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empet/LicentaCTI2014

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LicentaCTI2014
{
"metadata": {
"name": "",
"signature": "sha256:6a1782ca6b5c32b5c7ff37bac7d6d5f69739d9125ad2bce3c4e9017f38ba6072"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"Statistica rezultatelor la Licenta CTI, 2014"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import seaborn as sb#http://www.stanford.edu/~mwaskom/software/seaborn/"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"note=np.loadtxt('Licenta2014.txt')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'La examen s-au prezentat', note.shape[0], 'absolventi'"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"La examen s-au prezentat 135 absolventi\n"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"from IPython.display import HTML\n",
"df=pd.DataFrame({'Media multianuala': note[:,0], 'Media licenta': note[:,1]})\n",
"h = HTML(df.to_html());h\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Media licenta</th>\n",
" <th>Media multianuala</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 7.45</td>\n",
" <td> 7.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 6.43</td>\n",
" <td> 7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 9.26</td>\n",
" <td> 9.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 9.41</td>\n",
" <td> 9.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 8.08</td>\n",
" <td> 8.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 9.47</td>\n",
" <td> 9.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 7.98</td>\n",
" <td> 8.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8.54</td>\n",
" <td> 7.93</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 6.84</td>\n",
" <td> 6.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 9.19</td>\n",
" <td> 8.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10 </th>\n",
" <td> 7.84</td>\n",
" <td> 7.88</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11 </th>\n",
" <td> 9.46</td>\n",
" <td> 9.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12 </th>\n",
" <td> 7.26</td>\n",
" <td> 7.47</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13 </th>\n",
" <td> 8.07</td>\n",
" <td> 7.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14 </th>\n",
" <td> 6.38</td>\n",
" <td> 7.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15 </th>\n",
" <td> 8.30</td>\n",
" <td> 8.20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16 </th>\n",
" <td> 8.64</td>\n",
" <td> 8.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17 </th>\n",
" <td> 7.48</td>\n",
" <td> 7.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18 </th>\n",
" <td> 7.17</td>\n",
" <td> 7.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19 </th>\n",
" <td> 8.43</td>\n",
" <td> 7.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20 </th>\n",
" <td> 8.42</td>\n",
" <td> 8.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21 </th>\n",
" <td> 8.43</td>\n",
" <td> 8.11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22 </th>\n",
" <td> 8.04</td>\n",
" <td> 7.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23 </th>\n",
" <td> 9.04</td>\n",
" <td> 8.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24 </th>\n",
" <td> 8.73</td>\n",
" <td> 8.10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25 </th>\n",
" <td> 8.93</td>\n",
" <td> 8.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26 </th>\n",
" <td> 7.55</td>\n",
" <td> 8.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27 </th>\n",
" <td> 7.12</td>\n",
" <td> 7.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28 </th>\n",
" <td> 8.27</td>\n",
" <td> 8.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29 </th>\n",
" <td> 8.88</td>\n",
" <td> 8.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30 </th>\n",
" <td> 8.20</td>\n",
" <td> 8.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31 </th>\n",
" <td> 8.63</td>\n",
" <td> 8.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32 </th>\n",
" <td> 7.72</td>\n",
" <td> 7.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33 </th>\n",
" <td> 8.53</td>\n",
" <td> 7.96</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34 </th>\n",
" <td> 8.90</td>\n",
" <td> 8.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35 </th>\n",
" <td> 9.78</td>\n",
" <td> 9.55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36 </th>\n",
" <td> 8.42</td>\n",
" <td> 7.83</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37 </th>\n",
" <td> 9.26</td>\n",
" <td> 8.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38 </th>\n",
" <td> 9.39</td>\n",
