the_iucn_redlist.ipynb

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    "<html>\n",
    "<head>\n",
    "  <title>The IUCN Red List</title>\n",
    "  <meta name=\"author\" content=\"Giovanni Zedda\">\n",
    "  <meta name=\"editor\" content=\"VS Code\">\n",
    "  <link rel=\"stylesheet\" href=\"style.css\" type=\"text/css\">\n",
    "</head>  \n",
    "<body>\n",
    "<header>\n",
    "  <h1>The IUCN Red List</h1>\n",
    "  <h2>Una panoramica sulle specie in via di estinzione con Python</h2>\n",
    "  <p class = \"small\">ad opera di <b>G. Z.</b></p>\n",
    "</header>\n",
    "</body>\n",
    "</html>\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Prima di iniziare</h3>\n",
    "<ul>\n",
    " <li> Assicurarsi di possedere tutti i file necessari nella stessa cartella.</li>\n",
    " <li> Accertarsi di avere una versione Python uguale o superiore a 3.10 installata.</li>\n",
    " <li> Installare <tt>pandas</tt>, <tt>matplotlib</tt> e <tt>jupyter</tt> nel proprio ambiente di lavoro. <br>\n",
    "      A tal fine, eseguire <code>pip install -r requirements.txt</code> da terminale o scaricare manualmente i pacchetti.</li>\n",
    " <li> Il notebook fa uso di HTML. Per una corretta visualizzazione, usare un applicativo adatto (ad esempio Visual Studio Code). <br>\n",
    "      In alternativa, eseguire <code>jupyter notebook redlist.ipynb</code> da terminale.</li>\n",
    " <li> Attivare una connessione di rete funzionante.</li>\n",
    " <li> Buona lettura!</li>\n",
    "</ul>\n",
    "\n",
    "<h3>Cos'è la Lista Rossa IUCN?</h3>\n",
    "<p>L'International Union for Conservation of Nature (IUCN) è un'organizzazione parzialmente governativa ed è la massima autorità nel monitoraggio e nella salvaguardia dei beni naturali.<a href=\"#N1\"><sup>[1]</sup></a><br>\n",
    "Essa mette a disposizione un ampio catalogo con oltre 150'000 specie viventi, oltre a fornire descrizioni dettagliate sulle caratteristiche fisiche e tassonomiche della specie, sull'areale in cui vive e sui fattori attuali o potenziali che la minacciano.<br>\n",
    "Questo elenco è noto con il nome di Lista Rossa (in inglese, \"Red List\") ed è la raccolta più esaustiva esistente sullo stato di conservazione delle specie, distinte in base al grado di minaccia in<a href=\"#N2\"><sup>[2]</sup></a>:\n",
    "<ul>\n",
    "<li> dati insufficienti (DD - Data Deficient)</li>\n",
    "<li> a rischio minimo (LC - Least Concern)</li>\n",
    "<li> prossimo alla minaccia (NT - Near Threatened)</li>\n",
    "<li> vulnerabile (VU - Vulnerable)</li>\n",
    "<li> in pericolo (EN - Endangered)</li>\n",
    "<li> in pericolo critico (CE - Critically Endangered)</li>\n",
    "<li> estinto in natura (EW - Extinct In The Wild)</li>\n",
    "<li> estinto (EX - Extinct)</li>\n",
    "<li> non valutata (NE - Not Evaluated)</li>\n",
    "</ul>\n",
    "Si noti che le specie non valutate, a differenza di quelle i cui dati sono insufficienti, non sono contenute nella Lista Rossa, che, d'altro canto, contiene una percentuale minima del numero di specie esistenti, stimato a svariati milioni.<br>\n",
    "In questa sede, inoltre, per motivi didattici, ci si limiterà ad analizzare solo gli animali vertebrati, o, quantomeno, un loro sottoinsieme di 60'000 specie.</p>\n",
    "<p>Tutte le risorse dati che verranno utilizzate sono state reperite presso il <a href=\"https://www.iucnredlist.org\" target=\"_blank\">sito ufficiale</a> della IUCN Red List.</p>\n",
    "<p>Vediamo ora come manipolare queste informazioni con Python.</p>\n",
    "\n",
    "<h3>Creazione del DataFrame con gli animali</h3>\n",
    "<p>Le informazioni necessarie per cominciare sono contenute nel file <tt>simple_summary.csv</tt>.<br>\n",
    "Leggiamole in un DataFrame<a href=\"#N3\"><sup>[3]</sup></a> della libreria pandas.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assessmentId</th>\n",
       "      <th>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>kingdomName</th>\n",
       "      <th>phylumName</th>\n",
       "      <th>orderName</th>\n",
       "      <th>className</th>\n",
       "      <th>familyName</th>\n",
       "      <th>genusName</th>\n",
       "      <th>speciesName</th>\n",
       "      <th>infraType</th>\n",
       "      <th>infraName</th>\n",
       "      <th>infraAuthority</th>\n",
       "      <th>authority</th>\n",
       "      <th>redlistCategory</th>\n",
       "      <th>redlistCriteria</th>\n",
       "      <th>criteriaVersion</th>\n",
       "      <th>populationTrend</th>\n",
       "      <th>scopes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>495630</td>\n",
       "      <td>10030</td>\n",
       "      <td>Hexanchus griseus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>HEXANCHIFORMES</td>\n",
       "      <td>CHONDRICHTHYES</td>\n",
       "      <td>HEXANCHIDAE</td>\n",
       "      <td>Hexanchus</td>\n",
       "      <td>griseus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(Bonnaterre, 1788)</td>\n",
       "      <td>Near Threatened</td>\n",
       "      <td>A2bd</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>495907</td>\n",
       "      <td>10041</td>\n",
       "      <td>Heosemys annandalii</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>TESTUDINES</td>\n",
       "      <td>REPTILIA</td>\n",
       "      <td>GEOEMYDIDAE</td>\n",
       "      <td>Heosemys</td>\n",
       "      <td>annandalii</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(Boulenger in Annandale &amp;amp; Robinson, 1903)</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>A2cd+4cd</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>497499</td>\n",
       "      <td>132523146</td>\n",
       "      <td>Hubbsina turneri</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>CYPRINODONTIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>GOODEIDAE</td>\n",
       "      <td>Hubbsina</td>\n",
       "      <td>turneri</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(de Buen, 1940)</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>B1ab(i,ii,iii,iv)+2ab(i,ii,iii,iv)</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>498370</td>\n",
       "      <td>10767</td>\n",
       "      <td>Ictalurus australis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>SILURIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>ICTALURIDAE</td>\n",
       "      <td>Ictalurus</td>\n",
       "      <td>australis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(Meek, 1904)</td>\n",
