yogeshchappewar/Visualization_matplotlib_seaborn.ipynb

## Visualization_matplotlib_seaborn.ipynb

      
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## Visualization_matplotlib_seaborn_numpy.ipynb
{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# visualization basics"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.087843Z",
          "end_time": "2022-12-26T09:52:10.095945Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import warnings\nwarnings.filterwarnings('ignore')",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.115969Z",
          "end_time": "2022-12-26T09:52:10.153670Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df = pd.read_csv('Universities.csv')\ndf",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 13,
          "data": {
            "text/plain": "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n0          Brown  1310     89      22       13     22704        94\n1        CalTech  1415    100      25        6     63575        81\n2            CMU  1260     62      59        9     25026        72\n3       Columbia  1310     76      24       12     31510        88\n4        Cornell  1280     83      33       13     21864        90\n5      Dartmouth  1340     89      23       10     32162        95\n6           Duke  1315     90      30       12     31585        95\n7     Georgetown  1255     74      24       12     20126        92\n8        Harvard  1400     91      14       11     39525        97\n9   JohnsHopkins  1305     75      44        7     58691        87\n10           MIT  1380     94      30       10     34870        91\n11  Northwestern  1260     85      39       11     28052        89\n12     NotreDame  1255     81      42       13     15122        94\n13     PennState  1081     38      54       18     10185        80\n14     Princeton  1375     91      14        8     30220        95\n15        Purdue  1005     28      90       19      9066        69\n16      Stanford  1360     90      20       12     36450        93\n17      TexasA&M  1075     49      67       25      8704        67\n18    UCBerkeley  1240     95      40       17     15140        78\n19      UChicago  1290     75      50       13     38380        87\n20     UMichigan  1180     65      68       16     15470        85\n21         UPenn  1285     80      36       11     27553        90\n22           UVA  1225     77      44       14     13349        92\n23    UWisconsin  1085     40      69       15     11857        71\n24          Yale  1375     95      19       11     43514        96",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Univ</th>\n      <th>SAT</th>\n      <th>Top10</th>\n      <th>Accept</th>\n      <th>SFRatio</th>\n      <th>Expenses</th>\n      <th>GradRate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brown</td>\n      <td>1310</td>\n      <td>89</td>\n      <td>22</td>\n      <td>13</td>\n      <td>22704</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CalTech</td>\n      <td>1415</td>\n      <td>100</td>\n      <td>25</td>\n      <td>6</td>\n      <td>63575</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CMU</td>\n      <td>1260</td>\n      <td>62</td>\n      <td>59</td>\n      <td>9</td>\n      <td>25026</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Columbia</td>\n      <td>1310</td>\n      <td>76</td>\n      <td>24</td>\n      <td>12</td>\n      <td>31510</td>\n      <td>88</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cornell</td>\n      <td>1280</td>\n      <td>83</td>\n      <td>33</td>\n      <td>13</td>\n      <td>21864</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Dartmouth</td>\n      <td>1340</td>\n      <td>89</td>\n      <td>23</td>\n      <td>10</td>\n      <td>32162</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Duke</td>\n      <td>1315</td>\n      <td>90</td>\n      <td>30</td>\n      <td>12</td>\n      <td>31585</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Georgetown</td>\n      <td>1255</td>\n      <td>74</td>\n      <td>24</td>\n      <td>12</td>\n      <td>20126</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Harvard</td>\n      <td>1400</td>\n      <td>91</td>\n      <td>14</td>\n      <td>11</td>\n      <td>39525</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>JohnsHopkins</td>\n      <td>1305</td>\n      <td>75</td>\n      <td>44</td>\n      <td>7</td>\n      <td>58691</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>MIT</td>\n      <td>1380</td>\n      <td>94</td>\n      <td>30</td>\n      <td>10</td>\n      <td>34870</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Northwestern</td>\n      <td>1260</td>\n      <td>85</td>\n      <td>39</td>\n      <td>11</td>\n      <td>28052</td>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>NotreDame</td>\n      <td>1255</td>\n      <td>81</td>\n      <td>42</td>\n      <td>13</td>\n      <td>15122</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>PennState</td>\n      <td>1081</td>\n      <td>38</td>\n      <td>54</td>\n      <td>18</td>\n      <td>10185</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Princeton</td>\n      <td>1375</td>\n      <td>91</td>\n      <td>14</td>\n      <td>8</td>\n      <td>30220</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Purdue</td>\n      <td>1005</td>\n      <td>28</td>\n      <td>90</td>\n      <td>19</td>\n      <td>9066</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Stanford</td>\n      <td>1360</td>\n      <td>90</td>\n      <td>20</td>\n      <td>12</td>\n      <td>36450</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>TexasA&amp;M</td>\n      <td>1075</td>\n      <td>49</td>\n      <td>67</td>\n      <td>25</td>\n      <td>8704</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>UCBerkeley</td>\n      <td>1240</td>\n      <td>95</td>\n      <td>40</td>\n      <td>17</td>\n      <td>15140</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>UChicago</td>\n      <td>1290</td>\n      <td>75</td>\n      <td>50</td>\n      <td>13</td>\n      <td>38380</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>UMichigan</td>\n      <td>1180</td>\n      <td>65</td>\n      <td>68</td>\n      <td>16</td>\n      <td>15470</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>UPenn</td>\n      <td>1285</td>\n      <td>80</td>\n      <td>36</td>\n      <td>11</td>\n      <td>27553</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>UVA</td>\n      <td>1225</td>\n      <td>77</td>\n      <td>44</td>\n      <td>14</td>\n      <td>13349</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>UWisconsin</td>\n      <td>1085</td>\n      <td>40</td>\n      <td>69</td>\n      <td>15</td>\n      <td>11857</td>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Yale</td>\n      <td>1375</td>\n      <td>95</td>\n      <td>19</td>\n      <td>11</td>\n      <td>43514</td>\n      <td>96</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.158238Z",
          "end_time": "2022-12-26T09:52:10.169840Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df.shape",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "(25, 7)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.172475Z",
          "end_time": "2022-12-26T09:52:10.186000Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "pd.set_option('display.min_rows',None)",
      "execution_count": 15,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.188187Z",
          "end_time": "2022-12-26T09:52:10.202517Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "df",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 16,
          "data": {
            "text/plain": "            Univ   SAT  Top10  Accept  SFRatio  Expenses  GradRate\n0          Brown  1310     89      22       13     22704        94\n1        CalTech  1415    100      25        6     63575        81\n2            CMU  1260     62      59        9     25026        72\n3       Columbia  1310     76      24       12     31510        88\n4        Cornell  1280     83      33       13     21864        90\n5      Dartmouth  1340     89      23       10     32162        95\n6           Duke  1315     90      30       12     31585        95\n7     Georgetown  1255     74      24       12     20126        92\n8        Harvard  1400     91      14       11     39525        97\n9   JohnsHopkins  1305     75      44        7     58691        87\n10           MIT  1380     94      30       10     34870        91\n11  Northwestern  1260     85      39       11     28052        89\n12     NotreDame  1255     81      42       13     15122        94\n13     PennState  1081     38      54       18     10185        80\n14     Princeton  1375     91      14        8     30220        95\n15        Purdue  1005     28      90       19      9066        69\n16      Stanford  1360     90      20       12     36450        93\n17      TexasA&M  1075     49      67       25      8704        67\n18    UCBerkeley  1240     95      40       17     15140        78\n19      UChicago  1290     75      50       13     38380        87\n20     UMichigan  1180     65      68       16     15470        85\n21         UPenn  1285     80      36       11     27553        90\n22           UVA  1225     77      44       14     13349        92\n23    UWisconsin  1085     40      69       15     11857        71\n24          Yale  1375     95      19       11     43514        96",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Univ</th>\n      <th>SAT</th>\n      <th>Top10</th>\n      <th>Accept</th>\n      <th>SFRatio</th>\n      <th>Expenses</th>\n      <th>GradRate</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>Brown</td>\n      <td>1310</td>\n      <td>89</td>\n      <td>22</td>\n      <td>13</td>\n      <td>22704</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>CalTech</td>\n      <td>1415</td>\n      <td>100</td>\n      <td>25</td>\n      <td>6</td>\n      <td>63575</td>\n      <td>81</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>CMU</td>\n      <td>1260</td>\n      <td>62</td>\n      <td>59</td>\n      <td>9</td>\n      <td>25026</td>\n      <td>72</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>Columbia</td>\n      <td>1310</td>\n      <td>76</td>\n      <td>24</td>\n      <td>12</td>\n      <td>31510</td>\n      <td>88</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>Cornell</td>\n      <td>1280</td>\n      <td>83</td>\n      <td>33</td>\n      <td>13</td>\n      <td>21864</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>Dartmouth</td>\n      <td>1340</td>\n      <td>89</td>\n      <td>23</td>\n      <td>10</td>\n      <td>32162</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>Duke</td>\n      <td>1315</td>\n      <td>90</td>\n      <td>30</td>\n      <td>12</td>\n      <td>31585</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>Georgetown</td>\n      <td>1255</td>\n      <td>74</td>\n      <td>24</td>\n      <td>12</td>\n      <td>20126</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>Harvard</td>\n      <td>1400</td>\n      <td>91</td>\n      <td>14</td>\n      <td>11</td>\n      <td>39525</td>\n      <td>97</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>JohnsHopkins</td>\n      <td>1305</td>\n      <td>75</td>\n      <td>44</td>\n      <td>7</td>\n      <td>58691</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>MIT</td>\n      <td>1380</td>\n      <td>94</td>\n      <td>30</td>\n      <td>10</td>\n      <td>34870</td>\n      <td>91</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>Northwestern</td>\n      <td>1260</td>\n      <td>85</td>\n      <td>39</td>\n      <td>11</td>\n      <td>28052</td>\n      <td>89</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>NotreDame</td>\n      <td>1255</td>\n      <td>81</td>\n      <td>42</td>\n      <td>13</td>\n      <td>15122</td>\n      <td>94</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>PennState</td>\n      <td>1081</td>\n      <td>38</td>\n      <td>54</td>\n      <td>18</td>\n      <td>10185</td>\n      <td>80</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>Princeton</td>\n      <td>1375</td>\n      <td>91</td>\n      <td>14</td>\n      <td>8</td>\n      <td>30220</td>\n      <td>95</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>Purdue</td>\n      <td>1005</td>\n      <td>28</td>\n      <td>90</td>\n      <td>19</td>\n      <td>9066</td>\n      <td>69</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>Stanford</td>\n      <td>1360</td>\n      <td>90</td>\n      <td>20</td>\n      <td>12</td>\n      <td>36450</td>\n      <td>93</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>TexasA&amp;M</td>\n      <td>1075</td>\n      <td>49</td>\n      <td>67</td>\n      <td>25</td>\n      <td>8704</td>\n      <td>67</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>UCBerkeley</td>\n      <td>1240</td>\n      <td>95</td>\n      <td>40</td>\n      <td>17</td>\n      <td>15140</td>\n      <td>78</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>UChicago</td>\n      <td>1290</td>\n      <td>75</td>\n      <td>50</td>\n      <td>13</td>\n      <td>38380</td>\n      <td>87</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>UMichigan</td>\n      <td>1180</td>\n      <td>65</td>\n      <td>68</td>\n      <td>16</td>\n      <td>15470</td>\n      <td>85</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>UPenn</td>\n      <td>1285</td>\n      <td>80</td>\n      <td>36</td>\n      <td>11</td>\n      <td>27553</td>\n      <td>90</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>UVA</td>\n      <td>1225</td>\n      <td>77</td>\n      <td>44</td>\n      <td>14</td>\n      <td>13349</td>\n      <td>92</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>UWisconsin</td>\n      <td>1085</td>\n      <td>40</td>\n      <td>69</td>\n      <td>15</td>\n      <td>11857</td>\n      <td>71</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>Yale</td>\n      <td>1375</td>\n      <td>95</td>\n      <td>19</td>\n      <td>11</td>\n      <td>43514</td>\n      <td>96</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:10.204835Z",
          "end_time": "2022-12-26T09:52:10.333675Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(df['SAT'])\nplt.show()",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:52:35.237791Z",
          "end_time": "2022-12-26T09:52:35.368443Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.hist(df['Accept'])\nplt.show()",
