From a13cb66670ffb23d15ba27dc9ac758968c272cb0 Mon Sep 17 00:00:00 2001
From: wyleung <w.y.leung@e-sensei.nl>
Date: Mon, 7 Jul 2014 18:57:18 +0200
Subject: [PATCH] More visuals and boxplots, functions like in R

---
 PaintingPandas.ipynb | 390 ++++++++++++++++++++++++++++++-------------
 1 file changed, 270 insertions(+), 120 deletions(-)

diff --git a/PaintingPandas.ipynb b/PaintingPandas.ipynb
index 3cd434c..f3924a2 100644
--- a/PaintingPandas.ipynb
+++ b/PaintingPandas.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:b83caaa08091e840c80d6b9587a45da9dd898900c0bdbad86c2bb52215020906"
+  "signature": "sha256:f4bfd304820a6c792a65b971256ff4c1e3c8c0b9201520c8d5bb56942f3b771f"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -10,7 +10,11 @@
    "cells": [
     {
      "cell_type": "markdown",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "source": [
       "\n",
       "More on IPython and Python libraries making science peanuts..\n",
@@ -20,16 +24,26 @@
     },
     {
      "cell_type": "markdown",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "source": [
+      "First of all, I would like to guide you through this lecture interactively.. so first syncronize our directories to:\n",
+      "    \n",
       "    cd ~/programming-course/day03/paintingpandas\n",
-      "    workon bio\n",
+      "    workon programming-course\n",
       "    ipython notebook"
      ]
     },
     {
      "cell_type": "markdown",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "source": [
       "Pandas\n",
       "===\n",
@@ -54,7 +68,11 @@
       "import matplotlib.pyplot as plt\n"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "output_type": "stream",
@@ -68,7 +86,11 @@
     },
     {
      "cell_type": "markdown",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "source": [
       "Learn by example\n",
       "===\n",
@@ -82,19 +104,24 @@
      "input": [
       "# Sample dataset\n",
       "participants = ['Anthony', 'Berend', 'Christine', 'Don', 'Esther', \n",
-      "                'Fer', 'Ginny', 'Hendrik', 'Ivo', 'John', 'Karl']\n",
-      "grades = [85, 65, 81, 73, 85, 45, 70, 60, 14, 64, 72]\n",
-      "sex = ['M','M','F','M','F','M','F','M','M','M','M']\n",
-      "university = ['AU', 'NL', 'NL', 'AU', 'DE', 'DE', 'NL', 'NL', 'NL', 'AU', 'DE']\n",
+      "                'Fer', 'Ginny', 'Hendrik', 'Ivo', 'John', 'Karl', \n",
+      "                'Lotte', 'Marjolijn', 'Nanda']\n",
+      "grades = [70, 65, 81, 73, 85, 45, 70, 60, 14, 64, 72, 75, 92, 84]\n",
+      "sex = ['M','M','F','M','F','M','F','M','M','M','M', 'F', 'F', 'F']\n",
+      "university = ['AU', 'NL', 'NL', 'AU', 'DE', 'DE', 'NL', 'NL', 'NL', 'AU', 'DE', 'NL', 'NL', 'AU']\n",
       "\n",
       "\n",
       "courseResults = zip(participants, grades)\n",
       "partipants_info = zip(participants, grades, sex, university)"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "outputs": [],
-     "prompt_number": 128
+     "prompt_number": 158
     },
     {
      "cell_type": "code",
@@ -103,14 +130,18 @@
       "courseResults"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 129,
+       "prompt_number": 159,
        "text": [
-        "[('Anthony', 85),\n",
+        "[('Anthony', 70),\n",
         " ('Berend', 65),\n",
         " ('Christine', 81),\n",
         " ('Don', 73),\n",
@@ -120,11 +151,14 @@
         " ('Hendrik', 60),\n",
         " ('Ivo', 14),\n",
         " ('John', 64),\n",
-        " ('Karl', 72)]"
+        " ('Karl', 72),\n",
+        " ('Lotte', 75),\n",
+        " ('Marjolijn', 92),\n",
+        " ('Nanda', 84)]"
        ]
       }
      ],
-     "prompt_number": 129
+     "prompt_number": 159
     },
     {
      "cell_type": "code",
@@ -133,14 +167,18 @@
       "partipants_info"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 130,
+       "prompt_number": 160,
        "text": [
-        "[('Anthony', 85, 'M', 'AU'),\n",
+        "[('Anthony', 70, 'M', 'AU'),\n",
         " ('Berend', 65, 'M', 'NL'),\n",
         " ('Christine', 81, 'F', 'NL'),\n",
         " ('Don', 73, 'M', 'AU'),\n",
@@ -150,11 +188,14 @@
         " ('Hendrik', 60, 'M', 'NL'),\n",
         " ('Ivo', 14, 'M', 'NL'),\n",
         " ('John', 64, 'M', 'AU'),\n",
-        " ('Karl', 72, 'M', 'DE')]"
+        " ('Karl', 72, 'M', 'DE'),\n",
+        " ('Lotte', 75, 'F', 'NL'),\n",
+        " ('Marjolijn', 92, 'F', 'NL'),\n",
+        " ('Nanda', 84, 'F', 'AU')]"
        ]
       }
      ],
-     "prompt_number": 130
+     "prompt_number": 160
     },
     {
      "cell_type": "code",
@@ -164,7 +205,11 @@
       "df"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "outputs": [
       {
        "html": [
@@ -181,7 +226,7 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>Anthony</th>\n",
-        "      <td> 85</td>\n",
+        "      <td> 70</td>\n",
         "      <td> M</td>\n",
         "      <td> AU</td>\n",
         "    </tr>\n",
@@ -245,16 +290,34 @@
         "      <td> M</td>\n",
         "      <td> DE</td>\n",
         "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Lotte</th>\n",
+        "      <td> 75</td>\n",
+        "      <td> F</td>\n",
+        "      <td> NL</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Marjolijn</th>\n",