" <td> 9.02</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39 </th>\n",
" <td> 8.20</td>\n",
" <td> 7.64</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40 </th>\n",
" <td> 8.65</td>\n",
" <td> 8.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41 </th>\n",
" <td> 7.88</td>\n",
" <td> 8.16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42 </th>\n",
" <td> 8.48</td>\n",
" <td> 8.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43 </th>\n",
" <td> 6.40</td>\n",
" <td> 6.95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44 </th>\n",
" <td> 7.09</td>\n",
" <td> 7.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45 </th>\n",
" <td> 8.46</td>\n",
" <td> 8.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46 </th>\n",
" <td> 8.26</td>\n",
" <td> 8.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47 </th>\n",
" <td> 9.25</td>\n",
" <td> 9.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48 </th>\n",
" <td> 9.87</td>\n",
" <td> 9.73</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49 </th>\n",
" <td> 8.08</td>\n",
" <td> 7.35</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50 </th>\n",
" <td> 7.40</td>\n",
" <td> 7.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51 </th>\n",
" <td> 9.85</td>\n",
" <td> 9.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52 </th>\n",
" <td> 8.22</td>\n",
" <td> 8.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53 </th>\n",
" <td> 8.79</td>\n",
" <td> 8.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54 </th>\n",
" <td> 7.83</td>\n",
" <td> 7.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55 </th>\n",
" <td> 7.39</td>\n",
" <td> 7.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56 </th>\n",
" <td> 6.77</td>\n",
" <td> 7.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57 </th>\n",
" <td> 6.31</td>\n",
" <td> 7.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58 </th>\n",
" <td> 6.45</td>\n",
" <td> 7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59 </th>\n",
" <td> 7.72</td>\n",
" <td> 7.34</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60 </th>\n",
" <td> 9.68</td>\n",
" <td> 9.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61 </th>\n",
" <td> 8.07</td>\n",
" <td> 7.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62 </th>\n",
" <td> 8.81</td>\n",
" <td> 8.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63 </th>\n",
" <td> 8.81</td>\n",
" <td> 8.32</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64 </th>\n",
" <td> 8.97</td>\n",
" <td> 8.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65 </th>\n",
" <td> 8.56</td>\n",
" <td> 8.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66 </th>\n",
" <td> 5.85</td>\n",
" <td> 6.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67 </th>\n",
" <td> 6.53</td>\n",
" <td> 7.21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>68 </th>\n",
" <td> 9.16</td>\n",
" <td> 8.41</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69 </th>\n",
" <td> 7.14</td>\n",
" <td> 7.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70 </th>\n",
" <td> 8.03</td>\n",
" <td> 7.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>71 </th>\n",
" <td> 8.86</td>\n",
" <td> 8.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>72 </th>\n",
" <td> 7.24</td>\n",
" <td> 7.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>73 </th>\n",
" <td> 6.72</td>\n",
" <td> 7.33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>74 </th>\n",
" <td> 8.42</td>\n",
" <td> 8.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75 </th>\n",
" <td> 8.68</td>\n",
" <td> 8.40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76 </th>\n",
" <td> 7.69</td>\n",
" <td> 7.18</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77 </th>\n",
" <td> 6.35</td>\n",
" <td> 6.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78 </th>\n",
" <td> 9.10</td>\n",
" <td> 8.79</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79 </th>\n",
" <td> 9.35</td>\n",
" <td> 9.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>80 </th>\n",
" <td> 7.01</td>\n",
" <td> 7.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>81 </th>\n",
" <td> 7.23</td>\n",
" <td> 7.65</td>\n",
" </tr>\n",
" <tr>\n",
" <th>82 </th>\n",
" <td> 7.20</td>\n",
" <td> 7.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>83 </th>\n",
" <td> 8.61</td>\n",
" <td> 7.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>84 </th>\n",
" <td> 8.42</td>\n",
" <td> 8.03</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85 </th>\n",
" <td> 8.13</td>\n",
" <td> 7.51</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86 </th>\n",
" <td> 8.15</td>\n",
" <td> 7.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87 </th>\n",
