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Commit bfb8dd49 authored by 6f892419cc99326ee525ed439d8ff5df's avatar 6f892419cc99326ee525ed439d8ff5df
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Ajout Pressure fait

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module2/exo5/exo5_fr.ipynb
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...@@ -686,6 +686,587 @@ ...@@ -686,6 +686,587 @@
"analyse et de regarder ce jeu de données sous tous les angles afin\n", "analyse et de regarder ce jeu de données sous tous les angles afin\n",
"d'expliquer ce qui ne va pas." "d'expliquer ce qui ne va pas."
] ]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Ajout de la pression"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'pd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-5a1206939612>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_option\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mode.chained_assignment'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# this removes a useless warning from pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMalfunction\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'pd' is not defined"
]
}
],
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-2-f168eeb23f8e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMalfunction\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Pressure\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Frequency\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"scatter\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mylim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>4/12/81</td>\n",
" <td>6</td>\n",
" <td>66</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3/22/82</td>\n",
" <td>6</td>\n",
" <td>69</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>11/11/82</td>\n",
" <td>6</td>\n",
" <td>68</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4/04/83</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6/18/82</td>\n",
" <td>6</td>\n",
" <td>72</td>\n",
" <td>50</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>8/30/83</td>\n",
" <td>6</td>\n",
" <td>73</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>11/28/83</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>100</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>10/05/84</td>\n",
" <td>6</td>\n",
" <td>78</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>11/08/84</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>4/12/85</td>\n",
" <td>6</td>\n",
" <td>67</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>4/29/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6/17/85</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7/29/85</td>\n",
" <td>6</td>\n",
" <td>81</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>8/27/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10/03/85</td>\n",
" <td>6</td>\n",
" <td>79</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>11/26/85</td>\n",
" <td>6</td>\n",
" <td>76</td>\n",
" <td>200</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"0 4/12/81 6 66 50 0\n",
"1 11/12/81 6 70 50 1\n",
"2 3/22/82 6 69 50 0\n",
"3 11/11/82 6 68 50 0\n",
"4 4/04/83 6 67 50 0\n",
"5 6/18/82 6 72 50 0\n",
"6 8/30/83 6 73 100 0\n",
"7 11/28/83 6 70 100 0\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"11 10/05/84 6 78 200 0\n",
"12 11/08/84 6 67 200 0\n",
"13 1/24/85 6 53 200 2\n",
"14 4/12/85 6 67 200 0\n",
"15 4/29/85 6 75 200 0\n",
"16 6/17/85 6 70 200 0\n",
"17 7/29/85 6 81 200 0\n",
"18 8/27/85 6 76 200 0\n",
"19 10/03/85 6 79 200 0\n",
"20 10/30/85 6 75 200 2\n",
"21 11/26/85 6 76 200 0\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"data = pd.read_csv(\"shuttle.csv\")\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Date</th>\n",
" <th>Count</th>\n",
" <th>Temperature</th>\n",
" <th>Pressure</th>\n",
" <th>Malfunction</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>11/12/81</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>50</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>2/03/84</td>\n",
" <td>6</td>\n",
" <td>57</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>4/06/84</td>\n",
" <td>6</td>\n",
" <td>63</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>8/30/84</td>\n",
" <td>6</td>\n",
" <td>70</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>1/24/85</td>\n",
" <td>6</td>\n",
" <td>53</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>10/30/85</td>\n",
" <td>6</td>\n",
" <td>75</td>\n",
" <td>200</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>1/12/86</td>\n",
" <td>6</td>\n",
" <td>58</td>\n",
" <td>200</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Date Count Temperature Pressure Malfunction\n",
"1 11/12/81 6 70 50 1\n",
"8 2/03/84 6 57 200 1\n",
"9 4/06/84 6 63 200 1\n",
"10 8/30/84 6 70 200 1\n",
"13 1/24/85 6 53 200 2\n",
"20 10/30/85 6 75 200 2\n",
"22 1/12/86 6 58 200 1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = data[data.Malfunction>0]\n",
"data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Il faut voir maintenant comment la pression peut agir sur les résultats"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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iSRreV9bet1XLt9V2Xwp7776AJzZtesqyJzZtYu/dF4wokSQN741LFm3V8m213ZfCHgt35L1vOZCd5s9h1x3nsdP8Obz3LQd6sFnSM8LRS17AAYt2ecqyAxbt0tnB5mfFgeZjD96L1+y3p2cfSXpGuuSsowZnH936DT55yqs8+2gm7LFwR8tA0jPW0UtewFfvX8BRHRYCPAt2H0mShmcpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJaloIkqWUpSJJanZZCkmVJbk2yLsnZW1ifJB9q1t+Y5JAu80iSptZZKSSZC3wUOAZYApyUZMmEYccA+ze35cDHu8ojSZpel1sKhwHrqur2qnocWAkcN2HMccD5NXAlsFuSF3aYSZI0hXkdPvZewF3j5tcDhw8xZi/gu+MHJVnOYEsCYEOSW2c26jbZE/j+qENMoe/5oP8Z+54PzDgT+p4Pti3jzw4zqMtSyBaW1dMYQ1V9AvjETISaaUmurapDR51jMn3PB/3P2Pd8YMaZ0Pd8MDsZu9x9tB5YPG5+b+CepzFGkjRLuiyFa4D9k+ybZAfgRODiCWMuBk5pzkI6AvhRVX134gNJkmZHZ7uPqmpjkjOBS4C5wHlVtSbJGc36FcAq4E3AOuAR4LSu8nSol7u1xul7Puh/xr7nAzPOhL7ng1nImKqf2oUvSXqW8hPNkqSWpSBJalkKWyHJbkk+n+RbSW5JcmSS5yW5NMm3m//uPuKMZyVZk+TmJJ9NstMoMyY5L8n9SW4et2zSPEne1Vz25NYkvzTCjO9r/j/fmOTvkuzWt4zj1r0jSSXZc1QZJ8uX5LebDGuSvHdU+SbLmOTgJFcmuSHJtUkOG1XGJIuTrG7eW9Yk+R/N8tl9vVSVtyFvwKeB05vpHYDdgPcCZzfLzgbeM8J8ewF3AAua+c8Bbx1lRuDfAYcAN49btsU8DC6H8k1gR2Bf4F+AuSPK+EZgXjP9nj5mbJYvZnAyx78Ce44q4yS/w6XAZcCOzfzz+/Y7BL4CHNNMvwn46gh/hy8EDmmmdwVua3LM6uvFLYUhJXkOgz+qTwJU1eNV9W8MLtXx6WbYp4FfHU3C1jxgQZJ5wM4MPvcxsoxVdQXwwwmLJ8tzHLCyqh6rqjsYnJV2GB3bUsaq+kpVbWxmr2TwGZpeZWx8APg9nvqhz1nPOEm+/wr8WVU91oy5f1T5pshYwHOa6efyk89JjeJ3+N2qur6Zfgi4hcE/9Gb19WIpDO8lwPeAv07yjSTnJtkFWFTNZyua/z5/VAGr6m7g/cCdDC4V8qOq+kqfMjYmyzPZZU9G7TeBf2ime5MxybHA3VX1zQmr+pLxZcDrklyV5GtJXt0s70s+gLcD70tyF4PXzrua5SPNmGQf4FXAVczy68VSGN48BpueH6+qVwEPM9iU641mX+NxDDYlXwTskuQ/jTbVVhnqsiezKcm7gY3A325etIVhs54xyc7Au4FztrR6C8tG8XucB+wOHAH8LvC5JKE/+WCwNXNWVS0GzqLZE8AIMyZZCFwIvL2qHpxq6BaWbXNGS2F464H1VXVVM/95BiVxX5oruzb/vX+S+8+GNwB3VNX3quoJ4CLg53uWkSny9OqyJ0lOBf498BvV7MSlPxlfyqD8v5nkO02O65O8gP5kXA9cVANXA5sYXNCtL/kATmXwOgG4gJ/sfhlJxiTzGRTC31bV5lyz+nqxFIZUVfcCdyU5oFl0NLCWwaU6Tm2WnQp8cQTxNrsTOCLJzs2/yI5msF+yTxlh8jwXAycm2THJvgy+Z+PqEeQjyTLgncCxVfXIuFW9yFhVN1XV86tqn6rah8EbxCHN32kvMgJfAF4PkORlDE7O+H6P8sHgTfQXmunXA99upmc9Y/Oa/SRwS1X9xbhVs/t66fJo+vZ2Aw4GrgVuZPAHvzuwB3A5gz+my4HnjTjjHwHfAm4GPsPgzISRZQQ+y+D4xhMM3rj+81R5GOwS+RfgVpqzQkaUcR2D/bU3NLcVfcs4Yf13aM4+GkXGSX6HOwB/0/wtXg+8vm+/Q+C1wHUMzuK5Chgb4e/wtQx2/9w47u/uTbP9evEyF5KklruPJEktS0GS1LIUJEktS0GS1LIUJEmtzr55Teq7JE8CNzF4HdwCnFpP/UyC9KzjloKezR6tqoOr6pXA48AZ41dmYNZeI0nmztZzSZOxFKSBfwT2S7JPcz37jzH4wNXiJG9M8vUk1ye5oLk2DUn+LMna5jsX3t8sOyGD77L4ZpIrmmVvTfKRzU+U5EtJjmqmNyT54yRXAUcmGWsuHnddkks2X95Ami2Wgp71msuMH8NgVxLAAcD59ZMLH/4+8IaqOoTBJ9p/J8nzgDcDr6iqA4H/3dz3HOCXquog4Nghnn4XBtf3P5zBJ2o/DBxfVWPAecCfzsTPKA3LYwp6NluQ5IZm+h8ZXHfmRcC/VtWVzfIjGHyZyT8NLk3DDsDXgQeBHwPnJvky8KVm/D8Bn0ryOX5yobWpPMngAmgwKKNXApc2zzWXwWUZpFljKejZ7NGqOnj8gubN+OHxi4BLq+qkiXduvrrxaOBE4EwG1/Y5I8nhwC8DNyQ5mMGlt8dvle80bvrHVfXkuOdaU1VHbtuPJT197j6SpnYl8Jok+8HgewySvKw5rvDcqlrF4ItaDm7Wv7SqrqqqcxhcEXQxg4vVHZxkTpLFTP7tWLcCP5PkyOax5id5RZc/nDSRWwrSFKrqe0neCnw2yY7N4t8HHgK+mGQnBv/CP6tZ974k+zfLLmdw9U0YfHf2TfzkiqFbeq7HkxwPfCjJcxm8Pj8IrJnxH0yahFdJlSS13H0kSWpZCpKklqUgSWpZCpKklqUgSWpZCpKklqUgSWr9fzzEKE0BUmAnAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"pd.set_option('mode.chained_assignment',None) # this removes a useless warning from pandas\n",
