUVSW32hNQluj20ZtdKMu9GHPa2udfG0VHEERzVlSEnnnmGTz44IM1r23btg2PPfYYHMfBfffdB8/zcMcdd6zkMJYVPxBNSzGHTstN2/CD2FLcnEtBuHBTd6RxaDixMQbkis3akGJU/TOV9xkDVxgGe5NQVSVy2ywoW1VrGm8QoBCFM/RkTPz6u9ZHTthKOYmJc1mWOm57zvMFPD/6WsKfiQuEqGZdTwKaKs1Ia9oo/6dQ8gFEb5uFYrS+PwVdixeQ1s8fDf0TBBHPiorQnj17sGfPnobX8/k87rrrLnR1deHRRx+FpjWPclpNLEcSogCaluIGAEVhTY1D5WQaf+4CtD5PkeNgTZ0XqldI8f007ysIRNNxLgaG1tcjfUeXJgCct+hnEdex1LMsgjif6Eh03L333ovNmzfjK1/5CnRdb/0DBEEQxJqk7WdChw4dwoEDB7Bjxw7ceOONAICBgQF8/etfb/dQCIIgiA7TdhF6xzvegddee63d3a4qWm3FAeEWVrN6MMuzxdXKBFUEaFmXRp65NKtts7hrbsoidreWw64tEC2ud5HjoFo+xFI5mxDtuFINqzE0O2T1jmyVEZ4F+X5z+53FIC1yVMzkbPgR5yWMAYWSW460q630GubY5IsubMeTRecizm0YAwyVIxACjldfeVWUS3T72NCfwvh0AY5XW9xOeqYBCUuFoSlwvKBBSIQQ8AOBl948ja1DGaQSWk2+jOcF8AOBt0/O4eTpPAa6rYaxhlVCezMmZnI2ckW3Zhxhik82qSNhapjLO5Xy3tWf0TUFmsqhcg434hkxAJahwDLUikdc/f1Syo4TTGsegZcw1VhPQHlOF576EcTZczYh2qs5FDsOEqEWhOJTbXB5ttQbXMrJzMf0fAmeL5MzLV2pJIvarg/H9aHrCgxNKYcbS4NTpxLK7ENRGKxyvk21wWk42ZtCwHZ82G6AIJBh0aOn87BdH5zLKLmS42Fm3objBVA4w+bBNIZ6k5WxLrThV8SnOjz8xTcnkbI0bBvOIJ3Q4PkCz714Ev/+yngl7Hp8uoi+rInBvgRUzmGZKoZ6kxX7ne6MKb3yJvPIFV0wAH1dFvq7rEouUSapI1dwMZN3IAIBQ1cw3CfbCK/XDwLYtg+37BSeSmg1VVcNjcsQ+XLEnqowdKcbw9CjqPYErA7VD8WUQrMJ4swgEWpByZZOyUvZ6dFVDl1TapIyATmhJUwVlpHE9FwpckUjANiOj6nZIubybpX4LOCX822G+hLQIiZBzqRTwnyhiLdH51ByGkPITV3FYK90jk5ZWoPLQGhyGgiBqbkSohaEuaKLl96axOGRGYxNFRtyfoQAJmZKCARw9a+sbzBJBeQqcetwFo7ryzIPEfcsndSRTmrQNQWm3ljCXOEcCYvDUBl0XW0oIcEYq6ycLFOFrp5ZMmzYRihGoSsDiQ9BnDkkQotgqUcN1cmmce/XT7b1BAINk3o9yiImwlZtJMzmpQgYYy1XgzO5Rjfwegy9+fU2M1ENxxElQNUoCo+sYVTdxtkIUH0bikLiQxBnCxW1IwiCIDoGiVALOG/+TZcxlGv8NG+jGc2qeAILgQStVktOORAgDlXh6M2asUfmjEmvtmZJsq4XtDT8vHhLN9b1xB+o9mZN9GWt2HsWesx5TYJAFrN40TWl6UooCARyBTf2ngkhawW5VCmVIFYM2o5rga4p0DWlUknT80OfNjlhh9tGJhqDF1SFw9Tji50FQsB2FmdwGgi5VSaj2moreoYmn7YTwHYCaIo8v6mfgLvTOrIpHduGMzgyOofxqSIEZHRZb9bCQI8FxmRV1Hp/NNv1MTNvV/zf6mEMGOpNYvNguuLkMDI+jx/92wjGpgoApK3O9VdtwY4NWXlPBHBquojJ2WIlrLn+HiqcVbzYUB5rK2+2pKmiq1xZlTFptVOqMiiVhqULJqnT83ZN8EKUPyAZlBLEykAi1IJwwlGUhUqaYYG56veBBcFyPb+qHHfjhCWEnBSbGZx6foCC7SGo+ggrT6qWocLUq8ouyDcrn3N9AbfgQldlQMKC3xyDwgCFK9ixIYutQxmMThakWzZqz60qVWFLLk5M5CvO11EM9iSwbTgDxlmN8G0ZyuC/X38xxiYLMHUFm4fSUDiv6WewJ4F13RbeOD6D0zNFeX+q2vYDgXzRhaYw9HZZ0Jqc4ViGgp6MWSnrHaKWDUpdL8DkbKkhuEMAmC+4yBVcpBIaVKXx3KvaoDYZEbhBEMtNszyhczEfKI5zb8QdJDyE5jw6ETF8rdlECaClwzYgc3iCmI+EYqTz5of3apPtO4VzMCaaVjhljGE250RG01VzwcZs7P3QVIbtGzKxQQRyMmdyNdSkD11XW97X3owZu2XJGIt06a5GlP8rRJPtV764rUCCWCrN8oTOxXygOOhM6CxYTC7JklmmI4jVsHXEliFxc1EtLOZDq+fREAQBEiGCIAiig5AIrQAUSbWKoUdDEKsKEqFlJhSgoEkcc6sdISEEGF+6mDUbQziOVmPRtBbh5wwtQ5j98uFW3GeEkPY7zc76/bgDsio8TzQdh8LZooxjmyECcoUjiOWERGiZEEIgCKSlzStvT+PYqXnYjl8zqUnPtSD2oF8IgUAI5IsepudsFEou/KB2gmeQk2lv1sT6/iQySa1hUpRmnSp6shaSphabK8MYQyapwdAafw3CHBlTV7GuOwGjTox42fBzXU8CuaKHkuOX85lqr9fzAkzPOxidLMhgi6rPhJ+3HR/bN3RhsDcJhbMaMWIM0DWOdNJoer5laBwF25Ul04PGcfh+AN8PkE5o0CLyvhiAdEJDV8ooRxQ29iHDw5sHgxAEcWZQdNwSCYVjes7G+HShkkc0l3cwl3eQSeoY7ktCURhKdnwCZlAuGx06a4fkCq4MMbY0KJyjK20gaS5EmnWnTWSSBubyNuYLLixd5siEIeQAkFJ1eH6AYslrSIxlZV85UxcoOT5KjhxjqUpADV3BYG8StuNjJmfD9QIMdFvozVqVUGXbDWC7AQyNw9AV+L5sL7wWPxAYny5CUzm600Y5r0katAIyD6evy0JP1sTUbAmnpgpQFIautAlTj/81DfsLE2xdL4DrORWzWCFknlWYI6RweS9l7pC81pRVa3AKSLNZzw9QtD0wUI4QQawUJELLwMuHp2Lfm8s7YACyKb3pBHZ8PBf7nucLqJxjqD8Z6WagcOkC3Z02Y9tQFTlZF20v8v1QjKbmShUhrcfQFazvTyJharHXEopRHGGuTsqKCdkui5Gpq/CabI1xJlcuceMIxSgOhTMkTRWZZPRzCQ1KNZVWPkRniMoTCvODzsV8oDjWzpUQBEGsIaLyhNZSflAInQkRBEEQHWPNipAfCBRKLkq2FxkxFfqU5YsuvJjorqDcRtF2m5qMbh5KIx1RGwcoV9oEKgfmcfR3mUgY0QtTBsALBKZmS7FnSp4XIF904ZSLzkWhcLnlFmdQqnCGDQMp9GSiTU4ZgKSlNS33oKkcfVlTbpVFfgI151VxGLoSGTARorZoQxYIVCuVac92HARBrCxrbjvOD4KGAADb9aFrvHLA7XoBSs5CeWbPD8B5WJ2URZqEOq4DXeMw9NpJnDGGbNJAytLheT5Oni5gvuDUGJwyxuB4Ao7nQlejTU0TpgZTV9EtBKbnbRRKHhhQqaoKAPmSh0LJQ8JU0ZUyoCgMfmhwWhY4GVQgi9SFFVpDOJeeapoqS2DbZYNShcvaPKFbeMrSMdibwNhkAdNzJYABaUtDNmVUosZ0lZfNVWUAQ3XAAYP0sOpKGbJkd8GFAMrPQL7f6oBfUzlUhcHUFZTKVWHDfk1dAWPRbTC2cO0hhqbIKrVl/7swaCGuDYIg2seaEqFCyY31BnPcAI4bf1AdBAL5ktu0fceVh92phN6wmlA4g6Kr2DSYxsx8CfMFN3KCczzpX5ZONJpgcs7AIcOvk5YH2wkiK62GYpRJRh/MCyFXXp7PGwxMQzSFQ0tIx+j69xkDOBiG+5LoyxooOn6kcGgqg6rI1ZWm1YoLK/+nK20gbWmVe3smk36lPLmuwNQViHK7cW2Y+oKref1nDK1cIr1FGwRBtJc1JUKtqnkuBwrnTZMVFc5gu37TSU6Wgo5vgzOGwG8+US5mDm0WUlz9etxnOGcIBJrWF2KMwWgSQs0ZgyOiDV8XS42wNaGZwWnl9SWOhSCI5YU2xVeEc2eSa23G2rqNc8mmiASIIFYXa2olRBAEsVaozxNSFb6m8oNC1t4VrQrOnZWBaLE9tZhFDq0uCGL5qc8TuvyiAaQTegdHtDKsqe24doTc+kFQ439WjxACVkyodUgQiAZ/s3p0rfnZUyCAZl6bQSBLh4d9RY2z/v+iPqNE+KzV43qNvnHVNCuut5x4fnMjVYIgVh9raiWUMLXIEG0ATUO0AXkIb+pqpbRzfYh2iBChnxuHaSiVEt6hh1zJ9sEZQyahoeT4DaWkgXI56aJXjixTasqAhyHGmaSOQAjM5pxKiHOIpjCYhhppTBoIAREITOds5IseOGfoz5ro606Uo8Lk56JDtGvHaNte5PjrKZS88v1TagQnvCZV4cgkdTiu31AmXOHyWlSFxz47Q1Mq+T6O68N2/ZpnF46/Ve4QQRCrjzUlQoCMXktaHH55JcAZk7krVVtGuqbIXJlyuLShKVCUWiFImJoM2y46kSsOzw+QKwRQFQ5d43DcoGby5JwhYaowy6HfUTmmni8wX5BilE3p0FWlJrdHYQw9GRPZlIHpuRIcL4CpK7Gu2EXbQ77oIl9a8IcLAoFT00VMzJYw1JtAOqHBdoKa5Fu/PEaFy3vlecGixKea0ICVc4akqTUENIRRdLqmwHF9eL4s31AtWgvPTgok543PrlUbBEGcW6w5EQpROEPCjHYxAOSkqGsLeSVRcM6gKrzphOz5QayLQdiGpirwY8o3yDZEeeKOdzJIJ3WUYsxHQ07PFGO36IJAJsFyxmLdDvyykCyFIJArQpVHC0MoRkaTNhTOkbDihWUxbRAEcW5AXyEJgiCIjkEiRBAEQXSMNbkdF5qThs4F1hIOreUZxEIBtDNFVTh6sxqEAKbn7dh6PsfHc0gYsiBd9RmHEAKeL8+3mo9TwcVbezBfcDA2WYBTV9NH4QwKZ5gvujA1RUbfRVjb6LoCv66oXXUbpq6CKwy24zX0AcjzNoWzlqHfBEE0J8wTWos1hKpZU1dVLT7hubsQ8tC92qD0TCZHeX7CkDAZgmDxYlQfPQcAfVkTfiAwM2+jUCdGQtQalGZTOgCGkt1YDTWk3uA0NFPNJAzMFRyMTebh+zJkXFoFyci/YrmCqvRak1GDYfQZYwxM4UhZvFJhlQE1BqeMsfLPoFIdVS9HsJEvG0EsD2Ge0FqsIVTNmhKhXNGNLZcgBcRD0ooPVmgGYwyKwmDoAl6xuQhZRqODNbDgYp1N6Q0iFBIalAaBaFqGgAFIJ/XK2KrHyRiQTeoQQmAu50R67wgAJcdHb5dZI5TV7allMap/vdIPFsxF698nCIJYDGvqTKhZvR4AsVFhZ8JiciEV3ny1JURrTzbeIkk0yhm7/n3PC5p2xMri08r0c7GfIQiCOFPWlAgRdSyDMCxGXEiACII4W0iECIIgiI5BItQpWm3rtcUCTSxL0QnyayMI4mxZUyIkq4jGvy9NQ5fWh6pw6C3CvT2vuZGmrnGkElqsAIQRZs2OsASijUmrySb1WCNUVjaSc7x4J4dWhH55juc3NXUlCIKIY01Fx4WecPVh2tUmmUuFMQbL1GCUvemqLX10jcPQ1JYBEKziCadXDEpDEpaGrpQOVZGlt31foFh2w5Y/K8Olo6Lv6tE1BUO9SZQcD9PzNtxyXg/jDF0pHSkr3ioojlBoBFCTK1SyfXn9uloRPTorIoiz55LtfRgaGliz+UEha+7qqo0yXS+o+L8tN5wviJHrB9AUfsbRdwrnFTEqlDxYdULJGIOqMqQUDb4vICCaluyOw9RVDPWqKDk+fD9AwlTPWiA8X8D1/EiHcccN4LgOUgkNSox3HEEQi6M3a67p/KCQNSdCIaFB6UrDOYPBl9aPwnnTYlWhGC0Vmc+z9HsSJUDV0K4cQRCLpaNfVw8dOoRLLrmkk0MgCIIgOkjHRKhYLOKBBx6A67qtP0ysKpaa8yuEgB+cuQ8fQRBrj46J0EMPPYTbb7+9U90TZ4mqMKQSOhJmYwCGLGinVgxM6xFCBnPM5WUwRq7gnJUpLEEQa4eOnAkdOHAApVIJH/nIRzrRPbEE6n3lPD+A4y5Up63+TIgQAna5tHc1fiCQL7rLGr1IEMS5xYqK0DPPPIMHH3yw5rVt27Yhl8vhscceW8muiRWmWoxC8YiLuHMiBKgaPxDgVP6BIM5LVlSE9uzZgz179tS8tn//fnzta1/DbbfdVnlt7969eOKJJ5BKpVZyOMQKsBjRWEywHJWAIIha8qXz47y87dtxt956K2699dbKvy+66CI89dRT7R4GQRDEqqZkn72bybkEbcITBEEQHaPjyaqvvfZap4fQdsLIsfNl+2kxVykAYAlnQtXReOfLfSWItQCthNqIKJt8Oq6Pgu3C95sbna4VdE1WX42TBoXL0ulnIx7hPfUDgULJhVs2jz0f7itBrAU6vhI6HwgnxHpj1ZznQlUYTH0h52Ytfouv9vMLI+UEAKV87WcTmh3eUz8QKNke/LLBq+d7KDkLJq9h/wRBrE5IhNqA7fpwyhNvPZ4vkCu6yCT1NT9ZVouREEsrty4EkC86iKpmIQRQtD0Aalv8AwmCOHtIhNoEbQ4twBhbjsrjkQJEEMS5BZ0JEQRBrEJM4/xYxZMIEQRBrEKSptbpIbQFEqE2oKkcvMX+k3eeRMotF4wBWouABrqnBLH6oTOhNqBwjlRCg+fLSK6gamLUVVkSeymH9OcjjDEkLA1BOTrOrXLjJkNUgjh3IBFqE4wxaCqDqkgx8v0AuqaQ+CwRzhfEyHY9aKpC4kMQ5xAkQm0mFKMwh4VYHjhnsIzzYw+dINYSNBMSBEEQHYNEiCAIgugYJEIEQRBExyARIgiCIDoGiRBBEATRMUiECIIgiI5BIkQQBEF0DBIhgiAIomOQCBEEQRAdg0SIIAiC6BgkQgRBEETHIBEiCIIgOgaJEEEQBNExSIQIgiCIjkEiRBAEQXQMEiGCIAiiY5AIEQRBEB2DRIggCILoGCRCBEEQRMcgESIIgiA6BokQQRAE0TFIhAiCIIiOQSJEEARBdAwSIYIgCKJjkAgRBEEQHYNEiCAIgugYJEIEQRBExyARIgiCIDoGiRBBEATRMUiECIIgiI5BIkQQBEF0DBIhgiAIomN0RITGx8fxqU99Cr/5m7+Jffv2YWRkpBPDIAiCIDpMR0To85//PHbv3o3vfe972Lt3L7785S93YhgEQRBEh1Hb3eHU1BReffVVfPOb3wQA3HzzzbjyyivbPQyCIAhiFdD2ldDx48cxPDyMhx56CDfffDPuvvtuaJrW7mEQBEEQq4AVXQk988wzePDBB2te27x5Mw4dOoRPf/rT+MM//EPs378f9913Hx5//PFFten7PgBgbGxs2cdLEASx0gwODkJV274JtWphQgjRzg6PHTuGG2+8EQcPHgQAFItFvO9978OLL764qJ9/4YUXcNttt63kEAmCIFaMAwcOYMOGDbHvj4yM4EMf+lDLz60V2i7HmzZtwuDgIP71X/8VV199NZ599lns2rVr0T9/ySWX4IknnkB/fz8URVnBkRIEQSw/g4ODLd8/cOBAy8+tFdq+EgKAw4cP4/7778f09DRSqRQeeughbNmypd3DIAiCIDpMR0SIIAiCIAByTCAIgiA6CIkQQRAE0TFIhAiCIIiOQSJEEARBdAwSIYIgCKJjkAgRBEEQHeOcFKGRkRHcdttt2Lt3L377t38bJ06c6Mg4VlNJikOHDuGSSy7pWP8HDx7ELbfcgr179+L2229v+zP5/ve/j49+9KO49tpr8cQTT7S175BHHnkE1113Ha677jr8+Z//eUfGUM2XvvQl3HfffR3r/8c//jFuuukm7NmzB1/84hc7Moannnqq8ky+9KUvtb3/XC6H66+/vjI3PP/887jhhhtw7bXX4uGHH277eFYl4hzkc5/7nHjiiSeEEEL89V//tfjsZz/bkXHcfvvt4sknnxRCCPHkk0+Ke+65pyPjKBQKYt++feLCCy/sSP9CCLF7927xyiuvCCGE2L9/v7jzzjvb1vfY2JjYvXu3mJ6eFvl8Xtxwww3ijTfeaFv/Qgjx05/+VHzsYx8Ttm0Lx3HEJz7xCfGDH/ygrWOo5vnnnxfvfe97xR/8wR90pP9jx46Jq666SoyOjgrHccTHP/5x8S//8i9tHUOhUBBXXHGFmJycFK7riltuuUX89Kc/bVv/v/jFL8T1118vdu3aJY4fPy6KxaK4+uqrxbFjx4TruuJ3fud32n5PViPn5EooCALkcjkA0nvONM22jyEsSbFv3z4AsiTF7//+77d9HADw0EMP4fbbb+9I3wDgOA7uuece7Ny5EwBw0UUXYXR0tG39P//883jf+96Hrq4uJBIJfPjDH8Y//dM/ta1/AOjv78d9990HXdehaRq2b9+OkydPtnUMITMzM3j44Ydx5513dqR/APjhD3+Ij370oxgcHISmaXj44Ydx2WWXtXUMvu8jCAIUi0V4ngfP82AYRtv6/7u/+zvcf//9GBgYAAC89NJL2Lx5MzZu3AhVVXHDDTe0/fd0NXJOWrnec8892LdvHx5//HG4rou//du/bfsYqktSvPDCC+jv78cf/dEftX0cBw4cQKlUwkc+8pG29x2i6zr27t0LQH5BeOSRR3DNNde0rf/x8XH09/dX/j0wMICXXnqpbf0DwAUXXFD5/99++20888wz+Na3vtXWMYT88R//MT7zmc+09YtAPUePHoWmabjzzjsxOjqKD37wg23/kpZKpXDPPfdgz549sCwLV1xxBS6//PK29f+nf/qnNf+O+j09depU28azWlnVIhRVCmLbtm2wbRt/8id/gmuuuQb//M//jN/7vd/DP/zDP4Ax1rZxLLUkxXKMYdu2bcjlcnjsscdWpM8zGcdjjz0Gx3Fw3333wfM83HHHHW0bUxAENc9eCLFivwuteOONN3DHHXfg85//fEf8EPfv34+hoSFceeWV+M53vtP2/kN838cLL7yAxx9/HIlEAnfddRe++93v4qabbmrbGF599VV8+9vfxrPPPot0Oo3Pfe5z+MY3voFPfvKTbRtDNavp93Q1sapFaM+ePdizZ0/Na1NTU9izZ0/lm/aHP/zhihlqT09P28YRlqTYvXs3AOD6669f0cPXqDHs378fX/va12pKW+zduxdPPPEEUqlU28YBAPl8HnfddRe6urrw6KOPtrVQ4eDgIF544YXKvycmJipbIO3k4MGDuPvuu/GFL3wB1113Xdv7B4B//Md/xMTEBPbu3YvZ2VkUCgX82Z/9Gb7whS+0dRx9fX248sorK3+T11xzDV566aW2itBzzz2HK6+8Er29vQCAm266CU8++WTHRGhwcBATExOVf3fq93S1cc6dCXV3d8MwjMqkc/DgQSSTyRUToDiqS1IAOOOSFMvBrbfeih/96Ed46qmn8NRTTwGQ0UArJUDNuPfee7F582Z85Stfga7rbe37/e9/P372s59hamoKxWIRP/jBD/Drv/7rbR3D6Ogofvd3fxdf/vKXOyZAAPDNb34TTz/9NJ566incfffd+I3f+I22CxAA7N69G8899xzm5ubg+z5+8pOftP3vY+fOnXj++edRKBQghMCPf/xjXHrppW0dQzWXXXYZjhw5gqNHj8L3fTz99NNt/z1djazqlVAUjDE88sgjeOCBB1AqlZBMJvHVr361I2P56le/ivvvvx9/8Rd/USlJcT5y6NAhHDhwADt27MCNN94IQO53f/3rX29L/+vWrcNnPvMZfOITn4Drurjlllvwzne+sy19h3zjG9+Abds1vwP79u3Dxz/+8baOY7Vw2WWX4ZOf/CR+67d+C67r4gMf+ABuvvnmto7hqquuwqFDh3DTTTdB0zRceuml+NSnPtXWMVRjGAYeeughfPrTn4Zt27j66qs7epa7WqBSDgRBEETHOOe24wiCIIi1A4kQQRAE0TFIhAiCIIiOQSJEEARBdAwSIYIgCKJjkAgRBEEQHYNEiCAIgugYJEIEQRBExyARIs57vvvd7+Kaa65BPp9HoVDAnj178L3vfa/TwyKI8wJyTCAIAJ/97GeRTqfhOA4URcEDDzzQ6SERxHkBiRBBQJZh3rt3L0zTxHe+8522Fj8jiPMZ2o4jCACTk5OwbRtzc3MYHx/v9HAI4ryBVkLEeY/ruti3bx/27duHIAiwf/9+fOtb32prTSSCOF+hlRBx3vOXf/mX6Ovrw6233oqPfexj6O7uxsMPP9zpYRHEeQGthAiCIIiOQSshgiAIomOQCBEEQRAdg0SIIAiC6BgkQgRBEETHIBEiCIIgOgaJEEEQBNExSIQIgiCIjkEiRBAEQXSM/w+H+/XweNWn4AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"x\", \"y\", data, kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7NVd959MJy4z"
   },
   "source": [
    "### Pair plots\n",
    "\n",
    "When you generalize joint plots to datasets of larger dimensions, you end up with *pair plots*. This is very useful for exploring correlations between multidimensional data, when you'd like to plot all pairs of values against each other.\n",
    "\n",
    "We'll demo this with the well-known Iris dataset, which lists measurements of petals and sepals of three iris species:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bCkHKm4ZJy4z",
    "outputId": "a1b111e2-b39b-42b9-dc7d-68f35d2a57fd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width species\n",
       "0           5.1          3.5           1.4          0.2  setosa\n",
       "1           4.9          3.0           1.4          0.2  setosa\n",
       "2           4.7          3.2           1.3          0.2  setosa\n",
       "3           4.6          3.1           1.5          0.2  setosa\n",
       "4           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "iris.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZBVFxwEhJy42"
   },
   "source": [
    "Visualizing the multidimensional relationships among the samples is as easy as calling ``sns.pairplot``:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g5qihii9Jy42",
    "outputId": "f65d4ccb-f278-431d-b681-33d61457d8ce"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\furqa\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 806.85x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue='species', size=2.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oBRGph9LJy45"
   },
   "source": [
    "### Faceted histograms\n",
    "\n",
    "Sometimes the best way to view data is via histograms of subsets. Seaborn's ``FacetGrid`` makes this extremely simple.\n",
    "We'll take a look at some data that shows the amount that restaurant staff receive in tips based on various indicator data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UiIaw0N9Jy46",
    "outputId": "c9248725-9caa-4b1e-c913-defbe49524e3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tips = sns.load_dataset('tips')\n",
    "tips.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "s8i0wEvcJy49",
    "outputId": "7dab2c15-82aa-4cb7-d506-f0068b2ca77f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tips['tip_pct'] = 100 * tips['tip'] / tips['total_bill']\n",
    "\n",
    "grid = sns.FacetGrid(tips, row=\"sex\", col=\"time\", margin_titles=True)\n",
    "grid.map(plt.hist, \"tip_pct\", bins=np.linspace(0, 40, 15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "9Ju9bPH_Jy4_"
   },
   "source": [
    "### Factor plots\n",
    "\n",
    "Factor plots can be useful for this kind of visualization as well. This allows you to view the distribution of a parameter within bins defined by any other parameter:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "9XiqLzR-Jy4_",
