BasicWorkGBIF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1625196b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "42409948",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        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>gbifID</th>\n",
       "      <th>datasetKey</th>\n",
       "      <th>occurrenceID</th>\n",
       "      <th>kingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>species</th>\n",
       "      <th>...</th>\n",
       "      <th>identifiedBy</th>\n",
       "      <th>dateIdentified</th>\n",
       "      <th>license</th>\n",
       "      <th>rightsHolder</th>\n",
       "      <th>recordedBy</th>\n",
       "      <th>typeStatus</th>\n",
       "      <th>establishmentMeans</th>\n",
       "      <th>lastInterpreted</th>\n",
       "      <th>mediaType</th>\n",
       "      <th>issue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3395912434</td>\n",
       "      <td>84d26682-f762-11e1-a439-00145eb45e9a</td>\n",
       "      <td>https://svampe.databasen.org/observations/1024...</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>...</td>\n",
       "      <td>Jens H. Petersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC_BY_NC_4_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Jens H. Petersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31/10/2021 21:14</td>\n",
       "      <td>StillImage</td>\n",
       "      <td>COORDINATE_ROUNDED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3393147503</td>\n",
       "      <td>b124e1e0-4755-430f-9eab-894f25a9b59c</td>\n",
       "      <td>urn:uuid:df80f278-4957-47cd-8157-1425fd1ee3d8</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gry Føyerland Blix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26/10/2021 03:27</td>\n",
       "      <td>StillImage</td>\n",
       "      <td>COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3393037337</td>\n",
       "      <td>b124e1e0-4755-430f-9eab-894f25a9b59c</td>\n",
       "      <td>urn:uuid:41a99575-8ce8-413f-ba64-95c217339d60</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gry Føyerland Blix</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26/10/2021 03:22</td>\n",
       "      <td>StillImage</td>\n",
       "      <td>COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3389784557</td>\n",
       "      <td>6ac3f774-d9fb-4796-b3e9-92bf6c81c084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-264761682</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29/10/2021 06:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3389781651</td>\n",
       "      <td>6ac3f774-d9fb-4796-b3e9-92bf6c81c084</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Fungi</td>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CC_BY_4_0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1510526472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29/10/2021 06:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       gbifID                            datasetKey  \\\n",
       "0  3395912434  84d26682-f762-11e1-a439-00145eb45e9a   \n",
       "1  3393147503  b124e1e0-4755-430f-9eab-894f25a9b59c   \n",
       "2  3393037337  b124e1e0-4755-430f-9eab-894f25a9b59c   \n",
       "3  3389784557  6ac3f774-d9fb-4796-b3e9-92bf6c81c084   \n",
       "4  3389781651  6ac3f774-d9fb-4796-b3e9-92bf6c81c084   \n",
       "\n",
       "                                        occurrenceID kingdom  \\\n",
       "0  https://svampe.databasen.org/observations/1024...   Fungi   \n",
       "1      urn:uuid:df80f278-4957-47cd-8157-1425fd1ee3d8   Fungi   \n",
       "2      urn:uuid:41a99575-8ce8-413f-ba64-95c217339d60   Fungi   \n",
       "3                                                NaN   Fungi   \n",
       "4                                                NaN   Fungi   \n",
       "\n",
       "                phylum                 class             order  \\\n",
       "0  Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "1  Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "2  Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "3  Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "4  Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "\n",
       "              family          genus               species  ...  \\\n",
       "0  Entomophthoraceae  Entomophthora  Entomophthora muscae  ...   \n",
       "1  Entomophthoraceae  Entomophthora  Entomophthora muscae  ...   \n",
       "2  Entomophthoraceae  Entomophthora  Entomophthora muscae  ...   \n",
       "3  Entomophthoraceae  Entomophthora  Entomophthora muscae  ...   \n",
       "4  Entomophthoraceae  Entomophthora  Entomophthora muscae  ...   \n",
       "\n",
       "       identifiedBy dateIdentified       license rightsHolder  \\\n",
       "0  Jens H. Petersen            NaN  CC_BY_NC_4_0          NaN   \n",
       "1               NaN            NaN     CC_BY_4_0          NaN   \n",
       "2               NaN            NaN     CC_BY_4_0          NaN   \n",
       "3               NaN            NaN     CC_BY_4_0          NaN   \n",
       "4               NaN            NaN     CC_BY_4_0          NaN   \n",
       "\n",
       "           recordedBy typeStatus establishmentMeans   lastInterpreted  \\\n",
       "0    Jens H. Petersen        NaN                NaN  31/10/2021 21:14   \n",
       "1  Gry Føyerland Blix        NaN                NaN  26/10/2021 03:27   \n",
       "2  Gry Føyerland Blix        NaN                NaN  26/10/2021 03:22   \n",
       "3          -264761682        NaN                NaN  29/10/2021 06:59   \n",
       "4         -1510526472        NaN                NaN  29/10/2021 06:59   \n",
       "\n",
       "    mediaType                                            issue  \n",
       "0  StillImage                               COORDINATE_ROUNDED  \n",
       "1  StillImage  COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84  \n",
       "2  StillImage  COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84  \n",
       "3         NaN  COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84  \n",
       "4         NaN  COORDINATE_ROUNDED;GEODETIC_DATUM_ASSUMED_WGS84  \n",
       "\n",
       "[5 rows x 50 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('examples/GBIF_World.csv', sep = \",\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b4f8db7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gbifID</th>\n",
       "      <th>individualCount</th>\n",
       "      <th>decimalLatitude</th>\n",
       "      <th>decimalLongitude</th>\n",
       "      <th>coordinateUncertaintyInMeters</th>\n",
       "      <th>coordinatePrecision</th>\n",
       "      <th>elevation</th>\n",
       "      <th>elevationAccuracy</th>\n",
       "      <th>depth</th>\n",
       "      <th>depthAccuracy</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>taxonKey</th>\n",
       "      <th>speciesKey</th>\n",