       "      <td>Data Deficient</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>498476</td>\n",
       "      <td>10769</td>\n",
       "      <td>Ictalurus mexicanus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>SILURIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>ICTALURIDAE</td>\n",
       "      <td>Ictalurus</td>\n",
       "      <td>mexicanus</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(Meek, 1904)</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>D2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   assessmentId  internalTaxonId       scientificName kingdomName phylumName   \n",
       "0        495630            10030    Hexanchus griseus    ANIMALIA   CHORDATA  \\\n",
       "1        495907            10041  Heosemys annandalii    ANIMALIA   CHORDATA   \n",
       "2        497499        132523146     Hubbsina turneri    ANIMALIA   CHORDATA   \n",
       "3        498370            10767  Ictalurus australis    ANIMALIA   CHORDATA   \n",
       "4        498476            10769  Ictalurus mexicanus    ANIMALIA   CHORDATA   \n",
       "\n",
       "            orderName       className   familyName  genusName speciesName   \n",
       "0      HEXANCHIFORMES  CHONDRICHTHYES  HEXANCHIDAE  Hexanchus     griseus  \\\n",
       "1          TESTUDINES        REPTILIA  GEOEMYDIDAE   Heosemys  annandalii   \n",
       "2  CYPRINODONTIFORMES  ACTINOPTERYGII    GOODEIDAE   Hubbsina     turneri   \n",
       "3        SILURIFORMES  ACTINOPTERYGII  ICTALURIDAE  Ictalurus   australis   \n",
       "4        SILURIFORMES  ACTINOPTERYGII  ICTALURIDAE  Ictalurus   mexicanus   \n",
       "\n",
       "   infraType  infraName  infraAuthority   \n",
       "0        NaN        NaN             NaN  \\\n",
       "1        NaN        NaN             NaN   \n",
       "2        NaN        NaN             NaN   \n",
       "3        NaN        NaN             NaN   \n",
       "4        NaN        NaN             NaN   \n",
       "\n",
       "                                       authority        redlistCategory   \n",
       "0                             (Bonnaterre, 1788)        Near Threatened  \\\n",
       "1  (Boulenger in Annandale &amp; Robinson, 1903)  Critically Endangered   \n",
       "2                                (de Buen, 1940)  Critically Endangered   \n",
       "3                                   (Meek, 1904)         Data Deficient   \n",
       "4                                   (Meek, 1904)             Vulnerable   \n",
       "\n",
       "                      redlistCriteria  criteriaVersion populationTrend  scopes  \n",
       "0                                A2bd              3.1      Decreasing  Global  \n",
       "1                            A2cd+4cd              3.1      Decreasing  Global  \n",
       "2  B1ab(i,ii,iii,iv)+2ab(i,ii,iii,iv)              3.1      Decreasing  Global  \n",
       "3                                 NaN              3.1      Decreasing  Global  \n",
       "4                                  D2              3.1         Unknown  Global  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "species = pd.read_csv(\"simple_summary.csv\")\n",
    "species.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Per eliminare le informazioni superflue, mancanti, ridondanti, o comunque poco interessanti in questo contesto, usiamo il metodo <tt>drop</tt> della classe <tt>DataFrame</tt>.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assessmentId</th>\n",
       "      <th>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>kingdomName</th>\n",
       "      <th>phylumName</th>\n",
       "      <th>orderName</th>\n",
       "      <th>className</th>\n",
       "      <th>familyName</th>\n",
       "      <th>genusName</th>\n",
       "      <th>authority</th>\n",
       "      <th>redlistCategory</th>\n",
       "      <th>populationTrend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>495630</td>\n",
       "      <td>10030</td>\n",
       "      <td>Hexanchus griseus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>HEXANCHIFORMES</td>\n",
       "      <td>CHONDRICHTHYES</td>\n",
       "      <td>HEXANCHIDAE</td>\n",
       "      <td>Hexanchus</td>\n",
       "      <td>(Bonnaterre, 1788)</td>\n",
       "      <td>Near Threatened</td>\n",
       "      <td>Decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>495907</td>\n",
       "      <td>10041</td>\n",
       "      <td>Heosemys annandalii</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>TESTUDINES</td>\n",
       "      <td>REPTILIA</td>\n",
       "      <td>GEOEMYDIDAE</td>\n",
       "      <td>Heosemys</td>\n",
       "      <td>(Boulenger in Annandale &amp;amp; Robinson, 1903)</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>Decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>497499</td>\n",
       "      <td>132523146</td>\n",
       "      <td>Hubbsina turneri</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>CYPRINODONTIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>GOODEIDAE</td>\n",
       "      <td>Hubbsina</td>\n",
       "      <td>(de Buen, 1940)</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>Decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>498370</td>\n",
       "      <td>10767</td>\n",
       "      <td>Ictalurus australis</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>SILURIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>ICTALURIDAE</td>\n",
       "      <td>Ictalurus</td>\n",
       "      <td>(Meek, 1904)</td>\n",
       "      <td>Data Deficient</td>\n",
       "      <td>Decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>498476</td>\n",
       "      <td>10769</td>\n",
       "      <td>Ictalurus mexicanus</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>SILURIFORMES</td>\n",
       "      <td>ACTINOPTERYGII</td>\n",
       "      <td>ICTALURIDAE</td>\n",
       "      <td>Ictalurus</td>\n",
       "      <td>(Meek, 1904)</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   assessmentId  internalTaxonId       scientificName kingdomName phylumName   \n",
       "0        495630            10030    Hexanchus griseus    ANIMALIA   CHORDATA  \\\n",
       "1        495907            10041  Heosemys annandalii    ANIMALIA   CHORDATA   \n",
       "2        497499        132523146     Hubbsina turneri    ANIMALIA   CHORDATA   \n",
       "3        498370            10767  Ictalurus australis    ANIMALIA   CHORDATA   \n",
       "4        498476            10769  Ictalurus mexicanus    ANIMALIA   CHORDATA   \n",