+        "      <td> 92</td>\n",
+        "      <td> F</td>\n",
+        "      <td> NL</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Nanda</th>\n",
+        "      <td> 84</td>\n",
+        "      <td> F</td>\n",
+        "      <td> AU</td>\n",
+        "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
         "</div>"
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 131,
+       "prompt_number": 161,
        "text": [
         "           Grade Sex University\n",
-        "Anthony       85   M         AU\n",
+        "Anthony       70   M         AU\n",
         "Berend        65   M         NL\n",
         "Christine     81   F         NL\n",
         "Don           73   M         AU\n",
@@ -264,11 +327,14 @@
         "Hendrik       60   M         NL\n",
         "Ivo           14   M         NL\n",
         "John          64   M         AU\n",
-        "Karl          72   M         DE"
+        "Karl          72   M         DE\n",
+        "Lotte         75   F         NL\n",
+        "Marjolijn     92   F         NL\n",
+        "Nanda         84   F         AU"
        ]
       }
      ],
-     "prompt_number": 131
+     "prompt_number": 161
     },
     {
      "cell_type": "code",
@@ -280,7 +346,11 @@
       "summary"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "outputs": [
       {
        "html": [
@@ -295,15 +365,15 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>count</th>\n",
-        "      <td> 11.000000</td>\n",
+        "      <td> 14.000000</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>mean</th>\n",
-        "      <td> 64.909091</td>\n",
+        "      <td> 67.857143</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>std</th>\n",
-        "      <td> 20.544851</td>\n",
+        "      <td> 19.457477</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>min</th>\n",
@@ -311,19 +381,19 @@
         "    </tr>\n",
         "    <tr>\n",
         "      <th>25%</th>\n",
-        "      <td> 62.000000</td>\n",
+        "      <td> 64.250000</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>50%</th>\n",
-        "      <td> 70.000000</td>\n",
+        "      <td> 71.000000</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>75%</th>\n",
-        "      <td> 77.000000</td>\n",
+        "      <td> 79.500000</td>\n",
         "    </tr>\n",
         "    <tr>\n",
         "      <th>max</th>\n",
-        "      <td> 85.000000</td>\n",
+        "      <td> 92.000000</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
         "</table>\n",
@@ -331,29 +401,29 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 132,
+       "prompt_number": 162,
        "text": [
         "           Grade\n",
-        "count  11.000000\n",
-        "mean   64.909091\n",
-        "std    20.544851\n",
+        "count  14.000000\n",
+        "mean   67.857143\n",
+        "std    19.457477\n",
         "min    14.000000\n",
-        "25%    62.000000\n",
-        "50%    70.000000\n",
-        "75%    77.000000\n",
-        "max    85.000000"
+        "25%    64.250000\n",
+        "50%    71.000000\n",
+        "75%    79.500000\n",
+        "max    92.000000"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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5/iDoC8RrTA/6wx7mW3qPf38HZoGlcxhTnwazdRRMNxtTIwWzZRx8w3nEuy7+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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x6df7f10>"
+        "<matplotlib.figure.Figure at 0x749f350>"
        ]
       }
      ],
-     "prompt_number": 132
+     "prompt_number": 162
     },
     {
      "cell_type": "code",
@@ -362,9 +432,13 @@
       "groupGrades = df.groupby('University')"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
      "outputs": [],
-     "prompt_number": 133
+     "prompt_number": 163
     },
     {
      "cell_type": "code",
@@ -376,7 +450,11 @@
       "df\n"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "html": [
@@ -396,11 +474,11 @@
         "  <tbody>\n",
         "    <tr>\n",
         "      <th>Anthony</th>\n",
-        "      <td> 85</td>\n",
+        "      <td> 70</td>\n",
         "      <td> M</td>\n",
         "      <td> AU</td>\n",
-        "      <td> 42.2</td>\n",
-        "      <td> 105.3</td>\n",
+        "      <td> 46.1</td>\n",
+        "      <td>  98.15</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -408,8 +486,8 @@
         "      <td> 65</td>\n",
         "      <td> M</td>\n",
         "      <td> NL</td>\n",
-        "      <td> 36.0</td>\n",
-        "      <td>  94.0</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -417,8 +495,8 @@
         "      <td> 81</td>\n",
         "      <td> F</td>\n",
         "      <td> NL</td>\n",
-        "      <td> 36.0</td>\n",
-        "      <td>  94.0</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -426,8 +504,8 @@
         "      <td> 73</td>\n",
         "      <td> M</td>\n",
         "      <td> AU</td>\n",
-        "      <td> 42.2</td>\n",
-        "      <td> 105.3</td>\n",
+        "      <td> 46.1</td>\n",
+        "      <td>  98.15</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -436,7 +514,7 @@
         "      <td> F</td>\n",
         "      <td> DE</td>\n",