       "      <th>establishmentMeans</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.466000e+03</td>\n",
       "      <td>601.000000</td>\n",
       "      <td>4818.000000</td>\n",
       "      <td>4818.000000</td>\n",
       "      <td>2266.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1479.000000</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>4326.000000</td>\n",
       "      <td>4535.000000</td>\n",
       "      <td>4795.000000</td>\n",
       "      <td>6.466000e+03</td>\n",
       "      <td>4.583000e+03</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.286643e+09</td>\n",
       "      <td>2.206323</td>\n",
       "      <td>30.082042</td>\n",
       "      <td>-8.886388</td>\n",
       "      <td>4234.132392</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>574.517857</td>\n",
       "      <td>10.750000</td>\n",
       "      <td>74.097431</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.946140</td>\n",
       "      <td>6.276736</td>\n",
       "      <td>2002.020021</td>\n",
       "      <td>3.153161e+06</td>\n",
       "      <td>3.900745e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.467859e+08</td>\n",
       "      <td>7.077949</td>\n",
       "      <td>31.737285</td>\n",
       "      <td>81.018516</td>\n",
       "      <td>4667.427751</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>555.650394</td>\n",
       "      <td>6.876894</td>\n",
       "      <td>386.422113</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.683243</td>\n",
       "      <td>2.923887</td>\n",
       "      <td>29.371188</td>\n",
       "      <td>2.109748e+06</td>\n",
       "      <td>1.615556e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.123112e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-67.585600</td>\n",
       "      <td>-173.502000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1800.000000</td>\n",
       "      <td>8.590000e+02</td>\n",
       "      <td>2.559547e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.016257e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>31.237000</td>\n",
       "      <td>-73.974700</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.625000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>2.559620e+06</td>\n",
       "      <td>2.559670e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.568947e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.788000</td>\n",
       "      <td>-1.175062</td>\n",
       "      <td>5000.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>2.559692e+06</td>\n",
       "      <td>2.559932e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.941316e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>52.150000</td>\n",
       "      <td>8.104832</td>\n",
       "      <td>7071.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>948.250000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2018.000000</td>\n",
       "      <td>5.250887e+06</td>\n",
       "      <td>5.250887e+06</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.395912e+09</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>82.600000</td>\n",
       "      <td>176.828154</td>\n",
       "      <td>29066.000000</td>\n",
       "      <td>0.016667</td>\n",
       "      <td>1710.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>5551.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2021.000000</td>\n",
       "      <td>1.102224e+07</td>\n",
       "      <td>1.069865e+07</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             gbifID  individualCount  decimalLatitude  decimalLongitude  \\\n",
       "count  6.466000e+03       601.000000      4818.000000       4818.000000   \n",
       "mean   2.286643e+09         2.206323        30.082042         -8.886388   \n",
       "std    9.467859e+08         7.077949        31.737285         81.018516   \n",
       "min    3.123112e+07         1.000000       -67.585600       -173.502000   \n",
       "25%    2.016257e+09         1.000000        31.237000        -73.974700   \n",
       "50%    2.568947e+09         1.000000        40.788000         -1.175062   \n",
       "75%    2.941316e+09         1.000000        52.150000          8.104832   \n",
       "max    3.395912e+09       101.000000        82.600000        176.828154   \n",
       "\n",
       "       coordinateUncertaintyInMeters  coordinatePrecision    elevation  \\\n",
       "count                    2266.000000             2.000000    28.000000   \n",
       "mean                     4234.132392             0.016667   574.517857   \n",
       "std                      4667.427751             0.000000   555.650394   \n",
       "min                         1.000000             0.016667     0.000000   \n",
       "25%                       100.000000             0.016667    15.000000   \n",
       "50%                      5000.000000             0.016667   440.000000   \n",
       "75%                      7071.000000             0.016667   948.250000   \n",
       "max                     29066.000000             0.016667  1710.000000   \n",
       "\n",
       "       elevationAccuracy        depth  depthAccuracy          day  \\\n",
       "count          10.000000  1479.000000         1479.0  4326.000000   \n",
       "mean           10.750000    74.097431            0.0    13.946140   \n",
       "std             6.876894   386.422113            0.0     9.683243   \n",
       "min             0.000000     0.000000            0.0     1.000000   \n",
       "25%             5.625000     0.050000            0.0     5.000000   \n",
       "50%            15.000000     3.000000            0.0    14.000000   \n",
       "75%            15.000000     5.000000            0.0    22.000000   \n",
       "max            15.000000  5551.000000            0.0    31.000000   \n",
       "\n",
       "             month         year      taxonKey    speciesKey  \\\n",
       "count  4535.000000  4795.000000  6.466000e+03  4.583000e+03   \n",
       "mean      6.276736  2002.020021  3.153161e+06  3.900745e+06   \n",
       "std       2.923887    29.371188  2.109748e+06  1.615556e+06   \n",
       "min       1.000000  1800.000000  8.590000e+02  2.559547e+06   \n",
       "25%       5.000000  2007.000000  2.559620e+06  2.559670e+06   \n",
       "50%       6.000000  2014.000000  2.559692e+06  2.559932e+06   \n",
       "75%       9.000000  2018.000000  5.250887e+06  5.250887e+06   \n",
       "max      12.000000  2021.000000  1.102224e+07  1.069865e+07   \n",
       "\n",
       "       establishmentMeans  \n",
       "count                 0.0  \n",
       "mean                  NaN  \n",
       "std                   NaN  \n",
       "min                   NaN  \n",
       "25%                   NaN  \n",
       "50%                   NaN  \n",
       "75%                   NaN  \n",
