microbiota score classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataDir='./Data/'\n",
    "filename='microbiota_clean_wSCORE.xlsx'\n",
    "\n",
    "path = os.path.join(dataDir,filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(344, 314)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df.SCORE.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(336, 314)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ScoreClass']=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>SCORE</th>\n",
       "      <th>ScoreClass</th>\n",
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       "      <td>2.184495</td>\n",
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       "      <td>14.683229</td>\n",
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       "      <td>4.574546</td>\n",
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       "      <td>3.154071</td>\n",
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       "      <td>6.019058</td>\n",
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       "      <td>6.114808</td>\n",
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       "      <td>1.537867</td>\n",
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       "      <th>...</th>\n",
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       "      <td>4.646039</td>\n",
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       "      <td>0.902318</td>\n",
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       "      <td>5.901142</td>\n",
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       "      <td>11.182189</td>\n",
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       "      <th>288</th>\n",
       "      <td>3.908048</td>\n",
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       "      <th>289</th>\n",
       "      <td>0.274108</td>\n",
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       "      <th>290</th>\n",
       "      <td>0.036928</td>\n",
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       "      <th>291</th>\n",
       "      <td>3.692057</td>\n",
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       "      <th>292</th>\n",
       "      <td>5.845542</td>\n",
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       "      <th>293</th>\n",
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      "text/plain": [
       "         SCORE  ScoreClass\n",
       "0    22.879007           1\n",
       "1     1.353694           1\n",
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       "12    4.574546           1\n",
       "13    3.154071           1\n",
       "14    3.257595           1\n",
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       "16    4.354457           1\n",
       "17    4.231669           1\n",
       "18    2.520249           1\n",
       "19    3.885516           1\n",
       "20    6.019058           1\n",
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       "28    6.114808           1\n",
       "29    1.537867           1\n",
       "..         ...         ...\n",
       "274   4.646039           1\n",
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       "277   7.434890           1\n",
       "278   9.773401           1\n",
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       "281   4.822558           1\n",
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       "286  10.749321           1\n",
       "287  11.182189           1\n",
       "288   3.908048           1\n",
       "289   0.274108           1\n",
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       "293   6.830030           1\n",
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       "\n",
       "[300 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['SCORE','ScoreClass']].head(300)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "tresholds=(1,2,5,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Giammi\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "for i, row in df.iterrows():\n",
    "    for index, el in enumerate(tresholds):\n",
    "        if (row['SCORE']>el and row['SCORE']<tresholds[index+1]):\n",
    "            df['ScoreClass'][i]=index+2\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.019058</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>1.905593</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.500820</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>6.874374</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4.287369</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2.721086</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.705335</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3.080471</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6.114808</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1.537867</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>311</th>\n",
       "      <td>4.612675</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312</th>\n",
       "      <td>11.755201</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>5.381160</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>314</th>\n",
       "      <td>1.856538</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>315</th>\n",
       "      <td>7.153794</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>2.294207</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1.180673</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>3.797627</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>5.174855</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>1.716143</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>321</th>\n",
       "      <td>4.207728</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>7.979641</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>323</th>\n",
       "      <td>3.715576</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>2.878060</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>325</th>\n",
       "      <td>5.532249</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>2.348857</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>327</th>\n",
       "      <td>0.003986</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>8.013450</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329</th>\n",
       "      <td>2.392929</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>6.227326</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>3.197024</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>7.094034</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>17.287243</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>8.099615</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>335</th>\n",
