diff --git a/source-code/README.md b/source-code/README.md
index 684359c..65ed2fb 100644
--- a/source-code/README.md
+++ b/source-code/README.md
@@ -30,5 +30,6 @@ to create it.  There is some material not covered in the presentation as well.
 * [`pandas`](pandas): illustrations of using pandas and seaborn.
 * [`regexes`](regexes): illustrations of using regular expressions for validation
   and information extraction from textual data.
+* [1seaborn`]:(seaborn): illustrations of using Seaborn to create plots.
 * [`web-scraping`](web-scraping): illustration of web scraping using beautiful soup
   and graph representation using networkx.
diff --git a/source-code/seaborn/Data/data.txt b/source-code/seaborn/Data/data.txt
new file mode 100644
index 0000000..443f8b8
--- /dev/null
+++ b/source-code/seaborn/Data/data.txt
@@ -0,0 +1,101 @@
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diff --git a/source-code/seaborn/READNE.md b/source-code/seaborn/READNE.md
new file mode 100644
index 0000000..f49613b
--- /dev/null
+++ b/source-code/seaborn/READNE.md
@@ -0,0 +1,9 @@
+# Seaborn
+Seaborn works on top of matplotlib, and is especially strong for plotting
+data and its statistical properties.
+
+## What is it?
+1. `seaborn.ipynb`: Jupyter notebook that illustrates a number of Seaborn
+    features.
+1. `generate_data.py`: script to generate example data.
+1. `Data`: directory containing example data.
diff --git a/source-code/seaborn/generate_data.py b/source-code/seaborn/generate_data.py
new file mode 100755
index 0000000..3198a08
--- /dev/null
+++ b/source-code/seaborn/generate_data.py
@@ -0,0 +1,26 @@
+#!/usr/bin/env python
+
+import numpy as np
+
+nr_data = 100
+gaussian = np.random.normal(loc=1.5, scale=2.0, size=nr_data)
+poisson = np.random.poisson(lam=5.0, size=nr_data)
+labels = np.random.choice(['A', 'B', 'C', 'D'], size=nr_data)
+x = np.linspace(0.0, 100.0, nr_data)
+y = 1.3*x + 2.0 + np.random.normal(scale=8, size=nr_data)
+x1 = np.random.choice(np.arange(1, 10, 1), size=nr_data)
+y1 = 1.3*x1 + 2.0 + np.random.normal(scale=1.5, size=nr_data)
+x2 = np.sort(np.random.gamma(3, scale=4, size=nr_data))
+y2 = 1.3*x2 + 2.0 + np.random.normal(scale=1.5, size=nr_data)
+binary = np.random.choice(np.array(['yes', 'no']), size=nr_data)
+x3 = x2.copy()
+y3 = np.where(binary == 'yes', 1.3, 1.9)*x3 + \
+     np.where(binary == 'yes', 2.0, -1.0) + \
+     np.random.normal(size=nr_data)
+with open('Data/data.txt', 'w') as file:
+    print('gaussian,poisson,label,x,y,x1,y1,x2,y2,binary,x3,y3',file=file)
+    for i in range(nr_data):
+        print(f'{gaussian[i]},{poisson[i]},{labels[i]},{x[i]},{y[i]},'
+              f'{x1[i]},{y1[i]},{x2[i]},{y2[i]},{binary[i]},'
+              f'{x3[i]},{y3[i]}',
+              file=file)
diff --git a/source-code/seaborn/seaborn.ipynb b/source-code/seaborn/seaborn.ipynb
new file mode 100644
index 0000000..da272af
--- /dev/null
+++ b/source-code/seaborn/seaborn.ipynb
@@ -0,0 +1,717 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Seaborn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Import required libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "%matplotlib inline\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read data to visualize"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read the CSV file as a pandas dataframe."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "data = pd.read_csv('Data/data.txt', delimiter=',')\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Continuous data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Create a simple matplotlib scatter plot for $x$ and $y$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Wc10rb60c6K120gJdq6yAnqe6Jm+pZVqFS7Oqql1cK2+tHOitdvIEtKyAnqf8MG+pZdrh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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a4162df28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(x='x', y='y', data=data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now use Seaborn to plot the same data, but also include the result of a linear regression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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9ahcv7+rJPX716jruvHIlp9aXj7rOMJFpn/EGF+0cp0ZTzB3EQuANEdkMPAj8\n0szm9GulJkkqneFAb5R71+/iyc0duTLcpy4u40tXreLSk2qLWmeYyLTPeIOL1mVSoxnzb6sx5qsi\n8tfAB7Fag/5LtgvcA8aY3ZM9QKVmOmMMPaE4j7y2nwd/vZeBaBKAujI3t17ezO+es6Rg456RTGTa\n50SCi9ZlUiMpag3CGGNE5DBWI58UUAU8ISLPGWP+YjIHqNRMNhhL8sstnfzLizvZe8S6CHuctlzj\nnvoK77jzGSYy7aNrCqqUilmD+DLwaaAH+AHw58aYpIjYgJ2ABgg170QTaTbt6+U7L+zitT29AAjw\n4dMXcXvLCk5eVFZUb4ZCJjLto2sKqpSKuYOoBn7XGLMv/6AxJiMi10zOsJSamRKpDHu6w9y7fhdP\nv32Q7DIDZzVW8MWrVnFh8wL8ReYzTKQ/9Uh0TUGVkpb7VqoI6YyhazDGw6/u40ev7iMUTwFQX+nh\n9itW8Dtn1FPhcxa9zpC/2yj/m/7Xrj1NL+Zq0mm5b6VKwBhDfyTBM+8e5t71u2nviwJgE/C57Hjt\nwk827OP+1rZj7gLGykXQDGY1G2iAUGoEoXiKN/b08u0XdvLm/n7ACgwep51qnxObwL4+q9PbkkpP\nLufg4+39PLG5Y9RcBM1gVrOBBgilhokl0+zsCnHfut088+6hXOOe85qqGIyliKfSBNxO2rpD2EVA\noCeUoLk2QCSR4gcv76G2zJ27O0ilrempW3+8iXOWVnHr5c2620jNCtPSt1BEKkXkCRHZLiLbROQi\nEakWkedEZGf2Z9V0jE3NX4lUhsff2M+V/7COa7/zMv+VDQ52m9Bc4+czFzUxGEviz17UE+kMIiBi\n/Rmsu4BwIo03mykdjCY5OBAlkzGkM5nc3cRFzdUk04ZIIoUx1k/dbaRmmulqbPst4BfGmNXAmcA2\n4CvA88aYVcDz2d+VmnTpjKF7MMbfPb2Fu5/cwqFgLHfXIMCiMhfheJI//dnbdA3G2dUVYjCWxGW3\nYQwYA65srkM0mcbvshadAXpCcWwIIoLbYcfncuC0C6+29fK1a0+jrszDQDRJXZlHF6jVjDPlU0wi\nUgFcDnwGwBiTABIich3Qkn3aD4F1wF1TPT41fxhjCEZTvNrWw7+8sIstB4O5x+w2sBkQm9ATSpDJ\nHhdjSBlo74tS5XPQF0mBgUXl7txdwOcvXc4TmzuIJFLWXQaAEWrL3MDRtQbNYFYz3XSsQSwHuoF/\nFZEzgU2JMbDVAAAeEElEQVTAl4GFxphD2eccxqoBpdSkCMdTbD8c5L71bTy3tTN33O+yU1fm4UBf\nGLvdBgLxZAaX3YbYIJ2BJRVeOgdjBGNpVtb6ERFC8RR1ZZ7cbqUzGiqzuQhR6y6kwkOZx1qU1rUG\nNVtMR4BwAOcAXzTGvCYi32LYdFK2tEfBBA0RuQW4BWDp0qWTPVY1DUrZC2H4e33ukiZW1AV46JW9\n/PSNA8SzjXtW1PoRIJUxBDwO3A47qfTRv4IiR6eSyr1OyjwOBqJJfvEnVxT83KG7g6F8B7tNMMZo\nZrOaVaY8UU5EFgEbjDFN2d8vwwoQK4EWY8whEVkMrDPGnDzae2mi3NxTygSy/PfyOGwMxpMMRNPW\n1FLMSnSr8jm5+bJmfn9NI++29/N3P9+K0y6k0hk6+q0trHabkDEGQaivtO4EIgnrjuHRWy4sahya\n2axmkhmbKGeMOSwiB0TkZGPM+8DVwNbsPzcB92R/PjXVY1PTr5QJZPe3tuGwWd/6g7EUXYOJ3B2D\n0y584twGPn9ZM43VPpx2G1edshCbSO5iPjR91D0YYzCeptrvJOB2jHvHka41qNlquvIgvgj8RERc\nQBtWGXEb8LiI3AzsA66fprGpaVTKBLJ9vWHcdhsH+qK50hgAHoeNhz9/PqfXV+J1HVtQb6SLud4F\nqPloWgKEMeYtoNDtzdVTPRY1sxSbQDbaOkU0kWZ/b5hU2nCw/2hg8ThsVHgdLF3g57ymBeMal94F\nqPlIM6nVjFJMuerhbTX39IS49cebCLjtLK320bQgwLodXfRFrMY9dhFqylx4HDYyBu5sWTldp6fU\nrKIBQs0oxZSrzl+nCEaT9ITiAASjGbYcDPLmgQHAumO4dFUNPYNxekJx6it1akip8dAAoWacsaZz\nhtYpjLEyoMVYbQ6tDXnWrrwqn5MHbjqPUxaX59YZhqalvvrUlnFVXlVqvtIAoWadxiofhwei2G02\noskM+Ru1vU4bNQE3mYzh7KWVuf4Mw6elxlN5Van5SgOEmtGGf7v/7MVNfOysev7+mW2E4qljgkO1\nz0ldmZtEOsPCKt8xzXtG2j47