"import matplotlib.pyplot as plt\n",
"\n",
"data[\"Frequency\"]=data.Malfunction/data.Count\n",
"data.plot(x=\"Pressure\",y=\"Frequency\",kind=\"scatter\",ylim=[0,1])\n",
"plt.grid(True)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimation de l'influence de la pression"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Generalized Linear Model Regression Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Frequency</td> <th> No. Observations: </th> <td> 7</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>GLM</td> <th> Df Residuals: </th> <td> 5</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model Family:</th> <td>Binomial</td> <th> Df Model: </th> <td> 1</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Link Function:</th> <td>logit</td> <th> Scale: </th> <td> 1.0000</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Method:</th> <td>IRLS</td> <th> Log-Likelihood: </th> <td> -2.5168</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Fri, 05 Mar 2021</td> <th> Deviance: </th> <td> 0.20593</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>08:48:11</td> <th> Pearson chi2: </th> <td> 0.214</td> \n",
"</tr>\n",
"<tr>\n",
" <th>No. Iterations:</th> <td>4</td> <th> Covariance Type: </th> <td>nonrobust</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>Intercept</th> <td> -1.7283</td> <td> 3.593</td> <td> -0.481</td> <td> 0.630</td> <td> -8.770</td> <td> 5.313</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Pressure</th> <td> 0.0024</td> <td> 0.019</td> <td> 0.125</td> <td> 0.901</td> <td> -0.035</td> <td> 0.040</td>\n",
"</tr>\n",
"</table>"
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Generalized Linear Model Regression Results \n",
"==============================================================================\n",
"Dep. Variable: Frequency No. Observations: 7\n",
"Model: GLM Df Residuals: 5\n",
"Model Family: Binomial Df Model: 1\n",
"Link Function: logit Scale: 1.0000\n",
"Method: IRLS Log-Likelihood: -2.5168\n",
"Date: Fri, 05 Mar 2021 Deviance: 0.20593\n",
"Time: 08:48:11 Pearson chi2: 0.214\n",
"No. Iterations: 4 Covariance Type: nonrobust\n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"Intercept -1.7283 3.593 -0.481 0.630 -8.770 5.313\n",
"Pressure 0.0024 0.019 0.125 0.901 -0.035 0.040\n",
"==============================================================================\n",
"\"\"\""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import statsmodels.api as sm\n",
"\n",
"data[\"Success\"]=data.Count-data.Malfunction\n",
"data[\"Intercept\"]=1\n",
"\n",
"logmodel=sm.GLM(data['Frequency'], data[['Intercept','Pressure']], family=sm.families.Binomial(sm.families.links.logit)).fit()\n",
"\n",
"logmodel.summary()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"L'estimateur le plus probable du paramètre de température est 0.0024 et l'erreur standard de cet estimateur est de 0.019, autrement dit on ne peut pas distinguer d'impact particulier et il faut prendre nos estimations avec des pincettes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Estimation du dysfonctionnement des joints toriques"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
...@@ -705,7 +1286,7 @@ ...@@ -705,7 +1286,7 @@
"name": "python", "name": "python",
"nbconvert_exporter": "python", "nbconvert_exporter": "python",
"pygments_lexer": "ipython3", "pygments_lexer": "ipython3",
"version": "3.7.3" "version": "3.6.4"
} }
}, },
"nbformat": 4, "nbformat": 4,
......
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