       "max                   NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df. describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69b8c92c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2021, 2021, 2021, ..., 2003, 1987, 1910])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df['year'].array\n",
    "#Eliminación de NaN\n",
    "x = x[np.logical_not(np.isnan(x))]\n",
    "x = x.astype(int)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "880860e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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99FM8+uijGDx4MJ5//nl07twZNTU1yMjIwLFjxxAQEGDQpqSkRPrMFxQU4L333kNZWRn+/ve/NzuH1bHUxY/bybhx4wQA2VftnUYRERFi0qRJBu3qX7gtKCgQI0aMEK6urgKAWLt2rRBCiAsXLognnnhCqNVq4eDgIAYOHCgyMjKkdvUvtAohxM8//9zgAuDu3bsFAHHy5Emp3/fffy/uueceoVKphE6nE1OnThUlJSUG8zZ06FCxZMkS4e3tLRwcHERUVJR0x0vdYepav369qLt6VVZWiieffFJoNBoBQMyfP18IIX9XR/2L9ZWVlWLOnDnCx8dH2NnZia5du4oNGzYYtEG9i4G1XnnlFeHp6SkcHR3FiBEjxGeffWawXOSW3ZkzZ8To0aOFTqcTKpVK+Pv7izFjxohTp04JIYR4/fXXRffu3YWTk5NwdXUVAwcOFD///HODadeqvUgtN0xiYqK49957RZs2bYSLi4vo2bOndPOBENfXmwkTJkh3Kjk7O4sJEyaI0tLSVlk+n376qWjfvr2wtbWV1sWzZ8+KYcOGCUdHR+Ht7S3mzZsnJk6caPA3KSsrE08//bRwdXUVrq6u4qmnnhJxcXEGF6n1er146623pPF36NBButAvhBBHjhwRI0aMEF5eXtJyfuGFF0RFRUWjy9LYZXazz1pjjhw5Iv75z3+Kdu3aCTs7O+Hi4iL69esnFi9eLIqLi6XhAgICDD7nbm5uIjw8XGzevNlgfPWX+612F5NCCP6iHDVu/PjxyM7ORnJysqWj3JEGDRqEwMBAfPLJJ5aOQncgXoMgIiJZLBBERCSLp5iIiEgWjyCIiEgWCwQREcm6rb4HceHChWa10+l0sk8ntTTmMp41ZgKsM5c1ZgKYqylaM5OPj0+j7/EIgoiIZLFAEBGRLBYIIiKSxQJBRESyWCCIiEgWCwQREcligSAiIlksEEREJIsFgoiIZLFAEBHdonx9feDr2/g3oVuKBYKIiGSxQBARkSwWCCIikmW2p7k+88wzcHBwgFKphI2NDeLj41FSUoKEhATk5eXBw8MDs2fPhrOzMwAgMTERKSkpUCqVmDBhAkJDQ80VlYiIYObHfc+fPx+urq5Sd1JSEkJCQhAVFYWkpCQkJSUhJiYG2dnZSE9Px9KlS1FUVISFCxdi2bJlUCp5wENEdzZfXx9UVFSaZVoW3eJmZGQgIiICABAREYGMjAypf3h4OOzs7ODp6Qlvb29kZWVZMioR0R3HrEcQb7zxBgDg/vvvR2RkJK5cuQKNRgMA0Gg0KC4uBgAUFhYiKChIaqfValFYWNhgfMnJyUhOTgYAxMfHQ6fTNSuXra1ts9uaEnMZzxozAdaZyxozAczVFPUzmSqf2QrEwoULodVqceXKFSxatOiGv2IkhDBqnJGRkYiMjJS6m/sLS9b4i1EAczWFNWYCrDOXNWYCmMt4Pqiurv4r0/XtaEvyWcUvymm1WgCAWq1Gnz59kJWVBbVajaKiIgBAUVGRdH3C3d0dBQUFUtvCwkKpPRERmYdZCkR5eTmuXbsm/f/QoUPw9/dH7969kZqaCgBITU1Fnz59AAC9e/dGeno6qqqqkJubi5ycHAQGBpojKhER/cUsp5iuXLmCt99+GwBQU1OD/v37IzQ0FJ06dUJCQgJSUlKg0+kQGxsLAPDz80NYWBhiY2OhVCoxadIk3sFERGRmZikQXl5eWLJkSYP+Li4umDdvnmyb6OhoREdHmzoaERE1grvlREQkiwWCiIhksUAQEZEsFggiIpLFAkFERLJYIIiISBYLBBERyWKBICIiWSwQREQkiwWCiIhksUAQEZEsFggiIpLFAkFERLJYIIiISBYLBBERyWKBICIiWSwQREQkiwWCiIhksUAQEZEsFggiIpLFAkFERLJYIIiISBYLBBERyWKBICIiWSwQREQkiwWCiIhksUAQEZEsFggiIpLFAkFERLJYIIiISBYLBBERybI158T0ej3i4uKg1WoRFxeHkpISJCQkIC8vDx4eHpg9ezacnZ0BAImJiUhJSYFSqcSECRMQGhpqzqhERHc8sx5BbNmyBb6+vlJ3UlISQkJCsHz5coSEhCApKQkAkJ2djfT0dCxduhQvv/wyVq9eDb1eb86oRER3PLMViIKCAuzfvx9Dhw6V+mVkZCAiIgIAEBERgYyMDKl/eHg47Ozs4OnpCW9vb2RlZZkrKhERwYynmNatW4eYmBhcu3ZN6nflyhVoNBoAgEajQXFxMQCgsLAQQUFB0nBarRaFhYUNxpmcnIzk5GQAQHx8PHQ6XbOy2draNrutKTGX8awxE2CduawxE8BcTVE/k6nymaVA7Nu3D2q1Gh07dsTRo0dvOrwQwqjxRkZGIjIyUurOz89vVj6dTtfstqbEXMazxkyAdeayxkwAcxnPB9XV1X9l8gHQ/G0fAPj4+DT6nlkKRGZmJn799VccOHAAlZWVuHbtGpYvXw61Wo2ioiJoNBoUFRXB1dUVAODu7o6CggKpfWFhIbRarTmiEhHRX8xyDWL06NH48MMPsWLFCsyaNQt33XUXnnvuOfTu3RupqakAgNTUVPTp0wcA0Lt3b6Snp6Oqqgq5ubnIyclBYGCgOaISEdFfzHqba31RUVFISEhASkoKdDodYmNjAQB+fn4ICwtDbGwslEolJk2aBKWSX9kgIjInsxeI7t27o3v37gAAFxcXzJs3T3a46OhoREdHmzMaERHVwd1yIiKSxQJBRESyWCCIiEgWCwQREcligSAiIlksEEREJIsFgoiIZLFAEBGRLBYIIiIr5OvrA1/fxh+kZw4sEEREJIsFgoiIZLFAEBGRLBYIIiKSxQJBRESyWCCIiEgWCwQREcligSAiIllN+kW5y5cvIysrC1evXoUQQuo/ZMiQVg9GRESWZXSB2Lt3L9577z20bdsW586dg5+fH86dO4cuXbqwQBAR3YaMLhCbNm3C9OnTERYWhgkTJuCtt97C9u3bce7cOVPmIyIiCzH6GkR+fj7CwsIM+kVERCAtLa3VQxERkeUZXSBcXV1x+fJlAICHhwd+//13XLp0CXq93lTZiIjIgow+xTR06FCcOHEC9957Lx566CG89tprUCgUePjhh02Zj4iILMToAhEVFSX9PyIiAt27d0d5eTnatWtnilxERGRhTbrNtS6dTteaOYiIyMrcsEDMnj0bCQkJAIBp06Y1OtzKlStbNxUREVncDQvElClTpP/PmDHD5GGIiMh63LBAdOnSRfp/t27dTB6GiIgM+fr64Pz5CxaZttG3ub799ts4fvy4Qb/jx4/jnXfeafVQRERkeUYXiGPHjqFz584G/YKDg3H06NFWD0VERJZndIGws7NDeXm5Qb/y8nLY2Ni0eigiIrI8o29z7dmzJz766CM8/fTTcHR0RFlZGVavXo3Q0NCbtq2srMT8+fNRXV2Nmpoa3HvvvXj88cdRUlKChIQE5OXlwcPDA7Nnz4azszMAIDExESkpKVAqlZgwYYJR0yEiotZjdIEYO3Ys3nvvPUycOBHOzs4oKSlBaGioUXc32dnZYf78+XBwcEB1dTXmzZuH0NBQ7N27FyEhIYiKikJSUhKSkpIQExOD7OxspKenY+nSpSgqKsLChQuxbNkyKJX8+QoiujNZ4mK10QXC2dkZc+fOxeXLl5Gfnw+dTgc3Nzej2ioUCjg4OAAAampqUFNTA4VCgYyMDCxYsADA9W9nL1iwADExMcjIyEB4eDjs7Ozg6ekJb29vZGVlITg4uMkzSEREzdPkb1IrFAq4uLigoqICly5dAgB4eXndtJ1er8ecOXNw8eJFDB8+HEFBQbhy5Qo0Gg0AQKPRoLi4GABQWFiIoKAgqa1Wq0VhYWGDcSYnJyM5ORkAEB8f3+xvd9va2lrlN8OZy3jWmAmwzlzWmAlgrhupnX7tv/UzmSqf0QXi4MGDWLlypfRE17o2bdp00/ZKpRJLlixBaWkp3n77bZw9e7bRYev+Wt2NREZGIjIyUurOz883ql19Op2u2W1NibmMZ42ZAOvMZY2ZAOZqyEf63/Xp+0j/VldXS///3/vNnIqPT6PvGV0gVq9ejZEjR2LQoEFQqVTNDuPk5IRu3brh4MGDUKvVKCoqgkajQVFREVxdXQEA7u7uKCgokNoUFhZCq9U2e5pERNR0Rl/1LSkpwf3339+s4lBcXIzS0lIA1+9oOnz4MHx9fdG7d2+kpqYCAFJTU9GnTx8AQO/evZGeno6qqirk5uYiJycHgYGBTZ4uERE1n9FHEEOGDMH27dub9fvTRUVFWLFiBfR6PYQQCAsLQ69evRAcHIyEhASkpKRAp9MhNjYWAODn54ewsDDExsZCqVRi0qRJvIOJiMjMjC4QJ0+exNatW7F58+YGdy+99tprN2wbEBCAt956q0F/FxcXzJs3T7ZNdHQ0oqOjjY1HREStrElHEM05eiAioluT0QVi0KBBJoxBRHTnsOQTWpvC6AIhhMBPP/2EXbt24erVq3j77bdx7NgxXL58GeHh4abMSEREFmD0ld9NmzZh+/btiIyMlO65dXd3x+bNm00WjoiILMfoApGamoo5c+bgvvvug0KhAAB4enoiNzfXZOGIiMhyjC4Qer1eep5SrfLy8gb9iIjo9mB0gbj77rvxn//8B1VVVQCuX5PYtGkTevXqZbJwRERkOUYXiLFjx6KwsBDjx49HWVkZxo4di7y8PIwZM8aU+YiI7gi+vj7w9W38uUiWYPRdTI6OjvjXv/7VrMd9ExHRrcfoAqHX6wEArq6u0kP1avvxMRhERLcfowvEk08+2eh7xjzum4iIbi1GF4j333/foLuoqAhJSUno3bt3q4ciIiLLM/rckIeHh8ErODgYzz77LL8oR0R0m2rRxYOysjLpZ0KJiOj2YvQppvfee0/6BjUAVFRU4Pjx4xgwYIBJghERkWUZXSC8vb0Nuu3t7XH//fejR48erR6KiIgsz+gC8dhjj5kyBxERWRmjr0GsWbMGmZmZBv0yMzOxbt261s5ERERWwOgCsWvXLnTq1MmgX8eOHbFz585WD0VERJZndIFQKBTSN6dr6fV6CCFaPRQREVme0QWiS5cu2Lhxo1Qk9Ho9vvjiC3Tp0sVk4YiIyHKMvkg9YcIExMfHY8qUKdDpdMjPz4dGo8GcOXNMmY+IiCzE6ALh7u6OxYsXIysrCwUFBXB3d0dgYCAf1EdEdJtq0tZdr9ejpqYGQggEBwejsrIS5eXlpspGREQWZPQRxNmzZ7F48WLY2dmhoKAA4eHhOHbsGFJTUzF79mxTZiQiuu3U/jjQ+fMXLJykcUYfQXz88cd44okn8O6778LW9npd6datG06cOGGycEREZDlGF4js7OwGz11ycHBAZWVlq4ciIiLLa9Ljvk+dOmXQLysrq8EzmoiI6PZg9DWIJ554AvHx8bj//vtRXV2NxMRE/Pjjj5gyZYop8xERkYUYfQTRq1cvzJ07F8XFxejWrRvy8vLwwgsvoGfPnqbMR0REFmLUEYRer8fMmTOxdOlSTJ482dSZiIhuS76+PlZ911J9RhUIpVIJpVKJqqoq2NnZNXki+fn5WLFiBS5fvgyFQoHIyEg8+OCDKCkpQUJCAvLy8uDh4YHZs2fD2dkZAJCYmIiUlBQolUpMmDABoaGhTZ4uERE1n9HXIB588EEkJCTg0UcfhVarNfh1OS8vrxu2tbGxwT//+U907NgR165dQ1xcHHr06IEdO3YgJCQEUVFRSEpKQlJSEmJiYpCdnY309HQsXboURUVFWLhwIZYtW8ZvbRMRmdFNC8Tly5fh5uaGNWvWAAAOHTrUYJhNmzbdcBwajQYajQYA0KZNG/j6+qKwsBAZGRlYsGABACAiIgILFixATEwMMjIyEB4eDjs7O3h6esLb2xtZWVkIDg5u6vwREVEz3bRAzJw5E59++qlUBJYsWYIXX3yx2RPMzc3F6dOnERgYiCtXrkiFQ6PRoLi4GABQWFiIoKAgqY1Wq0VhYWGDcSUnJyM5ORkAEB8fD51O16xMtra2zW5rSsxlPGvMBFhnLmvMBNw5ueqP62bddfvV/ls/k6mW200LRP3fezh27FizJ1ZeXo533nkH48ePh6Ojo9HTbExkZCQiIyOl7vz8/Gblqn06rbVhLuNZYybAOnNZYybgTsnl89e4fKQ+/xu3j2z3//r9r211dbXBeFqSz8fHp9H3bnpSv+61hpaorq7GO++8gwEDBqBfv34AALVajaKiIgBAUVERXF1dAVx/cmxBQYHUtrCwEFqttlVyEBGRcW5aIGpqanDkyBHppdfrDbqPHDly04kIIfDhhx/C19cXDz/8sNS/d+/eSE1NBQCkpqaiT58+Uv/09HRUVVUhNzcXOTk5CAwMbO48EhFRM9z0FJNarcbKlSulbmdnZ4NuhUKB999//4bjyMzMRFpaGvz9/aXrF08++SSioqKQkJCAlJQU6HQ6xMbGAgD8/PwQFhaG2NhYKJVKTJo0iXcwERGZ2U0LxIoVK1o8kS5duuDzzz+XfW/evHmy/aOjoxEdHd3iaRMRUfNwt5yIiGSxQBARkSwWCCIiksUCQUREslggiIhIltEP6yMiotbn69v4N5ktjUcQREQkiwWCiIhksUAQEZEsFggiIpLFAkFERLJ4FxMRkRWxpruaeARBRESyeARBRHSLsbdXoe4vzpkKjyCIiEgWCwQREcligSAiIlksEEREJIsFgoiIZLFAEBGRLBYIojucr6+PVX05i6wHCwQREcligSAiIlksEEREJIsFgoiIZLFAEBGRLBYIIiKSxQJBRESyWCCIiEgWCwQREcligSAiIllm+UW5Dz74APv374darcY777wDACgpKUFCQgLy8vLg4eGB2bNnw9nZGQCQmJiIlJQUKJVKTJgwAaGhoeaISUREdZjlCGLQoEF46aWXDPolJSUhJCQEy5cvR0hICJKSkgAA2dnZSE9Px9KlS/Hyyy9j9erV0Ov15ohJRER1mKVAdOvWTTo6qJWRkYGIiAgAQEREBDIyMqT+4eHhsLOzg6enJ7y9vZGVlWWOmEREVIdZTjHJuXLlCjQaDQBAo9GguLgYAFBYWIigoCBpOK1Wi8LCQtlxJCcnIzk5GQAQHx8PnU7XrCy2trbNbmtKzGU8a8wEWGeuxjJZOqc1Liug9XM1Z1y1bRpra6rlZrEC0RghhNHDRkZGIjIyUurOz89v1jR1Ol2z25oScxnPGjMB1pmrYabrj/q2dE5rXFZAa+fy+WtcTXu8em2bxtq2JJ+PT+NZLHYXk1qtRlFREQCgqKgIrq6uAAB3d3cUFBRIwxUWFkKr1VokIxFRa7hVf2/DYgWid+/eSE1NBQCkpqaiT58+Uv/09HRUVVUhNzcXOTk5CAwMtFRMIqI7lllOMb377rs4duwYrl69iqlTp+Lxxx9HVFQUEhISkJKSAp1Oh9jYWACAn58fwsLCEBsbC6VSiUmTJkGp5Nc1iIjMzSwFYtasWbL9582bJ9s/Ojoa0dHRJkxEREQ3w11zIiKSxQJBRESyWCCIiEgWCwQREcligSAiIlksEEREJIsFgoiIZLFAEBGRLBYIIiKSxQJBRESyWCCIiEgWCwQRWSVfX59b9jHZtwsWCCIikmV1vyhHRHeW2qOEiopKCyeh+ngEQUREslggiMgq2NureM3ByrBAEBGRLBYIIiKSxQJBRESyWCCIiEgWCwQREcligSAiAPzmMjXEAkFERLJYIIgs4FbYU7fUEQWPZKwHCwQR3XJYRMyDBYKoBbihsqzWWP71x8G/6f+wQBDRLeVGG29TbNzt7VVNbnO7FBk+zZWoEbUf8PPnLzR7HNc3Lj4tGgeZxu2wATc1FgiiVmBsMfH1NW2xaI2iZi6tuYG29Mb+VlruTcFTTHRHk9uwGHt6wFIbJUtvDG8FtX/D+suKy65pWCAsrDVW2NZa6Y3dMDbnnOyNpnknuxXmvzZjc7Ja67l4a8xkjaz6FNPBgwexdu1a6PV6DB06FFFRUZaOZLXqH+K25JD3dj1cbq66G5Pz5y/c8DSRKZddU05P1d8ANiWPsRvP+sulbr8bLZ+mLpvaNi05PWdMW2OPNuTm+0bjuJU/R1Z7BKHX67F69Wq89NJLSEhIwK5du5CdnW3pWFalJadHWtrmZjlagzG5rHUPtb6W3HljbfNoTUe95nQrZm4pqz2CyMrKgre3N7y8vAAA4eHhyMjIQLt27SycrGka25Orv2cht6dReweMnJbuSdbdK7tZ25YchcipvzdoTBGo21au/42Wldx0mzL95mju6Rig4d54a0yj7nyb4refb7b3favvSTfmdi8aCiGEsHQIOXv27MHBgwcxdepUAEBaWhpOnjyJSZMmScMkJycjOTkZABAfH2+RnEREtyurPcUkV7cUCoVBd2RkJOLj41tcHOLi4lrU3lSYy3jWmAmwzlzWmAlgrqYwVyarLRDu7u4oKCiQugsKCqDRaCyYiIjozmK1BaJTp07IyclBbm4uqqurkZ6ejt69e1s6FhHRHcNqL1Lb2Nhg4sSJeOONN6DX6zF48GD4+fmZZFqRkZEmGW9LMZfxrDETYJ25rDETwFxNYa5MVnuRmoiILMtqTzEREZFlsUAQEZEsFggioiayxjPzpsjEAnETd8qK0BqsMZc1ZgKYqymsKVN+fj5KSkqg1+stHUViykxWexeTpZw6dQoFBQVo3749XF1dYW9vD71eD6XScrXUGjNZay5rzMRct34mANi7dy82b94MR0dHBAUFoVOnTujVq9dtnYl3MdXx66+/Yv369QgICIC9vT3atGmDRx99FBqNxmIrqDVmstZc1piJuW79TABQXFyM1157DVOmTIGDgwNOnTqFvXv3Ijw8HP37979tM/EUUx379+9HTEwMYmNjMWLECDg5OWHdunW4fPmyxVbMAwcOWF0ma81ljZmsORfXd+PZ2NjAx8cH7du3h7+/P/r27YvBgwdj9+7dOHjw4G2biQXiL3q9HkIInD9/HgDQsWNHDB06FL6+vvjmm29QWdn6T8A0JpNer7eqTNaayxozWXsuru/Gc3JygkqlwvLlywEAjo6O6Nq1K+6++26cOnUKgPmvlTg5OcHOzg7vvfeeyTLd8QXi0KFDOH36NJRKJR544AHs2rULu3fvBnD9eVChoaG4cuUKysrKzJbp8uXLKC4uhlKpxPDhw60iEwBUV1ejqqpKypWeno709HSL5qqpqZEy3X///VazrAoKClBWVmZ1f0Ou78Y7dOgQtm3bhu+//x4AMGnSJLi6umLNmjUAAGdnZ3Tq1AmZmZkoLy9v8DBRUzh69CiSk5OlTNOmTYO9vT3WrVtnkkx3dIHYv38/Vq1ahYKCAgghEBAQgMceeww7duxAeno6FAoFgoODUVpairNnz5olU0ZGBt59913Ex8cjLS0N7du3t3gm4Prj15ctW4bFixfj119/hY+PD0aNGoW0tDSL5dq/fz9WrFiBxYsX4/Dhw+jYsSP+8Y9/WHxZnTp1CnFxcUhJScHly5et5m/I9d14J06cwLJly6BSqbBnzx6sWbMGf/75J4YNG4bKykq89dZbKC0tRXZ2NioqKlBdXW3yTPv378cnn3yCmpoabNmyBR9//DHs7OwQHR2N0tJSLFmypNUz3bF3MZWXl+Obb77B1KlTERISgsrKSigUCvTs2RN2dnb47LPPcOHCBbRp0waXLl2Cj4/pfxgkKysLn3/+OWbMmIH8/HwkJiaib9++6Nu3L5RKJTZt2mT2TABw4cIFbNq0CdOmTUNubi5++ukn5OXloVu3bhg+fDg2bNiAnJwcODg4mC3XgQMH8NlnnyEmJgb5+fl477338Morr0jLauPGjWbPVEun08HDwwMlJSVIT09H//79Lf435Pre9GwPPPAAhgwZgv79++Orr75CRkYG7r33XkyYMAGffPIJVq9ejQsXLmDq1KlwdnY2aZ78/Hx8/fXXmDhxIkJCQjBgwAC8+eabuHjxItq2bYunnnrKJJnu2AKhUCjg4OCAkJAQlJSU4MMPP0RNTQ06d+6M0NBQxMbGIiUlBWVlZZg5cyZ0Op3JM+Xn5yMgIAD+/v7Q6XTQ6/VYt24dunTpgtDQUMyaNQvbt283ayYAKC0thVqtRnBwMIKDg+Hh4YEdO3ZAoVBg2LBhcHd3R1paGkpLS82Sq7y8HAcOHMCoUaMQGhoKACgsLMSff/4Jf39/9OnTB97e3khNTTVbprqUSiXc3NygUqlw8eJFHDx4ED4+PvD398fMmTOxY8cOs/4NhRBQKpVc35vA398f3377LS5cuAAfHx/84x//wBdffIG0tDRMnjwZzzzzDCorK6HX6+Hg4GDyPLa2toiOjkZISAiqq6uhUqmgUqlw5coVeHt7Q6VSYfr06a2e6Y6+zfXjjz+Gk5MTLl68iF69esHLywuHDh2CXq/HqFGjIIQwy3nFWufPn8eGDRvg7OyMY8eOSU+wzcjIQIcOHfDggw+aPVOt999/H/fccw/69esHGxsbZGZm4quvvsIDDzyAe+65x6xZ9Ho9zp07Bw8PDzg4OEh7m1evXsXkyZPNmqUx3333Hfr06YPS0lJ8+eWXOHnyJGbMmIEePXpYLJO1re/nzp3Dxo0b4eTkZBXre35+PtRqNWpqamBrayvdbnv33XdDo9GgsrIS8+fPx/33348hQ4aYLZObmxuA60Wiro8++giDBw9GUFAQMjMzERQU1Op3et1RRxBHjhzBxYsXodfrMWzYMAwaNAjp6ekoLi5GWFgYVCoV2rRpg/Xr16OkpMTkh411M9nY2GDw4MF47LHHcP78eZSUlGDkyJEArt+d8NVXX2Hw4MFo06aNyTMB1w+xq6urYWtri8DAQHTr1g2///471Go1OnfujM6dOyM8PBw7duxAz549YWNjY7ZMDg4OaN++PYDrF6mB63t8p0+fBgDs3LkTOp0OXbp0MXmmurns7OzQqVMnAMC1a9dw8uRJuLu7448//kCnTp2Qm5uL4uJiuLq6mjWXjY0NgoKCMHToUOzYscOi63v9v+ETTzyBM2fOWHx9379/PzZs2IDg4GBcu3YNMTExGDhwILZt2wYhBLp06QJfX1/06tXLLOt63UxBQUG4du0aHn/8cfj6+kqfy7KyMlRUVGDnzp3YtGkTFi5cKBWT1nLHXKQ+cOAA1q5di7KyMuzatQt79uxB+/btERQUhKqqKnz55ZcArldscx1U1c20Y8cO7NmzBx06dED//v2h0+lw+PBhANdPqahUKrPtSdVezNy/fz+2bNmCTz/9FEOGDIFarUZGRoZ055JCoTBbrrqZvv32W6xatQoApA+ro6Mj7O3tsXv3bnzxxRdm2djVz7V161Z8+OGHAIC+ffti7969+OCDD/DUU0/hgQcewKVLl8x2L3/dXNu2bcPq1avRsWNH3HXXXRZb3+tm+uabb/DJJ5/A398fAwcOtNj6LoRAfn4+NmzYgIkTJ+KJJ55Ahw4d8Oqrr0KtVuOBBx7A+fPn8dFHH2Ht2rX44YcfpJ0Ac2UaNWoUAgMD8frrr+PcuXPSkYRWq0ViYiJ+/PFH/Otf/2r14gDcIUcQ5eXl2LJlC8aMGYN77rkHKpUKVVVVyMnJQXh4OHQ6HdLS0hAfH48rV65gypQpJt/AyGWqrq5GVlYWAgMDodPpkJycjK1bt6KgoADTpk0zy7nOiooKbNu2DWPHjkVISAjy8/Pxr3/9C0IIjB8/Hmlpafjtt9+QkpKCq1ev4tlnnzX5Rk8uU1xcHGpqajB9+nQAkDZ67du3x4svvoh27dqZNNONcn300Ud4+umn4ejoiHHjxkm/hBgcHAxHR0eL5JozZw6EEJg8eTLUajV27txp1vW9sWVVWVmJ6dOnw93dHT/99JPZ13eFQgGtVovg4GC0bdsWarUajzzyCGxsbPDqq69i0aJFGDt2LI4fP44LFy5gxIgR8Pb2Nnumv/3tb7CxscGiRYswf/58+Pj4wM3NDXv27MHLL78MX19fk2S5IwoEAKm6njlzBt9++y0CAwOxbds2eHp6YubMmQgODkZBQQEcHBzg5ORksUz5+fnw9fXF9OnTcebMGVy6dAkdOnSAp6enWTIJIeDg4AC1Wg3g+h05gwcPxr59+7Bx40aMGjUKAwcOxNmzZ6FWq6XhzJ0pIiICBw4cwH/+8x+MHTsWvr6+6NKlCyZNmmSW4nCjXPv27cOXX36JqVOnAoD0iAhzFIfGcg0aNAj79+/HZ599htGjR6Nz585mXd8bW1b79+/H559/jn/84x84ffq09Awmc6zvFy9eRElJCby8vFBWVoaff/4ZjzzyCADg4YcfRnV1NTZt2oTJkyeja9eu6Nq1q0UzPfjgg6iqqsLXX3+NqVOnolu3btIOrqnc1gWi9g6E2vOdO3bsQF5eHsLCwhATEwMAmDt3LtLS0jBw4EC4u7tbRaY5c+Zg9+7dCAsLk861mzNXu3bt8O9//xvjxo3D4cOHYWNjg7lz5yIxMVE6V+3v72/xTHPmzEFiYiLKysrg7u6O2NhYuLi4WDxX/WVlrtNKN8sVFxeHxMREXL16FS4uLmZf3xvL9NVXX6GyshKdOnUy+embWvv27cP//d//wcnJCf7+/hgwYADWrl0LvV6PRx99FAAQHh6OxMRE2NnZWU2msLAw5OTkSNcGTe22LRD79u1DQkICevfujVmzZuGhhx5CZGQk9uzZA41GIw3XvXt3qFQqq8oUEhJiljz1c/Xq1QuzZ8/G448/DmdnZ1y4cAGOjo4YNWoUbG1tUVxcjKqqKqvLVF5eDq1Wa5a/ozUuq6bmMseXupqSqaSkBNeuXTPLKSUAyMzMxPr16zFz5kx06NABq1atQlZWFhYuXIiXX34Zer0e9913H06cOIHTp0+jtLTU5KfgmpKp9qK+Oa6z3ZYXqcvLy7Ft2zaMHz8ednZ2WLZsGQDA3t4eQgisXLkSWVlZSE5OxqFDh8yyN9zUTAEBASbPVD+XSqXCu+++C+D64ezIkSMRExMDW1tbpKWl4erVq2a5g6Opmerf/mctucx1t4s15rLGTHVFRUWhQ4cOAIAnn3wSp0+fhlarxYIFC3Dp0iV899132LZtG6ZPn262Gx6sMdNt+z2IwsJCODo6orKyUvpK+nPPPQcA+PLLL3H+/Hlcu3YNo0ePNkuBsNZMcrlsbW0xc+ZMANdvIz18+DA2bdqEKVOmmO2UlzVmYq5bPxNw/ZpQeXk5HB0dodfrUVRUhMWLF2Pu3LnQaDTIy8uDVqtFRUWF2a4bWWMm4DYuEHVdvXoVq1atgq2tLWbNmoVLly7h2rVraNeundn2Pm+FTHVzqVQqPPfcczh79ixyc3MRGBhoktvobtVMzHXrZwL+97DHt956C/PmzUNaWhpOnDghHfnc6ZnuiAIBXP9xjf/+97/IzMyEEALz5883y0W6Wy2TXK4FCxZAq9UyE