       "\n",
       "            orderName       className   familyName  genusName   \n",
       "0      HEXANCHIFORMES  CHONDRICHTHYES  HEXANCHIDAE  Hexanchus  \\\n",
       "1          TESTUDINES        REPTILIA  GEOEMYDIDAE   Heosemys   \n",
       "2  CYPRINODONTIFORMES  ACTINOPTERYGII    GOODEIDAE   Hubbsina   \n",
       "3        SILURIFORMES  ACTINOPTERYGII  ICTALURIDAE  Ictalurus   \n",
       "4        SILURIFORMES  ACTINOPTERYGII  ICTALURIDAE  Ictalurus   \n",
       "\n",
       "                                       authority        redlistCategory   \n",
       "0                             (Bonnaterre, 1788)        Near Threatened  \\\n",
       "1  (Boulenger in Annandale &amp; Robinson, 1903)  Critically Endangered   \n",
       "2                                (de Buen, 1940)  Critically Endangered   \n",
       "3                                   (Meek, 1904)         Data Deficient   \n",
       "4                                   (Meek, 1904)             Vulnerable   \n",
       "\n",
       "  populationTrend  \n",
       "0      Decreasing  \n",
       "1      Decreasing  \n",
       "2      Decreasing  \n",
       "3      Decreasing  \n",
       "4         Unknown  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.drop(columns=['infraType', 'infraName', 'infraAuthority', \n",
    "              'redlistCriteria', 'criteriaVersion', 'speciesName', 'scopes'], inplace=True)\n",
    "\n",
    "species.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Ora si vuole effettuare la ricerca di uno specifico animale. Ad esempio dell'orso bruno (<i>Ursus arctos</i>). </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>assessmentId</th>\n",
       "      <th>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>kingdomName</th>\n",
       "      <th>phylumName</th>\n",
       "      <th>orderName</th>\n",
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       "    <tr>\n",
       "      <th>43659</th>\n",
       "      <td>121229971</td>\n",
       "      <td>41688</td>\n",
       "      <td>Ursus arctos</td>\n",
       "      <td>ANIMALIA</td>\n",
       "      <td>CHORDATA</td>\n",
       "      <td>CARNIVORA</td>\n",
       "      <td>MAMMALIA</td>\n",
       "      <td>URSIDAE</td>\n",
       "      <td>Ursus</td>\n",
       "      <td>Linnaeus, 1758</td>\n",
       "      <td>Least Concern</td>\n",
       "      <td>Stable</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       assessmentId  internalTaxonId scientificName kingdomName phylumName   \n",
       "43659     121229971            41688   Ursus arctos    ANIMALIA   CHORDATA  \\\n",
       "\n",
       "       orderName className familyName genusName       authority   \n",
       "43659  CARNIVORA  MAMMALIA    URSIDAE     Ursus  Linnaeus, 1758  \\\n",
       "\n",
       "      redlistCategory populationTrend  \n",
       "43659   Least Concern          Stable  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species.loc[species['scientificName'] == 'Ursus arctos']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Costruzione di una classe <tt>Animal</tt> </h3>\n",
    "<p>Per agevolare le operazioni successive, creiamo una classe <tt>Animal</tt> che prende come parametro di inizializzazione il nome scientifico della specie ed in base ad esso crea i dovuti attributi. </p>\n",
    "<p>Per accedere ad ulteriori informazioni sulla specie, si può far uso degli attributi <tt>assessmentId</tt> e <tt>internalTaxonId</tt> per accedere alla pagina web, per come essa è strutturata, ovvero <tt>https://www.iucnredlist.org/species/[internalTaxonId]/[assessmentId]</tt>. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: Ursus arctos (Linnaeus, 1758)\n",
      "Class: MAMMALIA\n",
      "Order: CARNIVORA\n",
      "Family: URSIDAE\n",
      "Conservation: Least Concern (Stable)\n",
      "Info: https://www.iucnredlist.org/species/41688/121229971\n"
     ]
    }
   ],
   "source": [
    "WEBSITE = \"https://www.iucnredlist.org\"\n",
    "\n",
    "class Animal:\n",
    "    def __init__(self, name: str):\n",
    "        self.name = name.capitalize()\n",
    "        self.info = species.loc[species['scientificName'] == self.name]\n",
    "        self.is_listed = False if len(self.info) == 0 else True\n",
    "        if self.is_listed:\n",
    "            self.kingdom = self.info.loc[self.info.index[0], 'kingdomName']\n",
    "            self.phylum = self.info.loc[self.info.index[0], 'phylumName']\n",
    "            self.classis = self.info.loc[self.info.index[0], 'className'] # \"class\" avrebbe provocato conflittualità\n",
    "            self.order = self.info.loc[self.info.index[0], 'orderName']\n",
    "            self.family = self.info.loc[self.info.index[0], 'familyName']\n",
    "            self.genus = self.info.loc[self.info.index[0], 'genusName']\n",
    "            self.authority = self.info.loc[self.info.index[0], 'authority']\n",
    "            self.status = self.info.loc[self.info.index[0], 'redlistCategory']\n",
    "            self.trend = self.info.loc[self.info.index[0], 'populationTrend']\n",
    "            self.assessmentId = self.info.loc[self.info.index[0], 'assessmentId']\n",
    "            self.internalTaxonId = self.info.loc[self.info.index[0], 'internalTaxonId']\n",
    "            self.url = f\"{WEBSITE}/species/{self.internalTaxonId}/{self.assessmentId}\"\n",
    "\n",
    "\n",
    "    def __str__(self) -> str:\n",
    "        if self.is_listed:\n",
    "            return (\n",
    "                f\"Name: {self.name} ({self.authority})\\n\"\n",
    "                f\"Class: {self.classis}\\n\"\n",
    "                f\"Order: {self.order}\\n\"\n",
    "                f\"Family: {self.family}\\n\"\n",
    "                f\"Conservation: {self.status} ({self.trend})\\n\"\n",
    "                f\"Info: {self.url}\"\n",
    "            )\n",
    "        return f\"{self.name} (not listed)\"\n",
    "\n",
    "animal = Animal(\"Ursus arctos\")\n",
    "print(animal)\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Interazione con altri DataFrame</h3>\n",
    "<p>Cercare un animale tramite il solo nome scientifico può risultare complicato, perché non sempre è noto all'utente. <br>\n",
    "Di solito è più semplice conoscere il nome comune. A questo scopo il dataset <tt>common_names.csv</tt> può essere d'aiuto.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>name</th>\n",
       "      <th>language</th>\n",
       "      <th>main</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10030</td>\n",