         "      <td> 22.8</td>\n",
-        "      <td> 114.2</td>\n",
+        "      <td> 114.20</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -445,7 +523,7 @@
         "      <td> M</td>\n",
         "      <td> DE</td>\n",
         "      <td> 22.8</td>\n",
-        "      <td> 114.2</td>\n",
+        "      <td> 114.20</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -453,8 +531,8 @@
         "      <td> 70</td>\n",
         "      <td> F</td>\n",
         "      <td> NL</td>\n",
-        "      <td> 36.0</td>\n",
-        "      <td>  94.0</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -462,8 +540,8 @@
         "      <td> 60</td>\n",
         "      <td> M</td>\n",
         "      <td> NL</td>\n",
-        "      <td> 36.0</td>\n",
-        "      <td>  94.0</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -471,8 +549,8 @@
         "      <td> 14</td>\n",
         "      <td> M</td>\n",
         "      <td> NL</td>\n",
-        "      <td> 36.0</td>\n",
-        "      <td>  94.0</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
         "      <td>  True</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -480,8 +558,8 @@
         "      <td> 64</td>\n",
         "      <td> M</td>\n",
         "      <td> AU</td>\n",
-        "      <td> 42.2</td>\n",
-        "      <td> 105.3</td>\n",
+        "      <td> 46.1</td>\n",
+        "      <td>  98.15</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "    <tr>\n",
@@ -490,7 +568,34 @@
         "      <td> M</td>\n",
         "      <td> DE</td>\n",
         "      <td> 22.8</td>\n",
-        "      <td> 114.2</td>\n",
+        "      <td> 114.20</td>\n",
+        "      <td> False</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Lotte</th>\n",
+        "      <td> 75</td>\n",
+        "      <td> F</td>\n",
+        "      <td> NL</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
+        "      <td> False</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Marjolijn</th>\n",
+        "      <td> 92</td>\n",
+        "      <td> F</td>\n",
+        "      <td> NL</td>\n",
+        "      <td> 31.4</td>\n",
+        "      <td> 109.10</td>\n",
+        "      <td> False</td>\n",
+        "    </tr>\n",
+        "    <tr>\n",
+        "      <th>Nanda</th>\n",
+        "      <td> 84</td>\n",
+        "      <td> F</td>\n",
+        "      <td> AU</td>\n",
+        "      <td> 46.1</td>\n",
+        "      <td>  98.15</td>\n",
         "      <td> False</td>\n",
         "    </tr>\n",
         "  </tbody>\n",
@@ -499,52 +604,73 @@
        ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 134,
+       "prompt_number": 164,
        "text": [
-        "           Grade Sex University  Lower  Upper Outlier\n",
-        "Anthony       85   M         AU   42.2  105.3   False\n",
-        "Berend        65   M         NL   36.0   94.0   False\n",
-        "Christine     81   F         NL   36.0   94.0   False\n",
-        "Don           73   M         AU   42.2  105.3   False\n",
-        "Esther        85   F         DE   22.8  114.2   False\n",
-        "Fer           45   M         DE   22.8  114.2   False\n",
-        "Ginny         70   F         NL   36.0   94.0   False\n",
-        "Hendrik       60   M         NL   36.0   94.0   False\n",
-        "Ivo           14   M         NL   36.0   94.0    True\n",
-        "John          64   M         AU   42.2  105.3   False\n",
-        "Karl          72   M         DE   22.8  114.2   False"
+        "           Grade Sex University  Lower   Upper Outlier\n",
+        "Anthony       70   M         AU   46.1   98.15   False\n",
+        "Berend        65   M         NL   31.4  109.10   False\n",
+        "Christine     81   F         NL   31.4  109.10   False\n",
+        "Don           73   M         AU   46.1   98.15   False\n",
+        "Esther        85   F         DE   22.8  114.20   False\n",
+        "Fer           45   M         DE   22.8  114.20   False\n",
+        "Ginny         70   F         NL   31.4  109.10   False\n",
+        "Hendrik       60   M         NL   31.4  109.10   False\n",
+        "Ivo           14   M         NL   31.4  109.10    True\n",
+        "John          64   M         AU   46.1   98.15   False\n",
+        "Karl          72   M         DE   22.8  114.20   False\n",
+        "Lotte         75   F         NL   31.4  109.10   False\n",
+        "Marjolijn     92   F         NL   31.4  109.10   False\n",
+        "Nanda         84   F         AU   46.1   98.15   False"
        ]
       }
      ],
-     "prompt_number": 134
+     "prompt_number": 164
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "df.plot(style='-')\n"
+      "df.plot(style='-')"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 135,
+       "prompt_number": 165,
        "text": [
-        "<matplotlib.axes.AxesSubplot at 0x7c8a310>"