vPKq9mVQ6igNEGrGyv/WX+5x0NEX5q4n38Fhs+US3cDq0bDA76S2\nzEM8lSGV4bgcheHbZ4fWLsKJNKl0hrryo6UwtC+DUhYNEGpCJnP+fijRzWm3EY6n6MxLdAM4v6ma\nv/rtUzjcH+WhV/eNmqOQv312qAQ3WMElmTEc7I9RXwllHqfWSlIqSwOEOmEjzesPn78/kSCSyRj2\nHgnjcdo4NBBlIJo65vFF5W4O9kfpDcX50AcW86EPLB71/fK3zw7tehKEGr+TvmgSg6ErGMNuE62V\npFTWdPWDUHNA/ry+iOR6Hdzf2pZ7zlAQ6RqMHRNE1m3vKvieVp2kJLu7QwiwtydyTHCw28DrEGoC\nbtxOG2tf2lPUWFtW1+X6L8RSGZw2q67Swgov9RVeXHYb8bTRvgxK5dE7CDWmke4AiimLMZ7aSuF4\niiOhOC+838X969s4NGAVy7Nl15ptWN/6F1Z4EZFxrxUMbZ+9ce2GY7K1y71OHHYpuvieUvOFBghV\n0FBQ2NEZJJQtVLfA7z5mGqmYshjFBJFYMk1vOME77f3cu243b7dbiW52m3DB8moGo0ne7xzEJlKS\nvgrFZGsrpTRAqALy1xZiyQwZYzgSSuJ22HOlru9vbSvqQjtaEEmmM/SFE+w9EuaBl/fw7HuduW2r\nFzZXc0fLSs5oqKDC62T9+90l66tQTLa2UkoDhCogf1ookc5gF8EA3YNxyjzOY1pmjnWhLRREEqkM\nN57fyO6uED/deIBHX99PLGntTlpe4+f2K6z3qPa5cGR7PZf6oq7F95QamwYIdZz8aSGX3UYqbRAb\nJNLWRTx/amesC23+hf1Abxiv047TJnz1qS1EE9ZdAECl18lnL2niY+csoa7Mg8dpL/heelFXaupo\ngFDHyZ8Wqgm4rZyBDDhtMu5mOWBd2Ncsr+a/3znEPb/YzmAsSSKvnecVq2q46yOnsHSBj4Bb/0oq\nNVPoNld1nFsvbyaZNkQSKco8Dhb4XdhE8Lkd494GGkum6eiP8m57P19/ZhtHwolccAi47SwudxNO\npDllcZkGB6VmGP0/Uh1n+Hz/8poA94xzvn9oAbp7MM4jr+/nZ5vaSWSzoN0OG7UBNwGPA7tAZzB2\nTN0kpdTMMG0BQkTswEagwxhzjYhUAz8FmoC9wPXGmL7pGt98d6Lz/ZmMoS+SoC+S5JdbDvHAr/fS\nG04A4LBZNZVqAm6cDhuhWIrOwRjGwI1rN+hOIqVmmOmcYvoysC3v968AzxtjVgHPZ39Xs4QxhoFI\nkgN9Edbv6Oa2hzfxD8/uoDecwO2w8akLl/K3v3MKPreDtDGEYkk6+qOk0oZF5e4xM6yVUlNvWu4g\nRKQB+G3g68CfZg9fB7Rk//xDYB1w11SPbb4pRbG9UDxl5TP0hLmvdTe/3nUk99hvnFLHFy5rZtXC\nMiq9Thqr/Nzf2sbm/X04bMcmvmmZbaVmlumaYvpn4C+AsrxjC40xh7J/PgwsnPJRzTPFFtsbSTSR\n5kg4Tm8owcMb9vHvb3aQylgL0KcuLufOK1ewpqmaar8LZ14+Q8vqOi79xgtUep3HrD1omW2lZpYp\nDxAicg3QZYzZJCIthZ5jjDEiYgo9JiK3ALcALF26dNLGOReMdXcwnjpJ+eKpNH3hJIOxJP/5ziEe\nemUvA9EkQPb97LjsgsMmLCz3FHyPYsp0TMY5K6WKNx1rEJcA14rIXuAx4CoR+THQKSKLAbI/C05G\nG2PWGmPWGGPW1NbWTtWYZ51iqqge6IvgHZaQNtq3+FQ6Q/dgnI6+KOve7+LzP9rIt57fyUA0icth\no8ztYGG5m/oKL8FYkq8/s33ENYX8rbRDvaXb+6Ls7BrkxrUbTmgtYryVY5VSo5vyAGGMudsY02CM\naQJuAF4wxvwh8DRwU/ZpNwFPTfXY5pJiSnE3VvmIJtPHvK7Qt/hMxtAXTtDeF+Xdjn6+8m/v8JUn\n32XfkQgCfOT0RayqDVAdcFHptcpj+N3O4z4vX3757cMDUfoiSar9ThaVe074wl7MOSulijeT8iDu\nAR4XkZuBfcD10zyeGWc80yfFVFEtptheMJakP5zkSCjOQ6/u5edvHyS7zMBZjRXc0bKS05dU8In7\nXqHK5xrXmsJI5bdPtC90MeeslCretAYIY8w6rN1KGGOOAFdP53hmsvEuKBczxz9aAbxIIkVvOEE4\nnuI/3uzg4Q37CcWtxj1LKr3cenkzV5xUy4IyNwG3g6XV/hNeUyjVhX2y1jWUmq9m0h2EGsVoC8pD\njx/oixBw2RERugZjx/RxGKk89vCEuHgqzeGBGOF4kl/vOsL9rW109Fv9m/1uO5++cBkfO6eB2oCb\nSt/RXUgT6bFQqgu79nlQqrQ0QEyT8e62Gelb9s7OYO7Owi6wqzsMwJJKD067jd5wkmTasKqubNTP\nSGcMveEEg7Eku7tCfHfdbt460A9YHd1+54x6PnNxE0uqvFT7j5bhHjKRctylurBrnwelSkuMKbib\ndFZYs2aN2bhx43QPY9zyp4vyL4ijFcEbPk8PVmJZ92Cc2jI3PpeDtu4QqbQBscpaNNcGiCRS1JV5\nuPXy5oIByRjDQDRJfyRJTyjOgy/v4b+3HM417jm/qYrbWlZw0sIyFvjdeF3Hl+Eu1b8TvbArNTVE\nZJMxZs2Yz9MAMfVGutiP1hN5pKASSaRYVO5BRNh+OIhdBMS6I1i9qBxjDIcHoviyu4ryX3v3R1Zz\n+pIKQrEkT2xu55HXDuR2NS1b4OP2K1Zw0YoFVPpcVAy7e1FKzV7FBgidYpoGJ7IoO9L0yf2tbblg\nM9TcB6xGP2DN5SfShoq89QuP004yneS+dbu59qx61r7URmcwDkC5x8FnL2nimjPqqfK7qPK5sNu0\n0qpS85EGiGlwoouyI1VYHZq/rwm46OiPgYFF5e5ccx+Xw4bXaSdjDOmMIZMxGGPYejjI2/81AFhT\nUh87ewmfunAZteVuqv0u3I7JmU5SSs0O2jBoGgzPIj6RLm1D8hPOMgZW1vpZVRcgY8g191lZG2Aw\nniKZzhBPpjk0EONAXyzX7vPSlTX862fO40tXr2JFXYDFFV4NDkopvYOYDqXebTPSnYUxhv5Iko+d\nvYRv/moHfeEEg7FUbgF6cYWHP//QyZyztIpKn5OKYcXzlFLzmwaIaXKiDXmKNZQBnUin6Y0kiCbS\nBGNWopvDJlx3Zj23taygwmflSeg6g1JqOA0Qc8xQBnQileGd9n6+t243OzpDgFVp9fo1jdx4fiPV\nfmudwePUqSSlVGEaIOaIeCpNb9i6Uzg0EOX+1jZad/TkHr/y5Fq+cHkzDZU+qgMuAm79T6+UGp1e\nJWa5VDpDbyRBKJYiHE/xk9f282+b23ML0KcsLuOOlhWcvqSSSq/zmPIYJ0p7Lig1P2iAmKVe2NbJ\n99bt5kBfhEVlHpprA7z4fhf92cY9tQE3t1y+nCtX11HucVKV19VtIhf4iXahU0rNHhogJsFkfsM2\nxvDMO4f4+2e24bBZXdu2Hg7ydoeVz+Bx2Ljx/KV8Yk0D5V4nNQH3MesME73An2gXOqXU7KMBosQm\n8xt2OG4tQH//pT0YY+gNJwknjjb8qfa5uO9T57Cw3EOV30W55/jyGBO9wGvPBaXmD02UK7HJ6GoW\nS6Y52B+lMxjjSCjOjq5BDgfjueDgddporPLgcggragM0VvkKBgcYf5vR4YrtQqeUmv00QJTYRC/A\n+ZLpDF3BGAf7o4RiSZ7c3M6nHnydSDYwOG3C4nIPDZVebCIsW+BnQcCNbZSchole4EuZBa6Umtl0\niqnEStH8JpMx9EUSBGMpMpkMG9p6uW/9bg70WY173A4bLoeNCo8Dn8tBMpPBINx+xYox33uivReK\nzQLXnU5KzX5THiBEpBH4EbAQMMBaY8y3RKQa+CnQBOwFrjfG9E31+E7U0AVxR2ewqE5uhRhjCEZT\n9EcTpDOGtu4Q965vY9M+61+DTeC3P7CYz1zSxK7OEE9saufQQJTGan/RF+BSlPkYKwtcdzopNTdM\neT8IEVkMLDbGbBaRMmAT8FHgM0CvMeYeEfkKUGWMuWu095op/SCG92roCcXpiyQp8zjG7OQ2JBRP\n0RdOkExn6IskeOjXe/mvdw+Ryf7nOXdZFbdf0UxzbYCAx0G17/iubjPFifS7UEpNnRnbD8IYcwg4\nlP3zoIhsA5YA1wEt2af9EFgHjBogpspY0yXDdwbVlnnwux1FXRBjyTRHwgniyTSJVIYnN7fzk9f2\n5xagG6u83HbFCi5srsbjcrBgFpTH0J1OSs0N07oGISJNwNnAa8DCbPAAOIw1BTXtipkuOZELYiJl\n3SmE49Zib+vOHta2tnFoIAaAXQSf2061z4XTLtSVeygbYWfSTFOKdRil1PSbtjkKEQkA/wb8sTEm\nmP+Ysea9Cs59icgtIrJRRDZ2d3dP+jiL2bY6np1B6YyhJxSnoz9KOJ5iR+cgf/zTt/mfP9/KoYEY\nNgGfy87CcjeLyt0MxJJ854VdbNo7a5ZjdKeTUnPEtAQIEXFiBYefGGOezB7uzK5PDK1TdBV6rTFm\nrTFmjTFmTW1t7aSPtZhtq8VcEK3eDAkO9EYIRpN0D8b4xi+2c/uPN/NuNgv6wuZqTqorY4HfRbnX\nicthp8zjxOWwTSiPYqrlNzEaiCZzjYt0gVqp2WU6djEJ8ACwzRjzzbyHngZuAu7J/nxqqsdWSDHT\nJWPtDBqMJekLJ0llMsSSaR7feIDHXj9ALJUBoLnGz5Un17FpXx87ugZx2wW7zZubUpqN8/eT3e9C\nKTX5pmMN4hLgU8C7IvJW9thfYgWGx0XkZmAfcP00jO04o+UNjLV4HU2kORKOk0hlyBjD89u6+MFL\ne+gOxQGo9Dr57CVN1AbcfOfFXbgdNjwOG8mM4WB/jPpKKPM4df5eKTUtpmMX08vASKm+V0/lWIox\n0t0BMOLi9UUrF9AXThJJWB3ctnQM8L11u9l+eBCwGvf83jkNfPKCpfjdDv78Z+/gddrwu50YAwcH\nohgMXcEYdptM2fy9JrcppfJpJnURCk2X3Lh2w3FF78Jxa0F56QLr2/7hYIzvt7bx4vtHF9MrvU4c\ndmH7oUHePzzINWfW0zkYy+2CKs/+7AnFiaUyOG2Cy27jq09tobF18i7amtymlBpOA8QJyt/aaowh\nnTHYbUJHf4RIIsUjr+3nZ5uONu5ZUukllkzjc9nxOO30RxN8+4Vd1ATcx61zlGeDiNMmRJKZKblo\naxlvpdRwMzMVdxZorPIRSaRIZTIk0hnSGUM0kcZpt/GpB17nkdcPkEwbFgRc3PXhk6nxu/C57ATc\nDtwOG2UeZ2677Ei7oESk5JVhR1LKIoNKqblBA8QJ+vSFy4glM4Ri1kW9L5LgcDDOgb4ofZEkboeN\nT1+4jC9fuYpfbunk3YMD9AzGiCbTuZafQxfgkbaFDsZTU3bR1jLeSqnhdIppnIZ2Jp28uIwvXbWK\nH76yl7YjYeLZLasAv3FKHV+4rJk93WG+/cJO3E4bXqedRDoz4u6kQuscja1Tl5E80SqvSqm5R+8g\nihRPpTk8EOPQQJREyrpz2LS/j53doVxwOK2+nO/+wdn85W+dQl25hyc2t+Nx2gi4rdafQG53UjHZ\nxVOZkazJbUqp4fQOYgypdIbeSIJQzNqyms4Yfv72QR56ZS/B7LGF5W5uvbyZK06qRUQIuB1U+10c\nDo68O6muzDPmjqRSlOYeD01uU0rl0wAxgkzG0B9NMhBNMlQS/fU9vdy7bjf7eq01AK/TzicvWMrH\nz23AlW3is8Dvxuuy1g1G2p00nrLXetFWSk0XDRDDGGMIxlL0R6ymPQB7j4S5b91uXs8WzBPgIx9Y\nxOcuWU6134XdJlT6XFQMq+iq8/pKqdlMA0SecDxFb7ZpD8BAJMlDr+zl5+8czDXuaa7x47TZ2LSv\nj4N9MT57SRPXnFmPvUAf6KmeIlJKqVLSAMGxTXsAkukM//FmBz/asI9w3Dq2pNLLVSfX8dy2wzjt\nNiq8ToKxBP/vuR1UeJ0jXvR1ikgpNVvN6wART6WPqZlkjOHXu45wf2sbHf1RAAJuB5+6aBkfPaue\nu554F5fD2pVktwluh12zjZVSc9a8DBCpdIa+SJLBWDJ3bFdXiO+t281bB/oBsAlce2Y9N13URIXP\niYjQORij2ufEljedpNnGSqm5al4FiEI7k3rDCR58eQ//veVwroXdBcuruaR5AS+8381tP9lEQ6WP\n21uaaVrgz+5KOpo+otnGSqm5at4EiGAsSV/46M6keDLNE5vbeeS1A7kSE8sW+LijZQUmA996YSdO\nu7DA56I/muBr/7mNj5+zhCc2d+iuJKXUvDDnA0QkkeJI6OjOJGMML77fzdrWNroGrcY9FV4nn7m4\niWvOWIzdJvzp42/jdtgIuLNF8uw2IokUr7b18rVrT9NdSUqpeWHOBohYMk1fJEE0cbQA3bZDQb77\n4m62HgrmjtUG3NxxxQquWG31ty7zOOkejFHlc+WK6sGxhfU0ICil5oMZFyBE5MPAtwA78ANjzD3j\neX0ynaEvnCAUT+WOdQVj/ODlPfxqW1fumNdpo67MTcbA2pfbKPc5KPc4efDXe+kJJTgSSrCowpPr\nC61rDUqp+WZGBQgRsQPfBX4TaAfeEJGnjTFbx3ptOmPojyQIZstvg1V59bE39vP4xvZcQT2v007A\nbafK5xr6TBKpNN9vbcs151lU7qajP0Z7X5QllQaH3aZrDUqpeWdGBQjgfGCXMaYNQEQeA64DRgwQ\nxhgGokn6I0ky2cCQMYbntnbyg5f3cCSUAKDa7+LmS5fzw1f2WCUxBOwi2G2C0+5gZ1eIhipvXmlt\na1vr4WCcc5ZW6VqDUmremWkBYglwIO/3duCCkZ6cMYYDvVFSmaO9GN5u7+fedbvZ0RkCwOWw8Ylz\nG7jx/EZ8LgfPvddJXyROmcfKbQhGk3QOxoinMhzqj1JXbk0rlXudlHkcDESTRRfWU0qpuWSmBYgx\nicgtwC0ADY2NueDQ0R9lbWsbL+3syT33ypNr+cLlzSwq9wBWsLi9pZmvP7OdaDJNKp2hoz8GgNth\nI5kxIzb0UUqp+WamBYgOoDHv94bssRxjzFpgLcCZZ59rQvEUP9mwjyff7CCZtqaYTllcxh0tKzit\nvgLgmGqrDVU+3A4797e2sXl/Hw6bsKjCgzFwcCCaa+hjt4muOyil5rWZFiDeAFaJyHKswHAD8Acj\nPbk/kuDTD7xOf9QqmVFX5uYLly3nytV12LJbVMu9Tqp8rmOqrQ5tVb30Gy9Q6XUes511PA19lFJq\nLptRAcIYkxKRPwJ+ibXN9UFjzHsjPf9wMIZEk3icNm48fymfOLcBj9Nq1uN12an2u3A77CN+Xika\n+iil1Fw1owIEgDHmGeCZYp//odMWcvOly3M9n512G9V+F3732KemDX2UUmpkMy5AjEfTAj93fXg1\nYOUzVPmcVAybMhqNNvRRSqmRzeoAMTSdFHA7qPa7cNhtY7zieFo6QymlCpvVAcImUF/pzQUKpZRS\npTP+r9wziN0mGhyUUmqSzOoAoZRSavJogFBKKVWQBgillFIFaYBQSilVkAYIpZRSBWmAUEopVZAG\nCKWUUgVpgFBKKVWQBgillFIFicn2cZ6NRKQb2DeBt6gBesZ81twy3855vp0v6DnPFxM552XGmNqx\nnjSrA8REichGY8ya6R7HVJpv5zzfzhf0nOeLqThnnWJSSilVkAYIpZRSBc33ALF2ugcwDebbOc+3\n8wU95/li0s95Xq9BKKWUGtl8v4NQSik1gnkZIETkwyLyvojsEpGvTPd4JoOINIrIiyKyVUTeE5Ev\nZ49Xi8hzIrIz+7NqusdaSiJiF5E3ReQ/s7/P6fMFEJFKEXlCRLaLyDYRuWgun7eI/En27/QWEXlU\nRDxz7XxF5EER6RKRLXnHRjxHEbk7ez17X0Q+VKpxzLsAISJ24LvAR4BTgRtF5NTpHdWkSAF/Zow5\nFbgQuDN7nl8BnjfGrAKez/4+l3wZ2Jb3+1w/X4BvAb8wxqwGzsQ6/zl53iKyBPgSsMYYczpgB25g\n7p3vQ8CHhx0reI7Z/69vAE7LvuZ72evchM27AAGcD+wyxrQZYxLAY8B10zymkjPGHDLGbM7+eRDr\norEE61x/mH3aD4GPTs8IS09EGoDfBn6Qd3jOni+AiFQAlwMPABhjEsaYfub2eTsAr4g4AB9wkDl2\nvsaYVqB32OGRzvE64DFjTNwYswfYhXWdm7D5GCCWAAfyfm/PHpuzRKQJOBt4DVhojDmUfegwsHCa\nhjUZ/hn4CyCTd2wuny/AcqAb+Nfs1NoPRMTPHD1vY0wH8I/AfuAQMGCMeZY5er7DjHSOk3ZNm48B\nYl4RkQDwb8AfG2OC+Y8ZawvbnNjGJiLXAF3GmE0jPWcunW8eB3AOcK8x5mwgzLDplbl03tl59+uw\nAmM94BeRP8x/zlw635FM1TnOxwDRATTm/d6QPTbniIgTKzj8xBjzZPZwp4gszj6+GOiarvGV2CXA\ntSKyF2va8CoR+TFz93yHtAPtxpjXsr8/gRUw5up5/wawxxjTbYxJAk8CFzN3zzffSOc4ade0+Rgg\n3gBWichyEXFhLe48Pc1jKjkREax56W3GmG/mPfQ0cFP2zzcBT0312CaDMeZuY0yDMaYJ67/pC8aY\nP2SOnu8QY8xh4ICInJw9dDWwlbl73vuBC0XEl/07fjXW+tpcPd98I53j08ANIuIWkeXAKuD1knyi\nMWbe/QP8FrAD2A381XSPZ5LO8VKsW9B3gLey//wWsABrB8RO4FdA9XSPdRLOvQX4z+yf58P5ngVs\nzP63/g+gai6fN/A/ge3AFuBhwD3Xzhd4FGuNJYl1l3jzaOcI/FX2evY+8JFSjUMzqZVSShU0H6eY\nlFJKFUEDhFJKqYI0QCillCpIA4RSSqmCNEAopZQqSAOEUkqpgjRAKKWUKkgDhFIlJCLnicg72R4F\n/mzfgtOne1xKnQhNlFOqxETk7wEP4MWqk/R/pnlISp0QDRBKlVi2xtcbQAy42BiTnuYhKXVCdIpJ\nqdJbAASAMqw7CaVmJb2DUKrERORprJLjy4HFxpg/muYhKXVCHNM9AKXmEhH5NJA0xjyS7Qv8iohc\nZYx5YbrHptR46R2EUkqpgnQNQimlVEEaIJRSShWkAUIppVRBGiCUUkoVpAFCKaVUQRoglFJKFaQB\nQimlVEEaIJRSShX0/wHlRZX15U1CtwAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f32462748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x='x', y='y', data=data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we have data where the $x$-values are integer, and we have multiple $y$-values for each $x$-value, adding some \"jitter\" to the $x$-values makes the plot more informative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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GqA56sWyNZWu01gS8Bj3DMfYdO0Pb/ghHesYXgFdXBbl7SzMf2dA4odpnyO80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\nIYRY5iQQCCHEMieBQAghlrn/H+mGl9Iv39ZHAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f3241a898>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x='x1', y='y1', data=data, x_jitter=0.1);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the plot below, the mean value for $x$ is shown, as well as a confidence interval."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FNW6ng5++ewQAg2H2jEJmFKa3JcRk+tFdSzN6fpGzRUEgk26krmAA0Zjl9YPH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\nuf8DcTwCyfv6LPwAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2ccad780>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.regplot(x='x1', y='y1', data=data, x_estimator=np.mean);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A joint plot shows the distribution of the $x$ and $y$ data, as well as their relationship."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dPgKhaJcLrTVPravlT2v3A/2vwVJKUei0kjFIFYKHOn20uP0SWkIIIY4vGI62\nZQqGo4EVjmh++8ZuVm05BMCc8hx+eNm0PuXrJoOBwizrSVUInorICC3FkNASQoiT4A+Faez0x/sI\n+oNhfvxSNf/cHV2DdX5VId+8qO8aLIvJQHGWDdNJVgieisgI3flAQksIIQbgC4Zp6DzS5aLTG+Q7\nf9vG9kNdQP9rsBwWE4VO65AVXPTqwTuiSGgJIcQJeAIhGrv88T3dGjp93P38h9SdYA1Wlt085H0F\nZaYlhBCiD5cvSIs7EA+smkYX3/rrNtq6A8ddgzVYFYKifxJaQgjRj05PkNbuI30E1+9v4/u99sH6\n8eXTmFmaE/+8IdZDcLAqBAciMy0hhBAAtHUH6OjVR/DVHY38/B8fHXcNlslgoCjbitU0uBWCJyLV\ng0IIIWh2+XH5on0Etdb8ZV0dj67dB0TXYP30szMocB45XzWUFYInIjMtIYQYwbTWNLv8uP3Rtkzh\niObBN3fzwuboGqzZZTncu6zvGqyhrhAcaLwjkYSWEGLEi0Q0jS4f3kC0LZM/GOYnL+1k7e4WAM6r\nKuSbF07GYjoym8q2m8lP4M7DcnhQCCFGoHCsj6A/1kfw6DVYV88v5bYllX3WYOVnWsm2J7ZCcIRO\ntCS0hBAjVygc4XCvtkxHr8H64tLxLJ9XGr+9QSkKs6w4LIl/6gyN0NXFiX/khRAiAY7uI7i7yc09\nK7fG12B96+IpLJ18ZA1WIioET6SnYe9II6ElhBhx/KFoW6Zw7MTQ+v1t/OB/duAJRNdg/WjZNGaV\nHVmDlagKwRPxS2gJIUT6O7qP4Cs7GvlFbA1WQWZ0Dda4UUfWYCWyQvBEZKYlhBBprncfwaPXYI0b\nFd0Hq/carOHoIXi6JLSEECKN9e4jeOwarGzuvWw6mbYjT4nJUCF4IoGwhJYQQqSlTm+QVne0j+DR\na7DOnVzA3RdVxddgJVOF4In0lOiPNMn9WxFCiDPU3h2gPdZHsCu2BmtbbA3WVfNKuf2cI2uwzMbo\nLsPJUiF4IjLTEkKINNPi9tPljfYRbOjycc/zW6lt8/S7BstqNlKcZcOYZAUXx+MJyExLCCHSwtF9\nBHc3ufnWyq20xtdgVbF0cmH89plWEwVOK0qlRmAZlMLtCyV6GAkhoSWESCtaaxq7/HgC0Sf1DQfa\n+f6q7XgCYTKsRn68bHqfNVg5Dgt5GZZEDfe0GBS4JLSEECK1hSOaxi4fvliRQu99sI5eg6WUIj/T\nQpYteSu+n9MaAAAgAElEQVQEj8doULj8wUQPIyEktIQQaSEUjtDQ5SMQiqC15pkP6njk7f7XYBmU\noijLht2S/AUX/TEoJTMtIYRIVb37CIYjmt+t3sNfNx0Ejl2DZTYaKMqy9dlmJNUYDYouCS0hhEg9\nvfsIBkIR/s9L1ayp6X8Nls1spCiFKgSPx2hQuH1yeFAIIVJK7z6CXd4g331hG1sP9r8GK9NmoiAz\ndSoET8QohweFECK1dPtDNLmifQQbunx86/mtHGjzAPDFcyq5an5Z/La5Dgu5KVYheCJGg6LDE0Rr\nnRYhfCoktIQQKcflC9LsirZl2hPbB6tnDdY9F1VxblV0DZZSigKnlUxrej3VmYyKQDhClzdEtiP1\nqh/PRHr9JoUQaa/TE6S1OxpYGw+0871ea7B+tGw6s2NrsIyGaIWgzZyaFYInYjIYCAHNbt+IC63U\nLZ8RQow4bd2BeGC9Vt3IPSu34gmEGZVp4TfXzI4HltloYEyOPS0DC6IzLYCm2GxzJJGZlhAiJTS7\n/Lh80fM4z6yv55E1ewGoyHdw3xUzKMyyAWC3GCly2pJu08bBZDYa8AEt7kCihzLsJLSEEElNa02T\ny0+3P3TMGqyZpdn8eNmRNVhOm5lRmZa0L04wxQK5qcuX4JEMPwktIUTSikQ0jS4f3kD4mDVY50wq\n4FsXH1mDlZdhIceRPhWCJ2I0KOxmI4c7JbSEECIphCPRUnZ/MBxbg7WdrQc7AVg+r4Q7zhmPQam0\nrRAcSFmenbpYif9IMrJ+y0KIlNC7LVNjbB+s/tZgpXOF4EBKcx3UtXsTPYxhJ6ElhEgqgVA0sEKR\nCHuaY2uw3NE1WHdfVMV5sTVYZqOB4mwbZuPILIIuy7Xzwb62EbfAOOG/baWUUSm1SSn1YuzjPKXU\nq0qpmtjb3ESPUQgxPHzBMIc7vYQiETbWtvO1pzfT6g6QYTXysytnxgPLbjFSkmMfsYEFUJbnwOUP\n0ekdWT0Ik+E3/lWgutfH9wCva60nAq/HPhZCpDlPIBRvfPt6dRP3PL+V7n7WYDltZoqz0ruk/WSU\n5joAqB1h57USGlpKqVLgUuDRXlcvA56Ivf8EcPlwj0sIMbzc/hCNXX7CkQjPfFDHT16qJhTRVOQ7\nePC6OVQWZALRCsECZ3o0vT1T4wuim1nuaXYneCTDK9HntO4Hvgk4e11XpLU+HHu/ASga9lEJIYZN\npzdIq9vPe3ta+dVru+ILZsflZ/Cba2eTaTON2ArBE6kYlYHZqNjZ4Er0UIZVwmZaSqlPA01a6w3H\nu43WWgP6OF9/m1JqvVJqfXNz81ANUwgxhNq7A7S6/fyzpoV7/3dHPLDsZiPeYIgdh7owGhSjs20j\nPrCOfs4zGw2ML8hkl4TWsFkMXKaU2g88DZynlPpvoFEpNRog9rapvy/WWj+itZ6vtZ5fUFAwXGMW\nQgySFrefdk8Aly/IfS/vxBeMAJBjN1OaE60KfGZ9XVr3EDwV/T3nTS52sqtxZB0eTFhoaa2/pbUu\n1VpXANcCb2itbwRWATfFbnYT8EKChiiEGAJaa5q6fHR5gzR1+fjK05vpDoQBKMi0UBg7Z+WwGGl2\n+UZ0heDxtHUHeOr9WryBMAc7vPzX2n2JHtKwScb59n3As0qpzwMHgKsTPB4hxCDp3Zap9xosBeRm\nmMmNtWEyGBSBUJiyvIzEDjjJFcWaBDeOoB6ESRFaWuvVwOrY+63A+YkcjxBi8PVuy7Sptp3vvbCd\n7kCYDIuR6xeW8+LWw3iDYTKsJoLhCKEI3L6kMtHDTmolOXaAEdUZIylCSwiR3kLhCIdjbZne2NnE\nfX/fSSiiyc+0cN8VMxhfkMn4wkxWbjzI4U4vpbkObl9SydLYYmLRvyy7mRy7eUSt1ZLQEkIMqZ62\nTMFwmOc21POHt6L7YI2N7YNVlGXDZDBw+ZwSrl1YnuDRpp7yfAcHWkdOaMkZTiHEkOlpyxQIh3lo\n9Z54YM0oyeaBa2dTlGXDYjIwJmdkNr0dDOV5Djq9QQ53joxDhBJaQogh4Q2EaeiMFl386MVqVm6M\nbty4ZNIofrF8Jk6bGYfFxJhsOyapEDxt5XnRdk4bD3QkeCTDQ/5ShBCDrtsfoqHLR6c3wDef/5C3\ndkUbAFwxp4TvfXoqFpOBLLuZ4mzpIXimRmfbMRkU6w+0JXoow0LOaQkhBlWXL0iLy09Tl497Vm5l\nf+x8y+1LKrl6filKKfIzrGQ7zAkeaXowGhQVozJYG9vROd3JTEsIMWg6PAFaXH72Nrv5979sYn+r\nB5NB8e1LqrhmQRlGg4GiLJsE1iCbVJhJTZObQx3pf15LQksIMSha3X7augNsqm3nq09vpsUdIMNi\n5L4rZ3D+lCJMBgOjc2xkjPAegkNhYlG05/iaXenfh1VCSwhxRrTWNLl8dHqDvLGziXtWRvfBys+0\ncP+1s5lbnhuvELSapEJwKBQ6rYzJtsXPHaYzeckjhDht0cDy0+0P8dz6On7fswYrz8F9V0bXYDks\nJgqdVim4GEJKKc6ZXMCLWw4TDEfSul9j+v5kQoghFYloDnf6cPmC/G717nhgzSjJ5jexNVhOm1QI\nDpdzJhXg8ofYXJfepe8y0xJCnJTVO5t4eM1e6to9lObYWT6vlOkl2dz3952sjh2WWjJpFP958RQs\nJoNUCA6zT0wYhdGgeOujZhZU5CV6OENGQksIMaDVO5v43qrtmI2KbJuJQ51efvHKRzgsJva1dAPw\n2TklfGnpeExGA4VOqxRcDLMsm5l55bm8tauZOy+cnOjhDBk5PCiEGNDDa/ZiNipsZiOhCBiVos0d\niAfWbUsq+fdzx2M1GRmdLRWCiXLO5AK2Huykxe1P9FCGjISWEGlq9c4mrnvkPc762Rtc98h7rN7Z\n7ybgJ6Wu3YPVZCAYjuALhqhr9xKMaAC+fUkV1y4ow2o2Sg/BBFsyMbqj8ds16VtFKC+HhEhDvQ/n\n5djNNLl8fG/Vdu6F09ruY0y2ncOdXrTWHOr0EdGggMpRGZw/pUgqBJPAU+/XEtGaDIuRJ945gDcQ\nOeY21y9K/S76MtMSIg31HM5zWEyxretNmI2Kh9fsPeX7cvtDXDG3BJcvRH1HNLAMCvIzLfzr2ZXS\nQzCJGJRiYpGTXY0uIlonejhDQkJLiDRU1+7BftRhOrvZSH37qe271OkN0tTl40BrNx3eIBDtdVdV\nlMWdF0zm4hmjGZVpHbRxizM3qSgTTyCcti2d5PCgEGmoLNdBk8uHw3LkX9wbDFOa6zjp+2jrDtDW\n7ecPb+1hxYbotiLTx2Tx48unk+2wSIVgkppQ6EQBuxrdp/T7ThUy0xIiDd2+pJJgWOMJhNA6+jYY\n1ty+pPKkvr451qX9xy9WxwPr7InRfbDyMqyMkR6CSSvTamJ0to09ze5ED2VIyF+dEGloaVUh9xI9\nt1Xf7qE018HtSyr7FGH0XixcFvv8OZMLaHL5aez08b1V29hc1wnA5bPH8G/nTsBuMVKcZZNNG5Pc\n+MJM3tnTSiAUwWJKr9+VhJYQaWppVeFxKwX7qy787gvb+PonJ1GSa+eelVuPrME6exzXLCgjw2qW\nCsEUMaEgk7drWtjf2s2kWAf4dCGhJcQI1Lu6EKJFGsFwhIfX7KWtO0Cz24/RoPjmhZO5YGoRWXaz\nFFykkLH5GRgNij1NbgktIUTqq2v3kGOP9gXUWhMMa8IRza5GFxpwWIz84DNTmV+RJz0EU5DFZGBs\nnoPdaXheK70OdgohTkpZrgNvMEwkFlhd3gAHO3xoID/Dwv3XzGbhuHzZZTiFjS/M5HCnD08glOih\nDCqZaQkxAhxddPHxyjye21BPMBzBEwjT4g4AUJBp5TfXzaY0x0FRtlU2bUxh5XnRcvf6dm9aHSKU\nmZYQaa6n6KLJ5YsXXTy7oZ7zqwrwBiLxwBqb5+CPn5tHeZ5DdhlOA6U5dhRQ23ZqC8qTnYSWEGnu\n6JZOVpMRtOZvmw/THOsGbjYoml0+bnn8A+58dgtra1oSPGpxpqxmI0VZtlPugpLsJLSESGOrdzax\nsbadA63d7G120+kJ4A+Gae0OxNsy2UwGIloTCEdweYMcaOvme6u2n1FXeJEcyvLs1LV506oPoYSW\nEGmq57CgUtFGqsFwhEMdXg60efAGox3As+0mQGNUCrPRiNFooMsbOu3muiK59BTctMYOAacDCS0h\n0lTPYcEip42I1kQimpCGUGwfLIfFSKHTRiii4wuGlYJAOHJazXVF8imLFWPUpdHvUkJLiDRV1+7B\nZjJgtxjJtlsI9TpC9PnFFUwuchKORLCajPQcPdIaLEbDKTfXFcmpwGnFajJQl0bFGBJaQqSp0hw7\nbn+ITk+Q1u4jh4dsJgNTRmfx7+dOIBQBp81EBE0oEiES0WTZTafUXFckL4NSlObaZaYlhEhukYjm\nqvmltHuCHO7yxa83All2E795vQajQXHvZdMYNyqTbJsJu9lItsNMRX4m91427bR2OBbJpyzPQUOn\nj0Do2J2MU5EsLhYizYTCEQ51enl/XxtdviPdEGwmRYHTRo7DgicQ4uE1e/nLbR+TcEpz5bkOIhqm\nlWQleiiDQkJLiDQSDEeobfXwk5eqeSNWsm41GSjNtWE1mTDGCi6k0GLkKI0VY2yqbWdBRV6CR3Pm\nJLSESBP+UJg9Td3851+3srmuA4Bls8awv7WbLl8wHlhw6rsYi9SVaTVRnudg44GORA9lUMg5LSHS\ngC8Y5sO6Tv79qY3xwPrCWeP4xgWT+PdzJxCOcNq7GIvUN39sLusPtKPTYJFxwmZaSikbsAawxsax\nQmv9faVUHvAMUAHsB67WWrcnapxCJDtPIMT7e9u4+/kPaXJF98G661OT+MzsEoqcVsaOysBsNJxw\nF2OR3uZV5LJy00Fq2zyMzc9I9HDOSCIPD/qB87TWbqWUGVirlPo7cAXwutb6PqXUPcA9wN0JHKcQ\nScvlC/LGzia+/ddtuP0h7GYjP7hsKudOLqTAaUWp6CHBE+1iLNLf/LHRc1nr97enfGgl7PCgjurZ\nocwcu2hgGfBE7PongMsTMDwhkl6nJ8iKDfXc+dwW3P4QeRkW7r9mFhdMLaYwyxYPLCEmFmaSZTOx\n/kBboodyxhJ6TkspZVRKbQaagFe11u8DRVrrw7GbNABFx/na25RS65VS65ubm4dpxEIkh7buAH98\new/3/s8OgmFNWa6dh66fwycmjCIvw5Lo4Ykh0Ps5z9VxauFjMCjmjs1l/f7UP9OS0NDSWoe11rOB\nUmChUmr6UZ/XRGdf/X3tI1rr+Vrr+QUFBcMwWiGSQ2OXj5/9vZoH39yDBqaOzuKhG+YypzwXp012\nGU5XvZ/znDmnXrq+oCKPmiY3LbHtaFJVUlQPaq07gDeBi4BGpdRogNhb2R9BjAirdzZx3SPvcdbP\n3uC6R947ZmsQrTX17R7+c+VWnllfD8DiCfk8cO1sqoqzsJll00ZxfGdPHAWQ8nulJSy0lFIFSqmc\n2Pt24AJgJ7AKuCl2s5uAFxIzQiGGT3+7C/fe0yoS0exucvOVv2zi9dh1y2aN4adXzGRcQSYWU1K8\n/hRJbPqYbPIyLKzZldqnUxJZPTgaeEIpZSQans9qrV9USr0LPKuU+jxwALg6gWMUYlj03l0YwGEx\nxVstnT2pgK0HO/nGs5vZ29wNRNdg/evZ46TgQpw0g0Fx1oRRrKlpIdJrO5pUk7DQ0lp/CMzp5/pW\n4PzhH5EQiVPX7iHH3vd8lN1spK6tm3/ubuHO57b0WYN19YJyKbgQp2zJpAJWbTnEzgYXU8ekZi9C\naeMkRBJwWk3sbnIT1hqL0UCB04pBgd1i4ktPboyvwfrhsmlcNL2YLCm4EKeh57zWmppmCS0hxMlb\nvbOJh9fspa7dQ6bFyKEOb3QHYRVtenug1YNSENtkmEyriV9dPYuzJxZgt0jBhTg9RVk2pozO4o3q\nJu44Z3yih3Na5OytEMPs6KKL/W0ePMEIeQ4zZqOBYFijORJYJoPCYTFgAAksccYumFrE+gNtKVv6\nLqElxDDrXXShlCIcm2F1B8KU5zkw9vqvtJkMjM13kGk18ad/7k/YmEX6uHBaERENr1c3Jnoop0VC\nS4hhVtfuwW420uUNsrfZTSisCYY13kCY+nYvPRvMGhSU5zuwm404LCbZ/0oMiqmjsyjJsfPK9tQM\nLTmnJcQwK8t1sK/FTWt3AAMKkwGCkWjrF5c/utOwAsZk27CaoocDPYGQ7H8lTttT79f2+bgi38Fb\nu5p5bO0+rEctSr9+UflwDu2UyUxLiEE2UGeL25dU0u4JAqD6+Q/MtZvJdZiwmo2y/5UYElPHZBOK\naHY1uQe+cZKR0BJiEA3U2QKi24Q4bSZMCoKhSPxwIECWzcQ73zqfX109h0KnjU5vkEKnjXsvmyZb\ni4hBMzbfgcNiZMehzkQP5ZTJ4UEhBtGJOlv0Dp0JBZnsa3HT7A6gAaWia7W01lzw67cok40axRAy\nKMWU0VlsP9RJKBLBZEid+UvqjFSIFNBTZNGb3WzsU0QRCkeYXOyk0RUgosFoUGTbTLj9ITJtpuPO\n0IQYTFNHZ+ELRtgXaw2WKiS0hBhEZbkOvMFwn+u8wXC8iCIYjvDQm7v587sHALCaDORlmAlHNEVZ\nVkZlRnsJOiwmzEbFw2v2DvvPIEaGCYWZWIwGdhzuSvRQTomElhCD6PYllQTD0eKJo4sovIEQ339h\nG79+rSa2D5aT//3KWXzw7QvIdljIz7D2ua+jZ2hCDCaz0cCkokx2HO4iovvdtjApSWgJMYiWVhVy\n72XTjimimFOey1ee3sxT6+oA+MT4fP7r5oVMKHQCA8/QhBgKU8dk4fKFqG/3JnooJ00KMYQYZEur\nCvsUUDR0+rj1iQ/YcCC61flls8bwo2XTyHYc6dJ++5JKvrdqO55AtDGuNxiWMncx5CYXZWE0KLYd\n7KQ8LzVeIEloCTEEHnhtF4+u3YfLFzqq8a2Rhg4vm2o7+gTb0qpC7iVafVjf7qFUqgfFMLBbjEwq\nzGTrwU4uml6MIQX2ZpPQEiKmd+f1Myk5f+C1Xfzmjd2go41ve04X2E0GxuY5aPUE+N6q7dwLxwSX\nhJQYbjNKc6huqKO21UPFqIxED2dAck5LCE5uUfDJenTtPtCa8FHntn2hCAaDQSoDRVKZMtqJ2aj4\n8GBHoodyUmSmJQQnvyj4aEfPzm47exwuX4j+arF6XyeVgSJZWE1GJhc52Xqwi0tnjEn0cAYkoSUE\nx9/u/kTB0jM7C4TCuHwhDnd4WbevNR5OCjAbFaGIJqKjXdt7SGWgSCYzS3PYdqiLfS3Jv9BYDg8K\nwemVnD+8Zi+BUJjW7gCB2Nf2PiSogUBYx4swcuxmaYArktLkYic2s4H1B9oSPZQBSWgJwYkXBR9P\nXbsHly8EEU