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:54:04.990085Z",
          "end_time": "2022-12-26T09:54:05.143814Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.histplot(df['SAT'])",
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 20,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='SAT', ylabel='Count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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r10/9+vWTJPl8PnXs2FHz58/XzTfffN5zRUZGqlOnTiHH6tevryZNmpQ7DgAArkwXdY3KnDlz9OijjyoqKqrcYx6PR+PGjdPs2bOrbDgAAHBlu6hQ+fLLLzVw4MCzPt6/f39lZmZWeph169Zpzpw5lf56AABQs1xUqBw6dKjCH0s+IyIiQj/99NMlDwUAACBdZKhcc8015S6A/bXt27crNjb2kocCAACQLjJU/vjHP+qFF17QyZMnyz1WXFysqVOnasiQIVU2HAAAuLJd1E/9PPfcc1q+fLnatGmjCRMmqG3btnK5XNq1a5def/11lZaW6tlnn71cswIAgCvMRYVK8+bNtXHjRv3lL39RcnKyHMeRJLlcLg0YMEBvvPGGmjdvflkGBQAAV56L/oVvLVu21EcffaQjR47ou+++k+M4+v3vf88nHwMAgCpXqd9MK0mNGjXSDTfcUJWzAAAAhKjUhxICAACEA6ECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsZTRU5s2bpy5duigqKkpRUVHq2bOnPv74Y5MjAQAAixgNlRYtWmjWrFnKyMhQRkaGbr/9dg0fPlw7d+40ORYAALBEhMknHzp0aMj9mTNnat68edq0aZM6duxoaCoAAGALo6Hya6WlpfrPf/6j48ePq2fPnhWuCQQCCgQCwft+vz9c4+E8cnNzVVhYaHqMaiE6Olrx8fGmxwCAasF4qOzYsUM9e/bUyZMn1aBBA61YsUIdOnSocG1KSoqmT58e5glxPrm5uWrXrr2Ki0+YHqVaqFfvau3evYtYAYALYDxU2rZtq+zsbB09elTvv/++Ro8erfXr11cYK8nJyUpKSgre9/v98nq94RwXFSgsLFRx8Qn1GDNVUbEJpsexmj9/rza/NV2FhYWECgBcAOOhUrduXV177bWSpO7du2vr1q167bXX9K9//avcWrfbLbfbHe4RcYGiYhPUOL6t6TEAADWIdb9HxXGckOtQAADAlcvoKypTpkzRoEGD5PV6dezYMS1dulTr1q3T6tWrTY4FAAAsYTRUDh06pMTEROXn58vj8ahLly5avXq1+vXrZ3IsAABgCaOhsnDhQpNPDwAALGfdNSoAAABnECoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBahAoAALAWoQIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxkNlZSUFN1www2KjIxUs2bNdOedd+rrr782ORIAALCI0VBZv369xo8fr02bNiktLU2nT59W//79dfz4cZNjAQAAS0SYfPLVq1eH3E9NTVWzZs2UmZmpW265xdBUAADAFkZD5bd8Pp8kqXHjxhU+HggEFAgEgvf9fn9Y5gIAAGZYczGt4zhKSkrSTTfdpE6dOlW4JiUlRR6PJ3jzer1hnhIAAISTNaEyYcIEbd++Xe+9995Z1yQnJ8vn8wVveXl5YZwQAACEmxVv/UycOFGrVq3Shg0b1KJFi7Ouc7vdcrvdYZwMAACYZDRUHMfRxIkTtWLFCq1bt06tWrUyOQ4AALCM0VAZP368lixZov/+97+KjIzUwYMHJUkej0f16tUzORoAALCA0WtU5s2bJ5/Pp9tuu02xsbHB27Jly0yOBQAALGH8rR8AAICzseanfgAAAH6LUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYy2iobNiwQUOHDlVcXJxcLpdWrlxpchwAAGAZo6Fy/Phxde3aVXPnzjU5BgAAsFSEyScfNGiQBg0aZHIEAABgMaOhcrECgYACgUDwvt/vv6zPl5ubq8LCwsv6HDXBrl27TI9Q7fA9Oz++RwCkahYqKSkpmj59elieKzc3V+3atVdx8YmwPF9NUBI4ZXoE6xX7DktyadSoUaZHqTb4ewVc2apVqCQnJyspKSl43+/3y+v1XpbnKiwsVHHxCfUYM1VRsQmX5Tlqivwd6cpZtUCnT582PYr1Sk4ck+Tougcmq2mrdqbHsRp/rwBI1SxU3G633G53WJ8zKjZBjePbhvU5qxt//l7TI1Q7DZrF8/fqPPh7BUDi96gAAACLGX1FpaioSN99913w/p49e5Sdna3GjRsrPj7e4GQAAMAGRkMlIyNDffr0Cd4/c/3J6NGjtWjRIkNTAQAAWxgNldtuu02O45gcAQAAWIxrVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEAANYiVAAAgLUIFQAAYC1CBQAAWItQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1jIfKG2+8oVatWumqq65St27d9Pnnn5seCQAAWMJoqCxbtkyTJk3Ss88+q6ysLN18880aNGiQcnNzTY4FAAAsYTRUZs+erUceeURjx45V+/btNWfOHHm9Xs2bN8/kWAAAwBIRpp741KlTyszM1DPPPBNyvH///tq4cWOFXxMIBBQIBIL3fT6fJMnv91f5fEVFRZKkn/d9rdOB4io/f03iz98nSfLt/1Z1IlyGp7Eb36sLx/fqwvG9unB8ry6O/+Av73AUFRVV6X9rz5zLcZzzL3YM2b9/vyPJ+d///hdyfObMmU6bNm0q/JqpU6c6krhx48aNGzduNeCWl5d33l4w9orKGS5XaNE6jlPu2BnJyclKSkoK3i8rK9PPP/+sJk2anPVrzsfv98vr9SovL09RUVGVOgfCj32rnti36ol9q55s3jfHcXTs2DHFxcWdd62xUImOjlbt2rV18ODBkOMFBQVq3rx5hV/jdrvldrtDjjVs2LBK5omKirJuI3F+7Fv1xL5VT+xb9WTrvnk8ngtaZ+xi2rp166pbt25KS0sLOZ6WlqZevXoZmgoAANjE6Fs/SUlJSkxMVPfu3dWzZ08tWLBAubm5euyxx0yOBQAALGE0VO677z4dPnxYM2bMUH5+vjp16qSPPvpILVu2DNsMbrdbU6dOLfeWEuzGvlVP7Fv1xL5VTzVl31yOcyE/GwQAABB+xn+FPgAAwNkQKgAAwFqECgAAsBahAgAArFUjQmXDhg0aOnSo4uLi5HK5tHLlypDHHcfRtGnTFBcXp3r16um2227Tzp07Q9YEAgFNnDhR0dHRql+/voYNG6Yff/wxZM2RI0eUmJgoj8cjj8ejxMREHT169DL/6Wqu8+3b8uXLNWDAAEVHR8vlcik7O7vcOdi38DvXvpWUlGjy5Mnq3Lmz6tevr7i4OD344IM6cOBAyDnYt/A73z9v06ZNU7t27VS/fn01atRIffv21ebNm0PWsG/hd759+7Vx48bJ5XJpzpw5Icer+77ViFA5fvy4unbtqrlz51b4+Msvv6zZs2dr7ty52rp1q2JiYtSvXz8dO3YsuGbSpElasWKFli5dqi+++EJFRUUaMmSISktLg2seeOABZWdna/Xq1Vq9erWys7OVmJh42f98NdX59u348ePq3bu3Zs2addZzsG/hd659O3HihLZt26bnn39e27Zt0/Lly/XNN99o2LBhIevYt/A73z9vbdq00dy5c7Vjxw598cUXSkhIUP/+/fXTTz8F17Bv4Xe+fTtj5cqV2rx5c4W/kr7a79ulfrigbSQ5K1asCN4vKytzYmJinFmzZgWPnTx50vF4PM78+fMdx3Gco0ePOnXq1HGWLl0aXLN//36nVq1azurVqx3HcZyvvvrKkeRs2rQpuCY9Pd2R5Ozevfsy/6lqvt/u26/t2bPHkeRkZWWFHGffzDvXvp2xZcsWR5Kzb98+x3HYNxtcyL75fD5HkrNmzRrHcdg3G5xt33788UfnmmuucXJycpyWLVs6r776avCxmrBvNeIVlXPZs2ePDh48qP79+wePud1u3Xrrrdq4caMkKTMzUyUlJSFr4uLi1KlTp+Ca9PR0eTwe9ejRI7jmxhtvlMfjCa5BeLFv1YPP55PL5Qp+Lhf7Zr9Tp05pwYIF8ng86tq1qyT2zVZlZWVKTEzU008/rY4dO5Z7vCbsm/FPT77cznzo4W8/6LB58+bat29fcE3dunXVqFGjcmvOfP3BgwfVrFmzcudv1qxZuQ9WRHiwb/Y7efKknnnmGT3wwAPBD0Vj3+z1wQcfaMSIETpx4oRiY2OVlpam6OhoSeybrV566SVFREToiSeeqPDxmrBvNf4VlTNcLlfIfcdxyh37rd+uqWj9hZwH4cW+2aGkpEQjRoxQWVmZ3njjjfOuZ9/M69Onj7Kzs7Vx40YNHDhQ9957rwoKCs75NeybOZmZmXrttde0aNGii/7+Vqd9q/GhEhMTI0nlqrCgoCD4KktMTIxOnTqlI0eOnHPNoUOHyp3/p59+KvdqDcKDfbNXSUmJ7r33Xu3Zs0dpaWkhHzHPvtmrfv36uvbaa3XjjTdq4cKFioiI0MKFCyWxbzb6/PPPVVBQoPj4eEVERCgiIkL79u3TX//6VyUkJEiqGftW40OlVatWiomJUVpaWvDYqVOntH79evXq1UuS1K1bN9WpUydkTX5+vnJycoJrevbsKZ/Ppy1btgTXbN68WT6fL7gG4cW+2elMpHz77bdas2aNmjRpEvI4+1Z9OI6jQCAgiX2zUWJiorZv367s7OzgLS4uTk8//bQ++eQTSTVk38xcw1u1jh075mRlZTlZWVmOJGf27NlOVlZW8KcMZs2a5Xg8Hmf58uXOjh07nPvvv9+JjY11/H5/8ByPPfaY06JFC2fNmjXOtm3bnNtvv93p2rWrc/r06eCagQMHOl26dHHS09Od9PR0p3Pnzs6QIUPC/uetKc63b4cPH3aysrKcDz/80JHkLF261MnKynLy8/OD52Dfwu9c+1ZSUuIMGzbMadGihZOdne3k5+cHb4FAIHgO9i38zrVvRUVFTnJyspOenu7s3bvXyczMdB555BHH7XY7OTk5wXOwb+F3vn9P/tZvf+rHcar/vtWIUPnss88cSeVuo0ePdhznlx9Rnjp1qhMTE+O43W7nlltucXbs2BFyjuLiYmfChAlO48aNnXr16jlDhgxxcnNzQ9YcPnzYGTlypBMZGelERkY6I0eOdI4cORKmP2XNc759S01NrfDxqVOnBs/BvoXfufbtzI+SV3T77LPPgudg38LvXPtWXFzs/OlPf3Li4uKcunXrOrGxsc6wYcOcLVu2hJyDfQu/8/178rcqCpXqvm8ux3Gcy/VqDQAAwKWo8deoAACA6otQAQAA1iJUAACAtQgVAABgLUIFAABYi1ABAADWIlQAAIC1CBUAAGAtQgUAAFiLUAEQVgUFBRo3bpzi4+PldrsVExOjAQMGKD09PWTdxo0bVbt2bQ0cODB47KGHHpLL5TrnDUDNwq/QBxBWN998s0pKSpSSkqLWrVvr0KFDWrt2rbp06aLBgwcH140dO1YNGjTQv//9b3311VeKj4+Xz+dTcXFxcE1sbKxSU1NDYiYmJiasfx4AlxehAiBsjh49qkaNGmndunW69dZbz7ru+PHjio2N1datWzV16lR16NBBL7zwQrl1LpdLK1as0J133nkZpwZgEm/9AAibBg0aqEGDBlq5cqUCgcBZ1y1btkxt27ZV27ZtNWrUKKWmpor/TwVcmQgVAGETERGhRYsW6e2331bDhg3Vu3dvTZkyRdu3bw9Zt3DhQo0aNUqSNHDgQBUVFWnt2rUmRgZgGKECIKzuuusuHThwQKtWrdKAAQO0bt06XX/99Vq0aJEk6euvv9aWLVs0YsQISb/EzX333ae33nrL4NQATOEaFQDGjR07Vmlpadq3b5/+7//+T//4xz9Uu3bt4OOO46hOnTrKz89Xo0aNgse5RgWo+XhFBYBxHTp00PHjx3X69Gm98847euWVV5SdnR28ffnll2rZsqXeffdd06MCCLMI0wMAuHIcPnxY99xzj8aMGaMuXbooMjJSGRkZevnllzV8+HB98MEHOnLkiB555BF5PJ6Qr7377ru1cOFCTZgwwdD0AEwgVACETYMGDdSjRw+9+uqr+v7771VSUiKv16tHH31UU6ZM0b333qu+ffuWixTpl2tbXnzxRW3btk3XX3+9gekBmMA1KgAAwFpcowIAAKxFqAAAAGsRKgAAwFqECgAAsBahAgAArEWoAAAAaxEqAADAWoQKAACwFqECAACsRagAAABrESoAAMBa/w/s3CeQaSDCwAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T09:59:06.804291Z",
          "end_time": "2022-12-26T09:59:06.957824Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.histplot(df['Accept'])",
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 21,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='Accept', ylabel='Count'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:09:40.855407Z",
          "end_time": "2022-12-26T10:09:40.993845Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(df['SAT'],df['Top10'],'rs')\nplt.xlabel('SAT')\nplt.ylabel('Top10')\nplt.title('SAT vs Top10')",
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 24,
          "data": {
            "text/plain": "Text(0.5, 1.0, 'SAT vs Top10')"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:12:55.490495Z",
          "end_time": "2022-12-26T10:12:55.621276Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.plot(df['SAT'],df['Accept'],'rs')",
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 25,
          "data": {
            "text/plain": "[<matplotlib.lines.Line2D at 0x251f68556d0>]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:19:24.222167Z",
          "end_time": "2022-12-26T10:19:24.358978Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.scatterplot(x='SAT',y='Accept',data=df,)",
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 30,
          "data": {
            "text/plain": "<AxesSubplot:xlabel='SAT', ylabel='Accept'>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:23:24.440801Z",
          "end_time": "2022-12-26T10:23:29.028827Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.pairplot(df.iloc[:,2:])",
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 33,
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x251fc660880>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1250x1250 with 30 Axes>",
            "image/png": 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86zJs2DCVlZWpublZzz//vL7whS/EGmibNm2KPS4SiWjz5s264oorbIwWQDqUFObogrJC/eOYEbqgrLBfjTOp90+U/+3zFyb8RFnq/+WWfGIMpE5RnkfnjsrXx0YP17mj8geVP8leUt3XtpOZCcvl3MDA2ZE/p+a/v6RAk847U5edPbzbY06fjdZbHAM5N0hl/QOAZPRWs2puuLjHv5kOBts1Y8LZeqOxWf+09jXNfvJ1/dPa1/RGY7NmTDi71xsKWFXvXDPz7Pnnn1c0GtX555+v3bt361//9V91/vnn67bbblNWVpbmzJmj2tpa+f1++f1+1dbWKi8vTzNmzLA7dAAGO/3TjZzsIfq/2w/q4JH2uE+UT5XM5ZZ8YgyYx4pLqvs7E5bLuYGBMyF/yotztfgL47T7r+FusylOnY3WVxycGwDIJIlq1uGjkR7/Zhp6RpbueuoN3T5xTNzMs7ufekP/fefEtL8G1zTPgsGgFixYoPfff18jRoxQdXW1HnzwQWVnnxyY5s6dq7a2Ns2ePVvNzc2aMGGCNm7cqIKCApsjB2C6orwPT1aDrRHV//0LgBN9yeVALrc8dRsA7GfVJdUlhTl9zn7lcm5g4EzJn+K8bK3dundQcXBuACCTJKpZPdXkN/Yf0aWji/Xwi7t7fLwd5zpZ0Wg0mvatOlQoFFJRUZGCwaAKCwvtDgeA7MnLA0fa9MAvd+hL/zhaq1/eE3e3zWXVlSrjbnhwOSeMmQeOtGn++vpuJ3zpynE7tw1nc0Ju9sWU/DElDpjPDXkJ9+qpFk65cJS+9fmL9G+/2GFMjaR5lkIUNcA8duVlsDWiw0cjOn4iquMnomqNHFdRLpdUAF2cMmYGWyO2XTZl57bhXE7Jzb6Ykj+mxAGzuSUv4V6JaqFJNdI1l20CQDpxKQXgDnbmOnUGGDhT8seUOADATolqoUk10nV32wQAAAAAAAD6i+YZAAAAAAAAkACXbQKAy3V9l0CovVOFudnyDTNnejTgFOQZ4FzkN4BMRg3rH5pnAOBiB460ad76em057S42S6srVc6dvoCUIM8A5yK/AWQyalj/cdkmALhUsDUSN1hKUl1DQPPX1yvYGrEpMsA5yDPAuchvAJmMGpYcZp4B6KaxsVGBQKDvB6aIz+fT6NGj07Y9fCgQjsQNll3qGgIKhCNM2QYGiTwDnIv8BpDJqGHJoXkGIKaxsVEXXHCh2tpa07bN3Nw8/elPO2mg2SDU3tnr+pY+1gPoG3kGOBf5DSCTUcOSQ/MMQEwgEFBbW6sm3P6ACsvOsXx7oYN79YfHFykQCNA8G6SBfNFnYU52r+sL+lgPoG9W5hlf8AskJ9U5wzgKIJP1p4ZxrvEhmmcA4hSWnaMRo8+3Owz000C/6NOX71GV36e6HqZrV/l98uW7c2AEUsmqPOMLfoHkWJEzjKMAMllfNSwn+wzd+dQbnGv8HTcMAIAMNpgv+izK82hpdaWq/L5uy6v8Pi2rrnTtp0pAKlmRZ3zBL5Acq3KGcRRAJuuthtXedIkeeO5tzjVOwcwzAMhgg/2iz/LiXK2aPl6BcEQt7Z0qyMmWL9+907EBK6Q6z/iCXyA5VuYM4yiATJaohh0+GtH/7DzU4++49VyD5hkAZLBUfNFnUR4n+YDVUplnfMEvkByrc4ZxFEAm66mGvRc42uvvuPFcg8s2ASCD8WXFgPuQ90ByyBkASA51Mx4zzwAgg6Xjy4q5yw5gFl++R1MuHKXzywo1vqJYHcdOKCd7iF5vbNaugyG+pBwZy6rxhi/2B4DkpLtuZsLfGzTPACCDdX3R5/z19d0Gt1R9WTF39APMU5Tn0bc+f5EWbNiuh1/cHVs+cexI1d50iXEnm0B/WDneWD1WAoDTpLNuZsrfG65onh07dkw1NTV68skn1dTUpLKyMs2cOVP/9m//pjPOOHnlajQa1aJFi/Too4+qublZEyZM0A9+8ANdfPHFNkcPAL2z6suK+7o72arp4/mDA7BBsDWihb/YoZd3H+62/KXdh/Vvv9hBbiLjpGO84Yv9ASA56aibmfT3hiuaZ8uWLdOPfvQjrV27VhdffLFee+013XbbbSoqKtI999wjSVq+fLlWrFihNWvW6LzzztPixYs1ZcoU7dq1SwUFBTa/AgDoXV9fVjyQqdDc0Q9InVRejkBuwmnSdUxb8cX+mXCpEQAMVlSSslL/vJl0TuOK5tnvf/97feELX9B1110nSTrnnHP01FNP6bXXXpN0ctbZypUrtXDhQk2bNk2StHbtWpWUlGjdunWaNWuWbbEDwGANdCo0d/QDUiPVlyOQm3CaTD2mM+VSIwAYiHTUuEyq/6642+bEiRP1wgsv6M9//rMk6a233tJLL72kz33uc5KkPXv2qKmpSVOnTo39jtfr1aRJk7R161ZbYgaAVOhrKnSwNZLwd7nLDjB4g8nBRMhNOE0mHtNW5DYAmCJdNS6T6r8rZp7NmzdPwWBQF1xwgYYMGaLjx4/rwQcf1PTp0yVJTU1NkqSSkpJuv1dSUqJ9+/YlfN6Ojg51dHTEfg6FQhZEDyAZ5GV3g5kKzd3JkEpuzU0rLkcgN5FKJuRmJh7TmXSpETKPCXkJd0tXjcuk+u+KmWfPPPOMnnjiCa1bt06vv/661q5dq+985ztau3Ztt8dlZXW/iDcajcYtO9WSJUtUVFQU+1dRUWFJ/AD6j7zsbjBTobvuslPl93Vbzt3JMBBuzU0rLkcgN5FKJuRmJh7TmXSpETKPCXkJd0tXjcuk+u+KmWf/+q//qvnz5+vLX/6yJOmSSy7Rvn37tGTJEt16660qLS2VpNidOLscOnQobjbaqRYsWKB777039nMoFKKwATYjL7sb7FRo7k6GVHFrblp1OQK5iVQxJTcz7ZjOpEuNkHlMyUu4VzprXKbUf1c0z1pbW3XGGd0n2Q0ZMkQnTpyQJI0ZM0alpaXatGmTxo8fL0mKRCLavHmzli1blvB5vV6vvF6vdYEDSBp52V0qpkJbcXcyuI9bc9PKyxHITaSCSbmZScd0Jl1qhMxjUl7CndJd4zKh/rviss3rr79eDz74oH71q19p79692rBhg1asWKGbbrpJ0snLNefMmaPa2lpt2LBBO3bs0MyZM5WXl6cZM2bYHD0ADFwmTYUGnIgcBJyJ3AbgZNS4eK6YebZq1Sp961vf0uzZs3Xo0CGVl5dr1qxZ+vd///fYY+bOnau2tjbNnj1bzc3NmjBhgjZu3KiCggIbIweAwbNiKnSwNaJAOKJQe6cKc7PlG2b+p0WAXey+HIF8hWmcckzandsAYKWB1jin1PjTuaJ5VlBQoJUrV2rlypUJH5OVlaWamhrV1NSkLS4ASJdUToU+cKQt7tbVVX6fllZXqrw4NyXbAJzGrssRyFeYxmnHZCZcagQAA5VsjXNajT+VKy7bBACkRrA1EjcgSidvWT1/fb2CrRGbIgNwOvIVpuGYBADncnqNd8XMMwAwgROmMAfCkbgBsUtdQ0CBcCTjXhNgklTWCfIVpuGY/JATzgkAZBar647TazzNMwBIA6dMYQ61d/a6vqWP9QASS3WdIF9hGo7Jk5xyTgAgc6Sj7ji9xnPZJgBYzElTmAtzsntdX9DHegA9s6JOkK8wDceks84JAGSGdNUdp9d4Zp4BgMWcNIXZl+9Rld+nuh5eT5XfJ19+/14Hl6sglZxwPA2kTvT1ulOVr0CqcEym75zACXXRSrw/cKoPQu1qPhpRqP2YCnOHanieR60dx9JSd5xe42meAYDFnDSFuSjPo8U3jtP9G7brpd2HY8snjh2pxTeO69fAy+UqSCWnHE/J1on+vO6iPI+WVldq/vr6bieyVX6fllVX8oci0o5jMj3nBE6pi1bh/YFTNR4+qgUbtuvluHP0S+TL9ygQ7nmGWar+FnF6jad5BgAWc9IU5mBrRN/+v+/oY6OH67Yrx6jj2Al5h56hN/Yf0X/833f0nZs/2uvA2Ne08VXTx2f8wIr0cdLxlEydSOZ1lxfnatX08QqEI2pp71RBTrZ8+cywgH3cfkxafU7gpLpoBd4fONUHofa4xpkkvbT7sP7tF9u1rLpS/7T2tR5/N5V/izi5xtM8AwCL+fI9mnLhKJ1fVqjxFcXqOHZCOdlD9Hpjs3YdDGXUFOZAOKL/2XlI/7PzUML1vQ2OTrqEFfZz0vGUTJ1I9nUX5TnjpBXOYfUxafIleVZf1uSkumgF3h84VfPRSFzjrMtLuw9rwbUX9rjOysspo5KUZclT24LmGQBYrCjPo299/iIt2LBdD7+4O7Z84tiRqr3pkow6SRvs5SZOuoQV9nPS8ZRMnXDS6wZSzfRL8qy+rIn60DveHzhVqP1Yr+uPRo7FNe6tuJzS9Bo8GDTPAMBiwdaIFv5iR4Jp1Dsy6hKBwV5u4qRLWGE/Jx1PydQJJ71uIJUy5ZI8Ky9roj70jvcHTlWY03trpzAn2/LLKTOlBg/UGXYHAABO159LBDJF1+UmPenPtO/B/j5wKicdT8nUCSe9biCVMmm8Lcrz6NxR+frY6OE6d1R+yv6gpD70jvcHTjV8mEcTx47scd3EsSM1/O+Xr1tRd7pkUg0eCJpnAGAxJ10i0HW5yeknnv2d9j3Y3wdO5aTjKZk64aTXDaSSk8bbgaI+9I73B05VUpij2psuiWugdX39Q0lhjuUxOL0Gc9kmAFjMaZcIDPZyEyffhQfp55TjKdk64ZTXDaSS08bbgaI+9I73B041euQwffeLH1Pz0YhC7cdUmDNUw4d50tI4k5xfg2meAYDFrL6zlh0Ge6c07v6HVHLC8TSQOuGE1w2kkhPH24GiPvSO9wdOVVKYk7Zm2emcXoO5bBMALMYlAgD6Qp0ABo88AgD7OL0GM/MMANKASwQA9IU6AQweeQQA9nFyDaZ5BgBpwiUCAPpCnQAGjzwCAPs4tQZz2SYAAAAAAACQgCuaZ+ecc46ysrLi/t1xxx2SpGg0qpqaGpWXlys3N1eTJ0/W22+/bXP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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:23:07.706923Z",
          "end_time": "2022-12-26T10:23:13.159191Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "sn.pairplot(df)",
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 32,
          "data": {
            "text/plain": "<seaborn.axisgrid.PairGrid at 0x251f7f1d3a0>"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 1500x1500 with 42 Axes>",
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:30:54.258886Z",
          "end_time": "2022-12-26T10:30:54.365778Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.boxplot(df['SAT'],vert = True)\nplt.xlabel('SATscore')\nplt.ylabel('Values')\nplt.title('Boxplot af SAT')\nplt.show()",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:33:00.317128Z",