+        "<matplotlib.axes.AxesSubplot at 0xb1b24d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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GV0Fy4wiOwG2MPSSfZXvkiNnSJLJtG9Svr9M1Dxlibmk9e/36rtCbT0nVqvoz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\n1mkZzWc3p0/RPpS/Xt5UeW7H3Obd1e8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zLu4a8krAGeBL4DdgOtIjLwWcth1z2rYdUFjdz+aO/oIFJZRv5Urj9YDMIh06\nVIxpZjHPRte/PUTxhx8kF8mlS4Ze/ha80X4iI33nVrFy+7ey9sxwN448B3Av8DqwHZhAGi4U0km7\nGB4eTsWKFQEoUqQIoaGhhIWFATcr21+3d9pS7PmLHl/pb98+jCVLIDjYeH1r10LBgmE8/LB59R8Z\nGcZTT0H//rJwhb/Vf0bb330HixZ5R2+gtX8rbUdFRTHTFmJlt5fp4a6PvDSwBemZAzQGIoDKwAPA\nKaAMEAncmepc7SO3IGfPQuXKEhpoZIhbQoIMQE6ZkvHKNL5g2TJJ77B6tbk6XOH0ack1FBOjE8QF\nOt7wkZ8CjgLVbNvNgd3Ad8Bztn3PAd+6eX2Nn1G8uMQp//ijsdedPVuSc/lDxMWDD0q+nthYs5U4\nT2SkuIe0Ec/aeBJ+2AuYC0QDtYD3gPeBFkj44YO27YDC/uhjVTzRb3Tulbg4GDEC3n3X+Xwg3qz/\nggUlcmb9eq8VYbj+detkJqyvsHL7t7L2zPDEkEcD9wH3AO2RqJVzSO+8GtASuOCpQI3/8MQTMuAZ\nF2fM9b74Au64Axo3NuZ6RtC6tQx8WgVfDnRq/Beda0XjEo0awZAhYvA84fp1yXO+dKm4bPyFnTsl\nydb+/WYryZwjRyQPzqlTshqWJrDRuVY0hmGUe+Wzz8SN4U9GHKBWLQlB/Pdfs5VkTmSkuFW0Edfo\nJuAiVvezeaq/fXv49luJ/XaX2FjJdzJihOvnerv+s2WTlARGD+raMVK/GdPyrdz+raw9M7Qh17hE\npUqSm3zjRvevMXmyrBt5zz3G6TISbxpyo1BK51fR3ET7yDUuM3KkZEP8+GPXz710SXzj69dD9erG\nazOCM2dE45kz/jvt/e+/xa1y9Khexi2roH3kGkOx+8ndyU0yYYIMlPqrEQcoWRKqVYMtW8xWkj72\naBVtxDWgDbnLWN3PZoT+GjWgQAH49VfXzjt3DiZOhGHD3C/bV/XvrTBEo/Sb5Vaxcvu3svbM0IZc\n4xbuRK+MHy+x6FWqeEeTkfizn9zuH/flRCCNf6N95Bq32LEDOneWeGtnHu//+0/cKb//DiEh3tfn\nKQkJ4mLZuxdKlzZbTUr+/BPatYMDB8xWovEl2keuMZx775VFjf/807njx4yBrl2tYcRBlpp78EH4\n6SezldyKjlbRpEYbchexup/NKP1BQc67V06cgC+/hIEDPS/Xl/XfurXx7hUj9JvpVrFy+7ey9szQ\nhlzjNs5zwgGOAAAgAElEQVQa8lGj4IUXoEwZ72syklatpEfuTysHJSbCzz9r/7gmJdpHrnGbxERJ\nQbtxo8Rdp8Xhw+KG2bdPfM5Wo0YNWdOzbl2zlQg7dsCzz8Lu3WYr0fga7SPXeIXs2SUKJaNe+ciR\n0KOHNY04+F82RO0f16SFNuQuYnU/m9H6M3KvHDgg2Q379jWuPF/Xv9FhiJ7qN9uQW7n9W1l7ZmhD\nrvGIsDCZLn7s2K2fvfMO9O4NRYv6XJZhNG0qqW0vXjRbieSB37RJVgTSaBzRPnKNx4SHS0raXr1u\n7tuzR4z8gQNQqJBZyoyhdWvo3l2ePsxk0yap499+M1eHxhy0j1zjVdJyrwwfLi4Vqxtx8B8/uV4N\nSJMe2pC7iNX9bN7Q36KF9BLPnJHtnTthwwZ47TXDizKl/lu1EkNuxIOkJ/rN9o+Dtdu/lbVnhjbk\nGo/Jm1eM3bJlsj1sGAwYAPnzm6vLKO68UyZA7dtnnoZr12DbNmjSxDwNGv/FFB95UlKS3d+jceDU\nlVP8c+4fGoU0MluKyyxcCLNmiUulQwcZAM2Tx2xVxtG9u+SK+d//zCl/3ToYPBg2b07/mO///p5m\nFZuRL2c+3wnTAHAl7goL/1zIC7Vf8Jpt8zsf+fCo4bfsK1asGEFBQVn6VaZgGRpXaGzItYoVK+bT\n77RNG5kY1KePGJxAMuLgnen6rpDZtPwvfv+CNvPaMG7TON+J0gCQkJRAp8Wd2HLMvAT2phjy2X/M\n5svfv0yx7/z58yil9Mug1/nz59Ose2/5CQsWlLC4Y8fg+ee9UgRgnp/zoYckauTaNc+u467+jAY6\nfzzwIxFrI/jh6R+YuG0ip66ccl9gJljZz+wN7UopXlv5GolJiUx5ZIppngZTDPn3T39PxNoIfvrH\nD1PLadxm6FBxr/jr8mieULgwhIZKnhNfc/kyREdDw4a3fhZ9Kppnlj7Dko5LaFW1Fc+HPs+wSA9W\n7tC4xJhNY9h2YhuLnlpEzuw5zZbjU5RSSm04vEGVHFtS7Ty5U9kCy5XGOHR9Gs/IkUq9+abvy121\nSqmwsFv3H714VJX7sJxa+OfC5H3nrp5TJceWVH+e/tOHCrMmc/+Yq0I+ClHHLx33SXlAunFTpkWt\nNA5pzOQ2k2k7vy3HLqUxLVCj8TPMWjUorbDDi9cv0mZuG3rX603Huzom7y+atygRjSPov6a/j1Vm\nLdYfWs+bP7zJii4rCC4YbLYcc8MPO97Vkd71etNmbhszZWQprOzjBHP133svxMRIRkd3cUd/akMe\nlxjHk4uepGmFpvRp0OeW43ve15M9Z/aw7uA694Wmg5Xbj1Ha95zZQ8fFHZnfYT41S9U05JqeYnoc\neZ8GfWhaoanZMvyCihUrsnbtWrNlaNIhWzaZ/OTLXvm5cxLKed99sq2Uovt33cmbIy8ft/44zcG1\n3DlyM/qh0fT9qS9Jyo+SqQcAJy+fpM3cNoxrMY6HKj9ktpxkPDXk2YHfge9s28WA1cB+4CegSGYX\nCAoK4uPWH3sowzcsWLCAevXqUaBAAUqVKkX9+vWZMmWKYde3hw56k7CwMK9e39uYrd/TMERX9f/8\nswxy2geQ31n/DrvP7GZ+h/lkz5Y93fM63tWRnNlzMm/XPPfFpoHZ9e8Jnmq/EneFtvPb8mLtF3n2\nnmeNEWUQnhry3sAebjrhByCGvBqw1radKRk1SH9h/PjxvPnmm/Tv35/Tp09z+vRppk6dyqZNm4iL\ni7vl+CR/WlZGYxgtW8LatbJeqS9wdKt8+fuXfBX9FSu6rCB/roynzQYFBfFBiw8YtG4Q1xOu+0Bp\nYGOPFQ8tFcrgpoPNlmMo5YA1wAPc7JHvA0rZ3pe2bacm3RFZf+XChQsqf/786ptvvkn3mOeee071\n6NFDPfzwwyp//vxq7dq1asWKFSo0NFQVKlRIlS9fXg0fPjzFOV999ZUKCQlRxYsXV++9956qWLGi\nWrt2rVJKqaSkJDV69GhVpUoVVbx4cdWxY0d17tw5pzWnV5+RkZFOX8Mf8Qf9tWsrtWGDe+e6qv+u\nu5Tavl2pHw/8qG4bd5vae2avS+e3W9BOjdk4xqVzMsIf6t9d3NWelJSkui/vrlrObqniEuKMFeUC\neClq5SOgH+DY9SwFnLa9P+1g1C3Nli1buHHjBo8//niGx82fP58hQ4Zw5coVGjVqRIECBZgzZw4X\nL15k5cqVTJkyhWW2hCR79uyhZ8+ezJ07lxMnTnD27FmOOST1njhxIsuXL+fnn3/m5MmTFC1alNe8\nkYVK4zL2JFre5vRpOH4csgVH88w3z7D4qcXcWeJOl67x/kPvM3bTWGKuxnhJZeAzZtMYth7f6tex\n4u4a8rbAf4h/PD2nbrp3kPDwcIYPH87w4cOZMGGC06PJQUHGvFwlJiaGEiVKkC3bzepq2LAhRYsW\nJV++fGzYsIGgoCDatWtHgwYNAMidOzfNmjXjrrvuAqBmzZp07tyZ9evXA7B48WIeffRRGjduTK5c\nuXj33XdTXP+zzz5j5MiRBAcHkzNnToYNG8bixYtddtlERUXdUr+O26k/9/dtf9BfqlRUsp/cm/oj\nI6FqrUW0eq85kx6eRJMKTVwu7+SfJ2mS1IR3179ryP/vin5/2w4LC3P5/MFfDObD+R+ysutKCuUu\n5FO9UVFRhIeHJ9tLbzAKOAocBE4CscBsxJVS2nZMGQLEtbJq1SqVI0cOlZiYeMtn5cqVU1FRUSo8\nPFwNGjQoxWe//PKLCgsLUyVLllSFCxdWefLkUc8++6xSSqlXXnlF9evXL8XxZcqUSXat5M2bVxUq\nVEgVKVIk+ZU3b1514sQJpzT7c31anRs3lCpUSKn//vNuOc91v6DKjKipxm4c69F1/rvynyo+prj6\n++zfBinLGkQdjFIlx5ZUf5z6w2wpSinvuFYGAuWBSkBnYB3QDVgOPGc75jngWzev71c0aNCA3Llz\n8+23Gf87qSNOunbtSrt27Th27BgXLlygR48eKFtS6+DgYI4ePZp87NWrVzl79mzydkhICD/88APn\nz59Pfl29epUyZcp49L+k7l1ZDX/QnyuXJLBavdr1c53VH58Yz9dBT9K0YhP6NvRs0dOS+UvyVoO3\niFgb4dF1wD/q311c0b73zF6/ixXPCKPiyO13iveBFkj44YO2bctTpEgRhg0bRs+ePVmyZAmXL18m\nKSmJnTt3Ehsbm+55V65coWjRouTKlYtt27Yxb97NULAOHTqwYsWK5KiXoUOHpnCb9OjRg4EDB3Lk\nyBEAzpw5w/Lly733T2pcwpurBimleHpBdxKv5+WrLmnHirvKm/Xf5Jdjv7D56GYDFAY2p66ckkyS\nfhYr7m+k+9jg78ydO1fdf//9Kl++fKpkyZKqXr16avr06SouLk6Fh4erIUOGpDh+8eLFqkKFCqpg\nwYKqbdu2qlevXqpbt27Jn8+aNStF1EqlSpVSRK18+OGH6o477lAFCxZUVapUucV1kxFWqE8r8++/\nSt12m1JpeNs8ZnjkcFVpVF3VvtMVQ6878/eZqsHnDVRSUpKh1w0kLt+4rO797F41ImqE2VJugQxc\nK36z+HJQUBBp7de4h65P73PHHbBgAdSubdw1Z+6cyYj1I6gbvYWH6pXilVeMu3ZiUiJ1ptVhSNMh\ndKjRwbgLBwgJSQm0W9COUvlL8fljn/vd4jd+t7CExjys7OME/9LvThhiRvpX/7Oa/mv6s7LrKras\nLmX4+pzZs2VnXItxDFg7gLjEWyexOYM/1b+rZKRdKUWvVb2IT4pnatupfmfEM0Mbco3GTYxcNeiP\n03/w9DdPs/ipxeS4cCdKQdWqxlzbkRZVWlClaBWm/jrV+ItbmLGbxrL52Ga/jhXPCO1aCVB0fXqf\n2FgoXVom7RQq5P51jl06RoMZDRjXYhyd7+7MtGmybN5XXxmn1ZFdp3fRfHZz/nr9L4rkyTQdUsAz\nf9d8+q/pz5YXt1C2UFmz5aSLdq1oNF4gf36oX18m7riLPa94r/t70fnuzkDa+ceNpGapmrS9vS3v\nbwyIoDKPWH9oPb1/6M3Kriv92ohnhjbkWQwr+zjB//S76id31B+fGM+Ti56kcUhj+jXsB4BSmS+0\nbAQjHhjB9N+mc+TiEZfO87f6d4XU2u2x4vM6zLNErHhGaEOu0XiAPZ7cVS+WUoruK7qTJ0ceJj48\nMXlwbfducdNUqOAFsQ6ULVSW1+57jUHrBnm3ID/FHis+tvlYmldubrYcj9E+8gBF16dvUArKl5de\ndLVqzp/3TtQ7fLf/O9aHr0+RknbiRNi1C6ZP94LYVFy+cZlqk6uxsutK7i1zr/cL9BNi42JpNrMZ\nj93xGEObDTVbjtNoH7lG4yWCglx3r8zaOYtZ0bNY0fXWvOKRkd71jztSMHdBhjUbRr/V/bLMTT8h\nKYHOSzpTq1QthjQdYrYcw9CGPIthZR8n+Kd+V8IQx88bz9tr3mZl15WULlA6xWeJibB+vff94468\ndO9LnLh8gu8PfO/U8f5Y/84SGRnJG9+/wY2EG3zW9jPLxYpnRA6zBViBihUrMmPGDB56SOddMJMb\nCTc4E3uG45eOmy0lBTXqw/re8M8ZyJM7/eMOXTjEuz+/y/KI5VQvWf2Wz3fuhDJlJKTRV+TIloOx\nzcfSb3U/WlZpSY5s5pmEc9fOcS3+mteuP3fXXLbn2s6G5zdYMlY8I7QhdwJfrKWZGQkJCeTI4fnX\nZdU1F9cdXMeLy1/kRsINgv7yv55U3HNQ73PInYEhzx6Unc9e/yzdxca9HXaYHm2rtWX8lvF8+fuX\nvFzn5QyP9Ub7uZ5wnWGRw5jy6xQK5i5o+PXt3Jb/NlZ2kbziGs9JNyGMv+K4BJud69evq969e6vg\n4GAVHBys3nzzTXXjxg2llFJNmzZVS5YsUUoptXHjRhUUFKRWrlyplFJqzZo1KjQ0NPk6M2bMUNWr\nV1dFixZVrVq1UocPH07+LCgoSH3yySeqatWqqnLlyi5p9uf6dIXLNy6rnit6qnIfllMr9680W066\nDB+uVJ8+nl2jdWulMlhN0KtsP75dBY8PVpdvXPZpuVuPbVXVJ1dXT379pDp95bRPy7YaeGmptyzN\ne++9x7Zt24iOjiY6Oppt27YxcuRI4OZKJADr16+ncuXK/Pzzz8nb9l7NsmXLGD16NEuXLiUmJoYm\nTZrQpUuXFOUsW7aM7du3s2fPHkN0W8nHGXUoilpTahEbH8sfPf6gze1t/Fa/s37y9PTHx8OmTdCs\nmbG6nKVucF3CKoYxfvP4DI8zqv5vJNwgYk0Ej81/jOFhw1n01CJuy3+bIddOD39tO0ZgKddK0DvG\nPFKrYZ6P0M+bN4/JkydTokQJAIYNG8Yrr7zCiBEjaNq0KW+99RYAGzZsICIigs8//xwQQ/6///0P\ngKlTpxIREcEdd9wBQEREBKNGjeLo0aOUL18+eV+RIllrGnVsXCwD1gxg6b6lfNb2Mx6p9ojZkjKl\nbl04cQKOHYNy5Vw/f/t2ya1SrJjx2pzlvQffo860OnSv050yBT1bwCQjth/fTviycO4scSfRPaIp\nVSAglvbNcqT72OCvpOVayZs3r9qzZ0/y9t69e1WuXLmUUkrFxsaqPHnyqNOnT6vSpUuruLg4VbZs\nWRUTE6Py5s2rzp49q5RSqnr16qpAgQIplnPLly+f2rJli1JKXCsHDhxwS7M/12dGRB2MUpU/rqye\nXfqsOnf1nNlyXKJTJ6VmzHDv3Hff9dw1YwR9f+yrXl7+sleufT3+uopYE6FuG3ebmr9rvs6L7iJo\n14rxBAcHc+jQoeTtI0eOEBwcDEC+fPmoU6cOEyZMoGbNmuTMmZOGDRsyfvx4qlatSjFbtyskJIRp\n06alWM4tNjaW+vXrJ1/X7EFWXxEbF0vv73vT9ZuuTGg1gVntZlE0b1GzZbmEO2lt7Zg10JmagU0G\n8u2+b9n9325Dr/vriV+pM60Oe2P2Et0jms53d84ybdsXaEPuJHFxcVy/fj351aVLF0aOHElMTAwx\nMTGMGDGCbt26JR/frFkzPvnkE5rZnJ5hYWFMnjw5eRtkObdRo0Yl+78vXrzIokWLDNO85eiWW/b5\no59ww+EN3DP1Hs5dP8euV3fx6B2PpnusP+q306oVrFkDCQnpH5OW/mvXYNs2aNLEe9qcpWjeogxs\nMpD+a/qn+bmr9X8j4QaD1g7ikXmPMLDJQL7p+M0t8fO+wp/bjqdoQ+4kbdq0IV++fMmvGzduULdu\nXWrVqkWtWrWoW7cugwcPTj6+WbNmXLlyhaZNJdSsadOmxMbGJm8DtGvXjv79+9O5c2cKFy5MzZo1\n+dFhxMzTHkvf1X39esbe1firvPnDm3Ra3InxLccz+4nZFMtropPYQ4KDxT++fbtr523ZArVqQUHv\nRd65RM/7erI3Zi/rDq7z6Do7Tuyg7vS67D6zm+ge0XSt2VX3wgOIdP0/GuMAVK0ptdTi3YvNlpIm\nGw5vUFUnVlVdl3RVMbExZssxjH79lBo61LVzBg1SauBA7+hxl4V/LlS1p9ZWiUmuL0p6I+GGGrx2\nsCo5tqSaHT1b+8INAu0jz5p80OIDj5b18gZX46/y1o9v0XFRR8Y2H8vc9nMpnq+42bIMw51Vg3yZ\nX8VZnqrxFLmy52Lernkunffbyd+oO60u0aejie4RzTO1ntG9cB+gDXkAY1/W67NfP0veZ6afcPPR\nzYRODeXklZP88eofPFH9CZev4e9+zkaNYM8eOHs27c9T6798GaKjoWFD72tzhaCgID5o+QGD1g1K\nMW0+vfqPS4xjWOQwWs9pTd+GfVnWeZlXQxjdwd/bjidoQx7gjG0xlpEbRnLh+gXTNFyLv0afH/vQ\n4esOvN/8feZ3mE+JfCVM0+NNcueWST1r1jh3/MaNcN99kDevd3W5Q+OQxtQpU4eJWydmeNzOUzu5\nf/r97Di5g509dvLsPc/qXriP0fnIAxTH+nxh2Qvclv823m/u+6W9thzdQviycEJLhzL54cmUzF/S\n5xp8zeTJsGMHfPll5sf26ycLSQzx04yq+8/up+GMhux7fd8tN9+4xDhGbRjFJ9s/YVyLcTx3z3Pa\ngHuRjPKRa0MeoDjW5/FLx6k1tRa/v/I7IYVDfFL+tfhrDI0cyuw/ZjO5zWSerPGkT8r1Bw4cgKZN\nZVHmzOxanTqymESjRr7R5g69VvUiW1A2Pn744+R90aeiCV8WTpkCZZj+6HRLr3dpFfTCElmcsoXK\n0rNuTwavG+wTP+Evx37h3mn3cvjiYXa9ustQI24FP2fVquIq2bXr1s8c9Z87B3//La4Vf2Zos6HM\n3TWXv8/+zZq1axixfgTNZzfnjfvfsNSixVZoO+5iqVwrGvd5u9HbVJtcjdDgUKpequqVMpJUEp9s\n+4RZ0bOY+PBEOt7V0SvlWAH7Wp61aqV/zM8/yyBnrly+0+UOJfOXpE+DPvRY2YMjO49Q5d4q/Nb9\nN8oXLm+2NI0N7VoJUNKqz7l/zE13xp5RNAppxMTWE7N8IqTly+Hjj2Ht2vSPeeMNmUD09tu+0+Uu\n1+Kv0Xpua56t9Swv1H5B+8JNwBs+8vLAV8BtSJD6NGAiUAxYCFQADgEdgdThEtqQ+wBdn+Zy+bKs\n9nPqFBQokPYxd98NM2dK5kSNJjO84SOPB/4H3AXUB14DqgMDgNVANWCtbdvyZMuWjX///TfFvuHD\nh6fIrWIVrO4ntIr+ggXF951arl3/6dMyGFq7ts+leYRV6j8trKw9M9w15KeAnbb3V4C9QFngMWCW\nbf8soJ1H6vwYsx4tk5KSTClX4zp2P3laREZKZEv27L7VpAlMjIhaqQjUBrYCpYDTtv2nbdsBiaPb\nIioqinLlyjF69GhKlixJpUqVmDfv5tTm8PBwevToQcuWLSlUqBBhYWEcOXIk+fN9+/bRokULihcv\nzp133pkiA2J4eDivvvoqbdq0oUCBAh73Kqy6ZqcdK+lPa7q+Xb8/Tst3BivVf2qsrD0zPDXkBYAl\nQG/gcqrPMkzyEmicPn2as2fPcuLECWbNmkX37t3Zv39/8ufz5s1j6NChxMTEEBoaytNPPw1AbGws\nLVq04JlnnuHMmTMsWLCAnj17snfv3uRz58+fz5AhQ7hy5QqN/DngWJOCWrXgyhWJK0+Nv+Qf1wQG\nnoQf5kSM+GzgW9u+00BpxPVSBvgvrRPDw8OpWLEiAEWKFCE0NNS5Eo1yZ3hpEPDdd98lZ86cNG3a\nlEceeYSvv/46ObVt27Ztady4MSDrfRYuXJhjx46xadMmKlWqxHPPPQdAaGgo7du3Z9GiRQwdOhSQ\ndLcNGjQAIHdGy7Sngb0Hb++NTJgwgdDQ0OTt1J/7+7aV9AcFwT33RDFpEnz88U39ZcqEcvFiGHfd\n5V96ndm2Uv2n3nZ8mvUHPc7onTlzJkCyvTSaICRq5aNU+8cC9vi2AUBac8LTTdHor+TIkUPt27cv\nxb6BAweq559/XimlVGRkpCpZsmSKz/v166d69uyplFIqPDxc9evXL8XnJUuWVFu3blVjxoxRuXLl\nSrHcW4ECBVKcO3jwYJc1p1efkZGRLl/Ln7Ca/nnzlHr00ZvbkZGRauZMpTp2NE+TJ1it/h2xsnal\nvJPGthHwDPAA8Lvt1dpmuFsA+4EH0zHkliMkJISDBw+m2Hfw4MEUd8nz589z9erV5O3Dhw8nL/2m\nlOLo0aPJn125coVz585RtmxZQkJCaNasWYrl3i5fvswnn3zilf/F6n5Cq+lv0UIiV+JsmYTDwsIs\n7VaxWv07YmXtmeGuId9oOzcUGeisDfwAnAOaI+GHLbk1htySdOrUiZEjR3L8+HGSkpJYs2YNK1as\n4MknU049HzZsGPHx8WzYsIGVK1fy1FNPJX+2atUqNm3aRFxcHEOGDKFBgwaULVuWRx55hP379zNn\nzhzi4+OJj49n+/bt7Nu3D0DHglucEiXgzjth0ybZVkr84w88YK4uTWChc604wdChQ2nYsCGNGzem\nWLFiDBgwgHnz5lGjRo3kY0qXLk3RokUJDg6mW7dufPbZZ1SrVg2QUMWuXbvyzjvvULx4cX7//Xfm\nzJkDQMGCBfnpp59YsGABZcuWpUyZMkRERBBn68IFBQUZGupo9VhaK+p3DEOcNy8KpeD2202V5DZW\nrH87VtaeGTrXihPkyZOHsWPHMnbs2AyPGzhwIAMHDkzzsxIlSjBlypQ0P6tWrRorVqxI87MvncmF\nqvFrWrWC116DMWPgt9/EraJnuGuMRPfIfYA/uUes7ie0ov569eDwYTh5Eo4fD7OsfxysWf92rKw9\nM7QhN4iM3B9Gu0c01iJHDnjoIZkcpP3jGm+gsx8GKOnVZ1RUlKV7JlbV//nnMGkSxMREcfx4mNly\n3Maq9Q/W1g56YQmNxnRatYI//gBn575pNK6ge+QBiq5P/+Puu2HQIOjSxWwlGiui1+zMguj69D+O\nHIHgYPGZazSuol0rmmSsHktrZf0hIbBxY5TZMjzCyvVvZe2ZoQ25RqPRWBztWjGJQ4cOUblyZRIS\nEsiWLRtt2rShS5cuhq06lNXqU6MJdLRrxSBmzpxJzZo1yZ8/P2XKlKFnz55cvHjRqXMrVqzIunXr\n0v181apVllw6TqPRmI825E4yfvx4BgwYwPjx47l06RK//PILhw8fpkWLFsTHx2d6vrd6yAkJCS4d\nb3U/odZvLlbWb2XtmaENuRNcunSJ4cOHM3nyZFq2bEn27NmpUKECX3/9NYcOHWLOnDmEh4czZMiQ\n5HOioqIoX748AN26dePIkSM8+uijFCxYkA8++OCWMsLCwpgxY0by9hdffEGNGjUoVqwYrVu3TrE0\nXLZs2fj000+5/fbbueOOO7z4n2s0GiugDbkTbN68mevXr9O+ffsU+/Pnz0+bNm1YvXp1hlPwZ8+e\nTUhICCtWrODy5cv07dv3lmMcp/EvW7aM0aNHs3TpUmJiYmjSpAldUgUfL1u2jO3bt7Nnzx6X/hcr\nz2wDrd9srKzfytozw1IRrUEGPRopF7/QmJgYSpQoQbZst973ypQpw44dOyhbtqwh2gCmTp1KRERE\ncm87IiKCUaNGcfTo0eRefkREBEWKFDGsTI1GY10sZchdNcBGUaJECWJiYkhKSrrFmJ84cYISJUoY\nWt7hw4fp3bs3ffr0SbH/+PHjyYbc/tdVrJ5vQus3Fyvrt7L2zNCuFSdo0KABuXPnZsmSJSn2X7ly\nhR9++IHmzZuTP3/+FEu9nTp1KsWxrmQ/DAkJYdq0aSmWf4uNjaV+/fpuXU+j0QQ22pA7QeHChRk2\nbBi9evXixx9/JD4+nkOHDtGxY0fKly9Pt27dCA0NZdWqVZw/f55Tp04xYcKEFNcoVaoU//zzj1Pl\n9ejRg1GjRiX7vy9evMiiRYsM+V+s3iPR+s3FyvqtrD0ztCF3kn79+jFq1Cj69u1L4cKFqV+/PhUq\nVGDt2rXkzJmTbt26cc8991CxYkVat25N586dU/SaIyIiGDlyJEWLFuXDDz8E0u9Vt2vXjv79+9O5\nc2cKFy5MzZo1+fHHH5M/171xjUbjiJ7ZGaDofOT+idZvHlbWDnpmp0aj0QQ0ukceoOj61GgCC90j\n12g0mgBGG/IshtXzTWj95mJl/VbWnhnakGs0Go3F0T7yAEXXp0YTWGTkI/ebKfpFixbV8dEGUrRo\nUbMlaDQaH+EN10prYB/wN9Df2ZPOnTuHUsrvX5GRkaZrcOZ17ty5NOvZ6n5Crd9crKzfytozw2hD\nnh2YjBjzGkAXoLrBZZjKzp07zZbgEVq/uWj95mFl7ZlhtGvlfuAAcMi2vQB4HNhrcDmmceHCBbMl\neITW710SleJ8fDxnExKIiY9P8TobH8/WgwfJduwYJXLmvOWVL3t2s+Vnir/Xf0ZYWXtmGG3IywJH\nHbaPAfUMLkOj8QlJSnExIYGzqQxyRq8LCQkUzpEj2TgXdzDUxXLkICgoiL+vXWPLpUspzjsTF0e2\noJiPryAAAAfRSURBVKAUhr14GsY+xec5cpDHAsZf432MNuROhUk8umuXwcX6jt9//50dWr9peFO/\nUorLiYnJxvVcQgL5s2W71YDa/lbJm/eWz4rmyEGONBYgsbPvwgUm3X57mmVfTUpK7rmnvkHsjo1N\n88aR20Ff8Rw5yJlB2UZg5fbjbe3lc+fm02rVvHb9jDA6TKQ+MBzxkQNEAEnAGIdjDgBVDC5Xo9Fo\nAp1/gKq+KCiHrbCKQC5gJwE22KnRaDRZgYeBv5Ced4TJWjQajUaj0Wg0msChHeLvvsOJY98E8jps\nX/GKoswpjYRAHgB+BVYCLwPfOXn+O8BDGXz+OCldR5kdbxSJwO8Or7czODa1xiigjteUuUYpYB7i\njvsV2Iy0szrAxybq8hSz2runOOpugzxZu7LKdzgwyUA9ScBsh+0cwBmc//3aCQYyWysxzOG6j3Jz\nMuMrQDcXy/NrFgLLkcHMzDgIFHfYvuwNQZkQBGwBujvsqwUMxrmG4Mzw/0ygg8vKPMeV+pxJSo2R\nuG/IjYxySuv7CQFeN7AMszCjvRuBXfdDyMzsSi6cmwN4DmMN+WXgNyCPbfthpOOy3EVdzhCG6zcI\ny1EAmeQTws3JPWFI726Rbd8c2/43gBvAH8Ba277LwEhk8HMLcJttf0VgHRANrOHm3X8m0iPbhPTW\n7IZoFtLDtDMXeCwdzQ8C69PY3wwxZql1Y/sf3wd2AJ1IaQTfB3bbtI4DGgBngX+RxlY51fGHkJve\nDqQu7E8y+YEvgK2289LTnxHpGQpnNEbajtuK9Lga287Nbjtnm+18u4ENAzYAy2zHG8VDSPtJizBu\n/qiGI/UVibSFXrb9FZHvbxrwJ/Aj8oOvgtS5ndtTbfsC+/ezAOnZ2pkJtAdyA18i7eI35P/1By4D\nTZF6doyjexlpFzuBxdx82p4JTAV+AcbjHUM+kpu/qa+Qp09727gfeYr7DbEVds3hiLFfi7SbCkgb\nAWkjadV9mMN1wx3+j+FAH9v7KNL+7ViGp5EvDOBn4F7kH7+APLYEIRXa0HbMQaCYw/lJwCO292OA\nQbb333HzseV5YKnt/UzkCQDELfC37X1Th2MKIwYqvZ7zG8CHaezPTHdfh2O/RH54xZH8MXYKpfo8\n9fH2a71me/8qMN32fhRSnwBFkAaRL53/IT0SSOlaecoFjZGIwQbp4ay2ve/Oze8lN7AdMZZhyCN3\nBRc1ZkZ63w/casg3AjmR/zEGuelUBOKRpyyQ9mKv13XAPbb3o7j5PfgKuyFvh7RlkCiuI0jd9gE+\nt+2/Azhs+9xs4pEb/92p9jv+lt/l5lPTTMRg2sOYvWHIayKdrtxIW2/GzbZREGkLAM2RmwyIIT6K\n/L5A2oo9gDytus9N+oZ8GPCW7X16vx3TcXb2QBdu+pgW2bYVcpc+YXu/E6mwtIhD/NMgvSP7cfUR\nHylIz9h+h1PAt7b3exFfKshN5HaghE3DYuQmkRYZTU7KSPfCNI6/AFwHZgBPANccPssoFv8b29/f\nHMpoCQxAGmUk0ohc8UNiK7+2w2uRixrT0/WsTdcvyI/XHrO6DWnwRpL6+5mMfBfbUn2mkLZjNzL/\ncbM9HER6VpCyXX2OdAyyAR252cZ8zQ/AA4iRfhh5QrwBNOLmk+BfSN06M/bkbeKQnu1LqfbXRJ7K\n/kBuljVs+xXS9ryZL3kX8r124aYNsVMEsQG7kE5BDYfPfkJ+E6lJq+5dmcWT1m/HdJwx5MWQxjgD\n+eH0Q34cQUijtJNI+v6oeIf3SamOS88QxqVzzFdILz4ceeROj92k7wvOSHdsqmODbMfcjzSatsgP\n1E5GjdheTuoy2nPTCFfEGJeFKxrT0/W6g64qiLsLbq0TI9iNPNk5lv0QUDKNYx3bgqPm9L7HbxDD\n2RYZRD1vgF53uI48jrdCfjOOnYTU7d4fkscnITrvJ2Xo8EygJ/L08w4pAxmu+kDXcuADYD4p6+1d\nxH1SExmgdFaXJ3Wf3m/HVJwx5E8ixrMiMvgRghj0phmcc5mbj/YZsRnobHv/NNLjzoyZSFSMIqUr\nITXrkN7uyw77agFNnCgjNfmRu//3yGOW/bHd2f/TkR8Rt4Kd2m7oSQtPNf6I/FjtjbMarrt8XGEd\n4q/s4bAvfxrHuTP7+Dry/0xBXEtmshB4AWl39pvrBm66gaohvykjxx884TriBn0a0Q0yRnYKcW89\nQ/qGz1sLCnyBuNh2p9pfCHmyBnkCcwZ/rnu3ccaQd+amX9rOEtv+9L7QaUijtQ92pn5Utm/3Qr6A\naKRye6c6Lq33/wF7cO4H+gTiOzuADHa8B5zMQHdaKMQX951N5wbgf7bPFiBPKDuQgcSMrmEv813k\nB/GHTdM7Lmixk5eUPvJRHmi06/ocqdffkEfVKYhRd9RuNO0Qn+e/yADSTG6GUiqHv+mVn3q/4/Y8\npIf5kxFCXcRRx09Ip2c1MrYB8Cny2/sD+X6eI+VTq1nYdZ9H0mwMRnq6Q5DvZyO3ZjJN77dtpJ7j\niOstdRljgdFIm81Oxm3Gvp1e3asMzne2/WlcIB9imAuaLUTj1/TFvZukJvCoQ/oRUpkxCTH2GgNp\njoT1vZHJcZqszVJk4LRYZgdqAp66iOuknRvnvosM/Ot1EzUajUaj0Wg0Go1Go9FoNBqNRqPRaDQa\njUaj0Wg0Go1Go9EEGv8HwuXnWj8AmAoAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7c8ad50>"
+        "<matplotlib.figure.Figure at 0xaa14a50>"
        ]
       }
      ],
-     "prompt_number": 135
+     "prompt_number": 165
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "slide"
+      }
+     },
+     "source": [
+      "So, what does this mean?\n",
+      "===\n",
+      "\n",
+      "Let's grind the dataset a little bit more..."