" <td> 8.04</td>\n",
" <td> 7.28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88 </th>\n",
" <td> 7.96</td>\n",
" <td> 7.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89 </th>\n",
" <td> 8.52</td>\n",
" <td> 8.09</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90 </th>\n",
" <td> 9.18</td>\n",
" <td> 9.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>91 </th>\n",
" <td> 7.15</td>\n",
" <td> 7.04</td>\n",
" </tr>\n",
" <tr>\n",
" <th>92 </th>\n",
" <td> 7.35</td>\n",
" <td> 7.44</td>\n",
" </tr>\n",
" <tr>\n",
" <th>93 </th>\n",
" <td> 7.72</td>\n",
" <td> 8.23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>94 </th>\n",
" <td> 7.23</td>\n",
" <td> 7.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>95 </th>\n",
" <td> 7.57</td>\n",
" <td> 7.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>96 </th>\n",
" <td> 6.39</td>\n",
" <td> 6.97</td>\n",
" </tr>\n",
" <tr>\n",
" <th>97 </th>\n",
" <td> 7.54</td>\n",
" <td> 7.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>98 </th>\n",
" <td> 6.92</td>\n",
" <td> 7.98</td>\n",
" </tr>\n",
" <tr>\n",
" <th>99 </th>\n",
" <td> 7.89</td>\n",
" <td> 7.87</td>\n",
" </tr>\n",
" <tr>\n",
" <th>100</th>\n",
" <td> 7.11</td>\n",
" <td> 7.31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>101</th>\n",
" <td> 8.08</td>\n",
" <td> 7.91</td>\n",
" </tr>\n",
" <tr>\n",
" <th>102</th>\n",
" <td> 9.73</td>\n",
" <td> 9.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>103</th>\n",
" <td> 7.94</td>\n",
" <td> 7.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>104</th>\n",
" <td> 8.43</td>\n",
" <td> 8.06</td>\n",
" </tr>\n",
" <tr>\n",
" <th>105</th>\n",
" <td> 8.56</td>\n",
" <td> 8.67</td>\n",
" </tr>\n",
" <tr>\n",
" <th>106</th>\n",
" <td> 8.12</td>\n",
" <td> 7.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>107</th>\n",
" <td> 8.80</td>\n",
" <td> 8.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>108</th>\n",
" <td> 9.13</td>\n",
" <td> 9.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>109</th>\n",
" <td> 6.79</td>\n",
" <td> 7.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110</th>\n",
" <td> 8.70</td>\n",
" <td> 8.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>111</th>\n",
" <td> 8.77</td>\n",
" <td> 8.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th>112</th>\n",
" <td> 8.29</td>\n",
" <td> 7.82</td>\n",
" </tr>\n",
" <tr>\n",
" <th>113</th>\n",
" <td> 9.96</td>\n",
" <td> 9.92</td>\n",
" </tr>\n",
" <tr>\n",
" <th>114</th>\n",
" <td> 7.77</td>\n",
" <td> 8.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th>115</th>\n",
" <td> 9.54</td>\n",
" <td> 9.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>116</th>\n",
" <td> 7.94</td>\n",
" <td> 7.63</td>\n",
" </tr>\n",
" <tr>\n",
" <th>117</th>\n",
" <td> 8.90</td>\n",
" <td> 8.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>118</th>\n",
" <td> 8.69</td>\n",
" <td> 8.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>119</th>\n",
" <td> 8.20</td>\n",
" <td> 8.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>120</th>\n",
" <td> 7.81</td>\n",
" <td> 8.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>121</th>\n",
" <td> 6.82</td>\n",
" <td> 7.38</td>\n",
" </tr>\n",
" <tr>\n",
" <th>122</th>\n",
" <td> 8.57</td>\n",
" <td> 8.59</td>\n",
" </tr>\n",
" <tr>\n",
" <th>123</th>\n",
" <td> 8.32</td>\n",
" <td> 7.89</td>\n",
" </tr>\n",
" <tr>\n",
" <th>124</th>\n",
" <td> 8.30</td>\n",
" <td> 7.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>125</th>\n",
" <td> 8.61</td>\n",
" <td> 8.52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>126</th>\n",
" <td> 8.64</td>\n",
" <td> 8.42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>127</th>\n",
" <td> 7.47</td>\n",
" <td> 7.68</td>\n",
" </tr>\n",
" <tr>\n",
" <th>128</th>\n",
" <td> 9.68</td>\n",
" <td> 9.36</td>\n",
" </tr>\n",
" <tr>\n",
" <th>129</th>\n",
" <td> 8.59</td>\n",
" <td> 8.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130</th>\n",
" <td> 8.35</td>\n",
" <td> 8.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>131</th>\n",
" <td> 9.00</td>\n",
" <td> 8.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>132</th>\n",
" <td> 7.55</td>\n",
" <td> 7.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>133</th>\n",
" <td> 6.33</td>\n",
" <td> 7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>134</th>\n",
" <td> 6.15</td>\n",
" <td> 6.90</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"<IPython.core.display.HTML at 0x7f149ba23910>"
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Histogramele notelor"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.rcParams['figure.figsize'] = 5, 7\n",