       "      <td>Hexanchus griseus</td>\n",
       "      <td>Bluntnose Sixgill Shark</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10041</td>\n",
       "      <td>Heosemys annandalii</td>\n",
       "      <td>Yellow-headed Temple Turtle</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>132523146</td>\n",
       "      <td>Hubbsina turneri</td>\n",
       "      <td>Mexclapique Michoacano</td>\n",
       "      <td>Spanish; Castilian</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>132523146</td>\n",
       "      <td>Hubbsina turneri</td>\n",
       "      <td>Highland Splitfin</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10767</td>\n",
       "      <td>Ictalurus australis</td>\n",
       "      <td>Bagre del Panuco</td>\n",
       "      <td>Spanish; Castilian</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   internalTaxonId       scientificName                         name   \n",
       "0            10030    Hexanchus griseus      Bluntnose Sixgill Shark  \\\n",
       "1            10041  Heosemys annandalii  Yellow-headed Temple Turtle   \n",
       "2        132523146     Hubbsina turneri       Mexclapique Michoacano   \n",
       "3        132523146     Hubbsina turneri            Highland Splitfin   \n",
       "4            10767  Ictalurus australis             Bagre del Panuco   \n",
       "\n",
       "             language   main  \n",
       "0             English   True  \n",
       "1             English   True  \n",
       "2  Spanish; Castilian  False  \n",
       "3             English   True  \n",
       "4  Spanish; Castilian  False  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names = pd.read_csv(\"common_names.csv\")\n",
    "names.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Per accedere alla nomenclatura inglese dell'animale è consigliabile usare la colonna <tt>main</tt> e non <tt>language</tt>. <br>\n",
    "È intuitivo capire il motivo eseguendo la linea di codice qui sotto: </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>name</th>\n",
       "      <th>language</th>\n",
       "      <th>main</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84342</th>\n",
       "      <td>41688</td>\n",
       "      <td>Ursus arctos</td>\n",
       "      <td>Grizzly Bear</td>\n",
       "      <td>English</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84343</th>\n",
       "      <td>41688</td>\n",
       "      <td>Ursus arctos</td>\n",
       "      <td>Oso Pardo</td>\n",
       "      <td>Spanish; Castilian</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84344</th>\n",
       "      <td>41688</td>\n",
       "      <td>Ursus arctos</td>\n",
       "      <td>Ours brun</td>\n",
       "      <td>French</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84345</th>\n",
       "      <td>41688</td>\n",
       "      <td>Ursus arctos</td>\n",
       "      <td>Brown Bear</td>\n",
       "      <td>English</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       internalTaxonId scientificName          name            language   main\n",
       "84342            41688   Ursus arctos  Grizzly Bear             English  False\n",
       "84343            41688   Ursus arctos     Oso Pardo  Spanish; Castilian  False\n",
       "84344            41688   Ursus arctos     Ours brun              French  False\n",
       "84345            41688   Ursus arctos    Brown Bear             English   True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names.loc[names['scientificName'] == 'Ursus arctos']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p><tt>main</tt> ha valore True in corrispondenza del nome canonico (in questo caso 'Brown Bear'), False altrimenti, mentre con <tt>language</tt> è possibile trovare sinonimi dell'animale, anche in inglese ('Grizzly Bear'), dunque non è un indicatore univoco. </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Brown Bear'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "more_info = names.query(\"main and scientificName == \\'Ursus arctos\\'\")\n",
    "# scrittura compatta di\n",
    "# more_info = names.loc[(names['main']) & (names['scientificName'] == 'Ursus arctos')]\n",
    "\n",
    "# more_info = names.query(\"language == \\'English\\' and scientificName == \\'Ursus arctos\\'\")\n",
    "# restituirebbe un DataFrame a più righe, con 'Grizzly Bear' come prima occorrenza\n",
    "\n",
    "animal.vernacular = more_info.loc[more_info.index[0], 'name']\n",
    "animal.vernacular"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Può tornare estremamente utile effettuare l'operazione inversa, ossia ricavare il nome scientifico a partire dal nome comune, operazione che non verrà eseguita in questa trattazione, ma disponibile sull'applicativo finale <tt>launcher.py</tt>. </p> \n",
    "<p>Si noti invece come tra i file nella cartella sia presente anche un file più pesante degli altri denominato <tt>assessments.csv</tt>, contenente molte altre informazioni aggiuntive, come la descrizione, la distribuzione e l'habitat, e le minacce a cui è sottoposto.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>internalTaxonId</th>\n",
       "      <th>scientificName</th>\n",
       "      <th>redlistCategory</th>\n",
       "      <th>redlistCriteria</th>\n",
       "      <th>yearPublished</th>\n",
       "      <th>assessmentDate</th>\n",
       "      <th>criteriaVersion</th>\n",
       "      <th>language</th>\n",
       "      <th>rationale</th>\n",
       "      <th>habitat</th>\n",
       "      <th>...</th>\n",
       "      <th>populationTrend</th>\n",
       "      <th>range</th>\n",
       "      <th>useTrade</th>\n",
       "      <th>systems</th>\n",
       "      <th>conservationActions</th>\n",
       "      <th>realm</th>\n",
       "      <th>yearLastSeen</th>\n",
       "      <th>possiblyExtinct</th>\n",
       "      <th>possiblyExtinctInTheWild</th>\n",
       "      <th>scopes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>assessmentId</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>495630</th>\n",
       "      <td>10030</td>\n",
       "      <td>Hexanchus griseus</td>\n",
       "      <td>Near Threatened</td>\n",
       "      <td>A2bd</td>\n",
       "      <td>2020</td>\n",
       "      <td>2019-11-21 00:00:00 UTC</td>\n",
       "      <td>3.1</td>\n",
       "      <td>English</td>\n",
       "      <td>&lt;p&gt;The&amp;#160;Bluntnose Sixgill Shark (&lt;em&gt;Hexan...</td>\n",