          "end_time": "2022-12-26T10:33:00.431047Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "plt.boxplot(df['Accept'], vert=True)\nplt.show()",
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 640x480 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T10:36:08.101301Z",
          "end_time": "2022-12-26T10:36:08.280936Z"
        }
      },
      "cell_type": "markdown",
      "source": "sn.distplot(df['SAT'])"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Numpy basics"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:47:23.300211Z",
          "end_time": "2022-12-26T15:47:23.324025Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "import numpy as np",
      "execution_count": 50,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:57:02.880439Z",
          "end_time": "2022-12-26T15:57:02.898885Z"
        }
      },
      "cell_type": "markdown",
      "source": "#### a=np.array([(1,2,3,4),(4,5,6,7)])\na"
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:59:05.073935Z",
          "end_time": "2022-12-26T15:59:05.090865Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a = np.array([(1,2,3,4),(4,5,6,7)])\na",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 66,
          "data": {
            "text/plain": "array([[1, 2, 3, 4],\n       [4, 5, 6, 7]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:59:09.312101Z",
          "end_time": "2022-12-26T15:59:09.336045Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "type(a)",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 67,
          "data": {
            "text/plain": "numpy.ndarray"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:59:13.616900Z",
          "end_time": "2022-12-26T15:59:13.633179Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a.shape",
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 68,
          "data": {
            "text/plain": "(2, 4)"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T15:59:33.138334Z",
          "end_time": "2022-12-26T15:59:33.154811Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a.size",
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 69,
          "data": {
            "text/plain": "8"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:00:40.237340Z",
          "end_time": "2022-12-26T16:00:40.253653Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a = a.reshape(4,2)\na",
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 70,
          "data": {
            "text/plain": "array([[1, 2],\n       [3, 4],\n       [4, 5],\n       [6, 7]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:03:29.922534Z",
          "end_time": "2022-12-26T16:03:29.937255Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# access different elements ib array\na[0]",
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 71,
          "data": {
            "text/plain": "array([1, 2])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:03:45.372697Z",
          "end_time": "2022-12-26T16:03:45.390162Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a[0,0]",
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 72,
          "data": {
            "text/plain": "1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:33:02.821462Z",
          "end_time": "2022-12-26T16:33:02.828627Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a[0,1]",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 74,
          "data": {
            "text/plain": "2"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:33:27.668995Z",
          "end_time": "2022-12-26T16:33:27.680526Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a[1,1]",
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 75,
          "data": {
            "text/plain": "4"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:34:39.711425Z",
          "end_time": "2022-12-26T16:34:39.726384Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a[1,0]",
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 76,
          "data": {
            "text/plain": "3"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:36:31.489898Z",
          "end_time": "2022-12-26T16:36:31.515128Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a = a.reshape(2,4)",
      "execution_count": 77,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:36:36.272240Z",
          "end_time": "2022-12-26T16:36:36.290110Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a",
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 78,
          "data": {
            "text/plain": "array([[1, 2, 3, 4],\n       [4, 5, 6, 7]])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:37:01.731856Z",