3PddplqrVixAhqNBr/99hueeeYZsx7BW3Om2/IahBxXV1f4+/ujrKwML7zwglVsiK0xE9AwlzV8iK0xE8Bct3omIQSqq6tx4sQJ/Pzzz5g1a5bFi4M1Zbpt72Kqr6SkBAcOHMArr7xi8RWgljVmAqwzlzVmApirKawxk0KhgK2tLUaOHIlOnTqhbdu2lo5kVZnumFNMAFBZWWmx84qNscZMgHXmssZMAHM1hTVmAmCxh2DeiDVkuqMKBBERGe+OuQZBRERNwwJBRESyWCCIiEgWCwQREcligSAykZ07d+Kpp57C0aNH8e9//xuVlZWWjkTUJCwQRC20YMECTJgwocHTW48fP47XXnsNW7Zsgaenp1Xe3kl0I7zNlagFcnNzMWPGDDg6OuLpp59GWFiYpSMRtZo75pvURKaQlpaG4OBgBAYGIjU1VSoQK1asgL29PfLy8nD8+HG0a9cOzz33nPRzlZmZmVi3bp30gzrjx49H586dLTkrRA3wFBNRC6SmpqJ///4YMGAAfvvtN1y+fFl6b9euXXjsscewdu1aeHt7Y+PGjQCuP3IiPj4eI0aMwJo1a/DQQw8hPj4eV69etdBcEMljgSBqphMnTiA/Px9hYWHo2LEjvLy8sHPnTun9fv36ITAwEDY2Nujfvz/OnDkDANi/fz+8vb0xcOBA6T0fHx/s27fPQnNCJI8FgqiZduzYgR49esDV1RUA0L9/f6Smpkrv1/2dA3t7e5SXlwO4/kM6Hh4eBuPy8PBAYWGh6UMTNQGvQRA1Q2VlJXbv3g29Xo+nnnoKAFBdXY3S0lLpSKExWq0Wv/zyi0G//Px8hIaGmigtUfOwQBA1w969e6FUKvHOO+8Y/AJgQkIC0tLSbtj27rvvxpo1a7Bz506EhYXhl19+QXZ2Nu655x5TxyZqEhYIomZITU3F4MGDodPpDPoPHz4ca9euRUhISKNtXVxcEBcXh7Vr1+Ljjz+Gt7c34uLipFNVRNaC34MgIiJZvEhNRESyWCCIiEgWCwQREcligSAiIlksEEREJIsFgoiIZLFAEBGRLBYIIiKS9f8B5w9vqnIbEGoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervalos = range(1860, max(x)+2) #calculamos los extremos de los intervalos\n",
    "año = list(range(1860, 2021, 20))\n",
    "plt.style.use('ggplot')\n",
    "#plt.style.use('fivethirtyeight')\n",
    "plt.hist(x=x, bins=intervalos, color='blue', rwidth=0.85)\n",
    "plt.title('Entomophthorales reportados en GBIF')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.xticks(año,rotation=45)\n",
    "plt.savefig(\"Histograma.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fee8e909",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entomophthora muscae            1320\n",
       "Massospora cicadina              513\n",
       "Conidiobolus coronatus           369\n",
       "Conidiobolus firmipilleus        276\n",
       "Zoophthora aphidis               178\n",
       "                                ... \n",
       "Entomophthora philippinensis       1\n",
       "Entomophthora trinucleata          1\n",
       "Entomophthora helvetica            1\n",
       "Entomophaga kansana                1\n",
       "Furia ellisiana                    1\n",
       "Name: species, Length: 145, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para variables discretas\n",
    "pd.value_counts(df['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a2b6e92a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entomophthora muscae            20.414476\n",
       "Massospora cicadina              7.933808\n",
       "Conidiobolus coronatus           5.706774\n",
       "Conidiobolus firmipilleus        4.268481\n",
       "Zoophthora aphidis               2.752861\n",
       "                                  ...    \n",
       "Entomophthora philippinensis     0.015466\n",
       "Entomophthora trinucleata        0.015466\n",
       "Entomophthora helvetica          0.015466\n",
       "Entomophaga kansana              0.015466\n",
       "Furia ellisiana                  0.015466\n",
       "Name: species, Length: 145, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * df['species'].value_counts() / len(df['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a69e57a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_Mex = pd.read_csv('examples/GBIF_Mexico.csv', sep = \",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1863fe84",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_Mex = df_Mex['year'].array\n",
    "#Eliminación de NaN\n",
    "x_Mex = x_Mex[np.logical_not(np.isnan(x_Mex))]\n",
    "x_Mex = x_Mex.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efdd845f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NysrKTIZFRkYiOTkZQUFBVXJV/MbeqFEjPPTQQ3jnnXfw3XffISEhQfkWXFm7du2QkpKCvLw8ZVhycjIuXrxosty8vDwYDIYqy61pj6Aulb8JKrZHy5Yt4ezsXG37tmvX7oaW165dOyQnJ0Ov1yvDzp8/j6NHj1aZ92+//ab8n5eXh9TUVGX7JyYmYsiQIZgwYQI6deqEli1bmuyxlq9b5e3crl076PV6k2125coV7N27V1l+27ZtodPpTKbds2ePyXwiIyNx4sQJeHl5VdlugYGBdd4e1vi6c3R0xPjx4/HLL79g0qRJ1Y5jbjstWrQIR48exa5du7B//3689NJLNS63c+fOAFDjt3xztnFl/v7+uOWWW5CWllYlZ8uWLeHi4lJre9Rk0KBB8PPzu6HrUMpPzS0qKrruedSo1oNZ9ay6s6pErh6jLT/2nJKSIh4eHjJq1Cj5/fff5cSJE7Jt2zaZOXOmHD9+XET+16nbu3dv2bdvn+h0OunRo4dEREQonePfffed2Nvby7JlyyQtLU02bdpUbef4hAkTquQZOnSo9OvXT06dOiVZWVlSVlYm586dk6ZNm8qgQYMkMTFRTp48KTt27JD58+crnVrz58+XL7/8UlJTU+Xo0aPy+OOPi4eHR5XjxOUKCwslICBAhg0bJocOHZI9e/ZIt27dqnRSxsTESFhYmHz11Vdy/Phx2bdvn6xevfqaHaDlx5rLO34r/hkMBqUdQ0NDTabbsWOHyZlke/fuFQDy1VdfyYULF+TSpUsiIvL0008rneNHjx6tsXO84jFWEREHBwfl7KVyzs7OSt9Exc7g/fv3X7NzvEuXLpKQkCBJSUnyf//3f9KkSRPlJIsnn3xSmjRpItu2bZO0tDT517/+JV5eXtKiRQtluS+//LJotVo5fPiwZGVlicFgMOk43blzp/z55581do5PmjRJUlJSZNu2bdKlSxcBIB999JGIiFy+fFnatWsnkZGR8uOPP8rJkyflt99+kxdffFHpqyvv4/j7779rbCNz2sMaX3fliouLJSsrS+kPqtzHYU47bd++XRwcHGTr1q0iIvLFF1+Ik5OT7NmzR1lO5Y7fhx9+WPz8/GTjxo2Snp4ue/fulVWrVinrVts2rs6HH34oTk5OsnTpUvnzzz8lNTVV4uLiZPLkyco41X2mLF261OR1V52tW7eKs7Oz9O3bV7799ls5evSoHDlyRD788ENp2rSpjB8/3mRd586dq7yfDx8+LFOmTBF7e3uTs/Yqv/9sunO8usKxc+dOk87OpKQkueuuu8THx0dcXFwkNDRUJk2aJNnZ2SLyvw+8jRs3Kh0+/fr1U04jLbdhwwZp06aNODk5SWBgoMyfP7/K6bjVFQ6dTiedO3cWFxcXkw/Rv/76S0aNGiVarVY0Go00b95cHn74YeU04CVLlki7du3E3d1dvLy8pE+fPianHVbnwIED0qNHD9FoNBISEiKffvpplQ1cVFQkc+fOleDgYHFychJ/f38ZPHiw/PLLLzXOt7wDubq/8k5DcwqHiMisWbOkSZMmYmdnp/p0XLWFQ+Tq6ad33HGH0oE3bNiwak/H/fHHH6VNmzai0WgkMjLS5HTKvLw8GTFihHh6ekqjRo1k+vTp8txzz5m8gbOzs+WOO+4QLy+va56O26dPn2pPx23Xrp1oNBqJiIhQOscrnlqs1+tl6tSpShsFBgbK8OHD5cCBAyJiXuEwpz2s7XV3rbP5KheO2topOztbmjVrJnPmzDGZz+TJkyU4OFgpjpWzFxcXK9vbyclJgoKCTDrbzdnG1YmLi5MePXqIq6ureHp6SocOHUw61K+3cIiIHD58WB555BFp1qyZODk5iaenp3Tv3l1eeuklyc/PV8Zr0aKFyfvZx8dHevbsKV9//bXJ/OqqcNj9d2Y2b9GiRfjoo494/6B/qA0bNmDixIkoLS21dBRFYmIioqOjkZSUhIiICEvHIaozVtE5TnQzeOutt9ChQwcEBgYiJSUFs2fPRvfu3Vk06KbDwkFUR06dOoVly5bh/PnzCAgIwMCBA6/ZYUtkq26aQ1VERNQwrPJ0XCIisl4sHEREpIpN9nFkZGRc13RardbkoilrZSs5AdvJypx1z1ayMudVai4yrQ33OIiISBUWDiIiUoWFg4iIVGHhICIiVVg4iIhIFRYOIiJShYWDiIhUYeEgIiJVWDiIiEgVm7xynOhGBQYFmTzOOHvWQkmIbA/3OIiISBUWDiIiUoWFg4iIVGHhICIiVVg4iIhIFRYOIiJShYWDiIhUYeEgIiJVWDiIiEgVFg4iIlKFhYOIiFRh4SAiIlVYOIiISBUWDiIiUoWFg4iIVGHhICIiVVg4iIhIFRYOIiJShYWDiIhUYeEgIiJVWDiIiEgVR0sHICJqaIFBQcr/GWfPWjCJbeIeBxERqcLCQUREqrBwEBGRKiwcRESkSoN2jhuNRsybNw+NGjXCvHnzUFBQgNjYWGRlZcHPzw+zZ8+Gh4dHQ0YiIiKVGnSPY+vWrQiqcDbDli1bEBERgdWrVyMiIgJbtmxpyDhERHQdGqxwZGdn48CBAxgwYIAyTKfTITo6GgAQHR0NnU7XUHGIiOg6Ndihqg0bNmD06NG4fPmyMuzixYvw9fUFAPj6+iI/P7/aaePj4xEfHw8AWL58ObRa7XVlcHR0vO5pG5Kt5ARsJ2ttOa1lHWylPQHbycptX/capHDs378f3t7eCAkJQXJysurpY2JiEBMTozzW6/XXlUOr1V73tA3JVnICtpO1cs7ASs9byzrYSnsCtpO1upwVt7+1rEN9t2dgYOVX/fVrkMKRlpaGffv24eDBgyguLsbly5exevVqeHt7Izc3F76+vsjNzYWXl1dDxCEiohvQIIVj1KhRGDVqFAAgOTkZ3377LWbOnImNGzciISEBw4cPR0JCArp27doQcYiI6AZY9DqO4cOHIykpCTNnzkRSUhKGDx9uyThERGSGBr/JYbt27dCuXTsAgKenJxYsWNDQEYiI6AbwynEiIlKFhYOIiFRh4SAiIlX4Q05EDajiDwgB/BEhsk3c4yAiIlVYOIiISBUWDiIiUoWFg4iIVGHnOBH941U8aYEnLNSOexxERKQKCwcREanCwkFERKqwcBARkSosHEREpAoLBxERqcLCQUREqrBwEBGRKiwcRESkCgsHERGpwsJBRESqsHAQEZEqLBxERKQKCwcREanCwkFERKqwcBARkSr8ISciG1PxR