    "outputId": "1edc1beb-6ab1-47d8-bd3c-dce0fa748f84"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\furqa\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 436.35x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style(style='ticks'):\n",
    "    g = sns.factorplot(\"day\", \"total_bill\", \"sex\", data=tips, kind=\"box\")\n",
    "    g.set_axis_labels(\"Day\", \"Total Bill\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ESytnWO_Jy5B"
   },
   "source": [
    "### Joint distributions\n",
    "\n",
    "Similar to the pairplot we saw earlier, we can use ``sns.jointplot`` to show the joint distribution between different datasets, along with the associated marginal distributions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "B0VCHj2qJy5B",
    "outputId": "1e842f57-10cf-42c8-94fc-59e9a068dc0c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2s5lbq6QERos1ZBI6TCPcW6Jac1CYonvodPKlGpIxDXGzvT1/ouD3OdJg6AqUejAt9edMM2MI0ZzzPBk4gBqEALSQHUD98At/4d/Qw9+ZNd3220xMXV02k3Fje67x30QMIaK5MJvLSstsLmb40Fi8JkRERJFhCBERUWQYQkREFBmGEBERRYY3JhARLUDnbujExg09iAcofbQYLe1nR0S0SBm6ip6upd/0k9txREQUGYYQERFFhiFERESRYQjR3JvNN+Lb129uTk89q6csMacVxIkWE4YQzTlVEUgn9FDHSqDee0YGnpiF8Cs1hw2EXL2tQ5DzSinhSQlFCZ9CjuuxlA0tWwwhmhcxQ0Nn2oQaYnL2u676LSCCBpGhq+hKx6Cpwc97uq2D19J5pZRw623DHTf4SkYRAp1pE7GQlbuJlgKGEM0bv5mZCTNEdepGW4eq5QQOIkUR6EiZiJvBzyslkCvVUKpOf14pZbM5XZimeKauoitjhq4YTrRU8B1A80oIgUzSQCoebnuuWHWQLwffnhNCIBU3/O25EOetWA5Gi7UJ23OyPo58efruqdNJxTVkkga34IjAEKI2iZtas41zUDXbbxHe2J4LEkaGrqIzE3Z7zu/Q6rhe87yN7beaHbwDrCKAzrSJuBkukImWIoYQtY2mKujKmAhzDd9rXnsJPvmr9e25MEHU6LpaqjrN7bdW2pWfeX5TV+phyLcc0ViRvSMeffRR3Hzzzbj22mvx13/911ENg9pMCBG6dTcA2E64O8mEELPqnFqxnNDbb5qqIJUwQq0CiZa6SELo6NGj+NSnPoUvfvGLeOihh7Bv3z48/vjjUQyFiIgiFMm9oY888giuu+46rF69GgBw7733wjTNKIZCREQRimQldPjwYbiui9tvvx033XQTvva1ryGbzUYxFCIiilAkIeS6Lp588kncfffd+Ld/+zfs3bsXDz74YBRDISKiCEUSQitWrMD27dvR1dWFWCyGN7/5zdi7d28UQyEiWpBc10P/cBmFci3qocyrSELoqquuws6dO5HP5+G6Ln7xi1/gwgsvjGIoREQL0guHhrH7xX5Uqk7UQ5lXkdyYcMkll+BP/uRP8K53vQu2beONb3wj3v72t0cxFCIiilBklRNvueUW3HLLLVGdnoiIFgB+fZuIiCLDEKK28qSE7bihj9c1JXQDOENTQhdSnQ1DV0IVUSVaDtjIhNrGcT2/cVyIDFEVv2HdbJrHaaoCTfWDLFeqhWrBEIQQQCZhQNf4WY9oKgwhmnd+7x0HxUq4u3xMXW12ap1N+4PGsariV7MulGuhqmG3QlOVZpdXtmwgmhpDiOaVlBKFcg1WyMk+FdcRM9Q5nciFEBDwVynVmhu6MOlU4qaGZExj+BC1gCFE88bffvMb0gWl1LffVEXM22QuhEDMUP3tuZDjHP94p7ffGEBErWEI0byYTesDQ1eQTvgdUed7MhdC+NtzGRO5ogXHDRdEmiqQTZrcfiMKiCFEc871ZOgAamyTtXMib2zPhQ0gAMgkzVndNEG0XPG2HZp7s9nWWqTz+CIdNlHkWg6hXC6HYrE4n2MhIqJlZsYQOnjwIN7+9rdj+/bteMMb3oB3v/vdOHHiRDvGRkRES9yMIfRnf/ZnuPXWW7Fnzx78+te/xjXXXIM///M/b8fYiIhoiZsxhCqVCt7xjndA13UYhoHbbrsNg4OD7RgbEREtcTPeHbdp0ybs3r0b27ZtAwC89NJL6O3tnfeBEREtZ5t6s1i9uhNOvbldK+IxDemEMc8jm1szhtCJEydw2223YevWrdA0Dfv27cPKlStxww03AAAefvjheR8kEdFyc/BYDjkrHuiYbVt7ll4IfexjH2vHOIiIaBmaMoQOHDiAzZs3I5lMTvr3bMdNRESzNWUIff7zn8eXvvQl3HrrrVizZs24Hi6VSgVPPvlkWwZIRERL15QhdM8992B0dBSbN2/G/fffDyklhBCwbRvvfve72znGBaERwqwLtrA1XqeLSVRjjuo1zfcSjTVlCH30ox/FE088ASEEtm/f3vz/qqrimmuuacvgFgopJVxPQkpAU/nmmY6UEhJAzXZDVZOWEihVbSRjeqBjpfR/P7mSFaqQqOt5KFVsxE0tVBHSQrmGdDJ40VXX8zCYq6I7E4OiCChtem15nmw+X7WNr2kpJWzHg6L4hWP5XqIpQ+i+++4D4H9Z9bOf/WzbBrTQ+A3ZTvecSZgaEuwVMynPk6g5Lo6cKsJxPeiagkyIxm4Vy4XtyJabwnlSwnU9HDlVhGW7GMlbWL8qDVXFjJO6JyUcx8NzB4ZQrNgwdAUbVqWhaUqgQKg5HkYKVr39RGtj9jyJfa8OY6RgwdAVXLSpG4mYBlWZv5KOjbA+OVRCrliDogj0rkwiEdPnvQCrlBKlqoOK5Tc3nI9eUbT4zPhqX64BJKWEJyXy5dq4itBly2m2hpaz7D+zlHiexEjBwsHjeTiu38DOdjyM5KtwXC/wz8pxPQwXqrCd6Y/1PIliuYZXjuVg2S4AoFpzceBYDqWKPW0Lb9fzkCtYePqFvubvuGZ7OHA8h0KI9t+Nn0G15k47ZtfzUK7YePqFPowUrOZ5d784gJMDJbje/HR79Tx/FXLwRA65Yq35/470FdE/Up63dueyHrijxVozgACgWLFRKNv1YOR7abliK4dJNLbfclNMRLbjT5DZpAFNXd4NzPywBo4PFFEsT2zf4ElgtFBDIhZ8Belvr9Um7VTa+ER/ariM0fpEPv68Ekf7iuhMm1jVlZjwKd/1PBw6mcex/tKk5z0+UEI2ZWNNdzLwSq5YsVFzXGTq39cYe6zreTg5WMLB43lMNu0eOJHHSNHC+Wd1zWlDP8+TKFZqOD5QmrTI+XDeQsVy/BXkHJ5XSgnb9ZAv1SY9r2W7cAoeskkDCrfnliW2cjhDY/ttpGBN+8lQSmC0WEPZcpblp7jGp9ua7eLg8dykATRWueqc7l4a8OdVserH1lefnpRwXIlDJ/OTBtBYIwULh07m4dRXVJ6UsB0Xe18emjSAxsoVa3j1RD7USq5m+9tzbn3MUko4rocXXh3BgSkCqGE4b+GZF/pRqjrN48Nq/J5ODZVwrH/yAGqoWC5eOZZDuerMyapIStn8vU93XteTGC5YsOzpV5C0NHElNIZ/cdppbuu0olx1YDv+Jzlg+XySsx0PxYqNvqHytBPqmceM5KroyJhQA/6c7Pr2XCqmo+Z4ODFQarkdd7Xm4pXjOazpTsCTEvteHWluGc7Esv2J+aw1acSMYCs5t749F9NVQADPHxxu+bVl2S52v9iP88/qwopsrOVzjuU4XnNF2Op5PU/i8KkCejrj6M7GQr2ePU9CQqJQtmE7rW8tFso2arqHdCLYTSm0uDGExhjOT/+peiq240Fi5ovgS8nJwRJKVWfmf3gGT/o/L9UIvghvXFCvWK1/SGie15N46egoyiHGLOtbiqu7w71dTo2UcWqwhKCLCymBvqEyutImVDX4a8tyXBw9VQh8XsBfBXaG+LAAABIy9HupZrsA9FDH0uIUWQjddtttGB4ehqb5Q/j0pz+NSy65JKrhEBFRBCIJISklDh06hMcee6wZQkREtPxEcmPCwYMHAQDvec97cOONN+KrX/1qFMMgIqKIRbIMyefz2L59O/7iL/4Ctm3jD//wD3H22WfjjW98YxTDISKiiEQSQpdeeikuvfTS5p9vueUWPP744wwhIqK6RlM7TVVg6mpLx8Rji+/yRiQjfuaZZ2DbdrMmnZSS14aIiMZoNLXbtrUHPV2JqIczbyK5JlQoFPD5z38elmWhWCziwQcfxFve8pYohkJERBGKZPlx1VVXYc+ePXjb294Gz/Pwrne9a9z2HBERLQ+R7YF9+MMfxoc//OGoTk9ERAsAa8eNkYzpoaoeCAEstloJUvoFLWsBShSNlUmZSMXDfbNdDdkyQEqJfGl8JeYgVmRjWNMdbm89ZrR2YXgyuqqgK2TpnWRcR9hCHJ4nQ1+oNvRgrSzGE/WCs8GPnO92ErTw8G6AMeKmipipolCqodZizStNVZp14xZLV0/X9ZAr1eB6EhXLDdUjqSNlIJs0MJyvon+k0tIxQgDZlF95PMyYj/UX6wVj/dI/rdYYEwJYtzKJVNyAhER3NoYXDo3AbaGejRDAmu5kvS9SuN9tIqYhHtOQThg42leA47ZWR+esNRn09iRDNAaUKFsOqjUXcVODqavIFWstPV8AzcrjYSkCiJsaYoaGXMlq+fkauoJ0vfI4LR9cCY0hhN/ZMpM0kGzhE2Tc1NCRMqDUS98vhgCyai6G69WdG8qWg9Hi9FXDzySEgKIIdGViOHttBtoMtc10TUFXJhaq9UXFcprVnRs1S2u2/zxmKkRq6iq29GaRShj1bp4KOtMxXHb+KiRnWMnpmoLN67LIJI1ZfUJvvK5ihorN67JIzPDa0jUFl567Er09SahKsJ+X50nkirVmjTwhBFRVQWfGnPE2X0UA61elmq0vwr6eG+8FRRHoSJmIt7CKTMV1ZBIGlEXyPqK5wxCahBCiHjAmJpt7hACy9aBaLG8YKSUK5Rry5dqkf++4st5ELtj2nKLUJ9fe7JTBHTc1ZMeEdZAxD+UqOHQy77c0GPt3ON1EbqrtuWzKqAfk+K0lRREwdAWXnrsCq6fYnksndGxel4WuKXO2RdQIhA2r0ljZMfn2XDZl4LLzVyGd0AN3WG30ubInCWYhBNJJHenE5MFr6io293YgGZ/bDqtCCCTjur+SnOTvFSHQmTbZYXUZ43bcFIQQ0FSgMxNDvlRrlqTXVIFs0gzc6CxKrue1tB3T6JGUiGlImK0HrBACqhBYvyqNoXwVA/XtOSGATNKArgVf/bieh+P9JZSq9ozth0oVG47jIVXfnhMCWLsiiXRi6hVMY8xberPoysSw//AIPE9CAFi9IoFs0py36xOKItCdjSMZ13G0r9j8vWxcnao3lQsWPlJKVCxnxqrmQgiYhgpNU5r9mQB/a3V1iOZ9rRJCwNCU+nvp9PacoSlI18NpsbyXaO4xhKYhhICAv+opWw6kJ+sXihfPG8ayXRRKtZZ7/gD1Hkm2F3gbSlEEujMxJGMaTgyWkE4YoSa2quXgSF8BritbGreE/zztgoeejjjOaqx+Whi7qijozsRw2fk9eOHQMFZ2JKDP6qJ8axTFX21v7s3i1GAJm9ZlkQqx+vHqN2u02rfH/3Al0JkxUSzXsKIjjnR8dtuNrZ5XgURHykSxYkOtP//F9F6i+cEQaoEQAglTa/73YmE7flvlMFwpQ9/dZOoqsikz1Hk9T+LVk/mgzVch4TeR29SbDdyeujHm9avS/mqoTb/jRiBctLk7dJv4IAE0liIE1q1MwdDUtt2R1nh+jbsqF9N7ieYPrwm1aDG+YaSUoW8dVwQCrZ7mSqvdUqcSNIAahBCQMprf82xuAphNG25VEZHdEr0Y3080PxhCAfCNQzR7fB/RWAwhIiKKDEOIiIgiwxAiIqLI8O44IqIFqNHUznE99A+XQz1Go1zUQsYQIiJagBpN7WZj29aeBR9C3I4jIqLIMISIiCgyDCEiIooMQ6hN5CwrAYQ+b2QHz+K0EZ2XiNqPITTPpJTwpITnybYGkZQSqipC1X9rVGU+NVQK1XnV8ySK5dqMvX4moyoC2aQRupvo8cESXC/4eaWU0LXwb4cQffqarJob6rUhpYShhz9xNeR5ieYS746bR1JKOK5EvmTBk37hxnb0TZH10MuVaoFXFa7roWw5+PqPX0LfUBlvecN6XPOGjTBmaIgG+OFj2S5+8ORh9A2XsWVdBhdtWRGonpsQAut6UkgUqjg1VA48/gPHcsgVLJy3sbOlmmyNSbhQtkMVAgWAVNzvImq7fsHYIGNOxrRQ1aSllChXHVSscO3Z46aKZGxxVYSnpYkhNE8m6/FSrNjNttTA/NTQktIPgkLZDnys7bg4fLKAbz92AFZ9BfTjXx3FK8dyeN9NFyFmalCnKHjpOB76Ryv40S8Po1rzj33leB6DeQuXv2YNDD1YtebOdAxxU8PRU0U4nhdoYh/MVfHM/n5cvLkbpqFO2R5hbFi32vp6LEUA2ZTZbFduaCq60rGWWlr7jRHNwKsvKQYDnzgAACAASURBVCWkBPLlcNWzBYB00pixyypRu3A7bo41tt/ypdqkTcYs28VIwZrz7Tl/cpIoVOzQAfTTZ47iaz9+qRlADQeP5/HXX3kah0/mJ92ec1wPu1/qx3d/frAZQA2jBQs/+tUR9I9UAm/PxQy/304qrgfenqvWXDyzvx8DIxW4k5y3EdZntjpvVaNJm3bGPlyjpXWj9cdkdFVBVzoWKoAct949NUQAqYpoqc03UTsxhOaQlBKuJzGSt1CbZpJwPYnhggXLnps9+UbwjRQsWLVg2zOuJ1Gq2Pjn7+3HU8/3T/nvihUb//sbv8FPnz7aDCLPk6jWHPzHzlfx7P6BKY+1HQ8795zA8weHAgeRogj09qSwqisROIikBF48MooXj4zCdb1mUPutzsOFNeBvoWWSxpSN7xotrbOTtLROxE63Og/2XCTKloPRYvAtVgCIGSo602bgpnlE843bcXNESomq5aA4Q4vlsQplGzXDQ3oWTb6klKjVt9+Czk2O4+FIXwH//ugrE1Ywk54LwPefPIyXj+XwvpsuxGjRwg9/ebjl6xIvH81hKFfFFZeuC3ydqCsTQ8LUcOhkHkEXLgOjFRQrNi7ZsgKaKkJvvwXdQjN0tdnS2vUkskkDuhZsFeJ5EhDhm9cBQCZhwDS4+qGFiSE0R4Zy1VB3NFs1F6mYHrq52HB9ay+M+3+4H0f7ioGPe/noKL70nedg6MFfPsN5CycHS1i/Kh342JjpryJGCsG7xVYsB68cH8XKjniosDd1Bam4DiXgSkKtb88B4T5k2K4XuD17g6YIZFIGVz+0oEX66rznnnvwiU98IsohLGvuDBfPpzPThff5EtXdXEKE7346m2NnI6rzEgURWQg9+eSTePDBB6M6PRERLQCRhNDo6Cjuvfde3H777VGcnoiIFohIQuiTn/wkPvKRjyCTyURxeiIiWiDafmPCt771LaxZswbbt2/HAw880O7TExEtCo2mdmFoqgJTVxGPLfx7z9o+wu9///sYGBjATTfdhFwuh3K5jLvvvht33nlnu4dCRLRgzaap3batPejpSszxiOZH20PoK1/5SvO/H3jgATz11FMMICKiZYpfICAioshEumF488034+abb45yCEREFCGuhOaIaaihe+BUa06oqgeNOmhhNOrcha3UEJ+movZM+obLGBythDp2tGChHKA00lhxUwvdMyjszwnwf7+FUvAqD4Bf9TpszyAhMKF2XTt4nl9Bnr2KqBUL/9aJRSIV15GM6S2V8T9TqeqgXHWQSRkwWqwt5nkycO+aBst28dOnjgJSoittIl+uoWa3VpdMEQLnndWB7kwcQvj1786suj0VKf1iqc+80I/dLw5g+0WrcdHm7pa+1e95Es8dGMKB4zlIKbGiI44V2VjLFQE2rk5h/aoMFAGULSdQkMUNddqq2FORUmK0YOHUcBkAkCkbWNOdDBRouqZA1wyYtot8gIKrhqYgnTQCj3m2bMdt9rGqWA6yKTP0hxVaHhhCc8QvkQJ0pEwUK3ZLBUHHkgByxRoSMQ2JGZqcjX2jBzU4WsH3njgEq+bCk/64MwmjpR5EcVPDa7Z0w9RPN+ZLJ3SYtjLjBOm6HnKlWr2FhV8y6Je/PYVj/UVcfdn6aZvmVSwHT+w9iUL9eAAYGq2gXLHR25OCOk1bU01VcNGmLqQSenMyTJgaDE2Z8WfY6L1jaErg8jeeJ3F8oIhixW6eI1+qoVJ1sGF1uqUmgcDpMkWGrqIroyBXnLn4athGebMxWf8sv6J8FZmk0fLzpeWH23FzTAiBVFxHJqGH2gopVx3kirVJt+f8bpp2qHL+Ukr89uAQHvzZAZSrzriJTAiBmKGhK2NO2Z6gpzOO1523EjFj/OQmhIBZP3aqT7xWzcVI3oLnynHjdlyJo31FfOPHL2EoN/n2XN9wGT/65RHkiuP7/njSX9G8ciw35aomkzTw+gt6kEmOL+IphIDW6OkzRYBpqkBnJhYqgKyaiwPHc+MCCPBbS9QcDweO55ArWoEeUwgBRQh0pqfuB6TUPwS1O4AajQEn658lAeRKNRQrNW7P0aQYQvNACFEv4x9uK8KepHHZdG/0GR/PcfGjXx7Bk3tPTrtVqCoKujImDG3shA2ct7ED527onLYas6ooEyZIKSWK5VqzCvRkZ3Y9iVLVwQOPHcC+V4ebE5WUEs8dGMSuvSfhuJN3VpXSP/7wqTyGcpVxk9z6nhRes2UFdG3ydupCCCiKQDZlTNhqixkqOlImFBG8YOpooYqDJ3Kwnam7wUoJnBgs4cRAEV6AiblRkDSd0JvdeRv0ZpO99hYttZ3WmuxVLBejxXANBGlp43bcPBFCQAHQmQ65PSeB0aKFZEyDpirIl8Ntvw3lqvj+E4dQsZyWJgAhBDJJA9WaC8f18JotK2Aa6pQrpDOPTSd0GLaC0aKFfH3rqJVhO67EE3tO4FhfAdtfswZP7+traesJ8H9WAyMVlCo2Nq7J4OLN3fXVT2tjTsQ06LqCQqmGVFyHoU8eXNPxPIkTgyUUWvw9SemvEMqWgw2rWt+ea4zZ1FXoaX9L0axfs4p6+20mjisxUqgik+D2HJ3GEJpHjUnB1NXAIdQQZuXTMFqw8MBjrwS+UaIRJpt7s4F70QghoKsKRvMWpJx89TMVx5U4dLKAXKkGRRGBQteT/s9q29YeJGJaS6F55pi7MrHmn4M6fCqPas0NNGYpgZrtoWa70ANu+/krOf9DTuPP7VSqOqhYwV+bUvo3xgR9vrR0cTuuDaLagihW7NC3FmuqAhHyBt/G8w3zrL3mdlyoUyMZMIAaGhNi2InRsqfefpuJf3t/+8c8G27ANu1jaSoDiE5jCBHVLcaJcTGOmWgshhAREUWGIURERJFhCBERUWR4dxwR0QI0m6Z2juuhv14uaj7FYxrSidmVh2IIEREtQLNpatcu27b2zDqEuB1HRESRYQgREVFkGEJERBQZhtASF1Xh4sVYpnIxVnmezZgX4/OlpYch1AZ+naz2nzcd1+A4bqgkqlg2qpYF1w1T807C87xQk5xSb1kQptyQgN/6wfWCl5TxPL/TbJgOt54nQzW9A/wq5cWyHepn5Ul/vOG78oYPoqnaSbTCtl0GIDUxhNpAUxV0pv0y+0EIAWRTBrozU/e9mYzneShXa/jCv/4MP9u1G8OjOXgtT8wSjuNg9+5f40v//ABeefUYbKfVQpUStuPi8V/tw8+eeBan+gYCnBdQFYGLN3fj8kvWYE13ImARUqC7I479h0dwtK8QKIg8T2IoX8VLR0bRP1IONKl7nsRIwUKx0nrX07FjziQNZFNmqKrdxXINLx0dxbH+ol+tvMWJXUrZbMFwutFgsFCImRo6UkbgD1eqIpBMGCw3RE28RbtNVEWgI2WiVLVRsWZeXWiqQDZpNlcE2ZSBctVBeYbKxTXbwYn+HD77Dz/GcM7/nsBvfvsS1q9dhU1nrZ+2C6mUHsrlCp566imUy/6xD/3w57j4/M24+orXQ9dVYIqipp7noVSx8NBPnsFIrgQAeOHlQxgcyeGCczdBVaf+5KwoAoam4I2XrEFn2q9kvaW3A53pGF44NAxPyikXc6J+/PpVKSRifo+dw6eKGC3WcOHZ3dP215FSwpMSx/qKzWrlw3kLFcvB+lVpqMpMx8Lvnhqg7XZz3AJY051AR/35tqqxgjk1XMZowW+MV6zYOHg8h/WrUjA0ddpVpJRy3GvQdjy/vULShKYGq0Wnayq60jHkSzXYLRQ0jRkqUnGdAUTjMITayO+6asDQXOTrjd4mkzA1JGITO5gm4zp0XfGPneRgq+bg0V++iH/+zq8mVO4+eqIPo/kiLrnoXBi6hjPDxHVdnDp5Enufe27C6uW5Fw7gZN8QbrnxTUgm4lDOaO/gOC4OnxjAIzuf87f/xhgYHMFTxd/itRdtRSxmQIjxx6qqwIpsDG+4cPWEHjPd2Rh+5/we/PbAECo1d8IKRRFAzNDQuyoF7YxwzRVrePqFPly0qRvJuDahJYXnSVi2i6N9hQmtLiqWi1eO5bC+J4W4qU2Y1D3PX/Ed6SvO2MztTEL4K+MNq9IwjWBbWp6UcF2JI30FWGe0BrEdDweP57G6O+E35DtjzI3wypVqcM4IDK/eu6qV1vJnajQGnO4DUqNN+my28Gjp4nZcBPyuq7EJTdcEgGzSQHKaT4tG/dPn2K09z/NQqdq4958fxZcf+OWUrSMKxRJ++cxejOQK44LGcRzs2bMHv9mzZ8rts8HhUdz3rw/h4OHjcJrbc/5k/Iun9+EHP/vNhABqqFQt/Gr3c+gfHB73+KoicOHZXbj8krVTNjmLGRq2be3B6q4Exv64hPBDauOa9IQAarAdD79+aQDH+kvjtuc8T2I4X8WrJ/JT9lryPInDpwoYGK2MCz/PkxgtWjh4PB8qgNIJA5vXZYMHkCdRqtg4cCw3IYDGOjVUxvGB4rgttrHbb2cG0FjlquNvz8lg23OND0jZSbbnVEWgMzN1S3IihlBEVEWgM20iVp+MNFWgMxNrqeOkUt/aS5gaPM/Dif4cPvr5B/Ds80dnPNZxXPx67368euS4f+2oXMbOnTtx8uTJGY+1bQcPfu9neGzns3AcF8WyhX///pN4/uXjMx7reRLP7z+IF156FVJKGLqCKy5dh3M3dM74yVtRBM5Z34Hzz+qCovg3LWxYlcbKzkRLn9oPnczj+YPDcFwPrufhWH8R/SOVGY8D/M60h08V4LgePE/i+EARp4bKge/+EwJY3ZVAb08q0E0XjS3DvpEyjva11g68ULZx4HgONcerb7/54dJKrtiOh5G8FegaU8OZH5BihorOtBm4MSItL9yOi5DfwdRA3PSmvf4w1bHJuI6/+LvvYe+LJ6b9hDuZw0dP4sTRwygWC4FuHgD8a0wnh4qoWB7sKVY/U+kbGMI1l2/FtvPXB27xvKIjjgsFYDnelKufqYwULDz3yhCScS1wp9mK5eCVYzkoQgT+OQNAJqFjRUccsRB30BUrNvqHy7DsYOe1HQ9HTxXQmTER9OY5T0rkSzV0pM3AbQ0bH5BcTwb+HdHyFNmr5G/+5m9w3XXX4frrr8dXvvKVqIaxIMym02TfUCHUxAgA5XI5cAA15AvlwAHUoKsicAA1GLoaenJzXC9wADV4ngz9cxYi/PMFEHjbr0Eimq6+QggGELUskpXQU089hV/+8pd46KGH4DgOrrvuOlx55ZXYtGlTFMMhIqKIRPJx5fWvfz3+5V/+BZqmYWhoCK7rIpFIRDEUIiKKUGRrZl3X8bd/+7e4/vrrsX37dqxatSqqoRARUUQivTHhgx/8IN773vfi9ttvxze/+U38wR/8QZTDISJaMGbT1G4qmqrM6e3y8djsIySSEDpw4ABqtRrOP/98xONx7NixAy+++GIUQyEiWpDmo6ndtq096OlaWJc+ItmOO3bsGO666y7UajXUajX89Kc/xete97oohkJERBGKZCV05ZVXYu/evXjb294GVVWxY8cOXH/99VEMhYiIIhTZNaEPfOAD+MAHPhDV6YmIaAHgN8oWgHy9nH4YIb/H6BNTV8WeSU93BnFTD3VsMh68BUCDqasw9HAvW1UN16cI8MsqGVq484Y8ZVPYC8l+sdRwJ2/UmyOabyzbEyHPk3jp6Aj6hyuImxou2tyNeIulXRzXwz8+sBulmoZEqgOVch4yYPUDPZaCFkvCKo1Ceq1XP7jqja/FW654HSzbwdceehLHTo20fOw1l5+P7ZdshKL41cCDTHTZlIHV3UlIKfHCoREM56stH5tO6Fi3MgUhgELJhmW3/nyTcR29PUkICJwaKmG0WGv52ERMQ093InTopuI6knEdg6MVDI62/nxNXcWG1X518YrlNFtVtMqT/ocjtl+g+cYQikjFcvDcgUFYNQ8SQNly8Oz+fpy3sRMrOqa/I2ZgpIS//NLPcXKgACkBoaiIJztgVYpwnZknyMZ8IiEgIGAmO+FYRTi16Se5eMzEH976FmxY1wNNU6FpKv7o5t/FY7/ahyeefWXaY2Omjg/d9l9w8blroWn+J/tmC4AZJkghgLXdSaSTRn0lI3DB2Z04OVjGweO5GYuJntneIJ3UYdQUFFroA9TTGUdXJtY8dnV3EsmEjhMDpRkLgq7IxrCiIx569QX4JXAEgBXZOJJxHcf6ijOW4smmDKzpTkII//i4qUHXVORLVuA6ctWaC9vxkE0a0/aiIgqLIRSBgZEK9h8ZmbAF53oSLxwaxuruJDb3ZiftLPr08ydwzz8/AeuM/jpCCMQSKbh2DdVKccYxjJ1AhRD+qkg3US3lJv3369euxB+/461IxM1x/YR0XcVV/+kCbNmwCv/2vadQrU2c2Des6cSd//0aZFOxZgA1zpuIaTB0Bbni5FWeDU3BhtV+u4axk7mqKFizIoGOlIHnDg6hNkmBT11TsH5VGoaujPtZCiFgGio0zT/vZFuhmiqwflUapj6+SZyiCKTjBjav03CkrzDpeVVFoHeKXkRhKYpAwtSwuTeLo31FVCbp3SMEsHZFEumEMe68fi03oDMdQ74cbPUJ+K/L4YKFDHsC0TzgR5s28jyJF4+MYP/hiQHU/DcSODVUwu79/ajWTk80ruvh/3twNz775Z2oVJ0pjhfQDBOJVMeE5nFjp8LJP8ELKJqBWLobQhk/0fzuf7oYt//hDUglJza0A/wOm2f1rsAH/tubsbanY9zfvekN5+Duj9yIrmxiXAA1z1ovdtmViUE/45pLJmlg07osdE2ZdDJXFQWJuI7Lzl+FzrQ57u9ScR2b1mVhnhFAZ563M2NOuMaUiGnYvC6LmDF5l1JFEdA1BZvWZpFNGuP+Lm6q2NybRSI2dwF05pg3rk6jOzO+I6uhK9i8LjshgMYeqygC2aSBRMgvGOZLNRTKtcAtHoimw5VQm1QtB88dGEK15sy4JeJJoFR18MwL/Tj/rC4AHj79Dz/H0b78jNcymttzqdPbc805eIbzSgkIocBMdsKuFqArHt799jfj7A1roOvTv1QURUEqEcN7brkCjzzxPH7zwmHc8a4rsO2C9TCN6Y8VQkAIfxupVHFQtRysXpEY1958yvMKAUUVuHBTF44PlPDqiTxWdcXRmY61FAKKEMgkDVQtF8WKjZUdMXRnZ95Ca4x5zYokknEdJ4dK6Eyb6OlMzHn4TBizIrCyM45kQsex/iJScR1rV5zefptp3AlTg6EpLfcYGqu5PZcy2CeI5gRDqA2KFRu/eWkgcFl915N4Ys9x/MvDu1Gz3UDHCyFgxBKo1C+iB5lshBDIdnTh/e++Gsl4LNC1AF1XsePyC3D7H2yHafjXIoKcNxnX0NuThKpOvoKZiqooWLsigYSpwfVkoCAQQiBmqujpisPQJl/9TEVR/BBLJ3UIhL/7LihFEUjGNJzTm22uclrVWFHFDRVlK3g7DteTKFcd3rBAc4Ih1Ab+Fka4Y/uG/FbNYfrCSM+DIkRL3TjP1JFNwTT0UBej4zEDidjk20IzURQRur+SqiiQ9ccISggBU1dDnVdRRH0V2d4J2Q+fcOcVQqA2i1uwjZA/K6IzcT29GMzmvc6Joi2impBnd16+Nih6DCEiIooMQ4iIiCLDECIiosgwhIiIKDK8O46IaAGaj86qjuuhf7g8p485VjymIZ0wZv6HYzCEiIgWoPnorDrftm3tCRxC3I4jIqLIMISIiCgyDKEAZle4cRbHzqpeJItNEtHCxRBq0WwCyNSUUGV3ACARE6jZNYQJEyk9uE6wZmYN1WotUO22sVzXgydlqJ+Z40o4bsgyRVKG7mLa6CTqBWwMuJjpIbuuAoDteKymTXOCITQDKSU8KZEv1TBa7z3T6ptPSoma7eKx3cexe/8AarYb6NhqtYJv/OM9ePxfP4FqYRgI0P3UdWo49JsfYO9PvgirNAzI1o/VNRX/edtmrOxKQlVF4OIungQOHMuhYk3VcmJyjuvhyKk8vvrDF3BysBio742UEjXHC9y0DfBbbBQrNp7edwoDIxW4yySIknEdyRBtHRQBxAzWjqO5wRCahpT+J/KRvIWa48FxPQwXqrDdmT8FOq6H0YKFf/zu83j+4DCKFRtP7evDaMGacZJzHBuDA314/3+7Ef/x4NdQHD6Ox//1Y+g/tBvejJ1TPdjVIn7zoy/g6L7HYJVGsPcnf4+hY8/Dc6fvJKoqAomYjjv+63/B9VdeDF1V0JkyYRrBG5m5nsShkwUM5SszBpGUEo7r4el9fXh2/wCqNQ8P7zyEZ/b3wZkhiGR9xVWo2C11Sj2T50n0DZdxtK8IxwP2HxnFS0dG4bbwO17s/KaCOjpSZsslBg1NQWcmBo1dVmmO8BbtKUgpUa35PWbG/38gV6whYWpIxLRJPw3ajov9h0fw8C9eheOenshcV2LvK0Po7UnirLWZSfuxWNUKfv30Lnz6zjtQLp3ukOraFp753r1Yf+GbcMEVt0HVDJxZgNJzbRQGD2Pvo/fBqZ3+LoD0XBx45jvoWvcyNm27AYo28RZKQ1extqcDt7/jCmRSp28LFUIgnTBg6C4KpVrgTcGBkSrKFQe9PSkoipjw83I9D5Wqg517Tk74Wf/mpUGcHCzjuu0bYegTWyw0Vqm5Yi3w9l3jA8aRUwVUa+NXif0jFRTKNl6zuXvS8y41uqagq4Wuq8mYhrg5+WueKCyG0BmklJDw2y9M1rq5oWw5sF0PmYTRbCbWuK7w/V2HsPeVoSmPPdZfQq5Yw0VbuqHX2xZIKVGzqvjS396NB77xlSmPPfr8oxjtewWvv/F/wkxkAOGvUlynhiPPPYJDex/BVNePho8/j3LuFLa+8b/CjKebx+qaiqvesBU3XPWaSTunAoCpq9DSJnKl+pbklCOcqFR1cOB4bkK7bMf1cHygiGf3D0y5WuobLuPrj7yEa/7TRvR0xpufwBtbnYWyHTgYPU+iYjk42l+c8rwVy8HT+/uxdUMHurOxJd/ArdF1tWI5KFXHX0cUAsgmzQmdb4nmAl9VYzS33wrWtAHUYDv+9pzjenBdD/lSDfc9tG/aAGoolG08/XwfcsUabNvGyPAgPvB/3jxtADWPHTyCx+//GAaPPAfPtWFbJex55O9xaO+PMdMNDNXiEJ77yRcxcmI/BCRipo7b33EFbrr6tVMGUIOqKuhMh9uec1yJV0/kMZyvwvX87bdn9/fj6X39M27XVWsuvvvzg9j9Yj+c+jZZsWIjHzCApJTwPIn+kQoOnyrMeF7Pk3jh0AhePpqDG+Ba4GI12fZcY5XEAKL5wpXQGIWyPWP77DNJCYwWa3hizwm8cGgk0MV0x5XY8/Igdn33/8UTj/0AxWK+9WPtCp566PNYf+FVyPW/CtsqtXys5zp4+alv43/9P7+H7Zddgo5MouVjG9tzjlOFE+IugP6RCl49kcdQvopiwGs4z+4fQMzQ0NuTCnX33GjBwkjBmrD9NpO+4TLihor1q9LLoj1TI3hsx4Ohh2swSNSqyELoC1/4An7wgx8AAK688kp8/OMfj2ooTUEDaKygATTWEz//SaAAGmvw6F6EXdCevTYbKIDGUhSBULeiwQ+DoAHUUKzYoW93d1wZOIAaZtOFdDFSFBFqxUsUVCRr7F27dmHnzp148MEH8Z3vfAfPP/88HnnkkSiGQkREEYpkJbRy5Up84hOfgGH4d2lt3rwZJ06ciGIoREQUoUhC6Jxzzmn+96FDh/CDH/wAX//616MYChERRSjSW15efvllvOc978HHP/5xnHXWWVEOhYiIIhDZjQnPPvssPvjBD+LOO+/E9ddfH9UwiIgWpPloategqQpMfe5vPImHKAMVSQidPHkSd9xxB+69915s3749iiEQES1o89nUbtvWHvR0hbszdq5FEkL33XcfLMvC5z73ueb/e8c73oF3vvOdUQyHiIgiEkkI3XXXXbjrrruiODURES0grMUxxmy+GD67Ipfhj02lM+HPKpRIStFoqhK6V9FsfkdChP89sWYA0fxgCI3RnYkhFdcDHSOlxKGTeWxam8G6lclAk6SUEr9+8kcwUj3IrNwIiGC/jquv/3387Ze/h098+u+QTKUDHXvFVTvQ0dOL0aIVqOdPQyZpoDNtBg6TjpSBt125CX/wlnOQSU6s5j2drkwMZ63OhApOVRU4Z30H3nDBqsC/47ipYcPq5VGyh6jdGEJjCCEQM9SWJ1fbcbHnpUEc7StCCIFM0sDmdVkYLRR7tKplPPyv/xtPPvogAMAwU+hcvQWqZs54rBmL42N/+QX88fvvgqbp2HrBJbjnC9/ApnMumPFY3TDw0U98Gv/X//wrKIoKx5UYLlRRc4KVsxFCQFMVdGbMlp4vAJy1Oo2L6u0RskkDv3/1Fmxa29pK7twNHbji0rXQtOC1zGKGis6UCVURMHQVrz13BdauSLZ0bE9nHK/buhJ6iPMS0cwYQmcQQkBVxIyTa65o4ann+5Av15orCSH8SW7Tuuy0n/L7TxzC/V+4E0cPvQC7ZgEAPCkhFA2ZnrNhJjqmPLZ34xbc++Uf4HXb3wQz5t85o6oaMtlO3Pm/voC33viOKY9dvWYdvnjfN3Hl1dfAjMWa/7/RI6lUsQOvMpR6+E7XoVPXFLz2nBXoXZVutkQQQkDXVFz9O+tx5aVrp9wm0zUFV7x2LS44uytUI7VMQkcqrkOI072MVEXBpnUZXLSpC+oU5xUC2LqhA+du6ICqMoCI5guraE9CCL+ldWaS/ipSShztK+LwyfyU9TsVRWDtiiRScR0nB0vNdgNSSjz31KP4xY+/CWeKDqlCKEh2roGZyCA/dNRPiLord/wfeO+H/gqGGZt0UjTMGG551/tw8WvfgC/833ehUj5dWfs/X34VPvbnfw3TjE3ZsqFiObAdF5mkGejaiRACcVODoanIlaxxP5ds0sCFm7qhqRMb2gGApik4d0Mn1nQn8b1dh8Z1R+1Mm3jj06WXcQAAD/RJREFUJWtgaMEby6mKQDZlQBGTn1dVFHSmY7js/FV47uAgSpXTv+OYoeLizd0wDXXJ9xEiihrfYdNoTK4dKROK8PsH7X1l0O9FM8OCQalPgpt7s9A1BTWrgu994++w8yffmjKAxpwZRiyNjlVboGgGDMPEh//8Xrz3w38FMxaf9lO5YcZwwWteh89/4RvYuOlcaJqGD370Lnz8L+5GPJ6YtmeQBGDXt+fsMNtzmj+xN3rPrF+VwsVbumfcytJUBdmUid+/+hyctca/trWlN4srt61DzNACB5A5Zkt1uvMqioChK7j03JVY3e1/Z2JFRwy/c14P4qbGACJqA66EZuBf+/A/se/cc7LeVK31Yw1dRWeshi99/i5UywXYdmstDDwpoag6erdsw8fvuhvdK1ZB01u7kK+qGjIdXfjkZ78EXVjo6OiAbsx8ramh0SOpK2MGnogbHTrXr0ojGW99IlcUAUNR8ebfWY+Tw2XomhJq+y0V1xEz1Ja3z4QQUIXAlt4s1vekuPohajOGUAuEEOgbKgcKoLFe3LcXVqXYcgCNdc7WC9DZtbLlAGoQQiCRiCOTzMzYMXUymipC30Zt6CpScT3U7dCqqiBuhn9ZBgmgcedVFMTN6VdORDT3+JGvTcIEwVyIalJdjI2wGUBE7ccQIiKiyDCEiIgoMgwhIiKKDEOIiIgiw7vjiIgWoLBN7VppWBem+dx8WTgjISKiprBN7RZSw7pWcDuOiIgiwxAiIqLILKsQiqKBGy0PfG0RhbNsQshxPZSqTujJwtRViJD9NTs6O0Oft1IuQ1Wnv8g4nbBVAGYzp3qeDN2JdLZFC6LIgsbvlkFEFNyyCKGq5WCkYKFiOSiG6JnjeRIxU8OqrnjgSVIRwFvfciU++5lPIR6fuo3CZEzTxAXnbcH6VRkELcOmKgJrViRx0aYuaGqwg4UAEjEN2ZQZ+PkK4ddvCzshCyH8Cthh6s4pArLNBYOklPCkxGjRgu14DCKigJb03XFSShTKNiz7dFuCas2F43rIJv0JdrqVgpQSruf3D6pYDjozMcRjGo6cKsL1pi9mKoTf8O2CTV3oysSwad0OXHD+efjv/+ND6OvvR7VqTXmsoigwTRN33XUXrrjiCgBAMmHgyKkibMedsY2Eqghs27oSG9f4XUsvO9/Abw8OoVR1ZmzlLQSwqiuBzrRZbzwXQ75Ug+1405+0fuzKjji6s5P3O2qVpiroSpsTfnfTiRlqs3ldu0gpYdluswdSrlRD3NSQjGmsQ0fUoiW7EnJdDyMFa9JJzGn2zJn6k6vnSVQsBweO5VCxxjY807ClN1uf8CY/t6IIJGM6LrtgFboypzuYnrVxA777wNdxzY43Iz6ms+lYpmli/fr1+Kd/+qdmAAGArqnYtC4z7epEUQQSMQ1XX7a+GUCAX9X60nNXYt2K5JQrKgG/cvbZazLoypwOkUZfpMQM3ytQFYGNq9NY0TF9v6NWNdqlp+L69P8OfvPBdMJo28QvpYSUEsWKPa4JH+A3BswV/W67XBURzWxJhpBVczFcsOBO86lfSv+T62TXiTxPYjBXwaGThUkfQ1EEentSWNWVmBAIihBYsyKBbeethGlMvJYTi8Vwz91/hb/61J2Ix8avGGKxGHbs2IEvf/nLWLt27YRjhfC32NatTE0IE7XezXXHGzZM2lpcCIFN67K4cFM3VGX81S0hgGRcw+beLGKTtFEQQiAZ19GRNiY838bW3ebeLBKx6QMjjLip1RvUTfw7VfG37mb6Yt5camy/jRQsVGuTr9Js18NwoVpv/cEgIprOktuOK5RrU04Ok/FbWnvIJv0J1vMkjvYXUR7T0nsyQgh0ZWKImxqO9vlhpQiB88/uQnd28lXOWDfecB0uuvACvPd/fBADA4NQVQWf+MSf4U1vetOMx2aSBmJGFkf6CnAcD0IIvPbcFTh7bXbGY7syfktrf3vO/xTf0xkft/qZiq6p6MrEkCtacF0JCGBFNo4V/3979x9Tdb3Hcfz5hXMOQldDFnhKuDopXNkiq5WYC4+6I3KAg8wKbIe62Jabi+U/pOVqa5pELvxR3f6oa7VoZZs/BkuykWwmDKcz2JybjuJXIVGohMA5h3M+9w+Fe1HvhYPC14+8H/9x+MD384bPl9f5fM7h84m+seW3kVjCw4iZNoXuXh8+/+VlwYlefhsME9+V5beRomXwYMCoCMvQLFKW6IS41m0VQkqpkAJo0MCVZ64qCF3d/f93BnW1yAgLifHR9Pb7uSf2DqbYRv8jnTNnNhX797Drn/9i2bJlJCQkjPprbdZw5sy8E5QiYcZU7vzb6E9OjbCF88jcWM60nicywhrSIXJhYQbRUyNAwR1RVu4Yh9nP9RiGwbQoG/2+AGFhxoTOfgB8A0F8/kDI46v3ylLuSMuZQkxWpi7H9fT0kJGRQVtbm5ndAC4/c+280BdSAA0KDzOYfc+0kAJoUGTkFNYU/COkABoUZhgk/X16SAE0yDAM4qZHjekUU8O4HEQTFUD/fd3ICMuEBxBcniGP5QkOQCA40e/ZE0IfpoVQfX09eXl5NDU1mdUFIYQQJjMthPbs2cObb75JXFycWV0QQghhMtMWqrds2WLWpYUQQtwitHq1NBC4vCZ/7ty5635eXXnr7Fh1XRz71/p6r/824tE43+0d83YzkUYPVsvYJrQXL115l9sY9ETZxnxdHfX7AvT2+0dueB02a7j8A6sAwG63Y7Fo9Wd33Gn10+js7ATgueeeM7knQggRuqqqKuLj40fVNvWR+FG31ZlWIfTggw9SVlZGbGzsDW3qKYQQZrDb7aNqU1VVNaq2twOtQmjKlCk89thjZndDCCHGjcVimRQzoEGGkn1FhBBCmGTyvLIshBDiliMhJIQQwjQSQkIIIUwjISSEEMI0EkJCCCFMIyEkhBDCNFqFUHl5Oenp6TidTsrKyszuTsiuPrqipqaGzMxMnE4npaWlJvdu9N5//31cLhcul4uSkhJAz1p27NhBeno6LpeL3bt3A3rWMeidd95hw4YNAJw+fZqcnByWL1/O66+/zsDA/z+k8Vbh8XhwuVy43W7cbjf19fXa3vc//PADOTk5rFixgs2bNwN6j69xozRx7tw55XA41Pnz59WlS5dUZmamOnv2rNndGrWffvpJZWRkqHnz5qnW1lbV19enUlNTVUtLi/L7/aqgoEBVV1eb3c0RHT16VD377LPK6/Uqn8+n8vPzVXl5uXa11NXVqdzcXOX3+1VfX59yOBzq9OnT2tUxqKamRj3xxBPq1VdfVUop5XK51MmTJ5VSSm3cuFGVlZWZ2b1RCQaDatGiRcrv9w89put939LSohYtWqTa29uVz+dTeXl5qrq6WtvxNZ60mQnV1NSwYMECoqOjiYqKYvny5VRWVprdrVG7+uiKhoYGZs2aRUJCAhaLhczMTC3qiY2NZcOGDdhsNqxWK4mJiTQ1NWlXy+OPP87nn3+OxWLhzz//JBAI0N3drV0dABcuXKC0tJS1a9cC8Ouvv9Lf38/DDz8MQE5OjhZ1/PzzzwAUFBSQlZXFF198oe19//3335Oeno7dbsdqtVJaWkpkZKSW42u8aRNCv//+O7GxsUMfx8XF0dHRYWKPQrNly5ZhWw7pWs9999039MetqamJgwcPYhiGlrVYrVZ27tyJy+UiJSVF29/JG2+8wfr165k2bRpw7diKjY3Voo7u7m5SUlL44IMP+PTTT/nqq6/47bfftPydNDc3EwgEWLt2LW63my+//FLb8TXetAmhYDA4bCt8pZTWW+PrXs/Zs2cpKCigqKiIhIQEbWspLCyktraW9vZ2mpqatKvjm2++4e677yYlJWXoMV3H1vz58ykpKWHq1KnExMSwatUqdu7cqWUtgUCA2tpa3n77bb7++msaGhpobW3Vspbxps0Gpna7nePHjw993NnZqfWprHa7fehoCtCrnhMnTlBYWMhrr72Gy+Xi2LFj2tXS2NiIz+fj/vvvJzIyEqfTSWVl5bDd2XWo49tvv6WzsxO3283Fixfp7e3FMIxhv48//vjjlq8D4Pjx4/j9/qFAVUoxc+ZM7cYWwF133UVKSgoxMTEALFu2TMvxNRG0mQktXLiQ2tpaurq66Ovr49ChQzz11FNmd2vMkpOT+eWXX4am7RUVFVrU097ezrp169i2bRsulwvQs5a2tjY2bdqEz+fD5/NRVVVFbm6udnXs3r2biooKDhw4QGFhIUuWLGHr1q1ERERw4sQJAA4cOHDL1wHw119/UVJSgtfrpaenh3379vHuu+9qed87HA5+/PFHuru7CQQCHDlyhLS0NO3G10TQZiY0Y8YM1q9fT35+Pn6/n1WrVvHQQw+Z3a0xi4iIoLi4mJdffhmv10tqaippaWlmd2tEn3zyCV6vl+Li4qHHcnNztaslNTWVhoYGsrOzCQ8Px+l04nK5iImJ0aqO/2Xbtm1s2rSJnp4e5s2bR35+vtldGpHD4aC+vp7s7GyCwSCrV6/m0Ucf1fK+T05O5sUXX2T16tX4/X6efPJJ8vLymDNnzm0xvm4mOcpBCCGEabRZjhNCCHH7kRASQghhGgkhIYQQppEQEkIIYRoJISGEEKaREBJCCGEaCSEhhBCmkRAS2iooKKCrq+uG29TV1ZGRkTHi9ebOnXvd71VVVTV0XozH46GyspK2tjbmz58/4vcUYrLTZscEIa529OjRm9LmRi1dupSlS5eO+3WEuB3JTEhoaePGjQA8//zzHDt2DI/HQ2ZmJllZWezfv/+aNu3t7Rw+fJjc3FxycnJYvHgx27dvD/m627dvZ+XKlbjdbg4fPgzA3r17eemll25SZUJMLjITElraunUre/fu5bPPPuOZZ56hqKgIp9NJR0cHTz/9NLNmzRrWZvr06RQVFVFcXMzs2bPp6OjA4XCEvKdafHw8b731FmfOnMHj8XDw4MFxqlCIyUFCSGitsbERr9eL0+kELm9063Q6OXLkyLDXZAzD4KOPPqK6upqKigoaGxtRStHX1xfS9fLy8gBISkoiMTGRkydP3rxihJiEZDlOaM0wjGsOBlNKMTAwMOyx3t5eVq5cyalTp3jggQcoKirCYrEQ6v69YWH/uWWCwSAWizyPE+JGSAgJbYWHhzNz5kwsFguHDh0CoKOjg++++46FCxcOtRkYGKC5uZmenh5eeeUVlixZQl1dHT6fj2AwGNI19+3bB8CpU6doaWkhOTn55hYlxCQjT+OEttLS0njhhRf48MMP2bx5M7t27SIQCLBu3ToWLFgw1Mbj8bBjxw4WL17MihUrsNlsJCUlce+999Lc3IzNZhv1NVtbW8nOzsYwDN577z2io6PHqzwhJgU5T0gIIYRpZCYkxBUff/wx5eXl1/3cmjVryMrKmuAeCXH7k5mQEEII08gbE4QQQphGQkgIIYRpJISEEEKYRkJICCGEaSSEhBBCmObfxvNNdwg0W5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='hex')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Zh-3u7ylJy5D"
   },
   "source": [
    "The joint plot can even do some automatic kernel density estimation and regression:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MW8WPE_xJy5E",
    "outputId": "1dd4a2cb-0762-43c6-bfe6-3085a85e617d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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9xOOXJqvJ+Mw19dx5w+zUx3LMydSgmKYp76+FEEJYQmpZhBBCWEaSkBBCCMtIEhJCCGEZSUJCCCEsI0lICCGEZXKShAKBAPfeey9nz54F4Nlnn+Xee+9l06ZNfO973yMWi+UiDCGEEHkm60no008/5aGHHqK5uRmApqYmnnrqKZ555hm2bt2KYRg8/fTT2Q5DCCFEHsp6Enruuef4/ve/T3V1NQAOh4Pvf//7+Hw+FEVh0aJFnDt3LtthCCGEyENZ75jwxBNPjPi4rq6Ouro6AHp6evj3f/93fvjDH2Y7DCGEEHnIssKE9vZ2vvKVr/CFL3yBG264waowhBBCWMiS3nEnT57kscce45FHHuFrX/vahK7R3R3AyLOOnlVVRXR2+q0OY4R8jAkkrnRJXOnJ57jGKx/vcRN1uded8yQUCAR49NFH+fa3v83999+f66cXQgiRR3I+HffrX/+arq4ufvGLX7B582Y2b97M3/3d3+U6DCGEEHkgZyOhHTt2APDVr36Vr371q7l6WiGEEHlMOiYIIYSwjCQhIYQQlpEkJIQQwjKShIQQQlhGkpAQQgjLSBISQghhGUlCQgghLCNJSAghhGUkCQkhhLCMJCEhhBCWkSQkhBDCMpKEhBBCWEaSkBBCCMtIEhJCCGEZSUJCCCEsI0lICCGEZSQJCSGEsIwkISGEEJaRJCSEEMIykoSEEEJYRpKQEEIIy0gSEkIIYRlJQkIIISwjSUgIIYRlJAkJIYSwjCQhIYQQlpEkJIQQecgwDKtDyAlJQkIIkYcS0yMHSRISQoh8pOvTIwtJEhJCiDykG6bVIeSEJCEhhMhDCRkJCSGEsIpMxwkhhLCMPj1m4yQJCSFEPjIlCQkhhBDZlZMkFAgEuPfeezl79iwAu3btYtOmTdx+++389Kc/zUUIQggxtUyToVDWk9Cnn37KQw89RHNzMwCRSIQ/+7M/48knn+SVV17h4MGDvP3229kOQwghppRpkoOyn4See+45vv/971NdXQ3A/v37mTNnDrNmzULTNDZt2sSrr76a7TCEEFnS44/w5IsH+MnT+3jyxQP0+CNWh1QQpkkOyn4SeuKJJ7juuutSH3d0dFBVVZX6uLq6mvb29myHIYTIkme2N9LRFyYcS9DRF+bZNxutDqkgmNNkKKTl+gkNw0BRlNTHpmmO+Hi8Kip8mQwrY6qqiqwO4RL5GBNIXOnK17hiuonDbkt9HE2YeRFrPsQwGXanNuVfw3jkPAnNmDGDzs7O1MednZ2pqbp0dHcHMPKsrUVVVRGdnX6rwxghH2MCiStd+RyXw6bQF9dRFAXTNCn1OiyPNZ+/X+M14I/k5WuYiMu97pyXaK9cuZKmpiZaWlrQdZ3f/OY33HzzzbkOQwiRIQ9uXEhNmRu3Q6O61M2DGxdaHVJBmCazcbkfCTmdTn70ox/xh3/4h0SjUW655RbuvPPOXIchhMiQ8iIX37x/udVhFJxpkoNyl4R27NiR+u+1a9eydevWXD21EEJMQdMjDUnHBCGEyEPTZTpOkpAQQuQjSUJCCCGsMk1ykCQhIYTIRzIdJ4QQwjLTJAdJEhJCiHxkTpM0JElICCHykE2ZHrfn6fEqhRDCcmZaTUkThpHFWPKHJCEhhMgywzTpD8XT6ncpXbSFEEJMWixhMBCMkna/5emRgyQJCSFEtgQjcQLhOKYJqpr+kTXTgSQhIYTIMN0w8YdiRGL6hK8h03FCCCHSFosnp98SkzzvbHqkIElCQgiRESbJ6bfg4PTbpK83TbKQJCEhhJgkwzDoD8aJxic+/XYxmY4TQkx7Pf4Iz2xvJBBO4HNrPLhxIeVFLqvDyhuKApGYzkAwhj7J6beLTZMcJPuEhBBje2Z7Ix19YcKxBB19YZ59s9HqkPLKQChOXyCa8QQE06eaTkZCQogxBcIJFCV5M1QUBX8oYXFE+UE3TPqDMWIZnH67mNNuy9q184mMhIQQY/K5tdTahGma+NzT+32rokAkrtM9EMlqAgLQbNNjJCRJSAgxpgc3LqSmzI3boVFd6ubBjQutDskyJsnRT78/mlb7nYmyyXScEGK6Ky9y8c37l1sdhuUSukl/MEo8kbumopKEhBBi2jMJxXQCwThGjsvVhtbiCp0kISGEGIVhmvhDccJRa4oxZCQkhBDTVCxhMBCKkkhYt1lHkpAQQkw7JsGoTiAUs3yzqOwTEkKIacQwTQZCMSLR7JZej9c0yUGShIQQIlOdrzPJpk6PHTSShIQQ01amO19nkjJNhkKShIQQ05Ix2Honk52vM2ma5CBJQkKI6Scaz07n60xSZZ+QEEIUFtOEQCROKJJ/028Xk+o4IYQoIPrgwXPZbjyaKdOlY8L0KL8QQkxbFzpfRy1JQLph8t6BNn763CdpPU5GQkIIMcWZmAwEk613rJh+O93u56WdTbR1h9J+7DTJQZKEhBCFKWGYDASixHLY+XpIKBLn1Q/PsPdoBwAKsGZpdVrXMMzpkYgsS0IvvfQS//RP/wTAzTffzJ/8yZ9YFYoQoqCY+INRegYiOTn3ZzjDNPn4WCevfnCa0GDj05kVHjbfNJc5M4rTupauG2hq4Z+uakkSCofDPPHEE7z66qsUFxfz0EMPsWvXLtatW2dFOEKIAjHU+TqGmvME1NYd5KWdTZxuDwDJ47lvXzOLG66qmdD6Tj6Xj2eSJUlI13UMwyAcDuPxeEgkEjidTitCEUIUiOGdr93e3N1PIrEEb350lt0HzzOUN1YtqOSuG2dT5HFM+LoJPffTiFawJAn5fD6+9a1vcdddd+F2u1mzZg3XXHONFaEIIaY4k+QaTCDHrXdM0+TAqW627W7BH4oDUFXqZvOGBubNLJn09afLSEgxzdzXjBw9epQ//dM/5amnnqKoqIjvfve7rFixgsceeyzXoQghprBYXKcvkNtjtwHae0L8x+vHONrcA4BdU7ln/Vw2Xj8bzTb6zhdFgZoyD7YxPn+xk2f7mF9fmrGY85UlI6GdO3eydu1aKioqANiyZQtPP/10WkmouzuQ8znfK6mqKqKz0291GCPkY0wgcaVL4hpJUSAc0/GP0XqnvNxLT08w488bTxi8ta+Vdz49l3repXPKuHddA2VFTgb6w2M+VlUVaso8436u3v4Qnc7CKEyoqioa83OWJKElS5bwN3/zN4RCIdxuNzt27GD58uVWhCKEmGKs2vtz9HQvL7/XTK8/CkBZkZN71zWwdE5ZVp5vukzHWZKENmzYwOHDh9myZQt2u53ly5fz9a9/3YpQhBBTSFxPnvsTz+Gx273+KNt2N3O4uRdIHrt908qZ3Lp6Jg4teyMVQ5cklFVf//rXJfEIIcYpeex2MBTHyNHwJ6EbvHegjR0ft6bWnObNLGbzhrlUlbqz//wyEhJCCOtZcez2yXP9bN3ZTGdfco2nyG3n7rVzWDG/ImeNRaVEWwghLBZLDB67naOpKX8oxm/fP80nJ7qAZAHEjctm8Lnr6nE5cnu7zHXFn1UkCQmRh3r8EZ7Z3kggnMDn1nj8S6utDinHktNvgVAsJ8UHhmHyweF2Xt9zJnXS6qxqH5s3zGVmpTf7AYxCkpAQwjLPbG+koy+MoiiEonGe2nqQr921xOqwciLX029nOgK8tLOJc13Jkm63U+OuG2ZzzeIqS083lek4IYRlAuFEau1BURQGgjGLI8qN4a13si0cTfDah6fZc6SDoWe7bkk1d1w/C6/LnvXnvxIZCQkhLONza4SicRRFwTRNiifRg2xqMAlFdfw5mH4zTZN9jV389v0WgpFkp+vaCg+bN8xlds3YmypzTUZCQgjLPLhxIc++2Yg/lFwTenTz1ZjxhNVhZcVQ5+twNPuv73xPiK07m2g+n+zy4LCrfO66Wdy4bAa2PDu8Z5pUaEsSEiIflRe5+Ob9F7qIVJa687Jtz2TFdYP+YPan36JxnR0fneW9A22pm/uK+RXcfeMcir35OcrUZSQkhBDZkpx+C2R586lpmhxq6uE3u1tS62qVJS7uWz+XBfWT73SdTbImJIQQWZCr6bfu/ggv72ri+Jl+ADSbwm2r67lpZe2Yna7zyVCpeKGTJCSEyJlcVL/FEwa/2XmKV3c3pza5Lp5dyqZ1DZQXu7L2vJkWihTmGuDFJAkJIXIiGE1kffPp8TN9bH2viZ6BZKfrUp8j1ek6V+12MmXooLxCJ0lICJFVhmkyEIwRiWVveqk/EGXb7hYONiUPmVNVhZtW1HLb6joc9ql5Jk8gND32hkkSEkJkTSw+2PstS/XGumGw68B53vzoLLHBhfy5tcU8cs9SXHlWcp2uQCSOaZpTbgSXLklCQoiMM4FgJE4wHM/a9FtT2wBbdzbR3pvsdO1z27n7xjmsXFBBRYUvKyer5lI8YRCJ6bidhX2bLuxXJ4TIOcMw6A/Gs1bdFQjHefWDFj4+PqzT9VUz2HhdfcHdsAdCsYJ7TRcr7FcnhMgZRYFITGcgGMvK0dSGYbLnaAevfXg6tb5UX+Vl803zqLOo03W2DQRj1JR5rA4jqyQJCSEmzTTBH44TimRn+q21M9np+mznUKdrG3dcP5vrllRb2ul6PBQFHJoNr8uOLc39SR19EWZWJXDaNbT839o0IZKEhBCTog9Ov8WyMP0WjiZ4fc8ZPjzcnup0fe2iKu64YTY+t/Wdri9HUcCp2fC47Tjt6oSS88FTXcQTOmuW1qAV6LRcYb4qIUTWKUryJNLugShGhqffTNPkk8YuXvngNMFwcr9MTZmbzTfNpWFGcUafK9MUBVwODY9Lw6Elk89ER4fZLGvPF5KEhBBpMzEZCMZxmUrGE1B7b7LTdVPbYKdrTeWz19Wz7uoZ2NT8nZNSFQWX04bHaceuKZNKPgAOu41wjg72s5IkISHEqC4+YvzBjQspL3KR0E36g1HiCQOXx5mx54vFdXZ83MrO/W2ppqZXzyvnnhvnUOLL3PNkmqoquJ0aHqctlSQzsS7mtNuIxAq/dY8kISEyYKwb9lR28RHjL75zkoc/txh/MLOdr03T5EhLLy+/10z/YKfr8mIn962fy6JZpRl7nkxTVQWPS8Pt0LJyFpHToU6LE3UlCQmRARffsJ99s3HEeUBT0dAR44oCxV4H0ZhJfyCzN8WegQgv72rm2Ok+INnp+pZVddy8cib2PC0H01QFt0vD7dSyWpnntGtEZSQkhBiPoRs2gKIo+ENT/+bhc2vEdZ0ij4NgJI6ZwbWfhG7wzqfneGtfa6rT9aJZJWxaP5eKPO10rakKHrcdt9OGQvbLwh12NdWKqJBJEhIiA3xujVA0jqIomKaJzz31f7Ue+txCfrOrhbauEE5N5Z51DRm5buPZPra+10x3fwSAEq+De9Y1sKwhPztda5qC12XH7cjt/1O7TSUa1zGz2XY8D0z93xRR0DK51pLNdZsHNy7k2Tcb8YcuXHsqMwYbZ37uulkZu2Z/MMYru5s5cGqw07WisH75DD5zbT3OPOx0bddUvG47TrstB+OeSw2Vdw+NFAuVJCGR1zK51pLNdZvyIteUXwMaEksMdr7O0M1PN0x2HzzP9o/OEIsnp5caZhRx34a5zCjPr5Y0igJ2zYbXpVmeGO2Dzx9LFHaZtiQhkdcyudZSiOs2mWQCoUicQAY7X7ec9/PSzibO94QA8Lo07rpxDqsXVubV1Fuqu4HLjsOugCVjn5EW1Jfw2gewaFYZTnvh3qoL95WJgpDJtZZCXLfJFN0w8Ycyd/BcMBLntQ9Os/dYJ5C8pa9ZWs0d18/Oq67QipLcj+NxTby1TrYMrUEZulGwfeNAkpDIc5lcaym0dZtMicWTna8zcfCcYZp8dKyTVz84TTiaHGnWVXq5b8NcZlX7Jn39TMl0d4NscDmT03GhSGGP2CUJibyWybWWQlq3yQST5Nk8mep8fa4ryEs7mzjTEQDA5bBx+5pZXL+0BjVPTjkdSj5el5bR7gbZMDRiDEoSEkIUGsM06A9k5uC5SCzBG3vP8v6h86kb+uqFldx5w2yKPI5JXz8TRmutk++GpuNCkbjFkWSXJCEhphFFgXBMx5+Bg+dM02TP4fM898Zx/IOdrqtK3Wze0MC8mSWZCHfSbEOtdbLc3SAbnA4bigIBGQkJIQpFfzBGOJqY9BRUR7XJSdIAACAASURBVF+YrTubOHVuAEjuqfnsNfWsWz4DLc2D27JBVaDI68DtsE255DNEVZKbZIMyEsqOHTt28A//8A+Ew2HWr1/PX/zFX1gVihAFL2GYDARjkz54LpbQeevjVt7d35YaSV3VUMa96xoozYNO15ot2VqnusxDT0/Q6nAmzevSUucpFSpLktCZM2f4/ve/z/PPP09FRQVf+cpXePvtt7nlllusCEeIgqUoEIom8Ifikz7350hzDy/vaqZvsIlpWZGTh+9YQl25OxOhTopdU/C6HTi15BRWusdo5yuPyy7VcdnwxhtvcPfddzNjxgwAfvrTn+J0Wv8uSojLmWrHNQwdPBeOJJhM+un1R3j5vRaOnu4Fkusst6yayS2r6qipLrJsxKGQ7CrgdWk4LGqtk23FHjvdA1Grw8gqS5JQS0sLdrudb3zjG7S1tXHrrbfy7W9/24pQhBi3qXRcQ/LguQjxxMTTT0I32Lm/jd993EpcT7bbWVBXwn0bGqgssW70oyjg0Gx486i7QbZUlbo5eqYPc7CXXyGyJAnpus7evXv51a9+hcfj4Zvf/CYvvvgiW7ZsGfc1KiryZ+PbcFVVRVaHcIl8jAmsj6urL8xTLx1kIBSj2OPg0c1XXzaumG7iGNZPLJowc/oaxvNcpmkSCMXwh+IUFU+8L9vR5h7+4/VjtA+22ynxOfnSxoVcs7j6kpthebl3ws+TLqdmw+vRcDvtV/xaq3++Jsvu0Kiq8BKN6cRQqJ/ir2csliShyspK1q5dS3l5OQAbN25k//79aSWh7u5Axs+2n6yqqiI6O/1WhzFCPsYEk48rE1NjT754IDWy6fNHePL5ffzlf1s3ZlwOm0JfXE+1/Sn1OnL2vR3P98swTfyheKpTwUQMhGK8sruF/Se7gWSV2dqrZ7Dx2lk4HTZ6e0Mjvr683Jv16ThFAZdDw+PSQDEJDCQIELnsY/L553689h4+T3tXcuNvY3MPzik8ELrc67Zk9e62225j586dDAwMoOs67777LsuWLbMiFDFFDU2NhWMJOvrCPPtmY9rXSLeh6YMbF1JT5sbt0KgudedV259YwqBnIDLhBKQbJrsOnuenz36aSkCza3w8vmU596xtwOnIfUdpVUnu8akocVHqc2C35Vdvt1wo8SU3+7YOJqNCZMlIaOXKlTz22GM8/PDDxONx1q9fzxe+8AUrQhFTVCY6Yqfb0DRf2/4EowkCodiEb9BnOvy89G4T57qToxyPU+POG2ZzzeIqS/bYqEMbTB22vG+tk20+tx2Xw5baj1WILNsn9MUvfpEvfvGLVj29mOIy0RF7qjc0Nczk3p+Jdr4OReK89uEZ9h7tSFXPrVlSzR3Xz8LjuvKaS6ZpNgWPK3nTnaobTDNNURSqy9w0SRISIr9kIoHk68hmPGLxwYPnJrAuapgm+4538tsPTqf2oNRWeNi8YS6za3K7+K0A2tAJpoN7fMRI1aVu9h7rpC8QzYsNwZkmSUjkvbGKEKZqApkMk+RZPcEJHjzX1h1k685mWtqTi/ZOu43PrZnFDVfVYMthp+uRh8gVxsbSbKmpSFY57j/Zzc0rZ1ocTeZJEhJ5L5f7c7r6wjz54oG83JA6mc7X0ZjO9o/OsPvgeYYGTysXVHDXjXMozmGn69HO8RGXV17kpLLExYdH2iUJCWGFXB7L/dRLB/NuQ6qiQCAco7s/mnbna9M0OXCqh1d2NzMQSvYgqyxxsXnDXObX5a7T9dBRCl6nDXWaFxukS1EUrl1cxet7zjAQjFHszY/jMTJFkpDIe7k8lnsgFMtZwhuPodY7UVNJOwF19YXZ+l4zJ1r7AbDbVG67po4NK2pz1un6QqWbltPpvkJzzeLqZBHJsQ4+c0291eFklCQhkfdyWcVW7HHQ54/kJOFdSbL1TpR4wsDlGf+CdDxh8NYnrbzzyblU4loyu4xN6+dQlqOpxal8jk8+qq3wUF/l5d1P27htdV1BtfCRJCTyXi6LEB7dfDVPPr/P4rJtk1BUJxCKY6Q5Z3XsdC9b32um159selnqc7BpXQNLG8qzEeglpMw6c5bOLScx2LNPtamsW17LcztOcLill4ba4jEf57RraFOo1kOSkBDDVJa6LV0DMkyTgVCMSDS94oO+QJTf7GrmcPOFTtcbVtRy2zV1OLTsdzsYOkrBZc99Z4VCdaSpZ8TR3jZVQbMpvPjOKTasqB3zcWuW1qA5p86tfepEKkSBm8jeH90weG//ed78+CzxRPJd87yZxdy3YS7VpdntdK0oyXWm8iIn9gI9SiGf2DWV+XUlNJ7t57olVbgchXH7LoxXIcQUNtG9P01tA7y0s4mO3jAARW47d6+dw4r5FVldM1CU5P4ij8tOVZmbrq7JndYqxm/RrFKOne6j6ZyfpQ1lVoeTEZKEhLCQYRj0B9Pb++MPxXj1g9Psa+wCkklh7bIZbLyuPqvvjkfb41NIC+RTQVmRk4oSF41n+1gyp7Qgvv+ShITIoFNt/fzshQNEYwZOh8rjW5Yzr/bS/TiKAuGYjj8YG3fptWGYfHikndf3nEn1i5tV7WPzhrnMrMzemT7SUDS/LKgr4YPD7fQMRKkoyY+N1JMhSWiKmWpHTOfSZL43Q4+N6SYOmzLh7+vPXjhAIJTc0xQP6fzshQP87eMbRnxN6tjtaGLcN/PmtgF+te0wrV3Js3vcTht3Xj+ba5dUZ60KTVMV3G47bql0yytza4vYe7SDE639BZGExl3I19/fTyBQuGdaTBWZOEenUE3mezP02FAkPqnvazRmjNjsGo0ZIz6f0E16BqKEIuNLQOFogpd2NvHjf92TSkDXLqriO19axZqlNVlJDpqmUOJzUFnqxiv7fPKOw26jvtrH6XY/ZgEMSa84Ejp16hT//b//d44cOYKiKKxevZqf/OQnzJxZeD2MpoJctrCZaibzvekLROnqi2CS7OzsmOBGC6dDJR66cPqq0zF0nfT2/pimyb7GLn77fgvBwU7XM8qTna7nzMhOp+uhMmunVLrlvfoqLy3n/fT4o1QUT+3R0BWT0Pe+9z0eeOABvvCFL2CaJs8++yx//ud/zi9+8YtcxCcukssWNtk21vRZOtNqw7+2vTeIx6mhaTZM08RhV8fdjLR7IEI8oaOqKoZh0D0QGTUO4LKxPb5lOT974QDhqI5umJR4HPzytaPcunr8+3XO94TYurOJ5vPJTtcOu8p9N81n5bxybKpCfzDKtl3NhCI6HpeNe9Y1UOJNdlS43OdGowB2uw2vS8NxUfKRqd/8NWOws3ZHb3jKJ6Ervt0Lh8M8+OCD2O12HA4HjzzyCF1dXbmITYwin4+YTtdY02fpTKsN/1q3w0Yomkh9bzDNcV+nvMiFXbOhqGDXbJQXuUaN40qxzast4W8f38DVc8uZM6OI0mIXZzuD/OdbJ674/YjGdX77fgv/8J/7Uwlo+bxyvvOlVWy8fnaq99q2Xc10D0SJxHW6B6K8srs5dY3LfW44RUlO65QVOSkvco46+pGp3/zlddlxO2109YWtDmXSrvg2et68eXz88cdcc801ABw/fpz6+sJqoDeVFNI5OmNNn6UzrTb8a+12jWKviz9+eDUAP3l637ivU1bkJK4bOOw2YnGdsiLnmHGM55qGCV6Xg15/sveboY89CjJNk0NNPWzb3UJ/MAZARYmL+9Y3sLC+9JKvD0X0ETEEw/q4Ppf8N3BoNrwuOw67ApeZeJOpX2sNb9ujqSoX93/98HA7oajOmqU1I/7daZ9asyNXjPbcuXM88sgjLF68GE3TOHz4MFVVVWzatAmAl19+OetBisI01tRiOlOOl/vadK4z1CQ1mjAp9Tp4cONCntneOOrjr3RN3TBwOWw0nx8Akl/ncY2ehLoHIrz8XhPHzyQ7XWs2hVtX13Hzypljdrr2uGyEY4lUDMOvPdbnUofIue047eq4iiIKaep3KhretmfN0hq8F7XiqS5z88mJ7kv+faq5YvTf/e53cxGHmIbG6o6dTtfsy31tOtcZGmFWVRXR2em/7OPHuubQ3p+BYIybVtbiD0UJhi+szQwXTxi88+k53v6klYSezAiLZ5WyaX0D5VeY479nXQOv7G4e9doXf27T+rm4nIMjHy2ZfMZbUJXL7uUifeXFLgaCMeIJHXsO+gNmi2KOUeN38uRJ5s+fz6FDh0Z94LJly7Ia2JV0dwcw0jxfJduG38DyRT7GBIUXl4mJPxQnHElwpZ/K42f62PpeEz0DyU7XJV4Hm9Y3sHRO2Zg74MvLvfT0BMcdj6KAy6HhdWX3BNNC+/+YbVVV469s3PbuycuOhN470MZT247w11+/kRnlnozGmWmXe91jjoR+8pOf8I//+I888MAD1NbWjqhHD4fD7N69O7NRCjEB+VDBFdeTjUfjicvf6fsDUbbtbuFgUw+QbIOzYUUtn7mmDsdluk/3B6P859un6B2IXLHiLdVax6Vht6U38hFTy1BVXM9AJO+T0OWMmYR+/OMf09fXx/z58/nVr36FaZrJXeDxOH/wB3+QyxiFGNNQBZc1x3Ffee9PfzDKb95roq07TF8gytDgfW5tEfdtmEtN2ZVvHtt2NdMfjKEbEI4leGV3Mw9tXDzia4aSj9elSWudaaJ8sFtC90DE4kgmZ8wk9Ed/9Ee89957KIrC2rVrU/9us9m44447chKcEFdiVQXXeM/9eW7HCc50BFLrPppNYcvN81m5YPydri9UvJmXVLypioLbpeFxXujrJqaHMp8TBVLTulPVmEnoqaeeApKbVX/4wx/mLCAh0mFFBdd4zv0JhOO8+sFpmtourEt4XBpVJW5WLaxM6/k8Lhv9wWTiGap4U1UFt1OSz3Rm11SKfQ66+wt0JDREEpDIZ7ms4BrPuT+GabLnSAev7zlNeHCUZLeplBQ5sNtUijzpJ8l71jWw/aNWevsjlBU7uP/m+VQUu1KbV8X0NbPCy+mO/CvASMfULjAX014uNu/2+CNs29VCbyCKruvcvXb0woDWriAvvXuKs53JKjaXw8bNK2s51xUgFDFGLdUejxKvk69/fjnRcAy3Q5PkI1Lm15WwbXcz4WgC9xTdLzQ1oxYiRxQFtu1uoaXdTySmY5rmJYUB4WiCN/ae4YPD7akR0jWLKrnzhjn43PZJPb+qKnhdGlWlHvqk0kBcZFlDGb/Z1cyBU91cf1HnhKlCkpAQl9EfjHG2I5A6RG54YYBpmnx6optX3m8hEE7u56guc7N5w1zm1hZP6nltQwfJDR6lYJ9gV28xdQ1v26OoCsHopUU3M6t8FHsc7Dp4nmXzKsa8ltOuka8/QpKEhBhFwjAZCMaIxXWcdpVA2BzRCqejN8xLO5toahsAkkc/fPbaetYtnzGpQgE5SE4MGd6253Jm1fg4cLKb3310Fp9n9JH3mqU1aHk6XZefUYm8l41NohO9ZqZjicSTrXeGOnIMb4Xjcqj43A7+/tf7U3uDls0t5561cyj1jX1kwpVoqoLHneyMrMhpPiINS+aUcri5h0PNPdxw1dSbksvTAZrId9lo8z+Ra/b4Izzxy70cONXN6XY/57oCk4qlZyBCfyA6oiVUidfJg59dxLrlMzjXHeL9w+0Ypkl5kZOv3LmY3//cogknIM2mUOxzUFHqwuPUJAGJtHlddhbUldB4po+BwS7sU4mMhMSEZGOT6ESu+cz2xtRR2fGETl8Air3pxzI0/ebj0j5rPQMRXt7VzLHTfUAycdyyKtnpeqJrNZpNweu243bIr6CYvFULK2lqG2DvsU5uWz1z3Buh84H8BogJmegm0aGps5hu4rApI6bOJnLNQDiBTVVJ6AaKoqAbRlobVhUFzveG+PWOE/T6Y5QVu9h4XR0lXicJPdnp+q19FzpdL6wv4b71c6komdh0X+oIbc3GFLpPiDzndmqsmF/Bx8e7ON0eyNoR8Nkg03FiQiZ6wuvQlFsoEr9kym0i1/S5NcqKHGiaiqIkuxKMNxYTk/5gjGfeOM657hCRuE5Hb5BXdjdz4mw/f//r/Wzfe5aEblLsdfDwxoV89a4laScghWGnmBa7cdklAYnMu6qhnPJiJx8cbic8SiVdvrJ8JPTjH/+Y3t5efvSjH1kdikjDWJtEr1QkcPGUW68/ypMvHphwUcFQx4QijzOtxyd0k/5g8tRTf+hCTLphcuqcnwOnegFQFVi3vJbPXluP8zKdrkeTzimmQkyWqiqsX17LK7tb2Lm/jY3X1U+JaTlLk9Du3bt58cUXufXWW60MQ2TQlbpaD025JXSTjp4Qcd3ArgUpL3JOqAt2+h0TLu187XHZCEXjhKI6/lAstSY0Z0YRmzfMTbtN/tBZPm6nNu5TTIXIhLIiJ2uWVPP+4XYONfVw9WX2DuULy5JQX18fP/3pT/nGN77B0aNHrQqjIA0fjVSWurn/poYJlSxfaVTT44/wy1ePJffKmMnjCYKR5KgikdDp7o/Q2hnkW3//LnNri/nynYtTI5cjLb1EEzqmAbqu021EqKnwXlKMMN7y67G+7lRbPz974QCJhElVmYvP3zKPiiL3iMeuWljFr986mdqQ6nZq3H3jbK5ZVJXWO0lVVXA50j/LJx/ORBKFY+GsEtp6Quxr7KKmzENVmfvKD7KQZWtCf/mXf8l3vvMdiosnt7NcXGp4qXNb98RLlq9UMv3M9kYaz/QRjelEYgkaW/vp8UcwTZNef4xo3MAwTKIxncazfTz7ZmNq5GKzqdgUBUVJNgaNJYxRixHGW7Y91tf97IUD6LpJyWC34Z9vPZx6TCgS54V3TvFvrx8nEtNRgJtX1/FHv7eKaxdXjzsBqaqCz2OnothFsceBpqY3+slGubuYvhRFYe2yGrwuO+98eo5ILL/XhywZCT3//PPU1taydu1aXnjhhQldo6LCl+GoMiOd43uzJaabI07qjCbMCcV1pevEdBMGE4miKJjAjAovFSVuuvsjKIqCpimDe1+UkY83kzdvRVVIJAxQFIKRBIFIgn9+5SiPbr6aylL3uF/LWF/ndmooikL3QJSEbqCpCqVlHnbtP8eLb50kONhuZ1ZNEQ/fsZi5M0vG/f1RFPA47Xjd9km11Rnva8yHn63RSFzZsWJRVaoqcyLqa0v4u2f28cmJbu69aQFlxfk5urYkCb3yyit0dnayefNm+vv7CYVC/PVf/zV/9md/Nu5rdHcHRmwozAf5cq69w6bQF08ehKbZFBw2ZUJxDb+OaZqUeh0jruOwKWCamIN/NFXF69T42l1LeDIS51Bzz2CCAUxGxLFwThmHTnZhmuC02wYX8DXC0QSn2wd48vl9fPP+5VeMYaxYK4tdtLb1oygK7d3B1L+rmsYPf/EhZzoCQPK5b79+FjcsrUEd7E7d0xO87PdFHezr5nFqxCMx+iKT2yA4nteYLz9bF5O40pNOYtx/vHNcbXsu5+HPLeJff3uUf956kC/fsfjKD8iSy71u21/91V/9Ve5CSbr//vt5+OGHeeihhyguLsbhcPCDH/wgrWuEw7G8W/D1ep2EQtbvWJ5fX0Jz2wCGATXlXr542/wJtXkffp2yIicPblw44jrz60to7QziD8fRbCrzZxbz+3csxu3UmF9fwtmOAIFQAk1TmV9Xwu/fvij1+OuvnknLuX48Tjv1VV5UNTmSguSoyjBg/fLaK8YwWqwzKz3cs7aBaFxnRrmHo6f7MPRk77doTKd/cFf5qgWVfPnOxcyvK0lNvbndDsLh0X/xNVXB63FQ4nXgtNsyVnk0nteYLz9bF5O40uMd5QiQsTSe7iWeMCb1fGuW1gAm2/eepdjrmHRj3Ym63Ou2vERbZN7wirHJvCO8UuVZeZGLbz2wctTP9QWinOkIoBsmTofKfRtGFkdUlrpHXPvJFw+kquqGrw2Nt/pt6OsMw6A/GCca1zFNqK/ycdcNc3jl/Rb8oWRyqSp1s3lDA/PGOfWW7b5uuTgTSUxfX7h5Pq2dQZ5+4zjVZW6WNZRbHdIIliehLVu2sGXLFqvDEGm6UkXXz144QCCU7H4QD+n87IUD/O3jG8a83kROSB0eQ3mxk/s2NKCgoA9O03b2hdn6XhMnW5Odru2aymeuqWP98lo025XXcDRNweuS1jpialNVha9vWsaP/v0jfvbCAf70969hdk3+rJfJb5eYkCvtB4rGjBGbUqOxy08rTGQ08Mz2Rjr7whR5HfjDMZ7ZfpwvfWYRsYTOW/vO8e6n51IJ6aqGMu5d1zCuRqN2TcXrtien3NKKSMqtRX7yuDS+/cBKnvjVR/z0+U/5i0eum3DrqUyTtj1iQq7UbNTpUDEHF+1MMzkll2nhmE5psYt4wqSnP8pAMMHRll7+7vn9vLWvFd0wKSty8uU7F/MHty++bAJSlGRrndIiJxXFrmRrnQnEJOXWIl+VF7v4zpdWEosb/M/nPiE4yaKHTJEkJCbE59ZGJJmL9/c8vmU5Po8dzabi89h5fEtm1jx6/BGefPEA//PZffQHIjS19tHSNkAklqCzP8wvXztGrz+KTVW4bXUd33pgBUtml415PUVJVsiV+VyUFznwuiZ3HHc2uosLkSn1VT7+cMtyOvvC/K9f7yee0K0OSabjxMRcaQ1nXm3JZdeAJuqZ7Y2EYwkGgnE6esLEdRNFhcjAhV+mBXUl3Le+gcrSsXeKDyUfj8ue0dY6DrtKy/kQJsnK9IWzSjNzYSEyZMmcMh695yr+ceshfv6bI3xj8zJLT/GVJCRS0lnPGL6G0+OP8MvfHuVEaz+xhIHdpuCwa1SVuij1OVMJavi1H//SaoBUW51wVEc3DGZWeCjxuYjFdVq7gql2QF++awnlRS40m41YPMrZjkCqpNscXG4q8ti5Z20Dy+eVj1k+rSoKLqcNj9OOXVPG3Vpn3EwTFDANE0Ud3CCVR2TNSgDccFUNvf4oz/3uBM8XO/m9z4yv83w2SBISKVcqNrjc4xpb+4lEdUySHarDsRhx3SCWMHj2zUZMkxHXfmrrQb5215JUFZ1umBgmnO0M0dEbIRrX0WzJdaXG1n5eevcUX/rsIqLxOC3n/Zfc2iuKnTy+ZTmuMSrZVFXB7dTwOG3Y1OQsdDb2mcUSJtVlFxqexuL5lYQm+v9YFJ47rp9FV3+Y1z48w4xyD7esqrMkDklC09BY74Ynsp7R449w7HQfkZiemoIySU53GYY54jrDrz10DPFQFZ2JiQIYponJhfGDXVMpLXISiCTo90epKHETS1y4sSuA02HjsU1XpRJQfzDKtl3NhCI6ZcUO7r95PlXFrlTyyaaJHvaXK7JmNXUsnVtOQp/cZlVFVQhe5myh+26aR1tPiF+9fhyfx8GSOWOvn46X066RTher/PoNETkx1rvhidxAn9neOOIXJTVFZianvoZfZ/i1iz0OevwR4rpOfFhSsSlDneaSZaU+t0ZwMAE9+eIBznWHgGSSK/Y6cDtsVJa4KBm2I3vbrmbCMZ1ir4No3OCVXc38t03LJv19G4+J7HfKpXxPkuKCI009k27bMx4rF1TQ1hXkn7Ye4q4bZ49rG8PlrFlag5ZGhxb5CZyGxno3PJEbaCCcoKLERVd/mMjgXiC3Qx11TWj4tR/dfDU/e24f5UVOuvqjyT6ACtRXeSgtcuHUVNr7IvT0h1FVlVNtF7o+lHjtVJW4MczkWUD3rGtIfU5Tk8dnK2ocfzhOQjdIJHI3JZbv3Q/yPUmK3HNoNj5zbT2v7G7hdx+3cu+6hkk15E2XJKFpaKx3wxO5gQ5dq6bcm2y+6XPi0NRRF76HX7uy1E0gnMDltFNfnSyLdjs0/vjh1cTiBn3BCHuOdPDbD07jH5y602wKpT4ndk3F7bLx0MYLDRk1VcHttuNx2sA06Q/E5N3+KPI9SQpr+Nx2blk1k9c+PMOeIx2sWz4jZ88tv53TULrvhsdaQ+rxR4glDHr90VQVG6ZJR18YXTdoaR/gB7/Yw+JZpSOSUY8/wj+/cpTWrgAJ3aC8yIlmU6kpc+EPxznV2s9/7Wyi5Xxy9KMoYFdVSors2LXkkQfBcLIke3jyGerr9sBnFsi7fSHSVFPuYfm8cg6c6mHOjCLqqrw5eV5JQtNQuu+Gh68hDQQjPPHLvdSUeWnvDeJxapQXuwa7Itjwh5JTfb3+GImEkaqKG1p36vEnHx+O6skD7UyTSExnxYJS1iyt4fkdJ9h1sI2hUzpcDhslPgd9/igDwTgVJRpgUlnqTK4JjdJUNNPv9qWsWUwXKxZU0nzez95jHdRWNKSON8kmSULiioavIfUF4uiGQTiWIBRJEI0bVJW6U2tLQ9NzupGselMVZcS60zPbGwmEYgzVMpT4HMyu8VFb7uX/e/EgA4OdritLXNht6uBeGyj1OQlFE1QUu6gscXHv+jl4LrP4ObT/KBozcDpUHt+ynHm14z+wbjgpaxbThU1VuHZxFW/tO0dT2wDz6yb2O5MOSULiioavIemGkSp1tqkqum6g6wbd/RE0m0pdlZf+QIyEnjzorqLENWJdJhBOYJgKdruaTCyROAdO9bCvsRtIrvt85pp6Nqyo5bkdjXQPRLHZVHweJ1fNLecPbl88rp5u6XbxhgvThF194cuWrvcMJCv1ZGQkCtGsah/FHjvHz/RJEhLWuHj66c4bZ/PaB6fxhxKpE0UBSn12wkOHxClQ7LXT0p5cx5lR7qbHHyUQjqfWhCCZ0MqKHNhUla6+cKrLNcCS2WXcu24O5YPHEN+7roG3PzmHCdhtCvesnTPupqLpdvGG5IinNxAloZuXLV3v8UeJ64aMjERBUhSFBbNK+fhYJ/5QjCKPI6vPJ0lIXOKZ7Y2c6wqkpt4az/ZTW+6m+fwAkbhJrz9ZraYAi2eXEorE6eqP0tEbIq4n/70tFhpc19H55EQX53tCRGI6HqeNcMwgGI6OeE67pnCm3c/LO5v4/K3zqCxJlmrbVIXugSheV3o/qk6HSjx04chsp0MdkVwddhVMk1jCTI1mbkCZ3QAAIABJREFULox4zMuWrjs0lbieTJ6Z2PA5FFevP0qPP0JF8YXS9qERVldfWEZfImfqKj18fAw6esNZT0LSRVtcIhBO0BeIE08kTycdCMU43tpP5KIWNCbQ0u6nrSdEPKEzdBKxCQwb4KAbJv7BI7PPdgYJjnJ8djxhohs6faEYHx7poLzIya9/d4LTHQFC0fSPRRiti/fwYxYaz/TR2No/4siFsTqDDxU6/PHDq/k/Pr+cUp/zsh3E0zUUV1t3iEAozrnu0CWv96mXDsoRESJnSnxONJtCd38k688lIyFxCZ9bSxUWACgoqZvuxQzDxKaqqKpCIpoYrHi78HmXw0ax18FAKEZ/IDbisXYtWbRgGCZFHgdeV3Laq60z2RVhtE21461UG62L9/DrmSSbjA6/9mOblvJfO5vp6g1ftrQ70xs+h+Ia+p5f3O4Ikm8EpN3O9JKJtj1j0VSVKxW+vbHnDC6nxpqlNWld22lPL61IEhKXeHDjQk7+sp9QJJFMMIpBTDdHbfipqgouh40ij4OuvgjxhJ4aBZX4nBiGQWdveETDUXUwUWk2NdluR4FAKE5HbwhFgfNmkJ88vY/23iBuhw27XUuNOCZTqTZ8bUeBVOXd0LXLi1z8yZfX0Nnpv+x1Ml0CPhSXTVWJJ3RURb1khFXscdDnj8gG3Gkkm2171iytwXuF1jolHgfhSOKKXzdZMh0nLlFe5OLPv3wdK+ZXMLumiEWzy1hUX4LLPvKtk9uhsrCuhMe3LKemzE1thQefx86iWSXUV3sZCEbpC8QuNCO1qTjtCi67yoxyD7XlHkzTIBSOYZgGTruGy27H49QIxxKDf+u4HRrVpe6L1m3SHxE8uHEhNWVu3A6NhbNKWVhfMuLaVhmKa+j7N7PSc0lMj26+OhW71fGK6cFmU9Gz0Wr+IvJ2SgCjb8hM593+0NfGEgZv7DnNtt0tJAYX7+urvGy+aR51lV5sqsLLu5o52drH+d4wpmGyaFYp33pgJQA/eXof4VgysWiajRqPkz9+eHXqeSbTgDNfW9aMJ67KUndexi4Klzq4mTzbJAkJ4NKKuJO/7OfPv3zduCuwWtoHeGrbEdq6Qqmya7fTxu1rZrNmaTWaquJ2aXhdGgebuglHEqnpvVNtA6nrXCnJSANOIXJDUZWsnLl1MUlCAhhZEacoCqFIYpzrLSa9/ij/45lPCIYvTI3ZNYXvfGkVRR47LkfySIahTa564sL6kmmaIw4fvVKSydfRjBCFxqYoWSuMGE6SkAAurYizqeoV11viusHb+1rZ+l7ziAQEg4UHmkJZkQunXR3xjmpubREnzw2Q0A1sNjXZ+HSQJBkh8oPHpdHaFcz680gSKnDj3eT44MaFNP5LPwPBGIqqoKpGckPnKAzT5ERrP8++2UhT28hKMgUo8jrwujV2fHSWr9191SVD+i/ftWRcpdBCCOsUeRwMBGMYpomqZK+RqSShAje0yfFKJc3lRS7m1PhobO1P7VMZMU82qNcfZevOJt7d34YxmF2Wzytn1cJKXnznFD6Pg0hUp7cvxPkxSjvHWwp9sUx0s5aO2EKMT12ll1giucWiptyTteeRJFTgLt7keHHzzXXLZ/AvrxwhGNHRDRNVSe7fwYTGM3186+/fxePUWFBfTG2Flzf2nE32igPsWnLD25n2AF63RkWJk46eCLphYpgKLe1+fvL0vozd7DPRzXq6dcSWpCsmas6M5DT5idZ+SUJi4i7e5Hhx883/978OkkiYqTGPYUJCN4glwO3UKPY4CEfjvH+oPXX8gmZTKC9y0heM4HI6cDo0DpzswTCTIyhFMTENUDCTRz6McbNP9wY5mT1CmbzGVDLdkq7InFnVPsqLnXx0rJP1y2uz9jyShArco5uv5snn943ZfFMf+m8uTL7ZNZVirwOT5IF08cSFCplFs0rZtL6BbbuaUW0qkZhOV2+yWalNVZhZ5UVRFM53B7GpttTzjHazT/cGOZk9Qpm8xlQy3ZJuIclm2x5FVQhG///27jw6qvruH/j7LrNnsq9sSdgXBSkuBFBkMRBCCPCgoh7QUo/a2mJ5zilF62m1tdVaTnGh29P20frUltb+oAga1KJUIMimAmWNkJCEDNmTSWa9y/f3x02GBCYkE5K5M8nndQ5/zHDn3s8smc98t8+3+8/ClNHJ2HusCo4GN2JtoRcyNRlEiN2URBjYf4HkmkWOv952IvDFzxiDIHCBlpAoaMnHbjGgvtmDVm/nD+mIVBseXjgOFqOIjGQbSh0t8EsKGLQkZjYKSIgx4YLDqSU3pkBRVPA8F/TLPtQvyL5YIzTY1hkNtqQ7kPRn2Z6eirMZISsMW/51DlPHpoT8+NsmpEHspuwPfSIHmZXzx+CtXWdQWtUCcEBmWgxqm7wQBB4mgwC/pKC2yQ23Twk8xiBwyEqz4775Y5AQa4bZwCM/JxNHTlfD1/Y3wqBVRjCIPBLsJsRaDahr9uBygxsWk4j4GBMaWryduttC/YLsi+nbg20K+GBLuqRvxdq0nY/PVjThppFJMHTXrOkFSkKDTKLdDKMoICHWBIHnYTDw+Nq4VOROG4a9Jy7jX0cr4Je0LoCsDDsKZ2YjPckKi0lEjMUQmKqZaDcje0gcSiqbwJjWEjIZxUDrRhSFtu44FYmxZjS1+q7pbqMvyP432JIu6XuTshJRXt2Kry41Y0JmQp+fn5LQIOTyyLBZDDAZRbg9Ek6V1qP4xGX4JK31YzWLWDQ9E1PHJMNg4BFrMcFk1BacNrR48VbRGZQ6WuDyyTAZeCTHmSEIPHyS2uVW4F11t4WjLAghpPdSEixIiTfjdFkjxg2PB9/dHhAhoiQ06DCkJ1vhqHOhqqYFdc3eThvQWU0istJsmDYuBVazAbKi4o/vnQy0VvySgpJLzZBlFYwxeHwKGlp8SI4zB1oz7a2bjluBB+tu625iAk0vJiQyTMpOxJ4vqlBe04qsdHv3DwiBbklo8+bNKCoqAgDMnj0b69ev1yuUQYHjAJ+kwuWRcPuEFPz5g0bUNHXeNdEo8Ii3myAIIhLsZhhFvtNEBrdPQqPTF1jMKnAMKgAwBLYX6Nj909DivW53W3cTE2h6MSGRYVhqDGIsBpRUNA2MJFRcXIx9+/Zh27Zt4DgOjz76KD766CPcc889eoQz4PllFW6PBJ+soLLWhe17L+BSnbZ7KcdphQoBBtHAISnOhES7Eca2AchWjwxVZWhweqEyBllWIQhaNxrP8zDwHCZmJeBby4JXYbiRKdc0vZiQyMBzHEakxeDMxSZIstqnExR0SUIpKSnYsGEDjEZt3vmoUaNQVVWlRygDVnvLp7bRg8YWLzw+GR8drsRnpy4HxmHiY4wwGgRwAGRVRZzNBLvFgIKZ2YHzxFhEXLzshKIyMMYgijxEgYPXp0BhDBaTiIXTR/Qqxu4mJgRLUlS6hxB9ZCRZcaqsEQ1Ob59WUNAlCY0Zc+XLpqysDEVFRfjrX/+qRyhRKdiXKKB1X8kKQ2KsEXfdMhRev4ytn5aipLypUxU4jgNS4syIMYuob/XDZhYhqxzKLrfgQpUTxf+p7vLaPM/g8iqBRNbs8uOFPx3F+BHxeLRgYiCOYF/w7XE3tfrQ2OpHvM2IBLsJjxZMuCYJNLR44ZdVNLb4AKZV3l45fwzeKjoTqG8nySpOlTVgYlZil4kk2GsVrJvv/nna/S6fjMv1LiTazUiwmyhBEdLGZNTShV/u2wW0uk5MKCkpweOPP47169cjKytLz1CiSrAvUaMoQFJUyDJDSaUTtU0eeH0KSi45r3k8Y1rycHtlxNiMaG71w+XtWVeXTwr+ATxX2YS/7S4BY+hyHKc97romL2RFgccrQ1LUoGM97ckqMdYMxhhMRgGJdjNKHS2QZbWtPh3g9SuoafJ0OV4U7LUK1s3XflyD0wefX4bPr3YZGyGDkar2T/UG3ZLQ0aNHsXbtWjzzzDPIz88P+fFJSTH9ENWNS0np20G7YPwKg9GglcQxGQUIgoDKOhfqmrXtsmNjjLhUJ6PB6Q36eJ7nYLcawXEc6ps9kOQbnyfNGOBrO097bIB2X/tr0h43A8BxPFRotzseE+w5djwPz3PgeQ6yytA+UbSrc3R1nuR4Cxz1rYFuvuR4C5xuP4wGQSviyl8/Nr1EShxXo7j6x+SxKZCVMGyv3bZQvTvvF5cCAJbcNbrHJXwsZhF26/WP1SUJORwOPPnkk9i0aRNycnJ6dY76+laoamQtMklJsYe8PUFvGAUOXg6wmg3wywpKKhvR6pagqgyKyuCu93T5WLNRQLzdBLdXQosr9JIgwTd40O43ClpaaJKUwBd8vM0YeE2MAqf9HwDGVAjQKjR0PKbjcwx2nsx0O0oqm8BxHFTGAlUegp2jq/MsvTOr01jU0juztJaXpEDgOciyAgN//fOGW7g+W6GiuEITSmI8fq42LGV7bpuQBq6beQayomLfl5cwIi0GcWYBUJTrP6CN16XA6/Jd93kLzz333HMhxNsnXn31VZw4cQInTpzAli1bsGXLFgDAzTf3vNvD4/FH3EJHm80Et9vfr9fgOCB7SBwu1bWiucUPkefAVG3TKbdPDqz5EXgO08amoNnlhySr4DggPsaE+BgDfH4ZkszAcQyCwEPpJpnzHMDzgMgDBoMAjgM6tswFXitsujpvPG4amYQyhxOqisCYiqVtrdCoYXEoczhhEHmo4JASZ0ZynLnTMe3aj736PGNHxKO20QOLUQADQ1qCtctzdHWeRLsZt41Pw8ybM3DbhDRYTGLgOItZhF9WkBpvue55wy0cn63eoLhCY7OZenxsSXljp+LB/WVoSkxgNmxXPjhcjkOna7AmfwLSEkKflHC9580xFmlf5T0z2FpCHAdIMoPLK8HrlwMJWFZUbN56HDWNV7reYiwiHlsyCR8eKkezyw+O4xEXY4DdYoBfUtHqkWEUOXAchwtt22wnxZnB8xzSEixBx0CCrfnp6USAYMdF8i9ViqvnKK7QhNISem/v+bC1hGzX+aF1srQBr7xzDDePTMLaFZN7dY3rPW/9f+KRbsmqCpdH7pR8AG2zqXf3laKuWUtAAs9hSLIVD8wfoyWYS04IAo8YqwEVl72QFIakOHPbID1DWoIFQ1Ni4PFfmZTQ1VqcntYgowWmhAwcpQ4nNm87gYwkGx5dPLFfrkFJKIIpqgqXV4bH1zn5ON1+vH/gIo6frwegdZfNuCkD86YNg8moDTBu2X0ONqsBKgOqGz2BVmOsTdXWBrXNCuu4FkeSZDhdvhvaDZUWmBIyMHxRUovfvXsSsVYj1t03BVZz/6QLSkIRqD35eH0K1A7ZR1EZDp66jI8OVwaKjWam2bFkVhYykmyB43ieg8Wk1X1rbOncL17d4MbwNHsg4ciKGS1uPxLtZjj9CqwmscvdUHvS1Ub71xAS3Rhj+OBQBd755CtkptuxdsVkxMf0fCwrVPQNESE4DpAUFe4gyQcAyqtbsH1fKRz1Wrkdq1lE3h0jMHVsSmB7BUCbrhxrMwKMweu7dgaLygCR5wIJR1IY7FYjEmO1D5mj3h2ofm0QOg9WvrXrLEoqmgKb2P3frrN46t4pnY6h7RkIiV5Otx//+95pHD9fj2njUvDo4ok9mr59IygJ6ayrCQft3F4Juw5V4MiZGu14ALeOT8WC24fDajZ0Oo/VrE0+AIB7bh+OM+VN8Pg7JyKeAxJjTTCIQmAsqL3brKHFC0nWpjNLsoKGls7rjEodzk4z6S44rl0IS/vXEBKdTlyox/++dxour4wH54/BvGnDAl3r/YmSkE44Tit/4fJI8EnKNclHZQyfn63FroPlcLftBT8kyYols7IxIu3KTJNmlw8fHKyA0+OHs9WPIUlWrM4bj12flSMuxgi7zYjKmtYO5wWOnq29sr03DyiqtoUDAyCK2r5BPMcjKfaq8SAGKIoaqJytquya3VKDCdaN158LCak2HCE91+qR8JcPz+LAyWoMSbbhv++/BcNTw1cMgJJQmLUXFnV7gycfAHDUu7B9XynKq7XkYTIIuOe24bhjYhoEnkOzy4d//vsCqurdsJi0CgTNrT4wBri8Ev5v11lccDghySpE4dr5/yoDOMbAAKgKAgVM23/1pCVatYWdV/UDZ2fY8Z+yBjBVex6iwPVo9luwGXM/HBn6fvU9RTP0COkeYwylDif+357z8PoVLJmZhfycrH7Zwvt6KAmFSSD5tG2pECz5eP0ydh+pxIGTlwOLTm8ZnYy86SM6lb54r7gMjgY3Ym0GON0SWtwSOA4wCDxUleGCQ1v7oyjaxnPBA0Kg7I029sMh0W6C0yXBYhSDjueszhuP5984DElWwXMcEmNNPZr9Fu4ZczRDjwwEE7ITISu9W6wq8jyutwFqXbMHf9/9FU5fbMTIIbH4et54DE3RpxQaJaF+x+CXtDEffxfJhzGGExfq8d6Bi2hxa4vTUuLNWDIrG6OGxF1zvLb41IhGpy/QVQemnUcQeIABiXYTGlv8UNuK7BhEXutK63B9xrS1RYwx8BwPQeAxbkR80L2BAG28Z9zw+EAro7vZb+3dYpfqWiErKhLtJm3dUj/PmKMZemQgOF3a0OvFql0tQFVUFf86Uoltey+A4zg8dM9YzJk6tM+37A4F/XX2I7+koNUrQ5KUoPXWAKCuyYN395fhq0vNALRWydxpQzHz5oygXWlmk4DEWBPOVzVBklQt6SgqeJ6D2SgiO8MOv6TgYo1WUUIUeNgtImRVSzT+tlaM2ShAFHjE2Qxo8UhIijUjPsbU7Wy2UGa/tXeLxdmMqHd64XRJGDcivt9nzNEMPUKuVV7dgjeLzqDscgumjErCqgXjkHj1uK8OKAn1MQbAJylweaTr1n2SZBV7vriET49VBWacTcxKQH5OFhLs187J5zkOMVYDrCYBBTOz0NjiRWlVC8ABw1JiYRB4+GWt8nP7fh+MMXjaWko8x8Fo5HHziCSsXjiu1wP1ocx+a+8WEwQOqQlWWIxil62svkQz9Ai5QpIVvLu/DEWflSPGIuKJwkm4bXxqWGa+9QQloT7CGNDi9qPB2f3WCGfKG7Fjf5m2YRu0wpoFM7IwPjMh6PGiyCHOZgqs20m0m/Hde28J/P+vt53oNBDf6PQhNcGK2kYPJFnWxn84LUaTgQ/bTDHqFiNEX6UOJ/743mlU1bkw6+YM3Dd3NGIshu4fGEb0rXCDGBg8fgVuj4RYjrtuAmpq9WFncRlOlTUC0MZj7pwyBHdPHQKjGHxBmMUkwm41dFqQerWrB+LBaa0gRVUDExA4joOqsrAO0g/UbjGaAk4inSSr+MeB8yg6eBHxMSb8931TcNPIJL3DCoqSUC+pbV1dHq8MuZtq3rKiYv8JBz7+/FKgi27kkFgUzspGSrwl6GN4jkOMzQCrUcCVeWzBXd3iyM6IhcnAo9Hpg6Ro1Q/aJy2EszUyULvFaAo4iWR1zR68/PbnuNzgxp2TM3D/3DH9VvetL0RuZBGqPfm4vHKPtpK4UNWM7fvKUNukbTRntxiwKCcTk0clddknaxB5xNlMEIWe9dkGa3Ek2s3a9gufnMeZsgaAaet8grVG6Jd9aGgKOIlEqspw7Ks6/Ke0AXE2rejozRHa+umIklAPKSrTCnv2MPm0uP0o+qwcX35VB0Abk5k+KR333DoMZmPwl51D+3a4BnDdtH466qrFkWg344ffmN7tvir0yz40NNZFIo3LI+HTYw7UNnkwamgsHi+8CckRMPOtJ+ivpxuy2lZU1K/0KPmoKsPB09X46HAFvG1124anxqBwVjaGJNvQ7PJh67/Pw+1VYDULyJ+RhTibCTzPIdZm1HYMZV23Tvq61dLQ4sXZ8ib4ZQUCzyPBbgz5l/1ga0kN1LEuEp2q6lzYe8wBRVVx5+QMZA+JjYjdgHsqeiINI62iNYPbKwWtaN2VMocTb713ClV1LgDapIKFd4zAtHFXKl2/V1yGeqcPHMfB45dRdKAMq/MmItZm1LbqbrtUV62Tvm61bPlXCWRFBWNoK1rq67QtRE/PMZhaUgN1rItEF8YYzlxswpEzNYi3mzD7liFaBf0oQ0mog/aiolrL59qK1l3x+GR8cKgch0/XBBaltle6tpk7T4d0e5XAeIJB5GEyGpEYZF1QV+MOfT0e0eqRkRRnRkOLL7C4NdRf9jRGQkjfmzI6ucuyParK8PePv8LhMzWYPDoJqxeMD2xoCQAmQ/R8tUdPpP2KwS+z69Z1C/ooxvBFSR2KPrsIl1f74s1IsmLJzGxkpgevEm01a1soWM0GxFgNMIjBx366Gnfo6/GI9vOlxFvAGENqvCXkrjQaIyGk71mMAlT12qopsqLif3acwpEzNcjPycSyu0ZedwlHpAtvudQI5JcUNLT40djihbeLqtbBXG5w4392nMI/9pyHyyvDZBBw77wx+Naym7tMQACQPyMLIzNikRxnhknksXz2yKDHrZw/BmkJFliMIlLjLYHWSVf391ZfnK+vYyKEBCcrKjZvPYEjZ2pw/9zR+K/Zo6I6AQEAx7ossxzZ6utbezRRIJieltYJxudXsPvzShSfcASKgU4elYRF0zORNTwBDQ2uLh/LcYDNYoDNbAhh7tuNSUmxdzs7Tg8UV2gortBEclw9dfV3nMoYfr/jFA6eqsaqBeMwZ+rQ/gixX1zveQ+qfpMbST6MMZwsbcDOAxfhdPkBAMlxZiyZmY3Rw66tdH01UeAQazPBGOa9OgghA8M/PjmPg6eq8V+zR0ZVAurOoEhCV5KPv9u6bsHUN3uxo7gU5yq0SteiwOHuqUNx15QhQStdX60npXcIIaQrR87UYNehcsz52lAsmp6pdzh9akAnIcYAryTD5ZUg9yL5SLKKT49V4d9fXoKsaI8fNyIeBTOyelQCPZTSO4QQEkxdkwdvFJ1GdkYsHpg3JmKqX/eVAZuEPH4ZLo8USB6hOlfRhHf3l6LBqVW6jrMZsXhGFiZmJfToQxBq6R1CCLkaYwx//ugcVAZ8s3BSj3peos2ASkIMDB6fVtG6u6KiXWlu9eG9Axfxn9IGAFprZtbkDMz92lAYDcErXXfEcVe630IpvUMIIVf78qs6HD9fj/vnjkZyF8WOo92ASEIq07ZT8NxA8lFUFcUnLmP30crApnDZGbFYMisLaQnWHp2D54C4GFOg9A4hhPQWYwz/3FuKtEQr5k0bpnc4/Saqk1CoRUW7Uupw4t19pahu1Cpdx1gMWDQ9E1NGd13p+momg4CkOAuam9yUgAghN+xsRTMqalqxZtGEAdkN1y5qk5DLK6PF7b+h5NPqkbDr4EV8fu5Kpes7JqbhnluH97gAYMe1Pz3priOEkJ44fKoasVYDpk9K0zuUfhW1ScjtlXqdgFSV4fCZGnxwqDxQ6XpYig2Fs7IxNCWmx+cR+ba1P4aB+yuFEKKPU+UNuH1i2oBuBQFRnIR661JtK7bvK0VlbXulawG5t43AbRNSQ1rHYzYIsNuMEHiafEAI6XuKwnDb+FS9w+h3gyYJeXwyPjpcgYOnqgOVrr82NhkL78hEjMVw3cd2xHFAjNUIq0mkuW+EkH5jMgjIzojVO4x+N+CTEGMMX35Vh/c/K4fLIwEA0hIsKLwzG1npob3BBlHrfjMM8OYxIUR/I9JiBnxXHDDAk1B1oxvv7itDqcMJADAaeMyfNhw5N6VB4Hv+5tLaH0JIuA1J7vn4dDTTLQnt2LEDv/nNbyDLMh5++GE89NBDfXZuv6Tg488vYd9xR2BX1JuyE5Gfk4m4mGs3kLsegedg77DtNiGEhENyXGj7ekUrXZJQdXU1Nm3ahK1bt8JoNGLlypW44447MHr06Bs6L2MMpy82YmdxGZpatUrXibEmLJmZjbHD40M+n8kgIC7GAJ7jKQERQsIqLib6turuDV2SUHFxMaZPn474eC0xLFiwALt27cK3v/3tXp+zwenFjuIynC1vAqBVup59i1bp2hDi9gkcpy1YtYZx3x9CCOko1kpJqN/U1NQgJSUlcDs1NRXHjx/v1blkRat0veeLK5Wuxw6PQ8GMbCT1ojkrihxirbTvDyFEX7E2SkL9RlXVTuVwGGO9Kk/+VWUztu8vRX2zF4D2pi3OycSk7MSQz8cBMNO+P4SQCGExDY4KLLokofT0dBw5ciRwu7a2FqmpoS3K2nW4ArsPVwAAeJ7DvNuGI39mNszG0J8SxwF2iwExVuMN79URyva94RKJMQEUV6gortBEalw9lZJsH3B7BwWjSxKaMWMGXn/9dTQ0NMBiseDDDz/ET37yk5DOceIrrd5bVrodS2ZlIz3RCnerD274QjqP0SAgzmaA183gdftDeuzVInFf+0iMCaC4QkVxhSaS4+qphgbXDdXGjCTXe966JKG0tDSsW7cOq1evhiRJWLFiBSZPnhzSOawmESvuHoWpY5J79WshUHjUZMAg+LFBCCERSbd1QgUFBSgoKOj147+xeGKv67aJQtvkAyo8SgghuoraiglmowCpbfO5UFho8gEhhESMqE1CoeI5DjE2A6xGAaDVP4QQEhEGRRIyiDzibCaIAiUfQgiJJAM6CVHhUUIIiWwDNglR4VFCCIl8AzIJUeFRQgiJDgMqCQXW/lDhUUIIiQoDJgmJvLbrKa39IYSQ6DEgkpDZKCDWZqS1P4QQEmWiOglxHBBjNcJmorU/hBASjaI2CYkih1ibGQaBut8IISRaRW0SirOZAJr5RgghUS1qmxE0/kMIIdEvapMQIYSQ6EdJiBBCiG4oCRFCCNENJSFCCCG6oSRECCFEN5SECCGE6IaSECGEEN1QEiKEEKIbSkKEEEJ0Q0mIEEKIbigJEUII0Q0lIUIIIbqhJEQIIUQ3lIQIIYTohpIQIYQQ3VASIoQQohtKQoQQQnRDSYgQQohuKAkRQgjRDSUhQgghuqEkRAghRDeUhAghhOhGlyR09OhRrFixAoWFhXj44Ydx6dIlPcIghBBrbe7ZAAAKPElEQVSiM12S0Pe+9z288MIL2L59OwoKCvDCCy/oEQYhhBCdhT0J+f1+PPXUUxg/fjwAYNy4cXA4HOEOgxBCSAQIexIyGo0oLCwEAKiqis2bN2P+/PnhDoMQQkgEEPvz5EVFRXjxxRc73Tdy5Ei8+eab8Pv92LBhA2RZxuOPPx7yuXme66sw+1QkxhWJMQEUV6gortBEalykM44xxsJ9UZfLhW9+85uIj4/Hxo0bYTQawx0CIYSQCKBLEvrWt76FpKQkPP/88+B5miVOCCGDVdiT0KlTp7Bs2TKMHj0aoqj1BqampuL3v/99OMMghBASAXRpCRFCCCEAVUwghBCiI0pChBBCdENJiBBCiG4oCRFCCNENJSFCCCG6oSRECCFEN1GXhF555RW8/vrrgdtOpxOPPfYY8vLy8NBDD6G2tjbsMe3YsQOLFi1Cbm4u3n777bBf/2qtra1YvHgxKisrAQDFxcUoKChAbm4uNm3apEtMmzdvRn5+PvLz8/Hyyy9HTFyvvvoqFi1ahPz8fLzxxhsRExcA/PznP8eGDRsAAKdPn8by5cuxYMEC/OAHP4Asy2GPZ9WqVcjPz0dhYSEKCwtx7NixiPjsf/zxx1i+fDny8vICFfn1fg/feeedwOtUWFiIadOm4cc//rHucUUkFiWcTid7+umn2eTJk9lrr70WuP/5559nv/vd7xhjjG3bto099dRTYY3r8uXLbM6cOayxsZG5XC5WUFDASkpKwhpDR19++SVbvHgxmzRpEquoqGAej4fNnj2blZeXM0mS2Jo1a9iePXvCGtP+/fvZ/fffz3w+H/P7/Wz16tVsx44dusd18OBBtnLlSiZJEvN4PGzOnDns9OnTusfFGGPFxcXsjjvuYN///vcZY4zl5+ezL774gjHG2NNPP83efvvtsMajqiqbNWsWkyQpcF8kfPbLy8vZrFmzmMPhYH6/nz3wwANsz549EfEetjt37hy75557WFVVVUTFFSmipiW0e/duZGVl4etf/3qn+/fs2YOCggIAwOLFi/Hpp59CkqSwxVVcXIzp06cjPj4eVqsVCxYswK5du8J2/av9/e9/x49+9COkpqYCAI4fP47MzEwMHz4coiiioKAg7PGlpKRgw4YNMBqNMBgMGDVqFMrKynSP6/bbb8dbb70FURRRX18PRVHgdDp1j6upqQmbNm3CE088AQC4dOkSvF4vbrnlFgDA8uXLwx7ThQsXAABr1qzBkiVL8Oc//zkiPvsfffQRFi1ahPT0dBgMBmzatAkWi0X397Cj5557DuvWrUNFRUVExRUpoiYJLV26FI899hgEQeh0f01NDVJSUgAAoigiJiYGDQ0NYYur4/UBrQRRdXV12K5/tZ/+9Ke49dZbA7cjIb4xY8YEvkDLyspQVFQEjuN0jwsADAYDXnvtNeTn5yMnJyciXq8f/vCHWLduHWJjYwFc+x6mpKSEPSan04mcnBz86le/wptvvoktW7agqqpK99fq4sWLUBQFTzzxBAoLC/GXv/wlIt7DdsXFxfB6vcjLy4uouCJJxCWhoqIi3HXXXZ3+PfLIIz1+PGMsrEVRVVUFx10pGc8Y63Rbb5EUX0lJCdasWYP169dj+PDhERPX2rVrceDAATgcDpSVleka1zvvvIOMjAzk5OQE7ouE93Dq1Kl4+eWXYbfbkZiYiBUrVuC1117TPS5FUXDgwAH87Gc/w9/+9jccP34cFRUVusfVbsuWLYHem0h4HyNRv+4n1Bt5eXnIy8vr8fGpqamoq6tDeno6ZFmGy+VCfHx8P0bYWXp6Oo4cORK4XVtbG+gKiwTp6emdJmvoFd/Ro0exdu1aPPPMM8jPz8ehQ4d0j+v8+fPw+/2YMGECLBYLcnNzsWvXrk6t7XDH9f7776O2thaFhYVobm6G2+0Gx3GdXqu6urqwv1ZHjhyBJEmB5MgYw9ChQ3V/D5OTk5GTk4PExEQAwPz583V/D9v5/X4cPnwYL730EoDI+VuMNBHXEgrV7Nmz8c9//hOA9gd86623wmAwhO36M2bMwIEDB9DQ0ACPx4MPP/wQd911V9iu350pU6agtLQ00G2xc+fOsMfncDjw5JNPYuPGjcjPz4+YuCorK/Hss8/C7/fD7/dj9+7dWLlypa5xvfHGG9i5cye2b9+OtWvXYu7cuXjxxRdhMplw9OhRAMD27dvD/lq1tLTg5Zdfhs/nQ2trK7Zt24Zf/OIXun/258yZg3379sHpdEJRFOzduxcLFy7U/bMFAGfPnkVWVhasViuAyPjMR6KIawmF6qmnnsKGDRuQn58Pu92OjRs3hvX6aWlpWLduHVavXg1JkrBixQpMnjw5rDFcj8lkwksvvYTvfOc78Pl8mD17NhYuXBjWGP74xz/C5/MFfhECwMqVK3WPa/bs2Th+/DiWLl0KQRCQm5uL/Px8JCYm6hpXMBs3bsSzzz6L1tZWTJo0CatXrw7r9efMmYNjx45h6dKlUFUVDz74IKZNm6b7Z3/KlCl49NFH8eCDD0KSJMycORMPPPAARo4cqft7WFFRgfT09MDtSPhbjES0lQMhhBDdRH13HCGEkOhFSYgQQohuKAkRQgjRDSUhQgghuqEkRAghRDeUhAghhOiGkhAhhBDdUBIiUW3NmjXdFqztyTEHDx7E4sWLu73euHHjgp5r9+7dgb1sVq1ahV27dqGyshJTp07t9pyEDGZRXzGBDG779+/vk2Nu1Lx58zBv3rx+vw4hAw21hEjUevrppwEADz/8MA4dOoRVq1ahoKAAS5YsCdQT7HiMw+HAJ598gpUrV2L58uW4++678corr4R83VdeeQXLli1DYWEhPvnkEwDA1q1b8fjjj/fRMyNk8KCWEIlaL774IrZu3Yo//elPuO+++7B+/Xrk5uaiuroa9957LzIzMzsdk5CQgPXr1+Oll15CVlYWqqurMWfOnJDrsA0bNgw//vGPce7cOaxatQpFRUX99AwJGfgoCZGod/78efh8PuTm5gLQisrm5uZi7969ncZkOI7Db3/7W+zZswc7d+7E+fPnwRiDx+MJ6XoPPPAAAGDs2LEYNWoUvvjii757MoQMMtQdR6Iex3HXbA7GGIMsy53uc7vdWLZsGU6ePImJEydi/fr1EEURodbw7bhpoqqqEEX6LUdIb1ESIlFNEAQMHToUoijiww8/BABUV1fjgw8+wIwZMwLHyLKMixcvorW1Fd/97ncxd+5cHDx4EH6/H6qqhnTNbdu2AQBOnjyJ8vJyTJkypW+fFCGDCP2EI1Ft4cKFeOSRR/DrX/8aL7zwAl5//XUoioInn3wS06dPDxyzatUqvPrqq7j77ruRl5cHo9GIsWPHYvTo0bh48SKMRmOPr1lRUYGlS5eC4zj88pe/DOtOvoQMNLSfECGEEN1QS4iQDv7whz9gx44dQf/vG9/4BpYsWRLmiAgZ2KglRAghRDc0MYEQQohuKAkRQgjRDSUhQgghuqEkRAghRDeUhAghhOjm/wOH6Do+llG3BwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(\"total_bill\", \"tip\", data=tips, kind='reg');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "94l27sZrJy5G"
   },
   "source": [
    "### Bar plots\n",
    "\n",
    "Time series can be plotted using ``sns.factorplot``. In the following example, we'll use the Planets data that we first saw in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CioY9f1qJy5G",
    "outputId": "3f3dc1c7-f4b1-4634-db71-f175c7c1db2a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>method</th>\n",
       "      <th>number</th>\n",
       "      <th>orbital_period</th>\n",
       "      <th>mass</th>\n",
       "      <th>distance</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>269.300</td>\n",
       "      <td>7.10</td>\n",
       "      <td>77.40</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>874.774</td>\n",
       "      <td>2.21</td>\n",
       "      <td>56.95</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>763.000</td>\n",
       "      <td>2.60</td>\n",
       "      <td>19.84</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>326.030</td>\n",
       "      <td>19.40</td>\n",
       "      <td>110.62</td>\n",
       "      <td>2007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Radial Velocity</td>\n",
       "      <td>1</td>\n",
       "      <td>516.220</td>\n",
       "      <td>10.50</td>\n",
       "      <td>119.47</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            method  number  orbital_period   mass  distance  year\n",
       "0  Radial Velocity       1         269.300   7.10     77.40  2006\n",
       "1  Radial Velocity       1         874.774   2.21     56.95  2008\n",
       "2  Radial Velocity       1         763.000   2.60     19.84  2011\n",
       "3  Radial Velocity       1         326.030  19.40    110.62  2007\n",
       "4  Radial Velocity       1         516.220  10.50    119.47  2009"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "planets = sns.load_dataset('planets')\n",
    "planets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iH3OCWsoJy5I",
    "outputId": "f2ef5710-2175-4106-c8b9-f90eedfafbe7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\furqa\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py:3666: UserWarning: The `factorplot` function has been renamed to `catplot`. The original name will be removed in a future release. Please update your code. Note that the default `kind` in `factorplot` (`'point'`) has changed `'strip'` in `catplot`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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//e/T2tpae01bW1s6OjoaMT0AAOiVhsT0kUcemfnz5+cTn/hE9t1335xyyilZtGhRmpqaaq+pquodywAA0N80JKYff/zxPPbYY7Xlqqpy4IEHprOzs7aus7MzbW1tjZgeAAD0SkNi+g9/+EPmz5+frVu3ZvPmzbnrrrvyne98J4899lheffXVvPnmm3nwwQczevToRkwPAAB6pSFfQDz22GPzxBNP5MQTT0xPT0+++MUv5m/+5m9y0UUXZerUqdm+fXtOOeWUjBgxohHTAwCAXmnYfaa//vWv5+tf//o71rW3t6e9vb1BMwIAgB3jCYgAAFBITAMAQCExDQAAhcQ0AAAUEtMAAFBITAMAQCExDQAAhcQ0AAAUEtMAAFBITAMAQCExDQAAhcQ0AAPKtq7uATk2sHNqbvQEAGBHtDQPyqlzb6nL2LfNPqMu4wI7L2emAQCgkJgGAIBCYhoAAAqJaQAAKCSmAQCgkJgGAIBCYhoAAAqJaQAAKCSmAQCgkJgGqAOPvAbYNXicOEAdeOT1zmVbV3damgcNuLGB+hPTAPAh/HAEvB+XeQAAQCExDQAAhcQ0AAAUEtMAAFBITAMAQCExDQAAhcQ0AMU8nAbY1bnPNMBOohEPFnH/ZWBXJ6YBdhLCFqDvucwDAAAKiWkAACgkpgEAoJCYBgCAQmIaAAAKiWkAACgkpgEAoJCYBgCAQmIaAAAKiWkAACgkpgEAoJCYBvrUtq7uATk2ALyX5kZPANi1tDQPyqlzb6nL2LfNPqMu4wLA+3FmGnZhzhIDwEfjzDTswpwlBoCPxplpAAAoJKYBAKCQmAZ2eq4NB6BeXDMN7PRcGw5AvTgzDQAAhcQ0AAAUEtMAAFBITAMAQCExDQAAhcQ0AAAUEtMAAFBITNPveeAGANBfeWgL/d6u8sCNbV3daWkeNODGBoBdmZiGfmJX+aEBAHYmLvMAAIBC/S6m77333pxwwgkZO3ZsfvzjHzd6OgAA8L761WUeHR0dWbhwYe688860tLTk9NNPz1FHHZVDDjmk0VMDAIB36VdnpleuXJmjjz46e++9d/bcc8+MGzcuy5cvb/S0eqXed4Vw1wkAgP6nX52Z3rBhQ1pbW2vLbW1tWb169Q6PU+87F7zX+PX88ljy3l8ga8R+7ip3nNhV9hMA+Gj6VUz39PSkqamptlxV1TuWP0h391tnbl9++eUkyfmL7vn4J/j/XPt/Pv+e67dt3lS3ba5bt+491zdiP+u1zffbXlK/P9v3+3NNdp39tE3btE3b3NW22Yj/X+9M2+xP/y77cptDhgxJc/O707mpqqqqLjMocNddd+Xxxx/P1VdfnSS59tprU1VVLrjggg997+OPP54pU6bUe4oAAOyCHnrooRx00EHvWt+vYrqjoyNnnHFGbr/99uyxxx45/fTTc+WVV2bEiBEf+t4tW7bkySefTGtrawYN8lfoAAB8fAbEmenkrVvjXX/99dm+fXtOOeWUfPnLX270lAAA4D31u5gGAICBol/dGg8AAAYSMQ0AAIXENAAAFBLTAABQSEwDAEAhMQ0AAIV26pjevHlzJk6cWHsc5J133pkTTjgh7e3tueqqq9LV1ZXkrcdFTpkyJZMmTcpZZ52V9evXJ0k2btyY8847LxMnTsxpp52WX/3qVw3bFxqjt8fQ6tWrc/LJJ6e9vT3Tp09PZ2dnkuS8887LpEmTMmnSpLS3t+ewww7Lf/7nfzZsf+hbixcvzoQJEzJhwoTMnz8/SbJy5cq0t7dn7NixWbhwYe21Tz31VCZPnpxx48bliiuuqB1bq1atyimnnJJJkybl7LPPrn0+sfP7qMfPK6+8Uvv8mTRpUj772c/myCOPbNTu0AA7cgy97ZJLLsmdd95ZW/YZ1AvVTurXv/51NXHixOrwww+vXnzxxerZZ5+tjjnmmKqjo6OqqqqaM2dO9YMf/KCqqqr65je/Wf34xz+uqqqqfvSjH1UzZsyoqqqqZsyYUX33u9+tqqqqXnjhheq4446r3nzzzQbsDY3Q22Oop6enGjNmTPXYY49VVVVV9913XzV9+vR3jXfNNddUs2bN6tN9oHEeffTR6rTTTqu2bt1abdu2rZo6dWp17733VmPGjKleeOGFavv27dU555xT/eIXv6iqqqomTJhQ/epXv6qqqqouv/zy2mfSscceWz311FNVVVXVbbfdVp133nmN2SH61Md1/Lytu7u7OvPMM6t77rmnz/eFxtjRY+jll1+upk+fXo0YMaK64447auP4DPpwO+2Z6SVLlmTOnDlpa2tLkjz99NMZOXJkbfnYY4/Nz372syRJT09PNm/enCR58803s/vuuyd56yf9z33uc0mSoUOHZu+993Z2ehfS22No06ZN2bJlS44++uja+kceeSTbtm2rjfXcc89l6dKlufTSS/t+R2iI1tbWXHbZZWlpacluu+2Wgw8+OGvWrMmwYcMydOjQNDc3p729PcuXL8/69euzZcuWjBw5MkkyefLkLF++PNu2bcvXvva1DB8+PEly2GGH5aWXXmrkbtFHPo7j54/dcccd2WOPPdLe3t6I3aEBduQYSt56AvVxxx1X654kPoN6aaeN6auvvjqf/vSna8vDhw/PE088kZdeeind3d1Zvnx5Nm7cmCT52te+lh/+8Ic55phj8oMf/KD2CPO//Mu/zH333Zck+e1vf5vf/e53tfew8+vtMbTPPvtkzz33zCOPPJIkue+++7J9+/Zs2rSp9t7vfe97+dKXvpQ//dM/7fP9oDEOPfTQWtysWbMm999/f5qamtLa2lp7TVtbWzo6OrJhw4Z3rG9tbU1HR0daWloyadKkJG/90L948eL87d/+bd/uCA3xcRw/b+vu7s4///M/Z8aMGX23AzTcjhxDSXLuuefm1FNPfccYPoN6Z6eN6f/tU5/6VGbMmJGvfvWrmTJlSg477LDstttuSZJLL700c+fOzcMPP5xvfetbueCCC1JVVS6//PKsXbs27e3t+dGPfpSjjjqq9h52Pe93DDU1NWXRokW5/vrrc+KJJ+YPf/hD9t5779qx8vrrr+fRRx9914cUu4Znnnkm55xzTi655JIMHTo0TU1Ntd+rqipNTU3p6el5z/Vv27ZtW775zW+mq6sr06dP79P501gfx/Hz8MMP55Of/GQOO+ywPp07/UNvjqEP4zPog+0yMb1169aMGDEiS5cuza233pr9998/Q4cOzauvvprnnnuu9pPWuHHj0tnZmU2bNuWNN97IlVdemXvvvTdXXXVV1q1blz/7sz9r8J7QKO93DCVJc3NzbrrppixdujSf//zn09PTk7333jtJsmLFiowePTqDBw9u5PRpgFWrVmXatGmZMWNGTjrppAwZMqT25dQk6ezsTFtb27vWb9y4sXY50f/8z//k3HPPTVdXV6677jo/0O9CPo7jJ0l+9rOf5YQTTujTudM/9PYY+iA+gz7cLhPTb7zxRqZNm5bNmzdn27Ztufnmm3PCCSdkn332yeDBg/P4448neevA22uvvbLvvvvm5ptvzq233pok+fd///d0d3fXrhti1/N+x1CSzJw5M6tXr06S3HjjjRk/fnz+5E/e+s/r17/+9TsuF2HX8NJLL+X888/PggULMmHChCTJEUcckeeffz5r165Nd3d3li1bltGjR+fAAw/M4MGDs2rVqiTJ3XffndGjRydJLr744gwbNizXXHNNWlpaGrY/9K2P6/hJfAbtqnbkGPogPoM+XHOjJ9BX9tlnn5x//vk57bTT0tXVlYkTJ9a+iLF48eJceeWV2bJlS/baa6/80z/9U5LkK1/5SmbMmJG77747e+21VxYvXlwLJHY9H3QM/cM//EPmzJmTN998M4cddliuvvrq2vtefPHFfOYzn2nQrGmUG264IVu3bs28efNq604//fTMmzcvF154YbZu3ZoxY8Zk/PjxSZIFCxZk1qxZ2bx5cw4//PBMnTo1//Vf/5WHHnoohxxySE466aQkb13j+P3vf78h+0Tf+TiOn7e9+OKLGTJkSJ/vA421o8fQe/EZ1DtNVVVVjZ4EAAAMRE6zAgBAITENAACFxDQAABQS0wAAUEhMAwBAITENAACFxDQAABQS0wAAUGiXeQIiwM5u1qxZ2W+//XLRRRcleeux0g8++GBOPvnkXHfdddm+fXt23333XHrppTnyyCOzcePGzJ49O6+88ko6Oztz4IEH5pprrsl+++2Xz372sxkxYkSefvrpfOMb38jxxx/f4L0D6J+cmQbYSUyZMiV33HFHurq6kiRLlizJMccck4ULF+Zf/uVfsnTp0lx55ZW58MIL88Ybb+S+++7LyJEj85Of/CQPPfRQdt9999x999218Q499NDcf//9QhrgAzgzDbCT+Iu/+IscdNBB+cUvfpFPfepT2bBhQ7q7u7Nhw4ZMmzat9rqmpqa88MILOfvss/P444/nxhtvzJo1a/LMM8/kiCOOqL3u05/+dAP2AmBgEdMAO5G3z05/8pOfzBe+8IX09PRk1KhRueaaa2qveemll9LW1pbvfOc7Wb16dU4++eQcddRR6erqSlVVtdftueeejdgFgAHFZR4AO5Fx48blqaeeygMPPJCTTz45o0aNyqOPPppnn302SbJixYp8/vOfz5YtW/LII4/k7LPPzoknnpj99tsvK1euTHd3d4P3AGBgcWYaYCfS0tKScePGZePGjdl3332z7777Zu7cufnGN76RqqrS3Nyc6667LnvttVfOP//8zJ8/P9/97nez22675a//+q/zwgsvNHoXAAaUpuqP/04PgAHtjTfeyJlnnpnZs2dn5MiRjZ4OwE7PZR4AO4mHH344n/nMZ3LMMccIaYA+4sw0AAAUcmYaAAAKiWkAACgkpgEAoJCYBgCAQmIaAAAKiWkAACj0fwHLE7SEfO9crQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"year\", data=planets, aspect=2,\n",
    "                       kind=\"count\", color='steelblue')\n",
    "    g.set_xticklabels(step=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8xVH4BT0Jy5K"
   },
   "source": [
    "We can learn more by looking at the *method* of discovery of each of these planets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MSmrmIwFJy5L",
    "outputId": "2783a380-99d5-45a4-8e77-4a862ee8db8f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1624.47x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.factorplot(\"year\", data=planets, aspect=4.0, kind='count',\n",
    "                       hue='method', order=range(2001, 2015))\n",
    "    g.set_ylabels('Number of Planets Discovered')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jZboRxhEJy5N"
   },
   "source": [
    "For more information on plotting with Seaborn, see the [Seaborn documentation](http://seaborn.pydata.org/), a [tutorial](http://seaborn.pydata.org/\n",
    "tutorial.htm), and the [Seaborn gallery](http://seaborn.pydata.org/examples/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SfBK_noOJy5N"
   },
   "source": [
    "## Example: Exploring Marathon Finishing Times\n",
    "\n",
    "Here we'll look at using Seaborn to help visualize and understand finishing results from a marathon.\n",
    "I've scraped the data from sources on the Web, aggregated it and removed any identifying information, and put it on GitHub where it can be downloaded\n",
    "(if you are interested in using Python for web scraping, I would recommend [*Web Scraping with Python*](http://shop.oreilly.com/product/0636920034391.do) by Ryan Mitchell).\n",
    "We will start by downloading the data from\n",
    "the Web, and loading it into Pandas:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bsXThkq1Jy5O"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "\n",
      "  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0\n",
      "  0  836k    0     0    0     0      0      0 --:--:--  0:00:01 --:--:--     0\n",
      " 46  836k   46  388k    0     0   388k      0  0:00:02  0:00:01  0:00:01  214k\n",
      "100  836k  100  836k    0     0   418k      0  0:00:02  0:00:02 --:--:--  364k\n"
     ]
    }
   ],
   "source": [
    "!curl -O https://raw.githubusercontent.com/jakevdp/marathon-data/master/marathon-data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oJFyvTEvJy5Q",
    "outputId": "7f8ed4b7-5445-448d-f31a-909ba9e37024"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender     split     final\n",
       "0   33      M  01:05:38  02:08:51\n",
       "1   32      M  01:06:26  02:09:28\n",
       "2   31      M  01:06:49  02:10:42\n",
       "3   38      M  01:06:16  02:13:45\n",
       "4   31      M  01:06:32  02:13:59"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('marathon-data.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "AFKQ-6lHJy5S"
   },
   "source": [
    "By default, Pandas loaded the time columns as Python strings (type ``object``); we can see this by looking at the ``dtypes`` attribute of the DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dm9BAwIWJy5S",
    "outputId": "a0e0bf0f-d90e-4ffb-b7fc-c2f2c2dc0ac7"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age        int64\n",
       "gender    object\n",
       "split     object\n",
       "final     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-i2PIG8qJy5U"
   },
   "source": [
    "Let's fix this by providing a converter for the times:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "5jk7PJO-Jy5U",
    "outputId": "ea7031d4-cba8-4e5f-eac7-3079ba3c26aa"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender    split    final\n",
       "0   33      M 01:05:38 02:08:51\n",
       "1   32      M 01:06:26 02:09:28\n",
       "2   31      M 01:06:49 02:10:42\n",
       "3   38      M 01:06:16 02:13:45\n",
       "4   31      M 01:06:32 02:13:59"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "def convert_time(s):\n",
    "    h, m, s = map(int, s.split(':'))\n",
    "    return datetime.timedelta(hours=h, minutes=m, seconds=s)\n",
    "\n",
    "data = pd.read_csv('marathon-data.csv',\n",
    "                   converters={'split':convert_time, 'final':convert_time})\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JsXlC7GUJy5Y",
    "outputId": "394071e6-456f-45a0-adb1-a6eb66b8dc2e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age                 int64\n",