"colors=sb.color_palette()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig=plt.figure()\n",
"ax1=fig.add_subplot(211)\n",
"histo1=ax1.hist(note[:,0], bins=10, color=colors[0])\n",
"ax1.set_title('Histograma mediilor multianuale')\n",
"\n",
"ax2=fig.add_subplot(212)\n",
"histo2=ax2.hist(note[:,1], bins=10, color=colors[2])\n",
"ax2.set_title('Histograma mediilor la licenta')\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.text.Text at 0x7f149bb752d0>"
]
},
{
"metadata": {},
"output_type": "display_data",
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T/nJfFU/4Xwf82szeSvjxnUrlHbid8Fd+0MxeRxgBpNj4a/laSrxuJ7Tm1sck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\niIiIiIiMcf8fTQzh2YwaEFUAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f149ba71150>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"print 'Media anului inainte de licenta este: ', np.mean(note[:,0]).round(2), \\\n",
"'iar dupa licenta:', np.mean(note[:,1]).round(2)\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Media anului inainte de licenta este: 8.05 iar dupa licenta: 8.13\n"
]
}
],
"prompt_number": 17
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Corelatia celor doua seturi de note si aproximarea distributiei lor de probabilitate"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"plt.rcParams['figure.figsize'] = (6.0, 4.0)\n",
"sb.jointplot(note[:,0], note[:,1], size=6, kind=\"reg\");\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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xZmsDm/a1DlRFHoeZe6+txFuacfbjazpmk0qGx4bRYLjEo5+8znyy9KSfA6tP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\nUKMsoLglwgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f149ba23710>"
]
}
],
"prompt_number": 18
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Vizualizarea distributiei comune de probabilitate a celor doua seturi de date si a distributiilor marginale,\n",
"folosind `kernel density estimation`:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sb.jointplot(note[:,0], note[:,1], size=6, kind=\"kde\");"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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aXEl4KZBERBwmKzUBgKYj/TZXEl4KJBERh0mI95CeHEdDmwJJRERslpeZRE//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\ngg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f149be80c10>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "heading",
"level": 4,
"metadata": {},
"source": [
"Vizualizarea comparativa a mediilor multianuale si la licenta, pentru cei 135 candidati:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import plotly.plotly as py#https://plot.ly/python/\n",
"\n",
"py.sign_in('empet','my_api_key')\n",
"plt.rcParams['figure.figsize'] = (14.0, 6.0)\n",
"mpl_fig_obj= plt.figure()\n",
"plt.plot(note[:,0], color=colors[0])\n",
"plt.plot(note[:,1], color=colors[2])\n",
"plt.title('Vizualizarea comparativa a mediei multianuale si a mediei la licenta')\n",
"py.iplot_mpl(mpl_fig_obj, filename='comparaNote')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~empet/16\" height=\"525\" width=\"100%\"></iframe>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x7f149c038250>"
]
}
],
"prompt_number": 20
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Miscand mouse-ul peste grafic se identifica nota celui din pozitia $i=\\overline{0,134}$ (am sters liniile corespunzatoare absentilor din fisierul pdf postat pe site-ul facultatii)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Emilia Petrisor, UPT"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"./styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()\n"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.org/fonts/cm-unicode/fonts/otf/cmunss.otf');\n",
" }\n",
" div.cell{\n",
" width:800px;\n",
" margin-left:16% !important;\n",
" margin-right:auto;\n",
" }\n",
" h1 {\n",
" font-family: Helvetica, serif;\n",
" }\n",
" h4{\n",
" margin-top:12px;\n",
" margin-bottom: 3px;\n",
" }\n",
" div.text_cell_render{\n",
" font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width:800px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" .CodeMirror{\n",
" font-family: \"Source Code Pro\", source-code-pro,Consolas, monospace;\n",
" }\n",
" .prompt{\n",
" display: None;\n",
" }\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-size: 22pt;\n",
" color: #4057A1;\n",
" font-style: italic;\n",
" margin-bottom: .5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
" \n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<IPython.core.display.HTML at 0x7f149bc7b8d0>"
]
}
],
"prompt_number": 21
}
],
"metadata": {}
}
]
}
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