       "      <td>&lt;p&gt;&lt;span lang=\"EN-GB\"&gt;&lt;span lang=\"EN-GB\"&gt;The B...</td>\n",
       "      <td>...</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>The Bluntnose Sixgill Shark has a widespread, ...</td>\n",
       "      <td>&lt;p&gt;The species is utilized for its meat, liver...</td>\n",
       "      <td>Marine</td>\n",
       "      <td>&lt;p&gt;Since 2010, the European Union Fisheries Co...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495907</th>\n",
       "      <td>10041</td>\n",
       "      <td>Heosemys annandalii</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>A2cd+4cd</td>\n",
       "      <td>2021</td>\n",
       "      <td>2018-03-13 00:00:00 UTC</td>\n",
       "      <td>3.1</td>\n",
       "      <td>English</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Heosemys annandalii&lt;/em&gt; is considered ...</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Heosemys annandalii&lt;/em&gt; inhabits lowla...</td>\n",
       "      <td>...</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>&lt;p&gt;The range of &lt;em&gt;Heosemys annandalii&lt;/em&gt; i...</td>\n",
       "      <td>The species is collected for local consumption...</td>\n",
       "      <td>Terrestrial|Freshwater (=Inland waters)</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Heosemys annandalii &lt;/em&gt;is included in...</td>\n",
       "      <td>Indomalayan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497499</th>\n",
       "      <td>132523146</td>\n",
       "      <td>Hubbsina turneri</td>\n",
       "      <td>Critically Endangered</td>\n",
       "      <td>B1ab(i,ii,iii,iv)+2ab(i,ii,iii,iv)</td>\n",
       "      <td>2019</td>\n",
       "      <td>2018-04-17 00:00:00 UTC</td>\n",
       "      <td>3.1</td>\n",
       "      <td>English</td>\n",
       "      <td>The Highland Splitfin is now only known to be ...</td>\n",
       "      <td>&lt;p&gt;This species lives in quiet waters with cur...</td>\n",
       "      <td>...</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>The Highland Splitfin is a freshwater fish spe...</td>\n",
       "      <td>The Highland Splitfin is not a target species ...</td>\n",
       "      <td>Freshwater (=Inland waters)</td>\n",
       "      <td>No conservation actions targeting&amp;#160;&lt;em&gt;Hub...</td>\n",
       "      <td>Neotropical</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498370</th>\n",
       "      <td>10767</td>\n",
       "      <td>Ictalurus australis</td>\n",
       "      <td>Data Deficient</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2019</td>\n",
       "      <td>2018-12-06 00:00:00 UTC</td>\n",
       "      <td>3.1</td>\n",
       "      <td>English</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Ictalurus australis&lt;/em&gt; occurs in east...</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;I. australis &lt;/em&gt;inhabits quiet and sl...</td>\n",
       "      <td>...</td>\n",
       "      <td>Decreasing</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Ictalurus australis&lt;/em&gt; is distributed...</td>\n",
       "      <td>&lt;em&gt;I. australis &lt;/em&gt;is primarily harvested a...</td>\n",
       "      <td>Freshwater (=Inland waters)</td>\n",
       "      <td>&lt;p&gt;The American Fisheries Society Endangered S...</td>\n",
       "      <td>Neotropical</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498476</th>\n",
       "      <td>10769</td>\n",
       "      <td>Ictalurus mexicanus</td>\n",
       "      <td>Vulnerable</td>\n",
       "      <td>D2</td>\n",
       "      <td>2019</td>\n",
       "      <td>2018-12-06 00:00:00 UTC</td>\n",
       "      <td>3.1</td>\n",
       "      <td>English</td>\n",
       "      <td>&lt;em&gt;I. mexicanus &lt;/em&gt;is herein categorized as...</td>\n",
       "      <td>&lt;p&gt;Specifics details are unknown for this spec...</td>\n",
       "      <td>...</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>&lt;p&gt;&lt;em&gt;Ictalurus mexicanus&lt;/em&gt; is a species e...</td>\n",
       "      <td>This species is not utilised or traded.</td>\n",
       "      <td>Freshwater (=Inland waters)</td>\n",
       "      <td>&lt;p&gt;In Mexico,&amp;#160;&lt;em&gt;Ictalurus mexicanus&lt;/em...</td>\n",
       "      <td>Neotropical</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>Global</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              internalTaxonId       scientificName        redlistCategory   \n",
       "assessmentId                                                                \n",
       "495630                  10030    Hexanchus griseus        Near Threatened  \\\n",
       "495907                  10041  Heosemys annandalii  Critically Endangered   \n",
       "497499              132523146     Hubbsina turneri  Critically Endangered   \n",
       "498370                  10767  Ictalurus australis         Data Deficient   \n",
       "498476                  10769  Ictalurus mexicanus             Vulnerable   \n",
       "\n",
       "                                 redlistCriteria  yearPublished   \n",
       "assessmentId                                                      \n",
       "495630                                      A2bd           2020  \\\n",
       "495907                                  A2cd+4cd           2021   \n",
       "497499        B1ab(i,ii,iii,iv)+2ab(i,ii,iii,iv)           2019   \n",
       "498370                                       NaN           2019   \n",
       "498476                                        D2           2019   \n",
       "\n",
       "                       assessmentDate  criteriaVersion language   \n",
       "assessmentId                                                      \n",
       "495630        2019-11-21 00:00:00 UTC              3.1  English  \\\n",
       "495907        2018-03-13 00:00:00 UTC              3.1  English   \n",
       "497499        2018-04-17 00:00:00 UTC              3.1  English   \n",
       "498370        2018-12-06 00:00:00 UTC              3.1  English   \n",
       "498476        2018-12-06 00:00:00 UTC              3.1  English   \n",
       "\n",
       "                                                      rationale   \n",
       "assessmentId                                                      \n",
       "495630        <p>The&#160;Bluntnose Sixgill Shark (<em>Hexan...  \\\n",
       "495907        <p><em>Heosemys annandalii</em> is considered ...   \n",
       "497499        The Highland Splitfin is now only known to be ...   \n",