       "      <td>0.606035</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336</th>\n",
       "      <td>1.513243</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>338</th>\n",
       "      <td>2.108557</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>339</th>\n",
       "      <td>6.855821</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>340</th>\n",
       "      <td>1.963639</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>11.403071</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>336 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         SCORE  ScoreClass\n",
       "0    22.879007           4\n",
       "1     1.353694           2\n",
       "2     0.898345           1\n",
       "3     1.484264           2\n",
       "4     1.491918           2\n",
       "5     3.898358           3\n",
       "6     1.836433           2\n",
       "7     2.184495           3\n",
       "8     0.761695           1\n",
       "9    14.683229           4\n",
       "10    3.144521           3\n",
       "11   10.768774           4\n",
       "12    4.574546           3\n",
       "13    3.154071           3\n",
       "14    3.257595           3\n",
       "15    2.006362           3\n",
       "16    4.354457           3\n",
       "17    4.231669           3\n",
       "18    2.520249           3\n",
       "19    3.885516           3\n",
       "20    6.019058           4\n",
       "21    1.905593           2\n",
       "22    4.500820           3\n",
       "23    6.874374           4\n",
       "24    4.287369           3\n",
       "25    2.721086           3\n",
       "26    0.705335           1\n",
       "27    3.080471           3\n",
       "28    6.114808           4\n",
       "29    1.537867           2\n",
       "..         ...         ...\n",
       "311   4.612675           3\n",
       "312  11.755201           4\n",
       "313   5.381160           4\n",
       "314   1.856538           2\n",
       "315   7.153794           4\n",
       "316   2.294207           3\n",
       "317   1.180673           2\n",
       "318   3.797627           3\n",
       "319   5.174855           4\n",
       "320   1.716143           2\n",
       "321   4.207728           3\n",
       "322   7.979641           4\n",
       "323   3.715576           3\n",
       "324   2.878060           3\n",
       "325   5.532249           4\n",
       "326   2.348857           3\n",
       "327   0.003986           1\n",
       "328   8.013450           4\n",
       "329   2.392929           3\n",
       "330   6.227326           4\n",
       "331   3.197024           3\n",
       "332   7.094034           4\n",
       "333  17.287243           4\n",
       "334   8.099615           4\n",
       "335   0.606035           1\n",
       "336   1.513243           2\n",
       "338   2.108557           3\n",
       "339   6.855821           4\n",
       "340   1.963639           2\n",
       "341  11.403071           4\n",
       "\n",
       "[336 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['SCORE','ScoreClass']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Codice_PLIC</th>\n",
       "      <th>Eta</th>\n",
       "      <th>Alcool</th>\n",
       "      <th>Fumo</th>\n",
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       "      <th>Ipolipemizzanti</th>\n",
       "      <th>Antipertensivi</th>\n",
       "      <th>Ipoglicemizzanti</th>\n",
       "      <th>ALTEZZA_cm</th>\n",
       "      <th>...</th>\n",
       "      <th>g_92</th>\n",
       "      <th>g_93</th>\n",
       "      <th>g_94</th>\n",
       "      <th>g_95</th>\n",
       "      <th>g_96</th>\n",
       "      <th>g_97</th>\n",
       "      <th>g_98</th>\n",
       "      <th>g_99</th>\n",
       "      <th>Sesso</th>\n",
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       "    <tr>\n",
       "      <th>ScoreClass</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>57</td>\n",
       "      <td>...</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>105</td>\n",
       "      <td>...</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>119</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>116</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 314 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0  Codice_PLIC  Eta  Alcool  Fumo  Attivita_sportiva  \\\n",
       "ScoreClass                                                                  \n",
       "1                   58           58   58      58    58                 58   \n",
       "2                   46           46   46      46    46                 46   \n",
       "3                  112          112  112     112   112                112   \n",
       "4                  120          120  120     120   120                119   \n",
       "\n",
       "            Ipolipemizzanti  Antipertensivi  Ipoglicemizzanti  ALTEZZA_cm  \\\n",
       "ScoreClass                                                                  \n",
       "1                        58              58                58          57   \n",
       "2                        46              46                46          45   \n",
       "3                       112             112               112         105   \n",
       "4                       120             120               120         116   \n",
       "\n",
       "            ...  g_92  g_93  g_94  g_95  g_96  g_97  g_98  g_99  Sesso  SCORE  \n",
       "ScoreClass  ...                                                                \n",
       "1           ...    58    58    58    58    58    58    58    58     58     58  \n",
       "2           ...    46    46    46    46    46    46    46    46     46     46  \n",
       "3           ...   112   112   112   112   112   112   112   112    112    112  \n",
       "4           ...   120   120   120   120   120   120   120   120    120    120  \n",
       "\n",
       "[4 rows x 314 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('ScoreClass').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename=\"./microbiota_clean_wCLass.xlsx\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_excel = df.to_excel (filename, index = None, header=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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