+     ]
     },
     {
      "cell_type": "code",
@@ -553,26 +679,30 @@
       "df.boxplot(column=['Grade'], by=['University'])"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 147,
+       "prompt_number": 166,
        "text": [
-        "<matplotlib.axes.AxesSubplot at 0xa700910>"
+        "<matplotlib.axes.AxesSubplot at 0xb194750>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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BEQYwEP6ABtQfIcf5AJXGIKLKCuDdwNHAZuAY4E5igvQasKxje3P6Q1aPYZan7zBPdRlG\nefr+8lSXbtLO6Z8CXJosLyYCPsnz4gG8vyRpQPoN+jsDLwE+2+Vnk8mjlMwdFodtWRy2JezU5+tP\nAK4D7k7KzbTOFmAJcFe3F1WrVSqVCgCjo6OMj48/Ool4s1GyLjcnwu799eTq9ylrGWrUav29X71e\nz83vU+byJCPUkoRF/LQ1VdF8y/XHuP1M5cnk37x8P83lRqPBXPrN6X8G+BKwJim/H9gKrCIu4o4y\n/WKuOf0hq8cwy9N3mKe6DKM8fX95qks3ac2RuxvwU2B/4L5k3RiwFlgKNICTgO0drzPoD1k9hlme\nvsM81WUY5en7y1NduknrQu79wF60Aj7ANmA50WVzBdMD/tDov1t2re/3WLQo629BYB64SGzL/nP6\nhTSIPXjejwQklVMWd//kPr0zCAb9fMhTO+SpLsMoT99fnurSjWPvSJIAg36KallXQANiHrg4bEuD\nviSVijn9lKxcGQ9lK0+51zzVZRjl6fvLU126Sauffq9KEfSVD3n648xTXYZRnr6/PNWlGy/kZsDc\nYXHYlsVhWxr0JalUTO+o0PJ0Gp6nugyjPH1/eapLN7Old7wjtwfJFzoQ7gAlLSTTOz2YnJyc87Fx\n48Z5baf8Mw9cHLalQV+SSsWcvgotT7nXPNVlGOXp+8tTXboxpy+pEAZ4Oa0vwzzseT/pnVHgcuA/\ngZuBI4hJVNYDtwDrkm1KydxhcdiW+TA52f8DagN5n23bsv42etdP0P8QcDXwVOAZwGZiasT1xCQq\nG5g+VaIkKUO9niw9AdgEPLlj/WbgGFoTpNeAZR3bmNPXgslT7jVPdSmrsrRBGsMw7A/cDVwEXA98\nnJgzdzER8EmeF/f4/pKkFPQa9HcCDgM+mjzfz/RUzmTyKCXzwMVhWxZJLesKZK7X3jt3JI/vJeXL\ngXOALURaZwuwBLir24ur1SqVSgWA0dFRxsfHmZiYAFp/YMNebspLfcpahhq1Wn/vV6/Xc/P7WJ69\nPJ+75efTA6iZgs7695lvubncaDTm/N366QD1deB0oqfOSmDXZP1WYBVx5D9KlzMAc/paKHnK4eap\nLiq2tMbTPwT4BLAzcCvwemBHYC2wFGgAJwHbO15n0NeCyVOgzVNdVGxOopKBWq3WlmJQVgZzM08N\nmOj7XRYtGu7+3UVQlr9L78hVaQ3i+MIjdBWJR/rSHAz6GjZOlyhJAgz6qWnvSqVhV8u6AhoQ/y4N\n+pJUKub0pTmsXBkPaVjYZVOSSsQLuRkwd1gctmVx2JYGfUkqFdM7klQwpnckSYBBPzXmDoujWq1l\nXQUNiH+XBn1pTmvWZF0DaXDM6UtzcOwdDRtz+pIkoL+g3wC+D2wCrk3WjQHridm01hEzZ5WSucMi\nqWVdAQ2If5f9Bf1JYmaJQ4FnJ+vOJoL+gcAGpk+VKEnKUD85/duAZxJz4jZtBo4B7iQmSK8Byzpe\nZ05fQ8WxdzRs0hp75yfAvcDDwL8CHwfuARa1vfe2tnKTQV+SUpTWhdwjidTOCcCbgaM7fj6ZPErJ\n3GFx2JbFYVv2N0fuL5Lnu4Eribx+M62zBVgC3NXthdVqlUqlAsDo6Cjj4+OPTlbcbJRhLzflpT6W\ney/X6/Vc1cdy7+V6vZ6r+gyq3FxuNBrMpdf0zq7AjsB9wG5ET53zgOVEjn8VcRF3lOkXc03vSFKK\n0sjp708c3UOcLXwaeC/RZXMtsJTo0nkSsL3jtQZ9SUpRGjn924Dx5PF0IuBDXLhdTnTZXMH0gF8a\n7addGm6OvVMc/l16R640J8feUZE49o40B8fe0bBx7B1JEmDQT425wyKpZV0BDYh/lwZ9SSoVc/rS\nHBx7R8MmrbF3emXQl6QUeSE3A+YOi8O2LA7bsr+xd6RCSI6K+uYZrIaB6R1JKhjTO5IkwKCfGnOH\nxWFbFodtadCXpFIxpy9JBWNOX5IE9B/0dwQ2AVcl5TFgPXALMZvWaJ/vP7TMHRaHbVkctmX/Qf9M\n4GZaE6CfTQT9A4ENTJ8qUZKUoX5y+vsCq4F/BN4KvATYDBxDa4L0GrCs43Xm9CUpRWnl9D8IvB14\npG3dYiLgkzwv7uP9JUkD1mvQfzFwF5HPn+lsYZJW2qd0zB0Wh21ZHLZl72PvPA94KfAi4A+BxwMX\n00rrbAGWEDuGaarVKpVKBYDR0VHGx8eZmJgAWo0y7OWmvNTHcu/ler2eq/pY7r1cr9dzVZ9BlZvL\njUaDuQyin/4xwNuInP77ga3AKuIi7ijTL+aa05ekFC1EP/1mFH8f8EKiy+YLkrIkKScGEfS/RqR6\nALYBy4kumyuA7QN4/6HUftql4WZbFodt6R25klQqjr0jSQXj2DuSJMCgnxpzh8VhWxaHbWnQl6RS\nMacvSQVjTl+SBBj0U2PusDhsy+KwLQ36klQq5vQlqWDM6UuSAIN+aswdFodtWRy2pUFfkkrFnL4k\nFYw5fUkS0HvQ/0Pgu0AduBl4b7J+DFhPTKKyjpg5q5TMHRaHbVkctmXvQf9B4FhgHHhGsnwUMTXi\nemISlQ1MnypRkpShQeT0dyVmz6oCnyPmzG1OkF4DlnVsb05fklKUVk5/ByK9cyewEfgBsDgpkzwv\n7uP9JUkD1k/Qf4RI7+wLPJ9I8bSbpDVheumYOywO27I4bEvYaQDvcS/w78DhtNI6W4AlwF3dXlCt\nVqlUKgCMjo4yPj7OxMQE0GqUYS835aU+lnsv1+v1XNXHcu/ler2eq/oMqtxcbjQazKXXnP5ewO+B\n7cDjgK8A5wHHAVuBVcRF3FGmX8w1py9JKZotp99r0D8YWEOkh3YALgY+QHTZXAssBRrAScSOoZ1B\nX5JSlMaF3BuBw2h12fxAsn4bsJzosrmC6QG/NNpPuzTcbMvisC29I1eSSsWxdySpYBx7R5IEGPRT\nY+6wOGzL4rAtDfqSVCrm9CWpYMzpS5IAg35qzB0Wh21ZHLalQV+SSsWcviQVjDl9SRJg0E+NucPi\nsC2Lw7Y06EtSqZjTl6SCMacvSQJ6D/pPojUZ+k3AGcn6MWA9cAuwjpg5q5TMHRaHbVkctmXvQf93\nwFnAQcBzgDcDTyWmRlxPTKKygelTJUqSMjSonP7ngY8kj2NoTZBeA5Z1bGtOX5JSlHZOvwIcCnwX\nWEwEfJLnxQN4f0nSgPQb9HcHPgecCdzX8bPJ5FFK5g6Lw7YsDtsSdurjtX9ABPyLifQOtNI6W4Al\nwF3dXlitVqlUKgCMjo4yPj7OxMQE0GqUYS835aU+lnsv1+v1XNXHcu/ler2eq/oMqtxcbjQazKXX\nnP4IsAbYSlzQbXp/sm4VcRF3lOkXc83pS1KKZsvp9xr0jwK+DnyfVgrnHOBaYC2wFGgAJwHbO15r\n0JekFKVxIfcbyWvHiYu4hwJfBrYBy4kumyuYHvBLo/20S8PNtiwO29I7ciWpVBx7R5IKxrF3JEmA\nQT815g6Lw7YsDtvSoC9JpWJOX5IKxpy+JAkw6KfG3GFx2JbFcf75tayrkDmDvqTSSIbeKTWDfkqa\nAyJp+NmWxVGpTGRdhcz1M8qmJOVerRYPgPPOa62fmIhH2dh7JyW1Ws0jxIKwLYujWq2xevVE1tVI\nnb13JEmAR/qSSqRWK0dKJ43x9Pth0JekFKWV3vkkMT3ijW3rxoD1wC3AOmLmrFKyb3dx2JbFYVv2\nF/QvAo7vWHc2EfQPBDYwfarE0qjbIbgwbMvisC37C/rXAPd0rHspMXcuyfPL+3j/obZ9e2knDSsc\n27I4bMvB995ZTKR8SJ4XD/j9JUl9SLPL5iStSdNLp9FoZF0FDYhtWRy2Zf+9dyrAVcDBSXkzMAFs\nAZYAG4FlHa+pA4f0+bmSpJndAIx3+8Ggh2H4InAasCp5/nyXbbpWRJKUb5cCPwceAn4GvJ7osvkf\n2GVTkiRJUhG8HHgEeEpSniCudbRbDZy4cFVSDx4GNgE3Edee3krrutcEcG/y8+bjBQtfRc3TI8A/\nt5XfBpybLK8E/nahK5QXDrg2GKcC/zd5nkmpezMNiQeAQ4GnAy8ETqAVKAC+lvy8+fjqQldQ8/YQ\n8Apgz6Tc/rdX6r9Dg37/dgeOAP4aOHmObbMY60i9uRv470S7Ntl+w+N3wMeAs7KuSN4Y9Pv3MuDL\nwO1EoDgs2+pogG4DdgT2TspHMzW9s39G9dL8fBT4C+DxWVckTwz6/TsV+Gyy/Nmk/MgM25b6tLIA\nrmFqeue2bKujOdwHfAo4I+uK5InTJfZnDDiWyAFPEkeFk8S4Q4u6bHv3gtZO/XoycXHXdhte5wPX\nEwNECo/0+/Uq4kiiQpzqLyWO/saAfWjdjbwfcReyQ/wNj72BC4ELsq6I+nIPsBb4S1pn2l6bUc++\nCqzoWPc3RC7xecC3idzvtcCfLWzV1IPfM73LZtMxwHam5vRfudAV1Lz9qm35icD9wHuS8rnEzuBn\nyeP2ha2aJEmSJEmSJEmSJEmSJEmSJOVOBfgNcaflfsCNHT9fydxD5x4OfGjQFevwzeR5P2YfgbXp\nZOBHTB+WWxoY78jVsPoxMbhdt7sr5zPG0XXAmQOox2xDmRyZPO8PvHoe73UZcHrfNZJmYdBXUTUD\nfw14H/Bd4IfAUcn6CeKIeoQYOuMJba/9ETEMw97A5cQd1dcSd1lDnElcDHyDGGfpoOTnm4gJqQ9I\ntvt18vw+WiN0voUYl/+Qts/7BnBwsuwQAUqVA66p6JoD4R1Ba1KUF3b8/AvEhBurk+1uIwZZuwT4\nIJGmWUoMof205HXLiB3Ib4EPEwN7XUL8Te3U9t4A7yRmbnpJUt4GVImx3g8EdmF6ikpKhUf6GnYz\npXLa11+RPF9PXA/odBmtCXBOScoAy4GPEEfoXwD2AHZL3vuLRMCHGGPp74B3JO//YMf7dx69Xw68\nmNg5vAFHgNQCMuhr2G1l+jDWewK/bCs3g/PDdD+7/Q7wJ8BexKQ4zZ3ECHHk3xw//0nEwF0QUys2\nXUocxf8GuJoYbns2DwDribmV/xz49BzbSwNj0New+zXwC1qBdgw4jsiTz9ckcCWRyrmZGIERYB1T\nJ+A4hO72J1JCFxBnBAd3/Pw+4iyh3SeItNC1xITr0oIw6KsIXge8m0jDbCAutM40q9VME2RfRkyt\nd1nbujOAZxIXZ38A/NUMrz2JGI55E3FR91Md29xAnGXUafUYup4I9p2pHWdXk6QOFYb/wuc+RG+i\nThPYT1+SptiXmPji+qwr0qPXEfU/sWP9ycQZxZoFr5EkSZIkSZIkSZIkSZIkSZKkQfn/RhI26QIo\nIOQAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x9c65ed0>"
+        "<matplotlib.figure.Figure at 0xac5c150>"
        ]
       }
      ],
-     "prompt_number": 147
+     "prompt_number": 166
     },
     {
      "cell_type": "code",
@@ -581,33 +711,37 @@
       "df.boxplot(column=['Grade'], by=['Sex'])"
      ],
      "language": "python",
-     "metadata": {},
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
      "outputs": [
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 148,
+       "prompt_number": 167,
        "text": [
-        "<matplotlib.axes.AxesSubplot at 0xa942310>"
+        "<matplotlib.axes.AxesSubplot at 0xb5a9590>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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3MTeVM4v3UOTK/KiGjW02f53ekXtDenwrlT8JnANsI9I624BlwPZ2J09NTVEulwEolUpU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\nR9MY/ilJkiRJkiRJkiRJkiRJUvL/ia8+8LiPvcIAAAAASUVORK5CYII=\n",
        "text": [
-        "<matplotlib.figure.Figure at 0xa55abd0>"
+        "<matplotlib.figure.Figure at 0xb5acd10>"
        ]
       }
      ],
-     "prompt_number": 148
+     "prompt_number": 167
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "groupGrades.plot()\n",
-      "\n"
+      "\n",
+      "type(groupGrades)"
      ],
      "language": "python",
      "metadata": {},
@@ -615,41 +749,57 @@
       {
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 136,
+       "prompt_number": 209,
        "text": [
-        "University\n",
-        "AU            Axes(0.125,0.125;0.775x0.775)\n",
-        "DE            Axes(0.125,0.125;0.775x0.775)\n",
-        "NL            Axes(0.125,0.125;0.775x0.775)\n",
-        "dtype: object"
+        "pandas.core.groupby.DataFrameGroupBy"
        ]
-      },
+      }
+     ],
+     "prompt_number": 209
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "\n",
+      "for group, data in groupGrades:\n",
+      "    fig, ax = plt.subplots(1, 1)\n",
+      "    ax.get_xaxis().set_visible(False) # hide ticks\n",
+      "    data.plot( style='-', table=True, ax = ax, secondary_y=['Outlier'], mark_right=False)"
+     ],
+     "language": "python",
+     "metadata": {
+      "slideshow": {
+       "slide_type": "subslide"
+      }
+     },
+     "outputs": [
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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HRETI37k4HPgNq/crFVgK3JVh+/8B/8zvB9olMRngS2Ab8P+c62oDx5yvjzmX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\nYrFRvXp1Ro8eTWBgINdeey2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A9u3b8fb2pmHDhpfFtXPnzmzcuJFVq1bx4IMP8vTTT+Pj\n40OPHj1YvHixh2puT4qn+yiW7qV4Zlaga0w333wzycnJLFiwALCuC40ePZohQ4bQqFEjdu7ciTGG\ngwcPsmXLFtd+ZcuWzXNo4q233sprr73mKrd3716Sk5MLejwlSmJiIr6+vnh7e7Nu3ToOHDhwWZm4\nuDhq1arF0KFDGTp0KDt27KB9+/Zs2rSJ/fv3A1b/9b59+4q6+rajeLqPYuleimdmBWoxgTVJ4IgR\nI3jhhRdIT0/njjvuYPLkyZQtW5aGDRvSsmVLWrRoQdu2bV37DBs2jODgYNq2bcuCBQsydQleej10\n6FBiY2Np06YNxhh8fX1Zvnw5Doej1Px+Ietx/v3vf+fOO+8kODiYdu3a0aJFi8vKrlu3jmnTplG2\nbFkqV67MO++8Q82aNZk3bx79+/fnzz//BGDSpEk0bdq06A7GBhRP91Es3UvxzJ1mfhAXxdO9FE/3\nUSzdq0QNFxcRESlsSkwiImIrec2Vd9bhcFQuqsoUlObPci/F070UT/dRLN2rTJkyZ+08V56IiIiI\niIiIiIiIiIiIiIiIiIiIuMv/B/aqTY+jz+aEAAAAAElFTkSuQmCC\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7c84310>"
+        "<matplotlib.figure.Figure at 0x163a33d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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Hjy+wPGlKIhERyZE/BltfUWASEfGQ3JrbilpzXH74+29JTXkihYy/NuXZkZryREREPECB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\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7c843d0>"
+        "<matplotlib.figure.Figure at 0x163a32d0>"
        ]
       },
       {
        "metadata": {},
        "output_type": "display_data",
-       "png": 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M4KuvvpKuNw+Q1vxj1arBiy/CsGFaITy32b4dDh+GgQMdf4zUYYzJCFPFhKF1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\nEEIIIYQQQgghhBBCiP+zfwAWi45j3oS4wAAAAABJRU5ErkJggg==\n",
        "text": [
-        "<matplotlib.figure.Figure at 0x7eab890>"
+        "<matplotlib.figure.Figure at 0x15989390>"
        ]
       }
      ],
-     "prompt_number": 136
+     "prompt_number": 229
     }
    ],
    "metadata": {}
-- 
GitLab