4eqk3H2LH+YiOoV9ziIiEgVFg4iIlKFhYOIiFRh4SAiIlVYOIiISBUWDiIiUoWFg4iIVGHhICIiVVRdAJiXl4f09HRcunQJIqIM79+/f50HIyIi62R24di7dy9ef/11NG3aFH///TduueUW/P3332jTpk2thaO4uBgLFy5EaWkpysrK0KNHDzzwwAMoKChAbGwssrKy4Ofnh9mzZ8PDw+OGV4qIiOqP2YVj06ZNmD59OqKiojBu3Di8/PLL+PXXX/H333/XOq2TkxMWLlwIFxcXlJaWYsGCBejYsSP27t2LiIgIDB8+HFu2bMGWLVswevToG1ohIiKqX2b3cej1ekRFRZkMi46ORmJiYq3T2tnZwcXFBQBQVlaGsrIy2NnZQafTITo6WpmXTqdTk52IiCzA7D0OLy8v5OXlwcfHB35+fjh69Cg8PT1hNBrNmt5oNGLu3Lk4d+4cBg8ejLCwMFy8eBG+vr4AAF9fX+Tn51/fWhARUYMxu3AMGDAAqamp6NGjB4YNG4bFixfDzs4Od955p1nT29vb45VXXkFhYSFWrFiB06dPmx0yPj4e8fHxAIDly5dDq9WaPW1Fjo6O1z1tQ7KVnIDtZK0tp6XWofJy66I9K09fX+t2s257S62TrbQnoKJwDB8+XPk/Ojoa7dq1g8FgQLNmzVQt0N3dHW3btsWhQ4fg7e2N3Nxc+Pr6Ijc3F15eXtVOExMTg5iYGOWxXq9XtcxyWq32uqdtSLaSE7CdrJVzBlZ6vqHWobblmtOeledRmV6vNxmnvtbNVrc9gCrt0xDtVZv6bs/AwNpeOea77us4tFqt2UUjPz8fhYWFAK6eYfXnn38iKCgIkZGRSEhIAAAkJCSga9eu1xuHiIgayDX3OGbPno3Y2FgAwLRp02oc76233rrmQnJzc7FmzRoYjUaICKKiotClSxe0atUKsbGx2LZtG7RaLebMmXMdq0BERA3pmoVjypQpyv8zZsy47oW0aNECL7/8cpXhnp6eWLBgwXXPl4iIGt41C0ebNm2U/9u2bVvvYYiIyPqZ3cexYsUKHDlyxGTYkSNHsHLlyjoPRURE1svswpGSkoLWrVubDGvVqhWSk5PrPBQREVkvswuHk5MTDAaDyTCDwQAHB4c6D0VERNbL7MLRoUMHvPvuuygqKgIAFBUV4f3330fHjh3rKxsREVkhsy8AHDNmDF5//XWMHz8eHh4eKCgoQMeOHW/obCsiIrI9ZhcODw8PPPvss8jLy4Ner4dWq4WPj089RiMiImuk+spxOzs7eHp64sqVKzh//jzOnz9fH7mIiMhKmb3HcejQIbz11lvIy8ur8tymTZvqMhMREVkxswvH+++/j/vuuw99+/aFRqOpz0xERGTFzC4cBQUFGDhwIOzs7OozDxERWTmz+zj69++PX3/9tT6zEBGRDTB7j+PYsWP4/vvv8fXXX1c5m2rx4sV1nYuI6lFgUJDJ44yzZy2UhGyR2YWjf//+6N+/f31mISIiG2B24ejbt289xiAiIlthduEQEfzyyy/YtWsXLl26hBUrViAlJQV5eXno2bNnfWYkIiIrYnbn+KZNm/Drr78iJiZG+V3cxo0b4+uvv663cEREZH3MLhwJCQmYO3cubr/9duWU3CZNmuDChQv1Fo6ILCcwKEj5I6rI7MJhNBrh4uJiMsxgMFQZRkRENzezC0enTp3w4YcfoqSkBMDVPo9NmzahS5cu9RaOiIisj9mFY8yYMcjJycHYsWNRVFSEMWPGICsrCw8//HB95iMiIitj9llVbm5ueOaZZ3hbdSKifzizC4fRaAQAeHl5wcvLy2SYvb3qu7MTEZGNMrtwPPTQQzU+x9uqExH9c5hdON544w2Tx7m5udiyZQsiIyPrPBQREVkvs48x+fn5mfy1atUKjz/+OC8AJCL6h7mhzomioiLk5+fXVRYiIrIBZh+qev31101+xOnKlSs4cuQIevfuXS/BiIjIOpldOAICAkweOzs7Y+DAgWjfvn2dhyIiIutlduEYMWJEfeYgIiIbYXYfx7p165CWlmYyLC0tDRs2bKjrTEREZMXMLhy7du1CaGioybCQkBDs3LmzzkMREZH1Mrtw2NnZKVeKlzMajRCROg9FRETWy+zC0aZNG3z22WdK8TAajfj888/Rpk2begtHRETWx+zO8XHjxmH58uWYMmUKtFot9Ho9fH19MXfu3PrMR0RkcZV/zCrj7FkLJbEOZheOxo0b46WXXkJ6ejqys7PRuHFjtGzZ0qwbHOr1eqxZswZ5eXmws7NDTEwMhg4dioKCAsTGxiIrKwt+fn6YPXs2PDw8bmiFiIiofpldOICrh6fKysogImjVqhUMBgMA1PorgA4ODnjkkUcQEhKCy5cvY968eWjfvj22b9+OiIgIDB8+HFu2bMGWLVswevTo618bIiKqd2b3cZw+fRqzZs3CO++8g7feegsAkJKSovx/Lb6+vggJCQEAuLq6IigoCDk5OdDpdIiOjgYAREdHQ6fTXc86EBFRAzK7cKxduxYjR47EqlWr4Oh4dUelbdu2SE1NVbXACxcu4OTJk2jZsiUuXrwIX19fAFeLC+97RURk/cw+VHXmzJkq96VycXFBcXGx2QszGAxYuXIlxo4dCzc3N7Oni4+PR3x8PABg+fLl0Gq1Zk9bkaOj43VP25BsJSdgO1lry2mpdai83Lpoz8rTmzO/2sap7vmbddvXRXtdD1tpT0BF4fDz88OJEydMLgJMT0+vcg+rmpSWlmLlypXo3bs3unfvDgDw9vZGbm4ufH19kZubq/yyYGUxMTGIiYlRHuv1enNjmyg/G8za2UpOwHayVs4ZWOn5hlqH2pZrTntWnkdler3eZJzq5mfO+tc2D1vd9kDVdauL9rpR9d2egYG1vXLMZ/ahqpEjR2L58uXYvHkzSktLERcXh1dffRUPPvhgrdOKCN5++20EBQXhzjvvVIZHRkYiISEBAJCQkICuXbtexyoQEVFDMnuPo0uXLnj22Wexbds2tG3bFllZWXjqqaeUTu9rSUtLQ2JiIpo3b46nn34awNWfoh0+fDhiY2Oxbds2aLVazJkz5/rXhIiIGoRZhcNoNGLWrFl49dVXMXHiRNULadOmDTZv3lztcwsWLFA9PyIishyzDlXZ29vD3t4eJSUl9Z2HiIisnNmHqoYOHYrY2Fjcc889aNSokcmvAfr7+9dLOCIisj61Fo68vDz4+Phg3bp1AICkpKQq42zatKnukxERkVWqtXDMmjULH3zwgVIcXnnlFaWDm4iI/nlq7eOo/HsbKSkp9RaGiIisX62Fo2JfBhERUa2HqsrKynD48GHlsdFoNHkMALfddlvdJyMiIqtUa+Hw9vY2uQOuh4eHyWM7Ozu88cYb9ZOOiIisTq2FY82aNQ2Rg4j+q+KvzWWcPVvlMZGlmX2vKiIiIoCFg4iIVGLhICIiVVg4iIhIFRYOIiJShYWDiIhUYeEgIiJVWDiIiEgVFg4iIlKFhYOIiFRh4SAiIlVYOIiISBUWDiIiUoWFg4iIVGHhICIiVVg4iIhIlVp/yImIrl/FH2Ei28Htdm3c4yAiIlVYOIiISBUWDiIiUoWFg4iIVGHnONE/QMXO3oyzZy2YhG4G3OMgIiJVWDiIiEgVFg4iIlKFfRxE/2WJfoDKF5oVX7nSIMulG/dP7jdqkMLx5ptv4sCBA/D29sbKlSsBAAUFBYiNjUVWVhb8/Pwwe/ZseHh4NEQcIiK6AQ1yqKpv376YP3++ybAtW7YgIiICq1evRkREBLZs2dIQUYiI6AY1SOFo27Ztlb0JnU6H6OhoAEB0dDR0Ol1DRCEiohtksc7xixcvwtfXFwDg6+uL/Px8S0UhIiIVbKJzPD4+HvHx8QCA5cuXQ6vVXtd8HB0dr3vahmQrOQHbyVpbzsrPWWqdHB1N35LXk6u2aaqbR23zre75f/K2r4/Xi620J2DBwuHt7Y3c3Fz4+voiNzcXXl5eNY4bExODmJgY5bFer7+uZWq12uuetiHZSk7AdrJWzhlY6Xm9Xm8yrK7WqfJyalNaWgpNpRyVc9U2z9qmqW4e1a1vbe1hq9seqLpuddHGN6q+2zMwUO2rsWYWO1QVGRmJhIQEAEBCQgK6du1qqShERKRCg+xxrFq1CikpKbh06RKmTp2KBx54AMOHD0dsbCy2bdsGrVaLOXPmNEQUIiK6QQ1SOJ544olqhy9YsKAhFk9ERHWItxwhIiJVWDiIiEgVFg4iIlLFJq7jIKL6Vflmi7Z80z7eOLL+cY+DiIhUYeEgIiJVWDiIiEgVFg4iIlKFneNERHXgn/SLgNzjICIiVVg4iIhIFRYOIiJShX0cRDW4mS6Kqwv/pGP4lmIrbcw9DiIiUoWFg4iIVGHhICIiVVg4iIhIFXaOE9UhW+ncJNtgrSdocI+DiIhUYeEgIiJVWDiIiEgV9nEQEdWDyv0TlVlLf8X14B4HERGpwsJBRESqsHAQEZEq7OMgm2SJ6yVu5mPWllIX1ynw2pmGxz0OIiJShYWDiIhUYeEgIiJVWDiIiEgVdo6T1bHWG7uROtayHTXOzgi0ghyVVW6f4itXLJREPe5xEBGRKiwcRESkCgsHERGpwj4OG1H5IqeGOH5c1xdn1TSP2i7gup552PLx438ya+kXoWuzeOE4dOgQ1q9fD6PRiAEDBmD48OGWjkRERNdg0UNVRqMR77//PubPn4/Y2Fjs2rULZ86csWQkIiKqhUULR3p6OgICAuDv7w9HR0f07NkTOp3OkpGIiKgWFi0cOTk5aNy4sfK4cePGyMnJsWAiIiKqjZ2IiKUWvmfPHvzxxx+YOnUqACAxMRHp6ekYP368yXjx8fGIj48HACxfvrzBcxIR0f9YdI+jcePGyM7OVh5nZ2fD19e3yngxMTFYvnz5DReNefPm3dD0DcVWcgK2k5U5656tZGXOumfRwhEaGorMzExcuHABpaWl2L17NyIjIy0ZiYiIamHR03EdHBwwfvx4vPDCCzAajejXrx9uueUWS0YiIqJaWPw6js6dO6Nz584NsqyYmJgGWc6NspWcgO1kZc66ZytZmbPuWbRznIiIbA/vVUVERKqwcBARkSosHERUZ2zlyDdz3hgWDljvxqnMVnICtpPVVnIC1p1Vr9ejoKAARqPR0lGuiTnrhsXPqrKEEydOIDs7G8HBwfDy8oKzszOMRiPs7a2rjtpKTsB2stpKTsB2su7duxdff/013NzcEBYWhtDQUHTp0sXSsapgzrrzjzurat++fdi4cSNatGgBZ2dnuLq64p577oGvr69VvSltJSdgO1ltJSdgO1nz8/OxePFiTJkyBS4uLjhx4gT27t2Lnj17olevXpaOp2DOuvWP2+M4ePAgRo8eja5du+LEiRPQ6XTYsGEDxo0bBx8fH0vHUxw4cMAmcgK2k9VWtj1gO1kdHBwQGBiI4OBgaDQaaLVauLu7Y/v27fDw8EDHjh0tHREA4OjoaBM5baU9reNrSwMxGo0wGo04+99fFQsJCcGAAQMQFBSEb775BsXFxRZOeJXRaISI2EROADaT1Ra2PWBbWd3d3aHRaLB69WoAgJubG8LDw9GpUyecOHECgHX0zbi5ucHZ2RlvvPGG8tgac7q7u8PJyQmvv/46AOvN+Y8oHCdPnkROTg7s7e0xZMgQ7Nq1C3v27AFw9UaLHTt2xMWLF1FUVGTRnElJSTh58qTV5wSuZk1MTAQADBw40Gqz5uXlIT8/H/b29hg8eLDV5gSA0tJSlJaWKll3796N3bt3A7CurElJSfjhhx/w3XffAQAmTJgALy8vrFu3DgDg4eGB0NBQpKWlwWAwwM7OziI5MzMzkZeXpzyeMGECXFxcsGHDBqvKmZycjPj4eKU9p02bBmdnZ6vLWdFNXzgOHTqE9957D4WFhQCAFi1aYMSIEdi+fTt2794NOzs7tGrVCoWFhTh9+rTFch44cADvvPMOsrOzISJWmxO4+sGxatUqvPfee8jIyEBISIhVZtXpdFi1ahWWL1+OxMREBAcHW2XO8qxvvvkmXnvtNRw8eBDNmjXDgw8+iMTERKvKmpqaitdeew0ajQa//fYb1q1bh1OnTmHQoEEoLi7Gyy+/jMLCQpw5cwZXrlxBaWmpRXLqdDosWbIEX3zxBfR6PQDAxcUFd911FwoLC/HKK69YRc4DBw7gvffeQ1lZGbZu3Yq1a9fCyckJ9957r1XlrOym7uPYv38/Pv30U0yfPh233HKL0qnYrVs3AMDmzZuRkZEBV1dXnD9/HoGBgRbJaTAY8M0332Dq1KmIiIhAcXEx7Ozs0KFDBzg5OeGTTz6xipzA1Rf6p59+innz5iE1NRWHDh1CYGAgIiIiYG9vj08//RSZmZlwcXGxaNb09HRs3rwZM2bMgF6vR1xcHLp164Zu3brB3t4emzZtspo2PX78ODZv3oxp06YhNzcXW7ZsQXp6OqKiojBo0CB88sknVtGmwNV2HTJkCPr3749evXrhyy+/hE6nQ48ePTBu3Di89957eP/995GRkYGpU6fCw8OjwTMWFRXhl19+QVRUFLy9vbF161bccccd8PPzQ1BQECZOnIj333/f4jn1ej2++uorjB8/HhEREejduzeWLVuGc+fOoWnTppg0aZJVtGd1btrCYTQasX//fpSUlCAkJAQGgwFffPEFLl26pHyABAQEIDExEUVFRZg1axa0Wm2D5xQR2Nvbw8XFBRERESgoKMDbb7+NsrIytG7dGh07dsScOXOwbds2i+YErh722bNnD8aOHYtWrVrh9OnT2LNnD4YMGQJXV1dERkbC398fiYmJKCwstGhWvV6PFi1aoHnz5tBqtTAajdiwYQPatGmDjh074oknnsCvv/5q8TYFgKysLAQHByMkJATA1d+liY+PR+PGjdG/f380btwYO3bssHibAkDz5s3x7bffIiMjA4GBgbj//vvx+eefIzExERMnTsRjjz2G4uJiGI1GuLi4WCSjm5sbxo8fDw8PD2RkZECn0ynFo0mTJnB2dsb06dNRUlKCsrIyi+V0dHTEvffei4iICJSWlkKj0UCj0eDixYsICAiARqPB9OnTLd6e1bmpT8ctLS3FBx98gBMnTkBE0KNHDzg5OeH333/HoEGD0LNnT0tHVKxduxbu7u44d+4cunTpAn9/fyQlJcFoNOLBBx+EiFj82GZZWRmuXLkCNzc3ZdgLL7yA5s2b45FHHrGKjOX+/vtvfPbZZ3B3d0dKSopyy36dTodbb70VQ4cOtZq8x48fx48//ohevXqhffv2+Oqrr3Du3DlkZmbioYceQtu2bS2aT6/Xw9vbG2VlZXB0dFROE+7UqRN8fX1RXFyMhQsXYuDAgejfv79V5Kz4IXvs2DHodDqUlJTgkUcewfHjxxEQEABPT0+L5Sw/M87R0fS7+7vvvot+/fohLCwMaWlpCAsLs5pTryu66fY40tPTUVpaCicnJ4SGhmLChAlYvXo1AgMDcddddwEAPD09sWPHDnTr1q3KhmvonA4ODggLC8OAAQOwfft25OfnIyoqChqNBq6urti4cSMKCgosuouanp6OkpISODs7K9+Ki4uLodFocPfdd2P37t1KRkt+GJe3qYuLC4KDgzFy5Ej89ddfKCgowH333Qfg6rfRL7/8Ev369YOrq6tFclbOGhoaimbNmpl0kD777LOIj4/H4cOHLVo4Dhw4gI8//hitWrXC5cuXMXr0aPTp0wc//PADRARt2rRBUFAQunTpAgcHB6vIWVhYiAcffFA5pBcWFgYAOHLkCBYsWIAzZ85gxYoVFikc5TnDwsJw+fJlPPDAAwgKCkJpaSkcHR1RVFSEK1euYOfOndi0aROWLl1qVadfl7upCkf58fdOnTohOzsbGo0GU6ZMwcyZM1FWVqaMV1paCk9PT4tV8so5ExMTMWHCBOj1ehw/fhxffPEFRo0aBb1eb/FT7ypmzcnJgUajweTJk6HRaAAAgYGB+Ouvv7B7924MGjTIYkWjYk69Xg83NzdMnDgRzZs3R3p6Ov78809ERETAYDBAo9FYdE+jclYfHx+MGTMGly9fxvnz59GsWTMAQEFBgcVeoyKC7OxsfPzxxxg/fjyCgoKQkJCAf//731i6dKly1l/5SQe7d+/GwoULrSLnjh07sHjxYjz33HPKD8OFhYVh165dyM3NxZIlSxr8cF9NOZcsWWKSs1GjRoiLi0NpaSmeeeYZqywaAAC5SRgMBnnhhRckKSlJRESysrJk/PjxsmbNGpPxtm3bJnPnzpVTp05ZImaNOdeuXSsiIqmpqfLee+/JsmXLZN68eXLy5EmL5Kwp64QJE6q06Z49e+T5558Xg8EgRqPR6nJu2bJFYmNj5aWXXpJnnnnG6tp03Lhx8uabb5qM991338mcOXPk77//tkRMEREpKyuTt99+W7Kzs5Xt+u2338rUqVNFr9eLiEhKSorEx8dLZmamVeX87rvvZPLkyXL27FkREbl06ZI89dRTcuLECavO+fXXX8u0adPkzJkzFstpjptmj0NE4OLiAm9vbwCAVqtF3759cfDgQXz44YcYM2YMMjIykJaWhunTp6N58+ZWlfPAgQP45JNPMGrUKLRu3RrZ2dlwcXGBu7u7RXLWlDU6OtqkTQGgffv2uO222+Ds7GxVOQ8cOIDNmzfj/vvvx8mTJ5X7PjVp0sQiOWvK2q9fP5M2LS4uRkFBAWbMmKHsfTSkc+fOoaCgAP7+/igqKsKOHTtw9913AwDuvPNOlJaWYtOmTZg4cSLCw8MRHh7e4Blryzl06FCUlJQgLi4OEydOhIeHB1544QVlT9nacn711VeYOnUq2rZti549e1r0BAhzWF+vi0oZGRkArp6j3axZM7z44ovYs2cP3n33XRQXF2Pu3LkoKiqCwWCAv78/xo0bZ5GiUVvOefPmIT8/H5cuXQJw9YIvSxUNc9u0/NoYNzc3i/TBmNOmer0excXFCA0NRbdu3SxWNNS0qUajwYgRIxAcHNzgOffv348VK1Zg48aN2Lx5M3r37o2ffvoJcXFxyjg9e/aEg4MDnJycGjyfmpxRUVFwcnJSioUl8pqb09HREY6OjmjZsqXVFw3Axvs49u/fj9jYWHTp0gWzZ8/GAw88oJyC5+bmhgcffBCOjo7Iz89HUVERGjVqZJEOPDU5LX2Bj5qsxcXFFitu5uYsKCjA5cuXLXoq4/W0qSX6YNLS0rBx40bMmjULt956K9555x2kp6dj6dKl+Ne//gWj0Yjbb78dqampOHnyJAoLCy3yhUFNzhMnTig5G7pN1eQsP4nDWq7TqI3Nno5rMBiwcuVKdO/eHWlpaSgpKcETTzxRZbzExET8/PPPePrpp+Hl5cWc12ArWW0lJ2BbWdPS0pCZmYm+ffsCuHqn1jVr1uDZZ5/F+fPn8eWXX0Kj0SA9Pd2ih3uZ0wpYsH/lhmVnZ8vly5fl4sWLsmLFClm1apXyXGlpqRw8eNDiHcwitpNTxHay2kpOEdvJWlZWJoWFhcr/er1enn76acnJyRERkQsXLkhpaakyjqUwp+XZdB9Ho0aN4OLiAi8vL0yePBklJSXKXTrPnj2L0tJSzJ071yLHim0xJ2A7WW0lJ2A7We3t7ZWLO0UE7u7u8PDwgK+vLxITExEXF4eysjKTC0CZ0/ZzXg+bPVRVnfz8fHz00UdIS0uDiGDRokVo1KiRpWNVYSs5AdvJais5AdvKumbNGvj6+uKPP/7AY489ZrWHU5izYdn0HkdlXl5eaN68OYqKivDUU09Z7ZvRVnICtpPVVnICtpFVRFBaWorU1FTs2LEDTzzxhFV+yDGnZdj0WVWVFRQU4ODBg3juueeseqPYSk7AdrLaSk7ANrLa2dnB0dER9913H0JDQ9G0aVNLR6oWc1rGTXWoCvjfPZSsna3kBGwnq63kBGwnq1jJjSBrw5wN66YrHEREVL9uqj4OIiKqfywcRESkCgsHERGpwsJBdIN27tyJSZMmITk5GS+++CKKi4stHYmoXrFwEJlp0aJFGDduHEpKSkyGHzlyBIsXL8bWrVvRpEkTmzhbiuhG8KwqIjNcuHABM2bMgJubGyZPnoyoqChLRyKymJvqAkCi+pKYmIhWrVqhZcuWSEhIUArHmjVr4OzsjKysLBw5cgTNmjXDzJkzERAQAODqHVI3bNiAjIwMBAYGYuzYsWjdurUlV4XohvFQFZEZEhIS0KtXL/Tu3Rt//PEH8vLylOd27dqFESNGYP369QgICMBnn30G4OoV4suXL8cdd9yBdevWYdiwYVi+fLnyY11EtoqFg6gWqamp0Ov1iIqKQkhICPz9/bFz507l+e7du6Nly5ZwcHBAr1698NdffwEADhw4gICAAPTp00d5LjAwEPv377fQmhDVDRYOolps374d7du3V35gqVevXkhISFCe9/HxUf53dnaGwWAAAOTk5MDPz89kXn5+fsjJyan/0ET1iH0cRNdQXFyMPXv2wGg0YtKkSQCA0tJSFBYWKnsWNWnUqBF+//13k2F6vR4dO3asp7REDYOFg+ga9u7dC3t7e6xcuRKOjv97u8TGxiIxMfGa03bq1Anr1q3Dzp07ERUVhd9//x1nzpxB586d6zs2Ub1i4SC6hoSEBPTr1w9ardZk+ODBg7F+/XpERETUOK2npyfmzZuH9evXY+3atQgICMC8efMs9pviRHWF13EQEZEq7BwnIiJVWDiIiEgVFg4iIlKFhYOIiFRh4SAiIlVYOIiISBUWDiIiUoWFg4iIVGHhICIiVf4fChag7m/YvOEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "intervalos_Mex = range(1950, max(x_Mex)+2) #calculamos los extremos de los intervalos\n",
    "año_Mex = list(range(1950, 2024, 10))\n",
    "# se le quito el +2\n",
    "plt.style.use('ggplot')\n",
    "#plt.style.use('fivethirtyeight')\n",
    "plt.hist(x=x_Mex, bins=intervalos_Mex, color='red', rwidth=0.85)\n",
    "plt.title('Reportes de Entomopatógenos de México en GBIF')\n",
    "plt.xlabel('Año')\n",
    "plt.ylabel('Frecuencia')\n",
    "plt.xticks(año_Mex,rotation=45)\n",
    "plt.savefig(\"Histograma_Mex.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9cd3f628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beauveria bassiana              274\n",
       "Metarhizium anisopliae          122\n",
       "Ophiocordyceps entomorrhiza      67\n",
       "Cordyceps militaris              50\n",
       "Hirsutella citriformis           30\n",
       "Conidiobolus coronatus            7\n",
       "Erynia radicans                   7\n",
       "Ophiocordyceps gracilis           7\n",
       "Akanthomyces lecanii              6\n",
       "Metarhizium rileyi                5\n",
       "Simplicillium lanosoniveum        5\n",
       "Ophiocordyceps melolonthae        5\n",
       "Ophiocordyceps gracilioides       4\n",
       "Entomophthora muscae              3\n",
       "Aspergillus aculeatus             2\n",
       "Ophiocordyceps sphecocephala      2\n",
       "Cordyceps fumosorosea             1\n",
       "Ophiocordyceps stylophora         1\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para variables discretas\n",
    "pd.value_counts(df_Mex['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "de67ce83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Beauveria bassiana              45.819398\n",
       "Metarhizium anisopliae          20.401338\n",
       "Ophiocordyceps entomorrhiza     11.204013\n",
       "Cordyceps militaris              8.361204\n",
       "Hirsutella citriformis           5.016722\n",
       "Conidiobolus coronatus           1.170569\n",
       "Erynia radicans                  1.170569\n",
       "Ophiocordyceps gracilis          1.170569\n",
       "Akanthomyces lecanii             1.003344\n",
       "Metarhizium rileyi               0.836120\n",
       "Simplicillium lanosoniveum       0.836120\n",
       "Ophiocordyceps melolonthae       0.836120\n",
       "Ophiocordyceps gracilioides      0.668896\n",
       "Entomophthora muscae             0.501672\n",
       "Aspergillus aculeatus            0.334448\n",
       "Ophiocordyceps sphecocephala     0.334448\n",
       "Cordyceps fumosorosea            0.167224\n",
       "Ophiocordyceps stylophora        0.167224\n",
       "Name: species, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100 * df_Mex['species'].value_counts() / len(df_Mex['species'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "966cbf51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = (pd.value_counts(df_Mex['species'])).plot(\n",
    "kind='bar', title='Especies de Entomopatógenos de México en GBIF')\n",