       "498370        <p><em>Ictalurus australis</em> occurs in east...   \n",
       "498476        <em>I. mexicanus </em>is herein categorized as...   \n",
       "\n",
       "                                                        habitat  ...   \n",
       "assessmentId                                                     ...   \n",
       "495630        <p><span lang=\"EN-GB\"><span lang=\"EN-GB\">The B...  ...  \\\n",
       "495907        <p><em>Heosemys annandalii</em> inhabits lowla...  ...   \n",
       "497499        <p>This species lives in quiet waters with cur...  ...   \n",
       "498370        <p><em>I. australis </em>inhabits quiet and sl...  ...   \n",
       "498476        <p>Specifics details are unknown for this spec...  ...   \n",
       "\n",
       "             populationTrend   \n",
       "assessmentId                   \n",
       "495630            Decreasing  \\\n",
       "495907            Decreasing   \n",
       "497499            Decreasing   \n",
       "498370            Decreasing   \n",
       "498476               Unknown   \n",
       "\n",
       "                                                          range   \n",
       "assessmentId                                                      \n",
       "495630        The Bluntnose Sixgill Shark has a widespread, ...  \\\n",
       "495907        <p>The range of <em>Heosemys annandalii</em> i...   \n",
       "497499        The Highland Splitfin is a freshwater fish spe...   \n",
       "498370        <p><em>Ictalurus australis</em> is distributed...   \n",
       "498476        <p><em>Ictalurus mexicanus</em> is a species e...   \n",
       "\n",
       "                                                       useTrade   \n",
       "assessmentId                                                      \n",
       "495630        <p>The species is utilized for its meat, liver...  \\\n",
       "495907        The species is collected for local consumption...   \n",
       "497499        The Highland Splitfin is not a target species ...   \n",
       "498370        <em>I. australis </em>is primarily harvested a...   \n",
       "498476                  This species is not utilised or traded.   \n",
       "\n",
       "                                              systems   \n",
       "assessmentId                                            \n",
       "495630                                         Marine  \\\n",
       "495907        Terrestrial|Freshwater (=Inland waters)   \n",
       "497499                    Freshwater (=Inland waters)   \n",
       "498370                    Freshwater (=Inland waters)   \n",
       "498476                    Freshwater (=Inland waters)   \n",
       "\n",
       "                                            conservationActions        realm   \n",
       "assessmentId                                                                   \n",
       "495630        <p>Since 2010, the European Union Fisheries Co...          NaN  \\\n",
       "495907        <p><em>Heosemys annandalii </em>is included in...  Indomalayan   \n",
       "497499        No conservation actions targeting&#160;<em>Hub...  Neotropical   \n",
       "498370        <p>The American Fisheries Society Endangered S...  Neotropical   \n",
       "498476        <p>In Mexico,&#160;<em>Ictalurus mexicanus</em...  Neotropical   \n",
       "\n",
       "             yearLastSeen possiblyExtinct possiblyExtinctInTheWild  scopes  \n",
       "assessmentId                                                                \n",
       "495630                NaN           False                    False  Global  \n",
       "495907                NaN           False                    False  Global  \n",
       "497499                NaN           False                    False  Global  \n",
       "498370                NaN           False                    False  Global  \n",
       "498476                NaN           False                    False  Global  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Essendo assessmentId univoco lo si può utilizzare come chiave\n",
    "assessments = pd.read_csv(\"assessments.csv\", index_col = 'assessmentId') \n",
    "assessments.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Volendo, è possibile creare una pagina html per visualizzare in modo comprensivo queste informazioni, senza dover accedere al sito. <br>\n",
    "Ad esempio: </p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<main = style=\"background-color: #444444; color: white;\"><h1>Brown Bear</h1><h2><i>Ursus arctos</i></h2><h2>Assessment</h2><p><b>Least Concern</b> (Stable).<p>The range of the Brown Bear has historically declined in North America, Europe, and Asia, and the species has been extirpated in North Africa. However, it remains widespread across three continents, and is still one of the world’s most widely distributed terrestrial mammals. Globally the population remains large, and is not significantly declining and may be increasing in some areas (Swenson <em>et al. </em>1998, Schwartz <em>et al. </em>2006, Mace <em>et al. </em>2012, Kaczensky <em>et al. </em>2013, Chapron<em> et al. </em>2014). There are many small, isolated subpopulations that are in jeopardy of extirpation, but others, under more protection and management, are expanding.</p></p></main>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "far_more_info = assessments.loc[animal.assessmentId]\n",
    "\n",
    "html_content = (\n",
    "    f\"<main = style=\\\"background-color: #444444; color: white;\\\">\"\n",
    "    f\"<h1>{animal.vernacular}</h1>\"\n",
    "    f\"<h2><i>{animal.name}</i></h2>\"\n",
    "    f\"<h2>Assessment</h2>\"\n",
    "    f\"<p><b>{animal.status}</b> ({animal.trend}).\"\n",
    "    f\"{far_more_info['rationale']}</p>\"\n",
    "    f\"</main>\"\n",
    ")\n",
    "\n",
    "### Queste due linee di codice permettono un output per ipynb.\n",
    "from IPython.display import display, HTML\n",
    "display(HTML(html_content))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3>Rappresentazioni grafiche</h3>\n",
    "<p>Finora ci si è limitati ad accedere e visualizzare i dati testuali, ma <tt>pandas.DataFrame</tt> fornisce molti metodi compatibili con la libreria <tt>matplotlib</tt> che permettono di avere un'analisi grafica delle informazioni contenute in tabella.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "\n",
    "tmp = species.value_counts('redlistCategory', normalize = True)\n",
    "\n",