       "gender             object\n",
       "split     timedelta64[ns]\n",
       "final     timedelta64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3OLMhA6uJy5a"
   },
   "source": [
    "That looks much better. For the purpose of our Seaborn plotting utilities, let's next add columns that give the times in seconds:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rewt-JyXJy5a",
    "outputId": "c6b1902c-fe99-44ae-95cc-477f9114c9be"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot astype a timedelta from [timedelta64[ns]] to [int32]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-39-af38af42802f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'split_sec'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'split'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m1E9\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'final_sec'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'final'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m/\u001b[0m \u001b[1;36m1E9\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, **kwargs)\u001b[0m\n\u001b[0;32m   5880\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5881\u001b[0m             new_data = self._data.astype(\n\u001b[1;32m-> 5882\u001b[1;33m                 \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5883\u001b[0m             )\n\u001b[0;32m   5884\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, **kwargs)\u001b[0m\n\u001b[0;32m    579\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    580\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 581\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    582\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    583\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)\u001b[0m\n\u001b[0;32m    436\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0malign_copy\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    437\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 438\u001b[1;33m             \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    439\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    440\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[0;32m    557\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    558\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 559\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_astype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    560\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    561\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_astype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36m_astype\u001b[1;34m(self, dtype, copy, errors, values, **kwargs)\u001b[0m\n\u001b[0;32m    641\u001b[0m                     \u001b[1;31m# _astype_nansafe works fine with 1-d only\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    642\u001b[0m                     \u001b[0mvals1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 643\u001b[1;33m                     \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    645\u001b[0m                 \u001b[1;31m# TODO(extension)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    691\u001b[0m         raise TypeError(\n\u001b[0;32m    692\u001b[0m             \u001b[1;34m\"cannot astype a timedelta from [{from_dtype}] \"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 693\u001b[1;33m             \u001b[1;34m\"to [{to_dtype}]\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrom_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_dtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    694\u001b[0m         )\n\u001b[0;32m    695\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot astype a timedelta from [timedelta64[ns]] to [int32]"
     ]
    }
   ],
   "source": [
    "data['split_sec'] = data['split'].astype(int) / 1E9\n",
    "data['final_sec'] = data['final'].astype(int) / 1E9\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "soPLcTm4Jy5c"
   },
   "source": [
    "To get an idea of what the data looks like, we can plot a ``jointplot`` over the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0SrEKZCSJy5c",
    "outputId": "89f28c48-3ffc-41a4-c25b-a0430a4692a6"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'split_sec'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-40-e65c6c92b2cb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes_style\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'white'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"split_sec\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"final_sec\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hex'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m     g.ax_joint.plot(np.linspace(4000, 16000),\n\u001b[0;32m      4\u001b[0m                     np.linspace(8000, 32000), ':k')\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mjointplot\u001b[1;34m(x, y, data, kind, stat_func, color, height, ratio, space, dropna, xlim, ylim, joint_kws, marginal_kws, annot_kws, **kwargs)\u001b[0m\n\u001b[0;32m   2282\u001b[0m     grid = JointGrid(x, y, data, dropna=dropna,\n\u001b[0;32m   2283\u001b[0m                      \u001b[0mheight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mratio\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mratio\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mspace\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mspace\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2284\u001b[1;33m                      xlim=xlim, ylim=ylim)\n\u001b[0m\u001b[0;32m   2285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2286\u001b[0m     \u001b[1;31m# Plot the data using the grid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, data, height, ratio, space, dropna, xlim, ylim, size)\u001b[0m\n\u001b[0;32m   1707\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1708\u001b[0m                 \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1709\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1710\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1711\u001b[0m         \u001b[1;31m# Find the names of the variables\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'split_sec'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with sns.axes_style('white'):\n",
    "    g = sns.jointplot(\"split_sec\", \"final_sec\", data, kind='hex')\n",
    "    g.ax_joint.plot(np.linspace(4000, 16000),\n",
    "                    np.linspace(8000, 32000), ':k')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vtoB-gbFJy5e"
   },
   "source": [
    "The dotted line shows where someone's time would lie if they ran the marathon at a perfectly steady pace. The fact that the distribution lies above this indicates (as you might expect) that most people slow down over the course of the marathon.\n",
    "If you have run competitively, you'll know that those who do the opposite—run faster during the second half of the race—are said to have \"negative-split\" the race.\n",
    "\n",
    "Let's create another column in the data, the split fraction, which measures the degree to which each runner negative-splits or positive-splits the race:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RjwraB9mJy5f",
    "outputId": "ca68b401-49a1-423a-b975-673e6eef0dea"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'split_sec'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_sec'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-07b9f1b49acb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'split_frac'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;33m-\u001b[0m \u001b[1;36m2\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'split_sec'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m/\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'final_sec'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_sec'"
     ]
    }
   ],
   "source": [
    "data['split_frac'] = 1 - 2 * data['split_sec'] / data['final_sec']\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "NtojCjNfJy5h"
   },
   "source": [
    "Where this split difference is less than zero, the person negative-split the race by that fraction.\n",
    "Let's do a distribution plot of this split fraction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JrG4dVeTJy5h",
    "outputId": "487d252c-7857-4542-b88f-319672c626df"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'split_frac'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_frac'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-42-cef9f0a42e1a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'split_frac'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkde\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxvline\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"k\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinestyle\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"--\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_frac'"
     ]
    }
   ],
   "source": [
    "sns.distplot(data['split_frac'], kde=False);\n",
    "plt.axvline(0, color=\"k\", linestyle=\"--\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UO3m73PVJy5m",
    "outputId": "619d9cbd-5116-4feb-8a02-badca734edf3"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'split_frac'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-43-21fc1dc653fc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit_frac\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5177\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5178\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5181\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'split_frac'"
     ]
    }
   ],
   "source": [
    "sum(data.split_frac < 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZM4IsERMJy5o"
   },
   "source": [
    "Out of nearly 40,000 participants, there were only 250 people who negative-split their marathon.\n",
    "\n",
    "Let's see whether there is any correlation between this split fraction and other variables. We'll do this using a ``pairgrid``, which draws plots of all these correlations:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "KH-Y8P2fJy5p",
    "outputId": "5faf08b2-3c4e-4d72-8bd0-51488191241f"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'split_sec'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2896\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2897\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_sec'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-44-848c3e5ae8d9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\n\u001b[0;32m      2\u001b[0m                  hue='gender', palette='RdBu_r')\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_legend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, **kwargs)\u001b[0m\n\u001b[0;32m   1338\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1339\u001b[0m                     \u001b[0mcolor\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpalette\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mkw_color\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mkw_color\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1340\u001b[1;33m                     func(data_k[x_var], data_k[y_var],\n\u001b[0m\u001b[0;32m   1341\u001b[0m                          label=label_k, color=color, **kwargs)\n\u001b[0;32m   1342\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2978\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2979\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2980\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2981\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2982\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2897\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2898\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2899\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2900\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2901\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'split_sec'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.PairGrid(data, vars=['age', 'split_sec', 'final_sec', 'split_frac'],\n",
    "                 hue='gender', palette='RdBu_r')\n",
    "g.map(plt.scatter, alpha=0.8)\n",
    "g.add_legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LwtpHSxIJy5q"
   },
   "source": [
    "It looks like the split fraction does not correlate particularly with age, but does correlate with the final time: faster runners tend to have closer to even splits on their marathon time.\n",
    "(We see here that Seaborn is no panacea for Matplotlib's ills when it comes to plot styles: in particular, the x-axis labels overlap. Because the output is a simple Matplotlib plot, however, the methods in [Customizing Ticks](04.10-Customizing-Ticks.ipynb) can be used to adjust such things if desired.)\n",
    "\n",
    "The difference between men and women here is interesting. Let's look at the histogram of split fractions for these two groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "EOlUltFhJy5r",
    "outputId": "8a9854fe-ddc6-4b1a-dc80-fc1ee8b577be"