          "end_time": "2022-12-26T16:37:01.738730Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a.min()",
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 80,
          "data": {
            "text/plain": "1"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:37:11.267706Z",
          "end_time": "2022-12-26T16:37:11.275737Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a.max()",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 81,
          "data": {
            "text/plain": "7"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:37:37.172444Z",
          "end_time": "2022-12-26T16:37:37.184428Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "a.sum()",
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 82,
          "data": {
            "text/plain": "32"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:38:13.891828Z",
          "end_time": "2022-12-26T16:38:13.911918Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# row wise calculations\na.sum(axis = 0)",
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 83,
          "data": {
            "text/plain": "array([ 5,  7,  9, 11])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:39:16.185234Z",
          "end_time": "2022-12-26T16:39:16.200956Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# column wise calculations\na.sum(axis = 1)",
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 84,
          "data": {
            "text/plain": "array([10, 22])"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:48:47.487523Z",
          "end_time": "2022-12-26T16:48:47.495333Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup = pd.DataFrame({'col1':['A','A','B','B','B'],'col2':[2,2,2,3,3]})",
      "execution_count": 93,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:49:05.367660Z",
          "end_time": "2022-12-26T16:49:05.372654Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup",
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 94,
          "data": {
            "text/plain": "  col1  col2\n0    A     2\n1    A     2\n2    B     2\n3    B     3\n4    B     3",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col1</th>\n      <th>col2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>A</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>B</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>B</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>B</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:51:09.493877Z",
          "end_time": "2022-12-26T16:51:09.508593Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup.duplicated()",
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 96,
          "data": {
            "text/plain": "0    False\n1     True\n2    False\n3    False\n4     True\ndtype: bool"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:52:14.435115Z",
          "end_time": "2022-12-26T16:52:14.441152Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup.drop_duplicates()",
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 98,
          "data": {
            "text/plain": "  col1  col2\n0    A     2\n2    B     2\n3    B     3",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>col1</th>\n      <th>col2</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>A</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>B</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>B</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:54:32.085078Z",
          "end_time": "2022-12-26T16:54:32.094788Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup.duplicated().sum()",
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 99,
          "data": {
            "text/plain": "2"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2022-12-26T16:55:28.100052Z",
          "end_time": "2022-12-26T16:55:28.108351Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "dup.isnull().sum()",
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 101,
          "data": {
            "text/plain": "col1    0\ncol2    0\ndtype: int64"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "",
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3 (ipykernel)",
      "language": "python"
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    "language_info": {
      "name": "python",
      "version": "3.9.13",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
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      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "gist": {
      "id": "6b0a5fa9d28a81de2fa3a49923086911",
      "data": {
        "description": "Untitled3.ipynb",
        "public": true
      }
    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/6b0a5fa9d28a81de2fa3a49923086911"
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