0YjjmH1XtmpYhuO1LT5MZiNEgDXJFUzEYDc8py2X6oi7buQKKHc0ISWkJw/EXBJwqW\nslwH3kCYkD5S0t4TVEYDTBuTzdg8B2ajwmRUmA2K0lw73YHwce9TiERZOC6PcESzYkNdoodyQnJO\nS4iYUy05/+zsEt7be+QcllFFZ1RKgdUYfT3Y4vZjQKEMEIzoky7wEGK4FWXZGJvn4Kn3a/nCWZUY\nDMm5ZktCS4jTsO1gB//3tY/6FF1YjIqcDAtt3UGyHdGijkA4glEptAZLLMikclAkk947FTttJr78\nl028vL2BS2aMTuCojk8ODwpxitbsauL6P75PY5cfm9nAHUsq+VhlPqOcNiryM/m3peMxG414AiEs\nRgNhrdEaCpzRhrhSOSiS1SUzRlNZkMFv39iNTtImuiecaSmlsoACrfWeo66fqbX+cEhHJkQS+uvG\ng9y98kMCoQg5djMP3TCXxRNGHXO7maU5PLxmL52eAKGIJi/DTKbVJJWDIqkZDYp/WzqB/3huC69V\nN3HB1KJED+kYxw0tpdTVwP1Ak1LKDNystf4g9unHgblDPzwhkscf3trDz1/eSURDSY6dR2+az5TR\nWf3etvf5sZ4FyNJTUKSCZbPH8JvXa3jg9RrOrypMunNbJ5pp/ScwT2t9WCm1EPh/Sqlvaa3/SvQQ\nvhBp5Xi9ByORCPe+uIPH34lu3DhltJM/3bSAMTn2k7pf6SkoUonJaOCr50/kP57bwvMb67lqflmi\nh9THiULLqLU+DKC1XqeUOhd4USlVBv12qREiZfV0tzAbVZ/eg98Nhfnr5kO8tK0BiO6D9Ycb55Jl\ntwxwj0Kkrs/OKeHJ9w/ws5d38qlpxWQf1S0mkU5UiOFSSo3v+SAWYOcCy4BpQz0wIYZT796DLl+I\nhk4f9W0e7nhyYzywls0ew+M3L5DAEmnPYFDcu2w6rd0B7n9tV6KH08eJQuuLgEEp9WWlVC6A1roL\nuAi4dTgGJ8RwqWv3YDcb6fIGOdTpJRAMoznS5eKS6cX8+upZWMzGhI5TiOEyvSSb6xeW8+d3D7D9\nUGeihxN33NDSWm/RWtcARcAHSqlnlVIXASGt9ZPDNkIhhkFP78EWtx8imqA+cgw8P8NCuyeIwSAr\nRMTIcteFk8nLsPAfz27BH0qOTi4D/hdqrb8DTAT+BNwM1Cil/k/vQ4dCpLqe3oPeQJhgrzO2BZkW\nRmfbZDGwGJFyHBZ+duUMdja4+NUryXGY8KReOuroKrOG2CUE5AIrlFI/H8KxCTFsllYVcvH0YiK9\nrivKslKcbZfFwGJEO6+qiOsWlvPI23t5b29rooczcGgppb6qlNoA/Bz4JzBDa/1FYB5w5RCPT4hh\n8ce39/LHt6M7CRsNirJcGwWZVlkMLATwnUunUJ7n4D+e3UKnJ5jQsZzMTCsPuEJrfaHW+jmtdRBA\nax0BPj2koxNiiGmt+dH/7OAn/1tNREfXYH3+ExV0ekNUN7hodvlZPrdE1lmJES3DauL+a2bT2OXj\nzhVbEtriacCGuVrr75/gc9WDOxwhTs/xFgYf73qAYDjC157ezP9uPQzA4vH53LionJ++/BEFTivl\nZiPeYJgVGw8yszRHgkuMaHPKc/nWJVP40Ys7ePTtffxrgo4+JKzLe2yR8p+JVidq4BGt9W+UUnnA\nM0AFsB+4WmvdnqhxiuS3emcTd67YgtsfIhzRNHZ6WX+gDZvJQCAc7fuXn2GNLxi+F5hXkcsXnljP\n+/uiO7VePruEXyyfwef+64P4ei1AthIRopdbF1fwwb427nt5J3PKc5hfkTfsY0hkDW8I+A+t9VTg\nY8C/KaWmAvcAr2utJwKvxz4W4rju+3s1HZ4gOgJoTTACwbDG7Q8T0ZpWdxC3P4TDYsJsVDzweg1X\n/v6deGDdcU4lv75mFmaTMb5eqzfZSkSIKKUUP79qJiU5dv79qU20uv3DPoaEhZbW+rDWemPsfRdQ\nDZQQ7bjxROxmTwCXJ2aEIlXsa/VgUNFV/D2LgRXR6btRKZSCZpc/fv2W+g52NboxKPjhZVO55+Ip\nKKVYvbOJLm+Q6oYu9ja7cfmCdHmD7G520+Tyc90j77F6Z1OifkwhkkKWzczvbphLmyfA157ZTDgy\nvOe3kmK1pFKqApgDvA8U9fQ8JFpi329vfKXUbUqp9Uqp9c3NzcMyTpH8es4P9/wb+UIRguEI/lAE\ntz/I3pZuwhpsJgMPXT+Xmz4xDjjSe9BhMWJQikA4Qm2rh/p2D6GwpjjryOFFCS6RCL2f81wdbQkd\ny/SSbH542TTermnht2/UDOv3TvjOxUqpTOB54Gta6y6ljjSQ11prpVS/Ma61fgR4BGD+/PnSwHcE\nqxyVQU2TG6U1Sh0Jrh4RDRGt2dcSPcSXYTXy+C0LWdDreHxP78Fsuw2ryUiL248nHEZrGJtnx2mL\nNgyV81siUXo/51VOmTloz3lPvV97uuNhTlkOv3mthk5PkIlFztO6n947J5+MhM60Yvt0PQ88qbVe\nGbu6USk1Ovb50YC8rB2hVu9s4rpH3uOsn71xwkNzd19URa7DHN0vZ4B/ZafNxAv/trhPYAF9zmVl\n2c1UFmRiMiqMBuKBBXJ+S4geSimWzS6hMMvKM+vr6PQOz/qthIWWik6p/gRUa61/1etTq4CbYu/f\nBLww3GMTiddzuK7J5euzVUh/wbW0qpBfLJ/FnPJcSnLtmA3quBu+RSIR6tu8x1zf03uwN6NBYTqq\n36B0xxDiCIvJwPULxxKKaP6yrnZYzm8lcqa1GPgX4Dyl1ObY5RLgPuACpVQN8MnYx2KE6b1ViFKK\nUFjT5PJx+39v6DPr6pmNfeeFbQD8aNl05lfkRQsz+kkuXyjSb/j19B70BEJoHX2baTXhtJn6XCfd\nMYToq8Bp5Yo5JdS2eXhlR8OQf7+EndPSWq/l+Dsgnz+cYxHJp67dQ05s47me7UIU0XNTPbOu5fUd\nrNh48JiNGz8zo5j39rYec6TQbFRYjAbMRnXMeamlVYXcSzQs69s9lOY6+O6lU+Go63ovThZCRM0s\nzWFPczdra1qoKs5i3KiMIfteCS/EEKI/ZbkOmlw+HBYTLW4/BhQosBoN8QW/j67dR4HTGl8IHApr\nGjo9/O6tvf0GlkJR4LT2e17qRJ0zJKSEGNglM4rZ0+xmxYY6vnLeRKxDtPdcUpS8C3G03ofrAuEI\nGo3W0UMREC2I6A6E48UTXd4g9e0eAuEjtRhWo4ofIgxHNLkOM06b+ZjzUqdy/kwI0T+rychV80rp\n8AR5advhgb/gNEloiaS0tKqQey+bRqHThkEpDEoxJscWr+TzBsNkWIzx4omGTm98YTFE98EKazCq\n6CzLaFC0dQdocfuOOS/Vc/4sFNbsa+mmts1Dk8vHfX+X1ppCnIqx+RksmVTAB/vbqWlyDcn3kNAS\nSWtpVSF/ue1jPHzjPAqzbBgNqk9BxBfOGhftYHG4E3+vxCrOsuIJhKOzLKUYk23HYjSggW5/mHsv\nm9bnkF9du4dQOMKhTi+hsMaoFJGIpqbZLbMtIU7R+VWF5GdYePHDw0NSTSihJZJe71lXpzdIodPG\nvWwCxfcAACAASURBVJdNY2ZpDp5AiFCvnRsNgM1sJBCOXmkxGuLrrqqKnWTbzcecoyrLddDoip43\nMxgUSkUvZoOBh9fsHcafVIjUZzIauGTGaJpd/iHZNFIKMURKWFpV2CdstNYsvu8NugPRcDIbo7Mj\ng0HR1OXDaIge7us5BwbHX2N1+5JKPv/n9RhV9HyY1tFLcbZVFhILcRqqip1MLMzk9Z2NzCrLIdM6\neFEjoSWSwomq947+fEmOHQUc6vQBkGExMjY/g25/iBa3H18owsSCDFq7A/FDit5g+LhrrJZWFTKp\nMJN9Ld2EIxqL0UCB04rRoCh02obrIRAibSiluHTGaB54o4Y3djZx2awxg3bfEloi4Xqq98xGhVHB\nprp2Pv/n9UwqzOTui6oA4p/PtBjZWNtOMHYOy6BgVKYFo0GRZTdjMkaD5vYlldz392r2tUZnSpWj\nMvjupVXHLV+/+6Kq+PewxzZ/lIXEQpy+wiwbc8tzWb+/jaWTC8jq1Q7tTEhoiYT72cs7aeryRUvb\ndbR9klHBvpZuvrdqOxkWI2ajwmxQ7GnpPhJYRFenH+yIzrhMRgPBsObjlXnxAJpYmIk3GKY7ED7+\nAOh/cbEsJBbizCydXMjG2nb+WdPCxTNGD8p9SmiJQTfQob6jb7uryY0xtp5KE11TZTAqwlpjNqr/\n396dx9lV1nke//zufqvq1pJaslVlJVCBJEASNhEMiwqOQ2gWATeERhjFcWydlqB00yI20to9PT1N\nT4Mtgg4ijIStZVFpMsieEJbsJGSrqqS21F53P+eZP86tm1qTQC13+71fr5iqU7eOT47l/dZ5zu/5\nPexu76euIjgksABswO8SDNDcE2P5nApuOnfBkBZQcOy7Dw9/bqaUGp9pxT5OnFXGhn2dXLB4Oj7P\n+Gv/tHpQTaiBqb497X109sdZv7eDm/7PW/zTH98f9fUDASPIkC1FEpbzbCnodWPZNruHBdZhTmiF\n/G4evvFMVtXX6O7DSmWRsxZUEklYbGrqmpDzaWipCXXvS7uJJy0O9cdJWgaPS7CN4Z51H4y65qmh\nM8z0kB97WOMlg9P9oq03imU7e2KNZuBwfFCgjdaxXbuzK5UZ8yqLqCz28XaDhpbKQg2dYXqjySFr\nntwuIWnbo655qqsowuN2pRYAO3OEBqcFU280QUtvHAPp6cPhEpYNhiHTDqN1bNeiCqUyQ0Q4pa6c\nPW399ETHv+eWPtNSE6quoojm7ige1+AdqJ1Gt6NNz9107oLDRRPTQ7T3xejojyMu4VC/8wNePyPE\nzpZehJF7PBpABHqjSa657/X08zMtqlAq8wZ2JT51TjkvbG+losjLVad9uJ2Kh9PQUhPqpnMXsHF/\nJ5YxzmLd1ELdsmLvqNNzwwNmbmUxcyuLeWNPBwAfW1jJl8+cy395aOOY/522gdll/nSj2zvQogql\nskn9jBCzygKs29E27tDS6UE1oVbV13DzqoW4REhYNm6ByhIvXrd7zOm5gR6D37u4nm0He9KBdeb8\naTx4/ek8+Nq+9NTh8FlCAWorgpQGfRT5POm9spRS2UNEOGNBJev3dmLM+PoRamipCffNC4/n3i+u\n4PR5lVSW+JlXWTKiSe1wT7zVyDcfeYeeaBKAsqCHpq4Ir+xsp6EzzMwyp2HugIGP5kwLpju/g1YJ\nKpWtls+toL0vRmNnZFzn0elBNSk+zPTczpZevvv4pnRJ+8yyAFUl/vTaqoENIWvLg7T3xYhbNm6X\n4ELwuIf+3qVVgkplp8UzQgDsbO2lbtpH//+ohpaaUGMtLB7r+Jt7DvHVX75FPGkjQN20IGVBH3D4\nrumHq5ekizXmVxWnWyxdsXw2v93YRDie1NZLSmW542pKANjd1s/59R/9PBpaasIM7iE4eAfgKxq7\n+O3GphHHl25o4Nktzek1WBVFnnRgweG7piNVAy6rLdcqQaVyQFnQS8DrorU3Nq7zaGipCTNW+6R/\ne3kP1SH/kON72/v43eZmALwuoTTooaM/QXekG9s4pexet7A61R16rOlGrRJUKjeICJXFftr7NLRU\nlmjoDFMeHNrJOeh10x+3qLBsdrf1EUs6nSoGNm70e1zMrywikrDp6E8wuFOTbRt+9fo+ltWWazAp\nlQd8HtcY7diOnVYPqgkzVvskn1to6ooST1jY5nBgCc6WIV6Pe8hvX0Gvm6DXjcftojea1BJ2pfKE\nCNha8q6yxVjtk6pK/Ni2IWmG9hD0uCAct9jd1kd/3BrR7UIEkratJexK5Ym+aJLQOHcx1tBSE6rI\n66KxM8LO1j5iCYtin5vGzoizjX3qNR4XVBV7SNrQ1BUhYdlDFg1bqWQzBjwul5awK5UHLNvQFU5Q\nVjS+zSD1mZb6SIaXsJ+1YFq6QnBRTQntfTEO9sSIpp5hDQRWRZHTzikcTxKOG2xjsIzB5xYSlsEm\n1QQXwTbO67WEXancd6ArQtyymVdZPK7zaGipIxptfRUworT9nnUfMK3YS1kwADgNbDGG9r7DXZ1d\nArGElZ42LPK7mVEaQMS5z+qJJGjujhCzDCLCoupibrmoXoswlMoDO5p7gcPrtT4qDS01prHWXRX7\n3CNK2y3b0B1OUFXihFYkVXQBTlhVlfjoj1lEkzY1oUB6h+HW3mj6PKVBLx63UBMK8PCNZ2bk36yU\nmhxv7DmEz+1i6eyycZ1HQ0uN2a1irHVXu9v7WZT6baknkqC9L4ZlG8K2oScSJ5a004ElwILqYoJe\n53uHB9JfP7VFO1ooVQBeer+dU+eUExi2q/iHpaFV4Ma6m7qDw+uuBoIpbtn43C5s2xBJWCQtw4Hu\nCMY26WdW+zpGNsNs6Ihg2Qa36/BiYRi5LYl2tFAqP+1o7mVHSy8/uOSkcZ9LQ6vAjXU3de9Luwn5\nPexo7iFhO3dMHpcQt2wMhp5Igt5YEts2jLZWUIBiL/QlIJbqK2iM4Z51HwBOJ3jQjhZKFYJH1jfg\ncQmfWTpz3OfS0CpwY3Wx2NnSAyLphcAACdvgcQnVIT9uoD1hj1hbNSDgEYy48IhFMvUir9uFlQou\n7XKhVP779Rv7icQtHlm/n88um0l1yD/uc+o6rQI3VheLuGUoC3pxuSS9hkrEudtKWjaN3bExAwsg\nmjREkzaDMg8Rwe0SkratXS6UKhAv72qnP25xwzkT86xaQ6vAjdXFwudxEfS6CXhc+NwuAl43fo+L\nuGWny9iP9jjVts3hgoxU8hkDfrdLu1woVQC6Iwle3tXGfz55FkvGWTU4QEOrwK2qr+GOS06iJhSg\nO5KgJhTgjktOYlFNiEjCclowYZwAGuhUgRNY1hHO6xLnzwCPS7CNwRgoSy0wVkrlL2MMT797AGPg\nu58+YcLOq6Gl0gZP9w3cgXncwqyyAOICyzg/MC45cmAJqR6DIrgAtwviliGWtAFDUsvalcp7m5q6\n2XqwhwsWTx/XTsXDaWgVuIGS99be6JCSdyB9B2YbOLWugp99cQXTin1Dmt6OxgAet+B1CcdPL6Gi\nyEfA68LrFlwuOeKzMKVU7uvsj/PEO03UVgT5+HFVE3rujFYPisj9wGeBVmPMktSxacAjwDxgL/A5\nY0xnpsaY745U8v7wjWemK/xiCYu7n9tOa1/8mM6btAy2y2nHVBb0MrMsmP7awPm1elCp/JO0bR5e\nvx9j4KqVdbgHPyeYAJm+03oAuGjYsTXAC8aYRcALqc/VJGnoDBMctkI96HUPKZToiyb49v99l/tf\n2QuA33P0H0IDLKouoTeWPOr5lVL5wRjDE28foLEzwuXLa6ksGX+J+3AZDS1jzEtAx7DDq4EHUx8/\nCFw6pYMqMGOVvA8USrT2Rrnhlxv43XsHAZhW5GVWWQCvWzhSdPk9LtZcvPio51dK5Y+XdrazcX8n\n59fXTFi14HCZvtMazXRjzMHUx83A9NFeJCI3isgGEdnQ1tY2daPLM2OVvN907gJ2t/Xx5Z+/yeu7\nnd8rvnzWXH5y+TIsI2DMmM+mvG7h5lULWVVfc8TzK6WO3eD3vN6u4b/rZ97G/Z08v6WZZbVlXDCJ\nU/9Z3RHDGGNEZNT3RmPMfcB9ACtXrtRn+x/RaP3/bjxnPqVFHr708zdp6orgEvjOp07g66sWOguE\n3S6+/tBGEvbIGkK/x8W9X1yRfl6l/QWVmhiD3/MWLF6WVe952w72sHZjIwuri7lieW16u6HJkI2h\n1SIiM40xB0VkJtCa6QHlu8H9/2zb8OKOVq7/xQa6Igl8Hhd3XrqEK1cM/UG0jEmvw3KJ4Pe4qCpx\nKguHB5L2F1Qqf20/2MOv39zPrPIgXzxjLh735E7gZeP04FPAtamPrwWezOBYCkrSsvntW43c/NBG\nuiIJSgMe/vULy/ncyrp0YA2UyA+0dPK6XbjE6Ufocbv0WZVSBWTbwR4eemM/M0oDXPex+fjHue3I\nschoaInIw8BrwAki0igifw78GPikiOwELkx9riZZLGnxsz/tYc3a94gmbWaUBvjV9Wdw/uKhjxQH\nSuSnhwLOM63UfzR3R/VZlVIFZOuBHn79xn5mlge4/uz5BH2TH1iQ4elBY8w1Y3zpgikdSAFbt72V\ne17cxbbmHvpizjOq46pLuO/LK1hQPXJb7IGu8OJz7rycfbacoow7LjlJpwGVKgBbDnTzmzcbmFnu\n3GFNVWBBdj7TUlNk3fZWvv/4JroiCfrjTmD5PC6+cd7CUQMLnBL51t4oRT4PpUEvpUFvekdiDSyl\n8t9b+zpZu7GR2oog1509f9w7EX9Y2fhMS02R//nCTg6F4+nACgU8zCrz88iGxjG/R0vYlSpcL+9s\n47GNjSysKeH6j099YIHeaeWlddtbufel3TR0hqlLlZgD6WO15UE+u3QWmw90k0htO1zid5NMWjR1\nWRzsjrFue+uod05awq5U4THG8PutLfy/99tYMruMz62onfQqwbGIMVlV7v+RrFy50mzYsCHTw5hS\nowXTqvqadHWf1y0EvW4iCYvuSAIBSoNeAh4XneE4Lb1xrFTn27KAh/54Ere4MBhcItSUBvQZlVJT\n75gWOC1YvMzc+cC/T8oAPn/GnCGfW7bhtic28/Cb+7nm9DnceemSCe8nmHJMJ9U7rRw0OJgGd2Yf\nuAMa3gC3qSsCJtXzryNMzDr8i0pFkfNMyiXi/MgYYUZZALdLtKmtUgUulrT4i0fe4ZlNzdx83kL+\n+6dOmNSFw8dCn2nloMHBJOL87XVL+s5reINay3b2smrsigwJLAGuPWsu4GzQ6HEJs8oDhAJebWqr\nVIHrjyW54cENPLOpmdv+02L+8tP1GQ8s0DutnDRQdj7YQMgMru4b4BJnOdXgmWCvy+lk8caeTpbP\nqRjxPdrUVqnC1RWOc90D63m3oYufXLGMK1fWZXpIaXqnlYOO1Dl9eHVfTySObQ/9frcAONOAjZ1h\nrQhUSqW19kS56t7X2dLUw798YUVWBRZoaOWkI4XMqvoa7rjkJKpL/Bzqi9HRnyAxbKth28C0Yl+6\n7dLA99SEAnRHEnhdQrHPzW1Pbuaa+15n3XZt/6hUIejoj3PFv75GQ2eYX1x3GhctmZHpIY2goZWD\nhodMTWhopd+5x1dz1+VLmVflbMIIEPS6cAv43c62992RxJC7qVX1NTx845n8cPUSwgmbuGUPKfLQ\n4FIqv7X0RLn3pQ/ojiR46IYzOPu4qkwPaVT6TCtHjdU5PWHZbD/Yw3cfe49tB3sBp0JwRmmAcNw6\natul0aoPw/GkVhIqlccaOsI88OpePG7h0ZvO4oQZoUwPaUwaWnkkmrB4e38nf/nb92jsjCDArPIg\nPrekFgI6z8GMAb939JvsIxV5KKXyz+72Pn752j6KfW6uP3t+VgcW6PRg3uiLJVm3o42vP7SRxs4I\nHpcwt7KISDzBge4ojZ39HOiOELdsBCj2u0ed9jtSkYdSKr/sbu/jwVf3Uhb0ctO5C6ks8Wd6SEel\noZUHusJx/v3dA3zrkbfpDCcIet2UBb2IwKzyIiqKvHRFkti2wed2MbsiSFVJIL22azCtJFSqMOxp\n7+fBV/dSXuTjho/Pp3TYDEu20unBHNfeF+PR9Q38/R/ex7INNSE/lcU+YkmLYr/zQ1gdCtDeF8fn\nliHd20eb9tPegkrlv3RgBZ3ACgVyI7BAQytn2bahpSfKz/60m/tf2QvAwupifnrFyXzj4Y1UFPmG\nvN7vcRFNHtu031hFHkqp3Lf/kBNYpUEvf35ObgUWaGjlpKRl09gV5u+ff5+n3zsIwPI55dx12VKO\nqwkxZ1rxiA4XoYCHZNiZ7htopKvTfkoVltaeKA++to9QwMMN58ynNMcCC/SZVs6JJS12t/XzvbWb\n04F14eIa/sdVp7CoJoTbJaM+l/J53Ny8auGYa7uUUvmtO5LgF6/uxe0Srjs7NwML9E4rp4TjSd5v\n7uV7j29ia2oN1tWn1fHNCxYxsyyQbmZ5pOdS38zg+JVSmRGJW/zilT1EExZfPWcB04p9R/+mLKWh\nlSO6Iwk2NXaxZu2m9Bqsb5x/HF86ay5bGrv5zqPvjthbS++ilFJJ2+ZXr+/lUF+cr5w9j1nlwUwP\naVx0ejAHHOqL8doH7fzXh9+msTOC1y3cfsmJfOVj89ja1MPtT2+ltTeqbZeUUiM8s+kgew+FuXxF\nLQsHVQ/nKg2tLGaMUyH4x20tfOuRd+gMJwgFPPz0ipO59JTZVJb4j7i3llKqsG3c38nruzv4+HFV\nnFJXnunhTAidHsxSlm1o7ony1DtN/PT3h9dg3X35UlbMm5Z+iKptl5QqPNOKfXz+jDlHfM3mpm5+\n8PQWzlwwjQeuOy3Vyi33aWhloVjSoqU7yoOv7eXnL+8FYEF1MXdfvowTZ5ZS7D/8P9tomz5q2yWl\nCltvNMHXHnqLiiIf//z55XkTWKDTg1knHE/S2BHhJ7/fkQ6s5XPK+aerT2Xp7LIhgQXadkkpNdJd\nz26nqTPCPV84laoc6Cf4YeidVhbpjiRo6gzzo99t45UPDgHOGqxbL15M7bQgfo97xPdo2yWl1GCv\n7mrn12/s56vnzGfF3GmZHs6E09DKEu19MRoOhfn+E5vZerAHgKtW1vL1845jVnkQ7xFu77W8XSkF\n0B9Lcsva95hfVcx3PnVCpoczKTS0Msy2Da29MT5o6+WWxw6vwbr5vOO4+vQ6ZpYFcbsk08NUSuWA\nf/jD+zR0RHj0prMIeEfOzOQDDa0MSlo2zT1RNjd1c+vaTXSGE3jdwvc/s5hPL5nB9FAAlwaWUuoY\n7G5z9sa65vQ6Tp+ff9OCAzS0MiSasGjtifHa7nb+5qmtRBIWJX4Pd156EmctrKIm5E+3ZVJKqaO5\n+7nt+D0uvv3J/JwWHKChlQH9sSStvTGe33yQnwxag/Xjy5eydHY51aH8qvZRSk2uLQe6eX5LC9+6\ncFHev39oaE2xrnCcQ30xHn6zgX97eQ/grMG668+Wsmh6KKcbWSqlMuOeF3cR8nu47uz5mR7KpNPQ\nmiLGGNr74nSF4/yv/9jFU+8eAOCUunLuWH0Sc6cVU1aUm1sFKKUyp7EzzHObm7nx3IWUBfP/PURD\nawoMVAh29se485ltvLLLWYN1fn0Nt1xUz6zyQM7tHqqUyg4PvbEfEeFLZ83N9FCmhIbWJBuoEGzr\njfH9x4euwbrxEwuZWRYY0oJJKaWOlW0bnnrnAOcuqmJ2jm85cqz03XISOT0EYzR2hrnlsfdoSK3B\n+vp5C7lyRR0zygJ5u5ZCKTX53m7opKkrwnc+dXymhzJlNLQmSTiepLUnxo7mHm59fDMd/XG8buF7\nn1nMBfXTmV7mH7Utk1JKHaun3z2Iz+PikydOz/RQpkzWNswVkYtEZIeI7BKRNZkez4fRHUnQ3B3l\nzT2H+ItH36WjP06J38PfXbGMCxdPZ2Z5QANLKTUuxhie3XyQVcdXF9Qz8ay80xIRN3AP8EmgEVgv\nIk8ZY7ZmdmRHd6gvRnckwe+3tvCT53ek12DdddlSTpgR0rZMSqkJsbu9n5aeGOcVWN/RrAwt4HRg\nlzFmN4CI/AZYDWRtaBnjVAj2RRND12BVFXPXZUuZU1mkbZmUUhNm/Z4OAE6bl78tm0aTraE1G2gY\n9HkjcMbgF4jIjcCNAHPmHHkHz8k2sMtwOJbkn/9jF08OW4M1vTSgbZmUUuMy/D3vzb0dVBb7WFhd\nnOGRTa2sfaZ1NMaY+4wxK40xK6urqzM2jnjS5kBXhJ5wnL95eks6sM6vr+HHly1lRlmA6aUBDSyl\n1LgMf897a18nK+dVFNx7S7beaTUBdYM+r00dyyqRuEVLT5TOcJzbntjMlgPOGqwrV9Ry0ycWUFHk\nozLPdg1VSmWebWDfoTCXL6/N9FCmXLaG1npgkYjMxwmrq4HPZ3ZIQ/VGE7T3xTnYFRmxBuvy5bVM\nK/ZRXqR9BJVSEy+WtABYWF2S4ZFMvawMLWNMUkS+ATwPuIH7jTFbMjystM7+OJ3hOLta+1izdlN6\nDdatFy9m1QnVVIX8lBZQCapSamrFkjYAx9VoaGUNY8wzwDOZHsdgxhja+mL0RZO8ta+T25/aQjhu\nUex3c+fqJZwyp4KakJ9if9ZeVqVUHoglLDwCcyuLMj2UKafvrsfIsg0tPVGiCYs/bG3h71JrsKpL\nnH2wFlaXML00QNCni4aVUpMraRlmlPgLsg2chtYxSFg2zd1R4kmL36xv4Gd/ctZgza8qHlIhWIg/\nQEqpqZewbWryfLPHsWhoHUU04VQIxpM297y4iyfeGViDVcYdlyyhotjH9NIAPk/Orh5QSuWYpGXy\nfofisWhoHUFfLElbb4xoPMmPntnOy7vaATjvhGpuuaieYr+HmWUBPG4NLKXU1ElYNtUFupxGQ2sM\nXeE4Hf1xeiIJbntiM5uHrcEK+jzMKA1oH0Gl1JSzDZQWwC7Fo9HQGmZwhWBzT5Q1j21if0cYAb62\naiFXrKilyOehJuTXPoJKqYywjSnYKuXC/FePYXCF4K7WPm5du4lD6TVY9aw6oYYSv4dq7SOolMqw\nEn9hFn5paKUMVAgmLHvUNVgn15VTGvRSVaDzyEqp7FLkK8y378L8Vw8zUCFo2WbIGqyqEh93X76M\n+VXFVBT5qCjWtkxKqexQotODhWmgh6Bt2zyyvoH7hq3Bqg75qSzxU1agDz2VUtmpqEAbGRR0aA30\nELRsw7+s+4DH33YayZ9cW8YPVy8hFPRSHfIX7G80Sqns5S3QtaEF+W48uEIwlrD422e386edzhqs\nVcdXs+biegJeNzWl/oKdN1ZKZTd3gRaDFdw78uAKwZ5Igr96cjObmoauwfK6XdqWSSmV1TwFuuSm\noEIrnrRp6XEqBJt7otz62Cb2dYQBZw3WlStq8bhczCjTtkxKqexWqOtECya0BlcIfpDaB2tgDdaa\ni+o5r74Gr9sJLK+2ZVJKZTm908pjAxWCxhg27uvkrwetwfrh6iWcUleOz+NiZllQ2zIppXKC3mnl\nqY7+OF3hOAAvbGvh7ud2kEytwfrxZUtZUF1CwOtmRmmgYH8IlFK5R++08owxhrbeGH2xJMYYHtnQ\nyH0v7QZgXmURP75sKTWlAYr9Th9BbcuklMolhTorlJehZdmG5p4osYSFZRv+97oPWDtsDVZJwENJ\nwENNKJDh0Sql1IenoZUnYkmL1p4YCcsmnrT522e38dL7zhqsTxxfza0X1+PzuCgLeqnUPoJKqRyl\n04N5IBJ3KgRtY+iNJrjtiS1sauoG4PLls/naqoW4RJhW7KO8SPsIKqVy08LqEmorijI9jIzIm9Dq\niSY4lKoQbOmJsmbtJvYdSq3B+sQCrlxZB0BVyE9pQPsIKqVyV5HPXbDND/IitCzb0N4bA+CDttQa\nrL44Hpdwy0X1XLC4BhGhJuQv2I3TlFIqH+TFO7htnL837u/k9ie30B+3KPa5uWP1SZw6pwKXCNNL\nAwQLtCuyUkrli7wILYAXtrVy93PbR6zB8rhcTC/z4/doYCmlVK7Li9Dq6I/xo2e2AUPXYGlbJqWU\nyi95EVqtvTFmAstqy7gztQZL2zIppVT+yYvQgqFrsLQtk1JK5ae8CK2qEh9/9dnFuES0LZNSSuWx\nPAmtAC4RbcuklFJ5Li9CC9C2TEopVQDyoqzO4xINLKWUKgB5EVr6+EoppQpDXoSWUkqpwqChpZRS\nKmdoaCmllMoZGlpKKaVyhoaWUkqpnJGR0BKRK0Vki4jYIrJy2NduFZFdIrJDRD6difEppZTKTpla\nXLwZuAy4d/BBETkRuBo4CZgF/FFEjjfGWFM/RKWUUtkmI3daxphtxpgdo3xpNfAbY0zMGLMH2AWc\nPrWjU0opla2y7ZnWbKBh0OeNqWMjiMiNIrJBRDa0tbVNyeCUUipT9D3PMWmhJSJ/FJHNo/xZPRHn\nN8bcZ4xZaYxZWV1dPRGnVEqprKXveY5Je6ZljLnwI3xbE1A36PPa1DGllFIq66YHnwKuFhG/iMwH\nFgFvZnhMSimlskSmSt7/TEQagbOA34nI8wDGmC3Ao8BW4DngZq0cVEopNUCMMZkew7iJSBuwL9Pj\nmABVQHumB5El9FocptfisHy/Fu3GmIuO9iIRee5YXpeP8iK08oWIbDDGrDz6K/OfXovD9FocptdC\nZdszLaWUUmpMGlpKKaVyhoZWdrkv0wPIInotDtNrcZheiwKnz7SUUkrlDL3TUkoplTM0tJRSSuUM\nDa0sISIXpfYQ2yUiazI9nqkkIveLSKuIbB50bJqI/EFEdqb+rsjkGKeCiNSJyIsisjW139x/Sx0v\nxGsREJE3ReTd1LX4Qep4wV0LNZSGVhYQETdwD3AxcCJwTWpvsULxADB8oeQa4AVjzCLghdTn+S4J\nfMcYcyJwJnBz6uegEK9FDDjfGHMycApwkYicSWFeCzWIhlZ2OB3YZYzZbYyJA7/B2VusIBhjXgI6\nhh1eDTyY+vhB4NIpHVQGGGMOGmM2pj7uBbbhbM1TiNfCGGP6Up96U38MBXgt1FAaWtnhmPcRC24h\nxAAAAnRJREFUKyDTjTEHUx83A9MzOZipJiLzgFOBNyjQayEibhF5B2gF/mCMKdhroQ7T0FJZzzjr\nMgpmbYaIlACPAd8yxvQM/lohXQtjjGWMOQVni6LTRWTJsK8XzLVQh2loZQfdR2ykFhGZCZD6uzXD\n45kSIuLFCayHjDFrU4cL8loMMMZ0AS/iPPcs6GuhNLSyxXpgkYjMFxEfcDXO3mKF7Cng2tTH1wJP\nZnAsU0JEBPg5sM0Y8w+DvlSI16JaRMpTHweBTwLbKcBroYbSjhhZQkQ+A/wj4AbuN8b8KMNDmjIi\n8jCwCmfbiRbgduAJnL3V5uBsO/M5Y8zwYo28IiIfB/4EbALs1OHv4TzXKrRrsQyn0MKN88v1o8aY\nO0SkkgK7FmooDS2llFI5Q6cHlVJK5QwNLaWUUjlDQ0sppVTO0NBSSimVMzS0lFJK5QwNLaWOgYic\nIiKvpTqOvyciV2V6TEoVIi15V+oYiMjxOJ2DdorILOAtYHGqW4NSaoronZZSw4jIaam7qYCIFIvI\nFsBnjNkJYIw5gNM+qDqjA1WqAOmdllKjEJE7gQAQBBqNMXcN+trpON0aTjLG2GOcQik1CTS0lBpF\nqgfkeiAKfMwYY6WOzwTWAdcaY17P3AiVKkw6PajU6CqBEiCEc8eFiJQCvwO+r4GlVGbonZZSoxCR\np3B2kJ4PzAS+DTwLPG2M+cdMjk2pQubJ9ACUyjYi8mUgYYz5tYi4gVdxtos5F6gUka+kXvoVY8w7\nGRqmUgVJ77SUUkrlDH2mpZRSKmdoaCmllMoZGlpKKaVyhoaWUkqpnKGhpZRSKmdoaCmllMoZGlpK\nKaVyxv8Hu/1mehQ2koAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f3207bdd8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(x='x2', y='y2', data=data, kind='reg');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Since we have a data set with a categorical attribute, the data set can be split per \"label\" category, showing a regression plot for each."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Cr4WptFm9alL5om+LjDU727jt0dp0pZTFR43lS31KeOVapqoGKJXVumvpuZ1Csc/F7vYw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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2f048080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='x2', y='y2', data=data,\n",
+    "           col='label', col_wrap=2, size=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For categorical data, it is also possible to plot the two \"binary\" categories in the same plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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O53PAaKCrNzOzgRWxWOzq8ePHlzR8opv+TxCi9di25n/rSrj170spD0RxORR3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\nqRiFioiIVIxCRUREKub/AfnAxWaMZ360AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2d3097f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='x3', y='y3', data=data, hue='binary');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the plots below, we have a plot per category \"label\", but a hue per \"binary\" category as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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FQtitqr5ZgcViIc3tINktO/c7igRdEVNq/UFufHUVb31rukQOzIjnvpkj6ZnsAqT3rehe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\nHVb+ednR5PVKwmk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Obaqk/YhO44tNxVz/0kr2h5sVXDSxL3efNQJHazcQNi7dGArCpg/g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\nEZGIqHRFREQiotIVERGJiEpXREQkIv8FfAgG37NhIDIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2cd69be0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='x3', y='y3', data=data, hue='binary',\n",
+    "           col='label', col_wrap=2, size=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plot below shows the mean values and confidence intervals for the $x$-values split per \"binary\" category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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11FYWUVl0eBeans1R+SwctbJPg5WJ+vl3m3lkQ1NyRuCUaSV8e/lc5kwuAqy1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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2c72e2b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.lmplot(x='x1', y='y1', data=data, x_estimator=np.mean,\n",
+    "           col='binary', size=3);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Categorical data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting as a function of the \"label\" categories.  Again, we add some jitter to gain some insight into the $y$-value distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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cdw+cdze06QzbF7kuIZ1cGAJQ+8j2bD+8vaodZAui1FEKQEb7DDLa135T6ZDk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\ngVJKBTgtBEopFeD+Pyimhzmlc3kXAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2c3798d0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.stripplot(x='label', y='y2', data=data, jitter=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The data can be colored by its \"binary\" category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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M/eg3go4sLkkCYJSrPr7RvMCc3c4F8N/O8OEoeKUrnNpR9TVOQhJBLXSPCrY5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\nXJwkAiGEcHGSCIQQwsX9P2WWAb7VvaGIAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2cd03240>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.stripplot(x='label', y='y2', data=data, hue='binary', jitter=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A box plot of this data shows the quantiles for this