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'split_frac'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-45-5c579a589826>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit_frac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'M'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'men'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msplit_frac\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgender\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;34m'W'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'women'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'split_frac'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5177\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5178\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5179\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5180\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5181\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'split_frac'"
     ]
    }
   ],
   "source": [
    "sns.kdeplot(data.split_frac[data.gender=='M'], label='men', shade=True)\n",
    "sns.kdeplot(data.split_frac[data.gender=='W'], label='women', shade=True)\n",
    "plt.xlabel('split_frac');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "TiZpbK4HJy5t"
   },
   "source": [
    "The interesting thing here is that there are many more men than women who are running close to an even split!\n",
    "This almost looks like some kind of bimodal distribution among the men and women. Let's see if we can suss-out what's going on by looking at the distributions as a function of age.\n",
    "\n",
    "A nice way to compare distributions is to use a *violin plot*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "41zHRDE2Jy5u",
    "outputId": "a7419ca3-0fe1-452e-ad37-e7567d8bfc08"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'split_frac'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-46-1bac087a9697>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m sns.violinplot(\"gender\", \"split_frac\", data=data,\n\u001b[1;32m----> 2\u001b[1;33m                palette=[\"lightblue\", \"lightpink\"]);\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mviolinplot\u001b[1;34m(x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2385\u001b[0m                              \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2386\u001b[0m                              \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2387\u001b[1;33m                              color, palette, saturation)\n\u001b[0m\u001b[0;32m   2388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2389\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)\u001b[0m\n\u001b[0;32m    560\u001b[0m                  color, palette, saturation):\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_densities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'split_frac'"
     ]
    }
   ],
   "source": [
    "sns.violinplot(\"gender\", \"split_frac\", data=data,\n",
    "               palette=[\"lightblue\", \"lightpink\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jO9LJBjNJy5x"
   },
   "source": [
    "This is yet another way to compare the distributions between men and women.\n",
    "\n",
    "Let's look a little deeper, and compare these violin plots as a function of age. We'll start by creating a new column in the array that specifies the decade of age that each person is in:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "M3_PQUKhJy5x",
    "outputId": "026e0051-89d7-45a8-e8f9-11ea27634b7c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>split</th>\n",
       "      <th>final</th>\n",
       "      <th>age_dec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>M</td>\n",
       "      <td>01:05:38</td>\n",
       "      <td>02:08:51</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:26</td>\n",
       "      <td>02:09:28</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:49</td>\n",
       "      <td>02:10:42</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>38</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:16</td>\n",
       "      <td>02:13:45</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "      <td>M</td>\n",
       "      <td>01:06:32</td>\n",
       "      <td>02:13:59</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age gender    split    final  age_dec\n",
       "0   33      M 01:05:38 02:08:51       30\n",
       "1   32      M 01:06:26 02:09:28       30\n",
       "2   31      M 01:06:49 02:10:42       30\n",
       "3   38      M 01:06:16 02:13:45       30\n",
       "4   31      M 01:06:32 02:13:59       30"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['age_dec'] = data.age.map(lambda age: 10 * (age // 10))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "MdnjIfE9Jy52",
    "outputId": "d24dee12-c9a7-4d5a-ebbe-2a5c859cb573"
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'split_frac'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-48-b7efb94570d7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      5\u001b[0m     sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n\u001b[0;32m      6\u001b[0m                    \u001b[0msplit\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"quartile\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m                    palette=[\"lightblue\", \"lightpink\"]);\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mviolinplot\u001b[1;34m(x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2385\u001b[0m                              \u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2386\u001b[0m                              \u001b[0mwidth\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minner\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msplit\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdodge\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2387\u001b[1;33m                              color, palette, saturation)\n\u001b[0m\u001b[0;32m   2388\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2389\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0max\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, bw, cut, scale, scale_hue, gridsize, width, inner, split, dodge, orient, linewidth, color, palette, saturation)\u001b[0m\n\u001b[0;32m    560\u001b[0m                  color, palette, saturation):\n\u001b[0;32m    561\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 562\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morient\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue_order\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    563\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    564\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mestimate_densities\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbw\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcut\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mscale_hue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgridsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    153\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstring_types\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    154\u001b[0m                     \u001b[0merr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"Could not interpret input '{}'\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 155\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0merr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    156\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    157\u001b[0m             \u001b[1;31m# Figure out the plotting orientation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'split_frac'"
     ]
    }
   ],
   "source": [
    "men = (data.gender == 'M')\n",
    "women = (data.gender == 'W')\n",
    "\n",
    "with sns.axes_style(style=None):\n",
    "    sns.violinplot(\"age_dec\", \"split_frac\", hue=\"gender\", data=data,\n",
    "                   split=True, inner=\"quartile\",\n",
    "                   palette=[\"lightblue\", \"lightpink\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5UiJma9CJy55"
   },
   "source": [
    "Looking at this, we can see where the distributions of men and women differ: the split distributions of men in their 20s to 50s show a pronounced over-density toward lower splits when compared to women of the same age (or of any age, for that matter).\n",
    "\n",
    "Also surprisingly, the 80-year-old women seem to outperform *everyone* in terms of their split time. This is probably due to the fact that we're estimating the distribution from small numbers, as there are only a handful of runners in that range:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TZCB5D6XJy55",
    "outputId": "c9de60f4-0ec5-4bbe-aed1-0d19c37233a4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(data.age > 80).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "a1uzvXQhJy58"
   },
   "source": [
    "Back to the men with negative splits: who are these runners? Does this split fraction correlate with finishing quickly? We can plot this very easily. We'll use ``regplot``, which will automatically fit a linear regression to the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "h5XgVgxxJy58",
    "outputId": "6a5c85f0-8155-44a3-eb9b-da3faf4091aa"
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['final_sec', 'split_frac'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-50-cb4808fa2efb>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n\u001b[1;32m----> 2\u001b[1;33m                markers=\".\", scatter_kws=dict(color='c'))\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxhline\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"k\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\":\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m;\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\seaborn\\regression.py\u001b[0m in \u001b[0;36mlmplot\u001b[1;34m(x, y, data, hue, col, row, palette, col_wrap, height, aspect, markers, sharex, sharey, hue_order, col_order, row_order, legend, legend_out, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, x_jitter, y_jitter, scatter_kws, line_kws, size)\u001b[0m\n\u001b[0;32m    549\u001b[0m     \u001b[0mneed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0munits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx_partial\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my_partial\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    550\u001b[0m     \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0ma\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mneed_cols\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0ma\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtolist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 551\u001b[1;33m     \u001b[0mdata\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    553\u001b[0m     \u001b[1;31m# Initialize the grid\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2984\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2985\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2986\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2987\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2988\u001b[0m         \u001b[1;31m# take() does not accept boolean indexers\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[0;32m   1283\u001b[0m                 \u001b[1;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1284\u001b[0m                 \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;34m\"raise_missing\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;32mTrue\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1285\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1286\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1287\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[1;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1090\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1091\u001b[0m         self._validate_read_indexer(\n\u001b[1;32m-> 1092\u001b[1;33m             \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1093\u001b[0m         )\n\u001b[0;32m   1094\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[1;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[0;32m   1183\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"loc\"\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1184\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1185\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"{} not in index\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnot_found\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1186\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1187\u001b[0m             \u001b[1;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['final_sec', 'split_frac'] not in index\""
     ]
    }
   ],
   "source": [
    "g = sns.lmplot('final_sec', 'split_frac', col='gender', data=data,\n",
    "               markers=\".\", scatter_kws=dict(color='c'))\n",
    "g.map(plt.axhline, y=0.1, color=\"k\", ls=\":\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fBZMZo-VJy6A"
   },
   "source": [
    "Apparently the people with fast splits are the elite runners who are finishing within ~15,000 seconds, or about 4 hours. People slower than that are much less likely to have a fast second split."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OxC_62XUJy6A"
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Geographic Data with Basemap](04.13-Geographic-Data-With-Basemap.ipynb) | [Contents](Index.ipynb) | [Further Resources](04.15-Further-Resources.ipynb) >\n",
    "\n",
    "<a href=\"https://colab.research.google.com/github/jakevdp/PythonDataScienceHandbook/blob/master/notebooks/04.14-Visualization-With-Seaborn.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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