    "plt.savefig(\"Especies_Mex.png\",dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17449d03",
   "metadata": {},
   "source": [
    "# Guardar y crear archivos csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cf12567b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# si es un data frame\n",
    "# df_numeros.to_csv('numeros.csv', header=False, index=False)\n",
    "\n",
    "# si es un array\n",
    "Filo = df_Mex['phylum'].array\n",
    "Clase = df_Mex['class'].array\n",
    "Orden = df_Mex['order'].array\n",
    "Familia = df_Mex['family'].array\n",
    "Genero = df_Mex['genus'].array\n",
    "Especie = df_Mex['species'].array\n",
    "\n",
    "#Para la localidad\n",
    "Loc1 = df_Mex['stateProvince'].array\n",
    "Loc2 = df_Mex['locality'].array\n",
    "\n",
    "Estados = ('Aguascalientes', 'Baja California', 'Baja California Sur', 'Campeche', 'Coahuila', 'Colima', 'Chiapas', 'Chihuahua', 'Durango', 'Distrito Federal', 'Guanajuato', 'Guerrero', 'Hidalgo', 'Jalisco', 'Mexico', 'Michoacan', 'Morelos', 'Nayarit', 'Nuevo Leon', 'Oaxaca', 'Puebla', 'Queretaro', 'Quintana Roo', 'San Luis Potosi', 'Sinaloa', 'Sonora', 'Tabasco', 'Tamaulipas', 'Tlaxcala', 'Veracruz', 'Yucatán', 'Zacatecas')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "44b8a491",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creacción de un array que contenga la localidad\n",
    "Localidad = []\n",
    "i = 0\n",
    "while i<np.size(Loc1):\n",
    "    if Loc1[i] in Estados:\n",
    "        Localidad.append(Loc1[i])\n",
    "    else:\n",
    "        Localidad.append(Loc2[i])\n",
    "    i=i+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "65a5698c",
   "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>Filo</th>\n",
       "      <th>Clase</th>\n",
       "      <th>Orden</th>\n",
       "      <th>Familia</th>\n",
       "      <th>Genero</th>\n",
       "      <th>Especie</th>\n",
       "      <th>Localidad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Ophiocordycipitaceae</td>\n",
       "      <td>Ophiocordyceps</td>\n",
       "      <td>Ophiocordyceps gracilioides</td>\n",
       "      <td>Morelos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Cordyceps</td>\n",
       "      <td>Cordyceps militaris</td>\n",
       "      <td>Morelos</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Entomophthoromycota</td>\n",
       "      <td>Entomophthoromycetes</td>\n",
       "      <td>Entomophthorales</td>\n",
       "      <td>Entomophthoraceae</td>\n",
       "      <td>Entomophthora</td>\n",
       "      <td>Entomophthora muscae</td>\n",
       "      <td>Jalisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Simplicillium</td>\n",
       "      <td>Simplicillium lanosoniveum</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Beauveria</td>\n",
       "      <td>Beauveria bassiana</td>\n",
       "      <td>Minatitlan, Colima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Akanthomyces</td>\n",
       "      <td>Akanthomyces lecanii</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Akanthomyces</td>\n",
       "      <td>Akanthomyces lecanii</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Akanthomyces</td>\n",
       "      <td>Akanthomyces lecanii</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Clavicipitaceae</td>\n",
       "      <td>Metarhizium</td>\n",
       "      <td>Metarhizium anisopliae</td>\n",
       "      <td>Colima</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>Ascomycota</td>\n",
       "      <td>Sordariomycetes</td>\n",
       "      <td>Hypocreales</td>\n",
       "      <td>Cordycipitaceae</td>\n",
       "      <td>Beauveria</td>\n",
       "      <td>Beauveria bassiana</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>598 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Filo                 Clase             Orden  \\\n",
       "0             Ascomycota       Sordariomycetes       Hypocreales   \n",
       "1             Ascomycota       Sordariomycetes       Hypocreales   \n",
       "2    Entomophthoromycota  Entomophthoromycetes  Entomophthorales   \n",
       "3             Ascomycota       Sordariomycetes       Hypocreales   \n",
       "4             Ascomycota       Sordariomycetes       Hypocreales   \n",
       "..                   ...                   ...               ...   \n",
       "593           Ascomycota       Sordariomycetes       Hypocreales   \n",
       "594           Ascomycota       Sordariomycetes       Hypocreales   \n",
       "595           Ascomycota       Sordariomycetes       Hypocreales   \n",
       "596           Ascomycota       Sordariomycetes       Hypocreales   \n",
       "597           Ascomycota       Sordariomycetes       Hypocreales   \n",
       "\n",
       "                  Familia          Genero                      Especie  \\\n",
       "0    Ophiocordycipitaceae  Ophiocordyceps  Ophiocordyceps gracilioides   \n",
       "1         Cordycipitaceae       Cordyceps          Cordyceps militaris   \n",
       "2       Entomophthoraceae   Entomophthora         Entomophthora muscae   \n",
       "3         Cordycipitaceae   Simplicillium   Simplicillium lanosoniveum   \n",
       "4         Cordycipitaceae       Beauveria           Beauveria bassiana   \n",
       "..                    ...             ...                          ...   \n",
       "593       Cordycipitaceae    Akanthomyces         Akanthomyces lecanii   \n",
       "594       Cordycipitaceae    Akanthomyces         Akanthomyces lecanii   \n",
       "595       Cordycipitaceae    Akanthomyces         Akanthomyces lecanii   \n",
       "596       Clavicipitaceae     Metarhizium       Metarhizium anisopliae   \n",
       "597       Cordycipitaceae       Beauveria           Beauveria bassiana   \n",
       "\n",
       "              Localidad  \n",
       "0               Morelos  \n",
       "1               Morelos  \n",
       "2               Jalisco  \n",
       "3                   NaN  \n",
       "4    Minatitlan, Colima  \n",
       "..                  ...  \n",
       "593                 NaN  \n",
       "594                 NaN  \n",
       "595                 NaN  \n",
       "596              Colima  \n",
       "597                 NaN  \n",
       "\n",
       "[598 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Construcción de un data frame con los parametros extraidos\n",
    "df_bd = pd.DataFrame()\n",
    "df_bd['Filo'] = Filo\n",
    "df_bd['Clase'] = Clase\n",
    "df_bd['Orden'] = Orden\n",
    "df_bd['Familia'] = Familia\n",
    "df_bd['Genero'] = Genero\n",
    "df_bd['Especie'] = Especie\n",
    "df_bd['Localidad'] = Localidad\n",
    "\n",
    "df_bd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "77df2304",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guadar el data frame en un archivo csv\n",
    "df_bd.to_csv('EspeciesMexLite.csv')"
   ]
  }
 ],
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