data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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WfjxWQgfnauzc1GgNdtVQVVWSeS8ITrIb2A2wZcuWZaurdSvhiwv88pKW03KfuHw+yWaA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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2cface48>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='label', y='y2', data=data);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2BsDd/wbUA1cUojgRkaDo3sv96zcI3H2tu7cB1cArZvaEmdUD3e7+SK4PNrNhZvayma3N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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2c58afd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.boxplot(x='label', y='y2', data=data, hue='binary');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A violin plot gives more intuitive information on the distribution of the $y$-values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Oxhxt0lDfwKJFi8J74Chky5YtAAy1YKyhSlGwfXtUCnBnPIpM4JtKqfOVUm8o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\n9kkAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2ccd2748>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(x='label', y='y2', data=data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, the data can be colored by \"binary\" category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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S8+bNY9vWrbg6n6brvMsp6eG9L8uWLdOtjWi2aNEiPG43/syTDIuhT0qALomR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\nzBL3FyKmQm56SR+GAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f3196bf28>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.violinplot(x='label', y='y2', data=data, hue='binary', split=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A count plot will simply give the counts of data points in each \"label\" category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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dbvyqBSdsexrw3qr6ReBh4E+SrAauBl5RVecA1wBvG2BuaVk8xYS0uAB/keR84MfAacDa\nbt03qur27vLfAG8APsXotOS3dL1YBeyZ6MTSETAE0uJ+n9GZOs+pqh8leYDRef0BDj43SzEKx+6q\n+pXJjSgdPV8akhb3M8DeLgIvBJ6yYN36JAf+4P8e8DngPmDmwPIkq5P80kQnlo6AIZAWdz0wm+Qu\n4DXAVxasuw94XZJ7gVOA91XVDxmdqvgdSe4EdgF+WbyOeZ59VJIa5x6BJDXOEEhS4wyBJDXOEEhS\n4wyBJDXOEEhS4wyBJDXOEEhS4/4XVCBpMBIOLPIAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2c4fc198>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='label', data=data);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The count can be colored by the \"binary\" category as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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hDAJJKpxBIEmFMwgkqXAGgSQVrpIgiIi9IuIHEbE8IpZFxDFV1CFJqmhhGuDrwH2ZeUZE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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f8f2cd40358>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.countplot(x='label', data=data, hue='binary');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distributions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting the distribution of data in a pandas dataframe is straightforward. The number of bins can be set conveniently, and the rug options indicates the value of individual points in the distribution. A kernel density estimation is automatically computed and plotted as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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e1nat17Xevr3XbO9xW+s6eo322rb1ntpa19H7aOu9t/W6He2b9p63V197rxnO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\nX0TEZxT8IiI+8/8Bw9Ws/TNeG/MAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35b67e10>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data.x3, bins=20, rug=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `kde` options controls whether the kernel density estimation is plotted or not, the `hist` option is used to show (default) or hide the histogram, e.g.,"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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lzKKsoY3HtxVZHUedhxa/l+vqcbDtRC3LZyTo9XWV11s2LZ5LZybw6zcLaDjT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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35a5f860>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.distplot(data.x3, hist=False, rug=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Plotting bivariate distritions is easy as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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JbHlxntI9wEXGmB3Ahd1fi4hIL6X5WaQEDJsO1XldSkylxuJJrLVLgaXdnx8D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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35cd6048>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.jointplot(x='x2', y='y2', data=data, kind='kde');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A facet grid can be used to display distributions or other types of plots as a function of parameters, e.g., the \"binary\" category."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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7Ey5Naoq90i5nUFMgyS93P/nt160KcDOwb1XdBlBVdzJYHmVuooVKE2avrCzOoKZEkncC\n+wGPBm6vqr9ZcNtzGSzT//RuHS1pZtkrK4cBNSWS7At8DfgJ8IKq+lm3/zDgSmBtVV07uQqlNtgr\nK4en+KbHIcCBwEEMfjokyWOAy4G32XDSdvbKCuEMakokuQy4CDgSOAx4M/AZ4FNV9d5J1ia1xF5Z\nOVZNugCNLslrgZ9W1UeT7AP8B3Aa8CLgkCRndHc9o6o2T6hMaeLslZXFGZQkqUm+ByVJapIBJUlq\nkgElSWqSASVJapIBJUlqkgElSWqSASVJatL/A9324zTDrwyiAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35b50208>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = sns.FacetGrid(data, col='binary')\n",
+    "graph.map(plt.hist, 'x2');"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both matplotlib and Seaborn plots can be mapped over the facet grid."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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R3KWrVbuLJig3OnOpg3nJkQTZvOvHvGJ2HIEBotN8yq/scbTx2jzfMwnqmvlJGIN2lXAj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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35d83668>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = sns.FacetGrid(data, col='label', col_wrap=2, size=3)\n",
+    "graph.map(sns.kdeplot, 'x2');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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pp8o3btz45H/91389e8UVVxz60Y9+dDzA1q1bx3V0dMTOOeecXsvcDY8Cr4iI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\nrkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f4a35b889b0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "graph = sns.FacetGrid(data, col='label', hue='binary', col_wrap=2)\n",
+    "graph.map(plt.scatter, 'x2', 'y2', alpha=0.8)\n",
+    "graph.add_legend();"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