    "### unisco le voci minori sotto un'unica categoria\n",
    "gt_percentile = tmp[tmp>=0.01]\n",
    "gt_percentile['Extinct / Other Categories'] = tmp[tmp<0.01].sum()\n",
    "###\n",
    "\n",
    "gt_percentile.plot.pie(title = 'IUCN Red List Categories', ylabel='', wedgeprops=dict(width=0.6))\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p>Grazie a questo areogramma, si può subito intuire come quasi un quarto delle specie sia esposto a un qualche livello di minaccia.</p>\n",
    "<p>Nelle successive due caselle di codice si prova a complicare leggermente questa operazione effettuando una distinzione tra le classi.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Diagramma a barre\n",
    "tmp = species.value_counts('className')\n",
    "fig = tmp.plot.bar(title = 'Distribution of chordata classes', xlabel='')\n",
    "fig.bar_label(fig.containers[0]) # scrive il valore sopra la rispettiva barra\n",
    "fig.grid(axis = 'x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1000 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "### Areogrammi \"a ciambella\"\n",
    "\n",
    "### riunisco le classi minori in un'unica categoria 'OTHERS'\n",
    "sel = species[['className', 'redlistCategory']].copy()\n",
    "tmp = sel.value_counts('className')\n",
    "gt1300 = tmp[tmp>=1300]\n",
    "gt1300['OTHERS'] = tmp[tmp<1300].sum()\n",
    "sel.loc[~(sel['className'].isin(gt1300.index)), 'className'] = 'OTHERS'\n",
    "###\n",
    "\n",
    "fig, axes = plt.subplots(2,3)\n",
    "plt.suptitle('CONSERVATION STATUS FOR EACH CLASS OF CHORDATA', size = 20, y=1)\n",
    "\n",
    "from itertools import product #prodotto cartesiano\n",
    "\n",
    "# itero sulle classi di animale e sulle posizioni dei\n",
    "# grafici nella figura di output (0,0), (0,1), ..., (1,2)\n",
    "for cls, pos in zip(gt1300.index, product((0,1),(0,1,2))):\n",
    "\n",
    "    tmp = sel.loc[sel['className'] == cls]\n",
    "    tmp = tmp.value_counts('redlistCategory', normalize = True)\n",
    "    gt_percentile = tmp[tmp>=0.01]\n",
    "    gt_percentile['Other Categories'] = tmp[tmp<0.01].sum()\n",
    "\n",
    "    # creazione del del subplot relativo alla classe su cui si sta iterando\n",
    "    gt_percentile.plot.pie(title = cls, ylabel = '', ax = axes[*pos], \n",
    "                           figsize = (18, 10), fontsize = 7.5, \n",
    "                           labeldistance = 1.15, wedgeprops = dict(width=0.6))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
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    "<h3>L'albero tassonomico</h3>\n",
    "<p>Nel 1700 il naturalista svedese Carlo Linneo, propose di catalogare piante e animali secondo principi rigorosi, introducendo di fatto quella che è la sistematica moderna.<br>\n",
    "Questa classificazione delle creature è di natura gerarchica, in virtù della quale è possibile affermare che una specie animale sia il ramo terminale o foglia (escludendo eventuali ulteriori ramificazioni quali sottospecie o popolazioni) di una struttura ad albero, solitamente non dicotomica, così strutturata:<br>\n",
    "Regno > Phylum (o Tipo) > Classe > Ordine > Famiglia > <i>Genere</i> > <i>Specie</i>,<br>\n",
    "con eventuali ripartizioni intermedie.<br>\n",
    "Riportando il solito esempio dell'orso bruno, esso è scientificamente così classificato:<br>\n",
    "Animalia > Chordata > Mammalia > Carnivora > Ursidae > <i>Ursus</i> > <i>Ursus arctos</i>.\n",
    "\n",
    "L'albero è stato implementato nella classe <tt>TaxonTree</tt> del modulo <tt>trees.py</tt>.<br>\n",
    "Questo albero eredita molti metodi e attributi da una struttura più generica <tt>Tree</tt>, la cui caratteristica più interessante è quella di poter inserire un ramo in tempo O(log N) grazie all'albero di ricerca binaria in cui sono disposti i figli, mantenendo dunque intatto il loro ordinamento, e di conseguenza permettendo l'estrazione di un ramo figlio sempre in tempo logaritmico, tramite ricerca binaria.<br>\n",
    "Di conseguenza la complessità computazionale per la creazione dell'albero è O(NlogN), al posto di O(N²) qualora si fosse scelto di utilizzare una lista classica. Per N > 60'000, come in questo caso, si tratta di un risparmio significativo.<br>\n",
    "Anche l'utilizzo di un insieme o un dizionario sarebbe stata una valida alternativa, tuttavia in fase di stampa dovrebbe comunque essere convertito in una lista ed ordinato, impiegando in ogni caso un tempo O(NlogN), oltre ad essere didatticamente meno attraente di un <i>binary search tree</i>, implementato in maniera assai simile a quella presentata nel libro <i>Data Structures and Algorithms with Python</i> di K.D. Lee e S. Hubbard<a href=\"#N4\"><sup>[4]</sup></a>.\n",
    "\n",
    "Qui sotto viene data una dimostrazione di come agisce la classe.</p>"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Length of animals list: 6\n",
      "ANIMALIA\n",
      "  CHORDATA\n",
      "    ACTINOPTERYGII\n",
      "      PERCIFORMES\n",
      "        ISTIOPHORIDAE\n",
      "          Istiophorus\n",
      "            Istiophorus platypterus\n",
      "          Makaira\n",
      "            Makaira nigricans\n",
      "    MAMMALIA\n",
      "      CARNIVORA\n",
      "        FELIDAE\n",
      "          Panthera\n",
      "            Panthera leo\n",
      "            Panthera tigris\n",
      "          Puma\n",
      "            Puma concolor\n",
      "\n",
      "There are only 5 different species in the tree!\n"
     ]
    }
   ],
   "source": [
    "# i moduli completi del progetto, precedentemente solo abbozzati\n",
    "from trees import TaxonTree\n",
    "from animals import Animal as An # evita conflitti con la classe Animal dichiarata localmente\n",
    "An.species = species\n",
    "An.names = names\n",
    "\n",
    "animals = [\n",
    "            An('Panthera tigris'),          # tigre\n",
    "            An('Istiophorus platypterus'),  # pesce vela\n",
    "            An('Panthera leo'),             # leone\n",
    "            An('Puma concolor'),            # puma\n",
    "            An('Panthera Leo'),             # leone (duplicato)\n",
    "            An('Makaira nigricans')         # marlin azzurro\n",
    "          ]\n",
    "\n",
    "print('Length of animals list:', len(animals))\n",
    "taxontree = TaxonTree('ANIMALIA')\n",
    "for animal in animals:\n",
    "    taxontree.add_animal(animal) # non aggiunge duplicati!\n",
    "\n",
    "taxontree.print() # visita in pre-ordine: prima la radice e poi i figli, in ordine alfabetico\n",
    "print('There are only 5 different species in the tree!')\n"
   ]
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    "<h3>Menù dinamico</h3>\n",
    "<p>L'interfaccia con il menù contestuale è gestita da una classe <tt>Menu</tt>, che si basa su un dizionario di dizionari, nonché della caratteristica di Python di trattare le funzioni come oggetti.<br>\n",
    "La voce principale del dizionario è il \"tasto\" che va premuto dall'utente, a cui sono associati una stringa di descrizione e la funzione che l'utente vuole chiamare.<p>"
   ]
  },
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   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "My Menu\n",
      "Actions:\n",
      "   1 - call fun1\n",
      "   2 - call fun2\n",
      "\n",
      "fun1 works!\n"
     ]
    }
   ],
   "source": [
    "class Menu():\n",
    "    def __init__(self, title:str = '', entries: list[tuple[str|int, str, callable]] = None):\n",
    "        self.title = title\n",
    "        self._menu = {}\n",
    "        if entries: # permette di convertire una lista di tuple (indice, etichetta, azione) in un menù\n",
    "            for idx, label, action in entries:\n",
    "                self.add_action(idx, label, action)\n",
    "\n",
    "    def add_action(self, idx: str|int, label: str, action: callable):\n",
    "        self._menu[idx] = {'label' : label, 'action': action}\n",
    "    \n",
    "    def execute(self, idx: str|int) -> None:\n",
    "        if self._menu.get(idx): # get serve a non alzare KeyError quando viene passata una chiave inesistente\n",
    "            self._menu.get(idx)['action']() # le parentesi tonde finali permettono la chiamata della funzione\n",
    "            # nelle parentesi tonde si possono anche passare dei parametri, se necessari\n",
    "\n",
    "    def __str__(self) -> str:\n",
    "        string = f\"\\n{self.title}\\nActions:\\n\"\n",
    "        for key, value in self._menu.items():\n",
    "           string += f\"   {key} - {value['label']}\\n\"\n",
    "        return string\n",
    "\n",
    "def fun1():\n",
    "    print('fun1 works!')\n",
    "def fun2():\n",
    "    print('fun2 works!')\n",
    "actions = [('1', 'call fun1', fun1),\n",
    "           ('2', 'call fun2', fun2)]\n",
    "menu = Menu('My Menu', actions)\n",
    "print(menu)\n",
    "choice = input('Enter choice: ')\n",
    "menu.execute(choice)"
   ]
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   "source": [
    "<h3>Uso e struttura dell'applicativo</h3>\n",
    "<p>Associati a questo notebook, sono presenti anche alcuni file Python, dei quali <tt>launcher.py</tt> è il pannello di controllo, ossia quello che l'utente dovrebbe lanciare.<br>\n",
    "In esso vengono invocate tutte funzionalità presentate fino ad ora, in particolare:<br>\n",
    "<tt>menu.py</tt> predispone di una classe <tt>Menu</tt> che semplifica la struttura del launcher;<br>\n",
    "<tt>animals.py</tt> contiene la classe <tt>Animal</tt>, in cui i DataFrame vengono assegnati come variabili di classe, per ottimizzare i tempi e lo spazio di lettura dei file csv. La classe contiene inoltre i metodi per istanziare un oggetto indifferentemente se a partire dal nome comune o dal nome scientifico;<br>\n",
    "<tt>explore.py</tt> contiene le funzioni per creare, salvare ed aprire una pagina html con le informazioni contenute nel DataFrame <tt>assessments</tt> relative all'animale passato come parametro, e una funzione per aprire semplicemente sul proprio browser predefinito il sito internet dell'animale in questione;<br>\n",
    "<tt>downloader.py</tt> fa uso di <tt>urllib.request</tt> della libreria standard per provare a cercare l'immagine dell'animale online e scaricarne i byte che la compongono, trasponendoli su un file di output jpg tramite scrittura binaria. In caso di eventuali errori in fase di download o salvataggio, le eccezioni vengono gestite senza interrompere il programma;<br>\n",
    "<tt>charts.py</tt> crea i tre grafici mostrati nella sezione precedente, a cui si aggiunge una ricerca per classe;<br>\n",
    "<tt>trees.py</tt> dispone di un contenitore <tt>TaxonTree</tt> adatto al salvataggio e alla stampa di un albero tassonomico.\n",
    "</p>\n",
    "\n",
    "<h3>Conclusioni</h3>\n",
    "<p>Questo progetto nasce da un personale interesse verso le specie animali e la biodiversità in generale, che mi ha condotto ad approfondire questi temi con l'analisi dati in Python, in parte anche a dimostrazione di come la programmazione e l'elaborazione di strutture dati siano applicabili ai più disparati settori di studio e non solo.<br>\n",
    "Spero inoltre di essere riuscito ad intrattenere il lettore e/o l'utilizzatore con un'opera gradevole ed intrigante, che comunichi una gamma di informazioni relativamente ampia sull'utilizzo di dati aggregati e formalmente esatta e coerente per quanto concerne il codice, ma che possa al contempo far scaturire una nuova consapevolezza sulla ricchezza della diversità biologica presente sul nostro Pianeta, e su come una rilevante porzione di essa sia vicina alla scomparsa, senza che mai nessuno, spesso neppure il mondo della biologia, venga a conoscenza dell'esistenza stessa di moltissime specie che ne fanno parte.<br></p>\n",
    "\n",
    "<h3>Riferimenti</h3>\n",
    "<ol>\n",
    "<li id=\"N1\"> IUCN. 2022. International Union for Conservation of Nature. <a href=\"https://www.iucn.org\" target=\"_blank\">https://www.iucn.org</a>. Consultato il 18 aprile 2023.</li>\n",
    "<li id= \"N2\"> IUCN. 2022. The IUCN Red List of Threatened Species. Version 2022-2. <a href=\"https://www.iucnredlist.org\" target=\"_blank\">https://www.iucnredlist.org</a>. Consultato il 18 aprile 2023.</li>\n",
    "<li id=\"N3\"> DataFrame - pandas 2.0 documentation, su <a href=\"https://pandas.pydata.org/docs/reference/frame.html\" target=\"_blank\">https://pandas.pydata.org/docs/reference/frame.html</a>.</li>\n",
    "<li id=\"N4\"> K. D. Lee and S. Hubbard. \"6.5.1 The BinarySearchTree Class\". In <i>Data Structures and Algorithms with Python</i>, 170-176. Springer, 2015.</li>\n",
    "<ol>"
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    "<html>\n",
    "<head>\n",
    "  <link rel=\"stylesheet\" href=\"style.css\" type=\"text/css\">\n",
    "</head>  \n",
    "<body>\n",
    "<footer>\n",
    "<p class=\"small\">22 maggio 2023: Giornata Internazionale della Biodiversità</p>\n",
    "</footer>\n",
    "</body>\n",
    "</html>\n"
   ]
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