239 lines
17 KiB
Plaintext
239 lines
17 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "unnecessary-combine",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "touched-clerk",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"../data/analyses/guesses.csv\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "lasting-buffer",
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Unnamed: 0</th>\n",
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" <th>date</th>\n",
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" <th>probability</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>count</th>\n",
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" <td>6239.000000</td>\n",
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" <td>6.239000e+03</td>\n",
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" <td>6239.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>mean</th>\n",
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" <td>3119.000000</td>\n",
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" <td>2.021030e+13</td>\n",
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" <td>0.632259</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>1801.188497</td>\n",
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" <td>1.239403e+06</td>\n",
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" <td>0.196005</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>0.000000</td>\n",
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" <td>2.021030e+13</td>\n",
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" <td>0.217052</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>1559.500000</td>\n",
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" <td>2.021030e+13</td>\n",
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" <td>0.479808</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>3119.000000</td>\n",
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" <td>2.021031e+13</td>\n",
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" <td>0.621566</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>4678.500000</td>\n",
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" <td>2.021031e+13</td>\n",
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" <td>0.789861</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>6238.000000</td>\n",
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" <td>2.021031e+13</td>\n",
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" <td>0.999820</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Unnamed: 0 date probability\n",
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"count 6239.000000 6.239000e+03 6239.000000\n",
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"mean 3119.000000 2.021030e+13 0.632259\n",
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"std 1801.188497 1.239403e+06 0.196005\n",
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"min 0.000000 2.021030e+13 0.217052\n",
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"25% 1559.500000 2.021030e+13 0.479808\n",
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"50% 3119.000000 2.021031e+13 0.621566\n",
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"75% 4678.500000 2.021031e+13 0.789861\n",
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"max 6238.000000 2.021031e+13 0.999820"
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]
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},
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"execution_count": 25,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df.describe()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "attractive-endorsement",
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"data = {'species': [], 'n_appearence': []}\n",
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"for i, sighting in df.iterrows():\n",
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" # print(sighting)\n",
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" species = sighting['predicted_species']\n",
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" if species in data['species']:\n",
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" data['n_appearence'][data['species'].index(species)] += 1\n",
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" else:\n",
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" data['species'].append(species)\n",
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" data['n_appearence'].append(1)\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "fitted-pittsburgh",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'species': ['pasdom',\n",
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" 'erirub',\n",
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" 'parmaj',\n",
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" 'caycae',\n",
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" 'felcat',\n",
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" 'fricoe',\n",
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" 'prumod'],\n",
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" 'n_appearence': [4374, 453, 988, 292, 104, 5, 23]}"
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]
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},
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"execution_count": 27,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 28,
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"id": "smooth-galaxy",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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L38zMlpVuJQhJ6wB7AGfkuIAdgJ/nJGcDe+XwnjlO1u+Y0+8JnBcR/4yIe4FZwJb9sA5mZrYMdPcJ4pvAZ4Dnc3xN4LGIeDbH5wITcngCMAcg6xfl9C+UV+Z5gaRDJM2QNGP+/PndXxMzM+tXXSYISW8D5kXEjQMQDxFxekRMjojJ48aNG4hFmplZRXf+l6PbAu+QtDswAlgD+BYwStLwfEpYB7g/p78fmAjMlTQcGAk82ijv0JzHzMyGmC6fICLisxGxTkRMorxkviIi3gdcCeydkx0AXJTDU3OcrL8iIiLL98mvnNYHNgSu77c1MTOzftWdJ4h2jgLOk3QCcBNwZpafCZwjaRawgJJUiIiZks4H7gCeBQ6NiOf6sHwzM1uGepQgImI6MD2H76HyFVJEPAO8q838JwIn9jRIMzMbeP4ltZmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVdZkgJI2QdL2kWyTNlPSlLF9f0nWSZkn6qaSVsnzlHJ+V9ZMabX02y++WtMsyWyszM+uz7jxB/BPYISJeA2wO7Cppa+Bk4JSI2ABYCByU0x8ELMzyU3I6JG0M7ANsAuwKfFfSsH5cFzMz60ddJogonszRFfNfADsAP8/ys4G9cnjPHCfrd5SkLD8vIv4ZEfcCs4At+2MlzMys/3XrHYSkYZJuBuYB04C/AY9FxLM5yVxgQg5PAOYAZP0iYM1meWWe5rIOkTRD0oz58+f3eIXMzKx/dCtBRMRzEbE5sA7lrn+jZRVQRJweEZMjYvK4ceOW1WLMzKwLPfqKKSIeA64EtgFGSRqeVesA9+fw/cBEgKwfCTzaLK/MY2ZmQ0x3vmIaJ2lUDq8CvBW4k5Io9s7JDgAuyuGpOU7WXxERkeX75FdO6wMbAtf303qYmVk/G971JKwNnJ1fHK0AnB8RF0u6AzhP0gnATcCZOf2ZwDmSZgELKF8uEREzJZ0P3AE8CxwaEc/17+qYmVl/6TJBRMStwBaV8nuofIUUEc8A72rT1onAiT0P08zMBpp/SW1mZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVtVlgpA0UdKVku6QNFPS4Vk+RtI0SX/N/47Ockk6VdIsSbdKem2jrQNy+r9KOmDZrZaZmfVVd54gngU+FREbA1sDh0raGDgauDwiNgQuz3GA3YAN898hwGlQEgpwDLAVsCVwTEdSMTOzoafLBBERD0bEn3P4CeBOYAKwJ3B2TnY2sFcO7wn8MIprgVGS1gZ2AaZFxIKIWAhMA3btz5UxM7P+06N3EJImAVsA1wFrRcSDWfUQsFYOTwDmNGabm2XtyluXcYikGZJmzJ8/vyfhmZlZP+p2gpC0GvAL4IiIeLxZFxEBRH8EFBGnR8TkiJg8bty4/mjSzMx6oVsJQtKKlORwbkRckMUPZ9cR+d95WX4/MLEx+zpZ1q7czMyGoO58xSTgTODOiPhGo2oq0PEl0gHARY3y/fNrpq2BRdkVdRmws6TR+XJ65ywzM7MhaHg3ptkW2A+4TdLNWfY54CTgfEkHAX8H3p11lwK7A7OAfwAfAIiIBZKOB27I6Y6LiAX9sRJmZtb/ukwQEfEHQG2qd6xMH8Chbdo6CzirJwGamdng8C+pzcysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqnCDMzKzKCcLMzKqcIMzMrMoJwszMqpwgzMysygnCzMyqukwQks6SNE/S7Y2yMZKmSfpr/nd0lkvSqZJmSbpV0msb8xyQ0/9V0gHLZnXMzKy/dOcJYgqwa0vZ0cDlEbEhcHmOA+wGbJj/DgFOg5JQgGOArYAtgWM6koqZmQ1NXSaIiLgaWNBSvCdwdg6fDezVKP9hFNcCoyStDewCTIuIBRGxEJjG0knHzMyGkN6+g1grIh7M4YeAtXJ4AjCnMd3cLGtXvhRJh0iaIWnG/PnzexmemZn11fC+NhARISn6I5hs73TgdIDJkyf3W7u2bEw6+pLBDmEJs0/aY7BDMHvR6O0TxMPZdUT+d16W3w9MbEy3Tpa1KzczsyGqtwliKtDxJdIBwEWN8v3za6atgUXZFXUZsLOk0flyeucsMzOzIarLLiZJPwG2B8ZKmkv5Gukk4HxJBwF/B96dk18K7A7MAv4BfAAgIhZIOh64Iac7LiJaX3ybmdkQ0mWCiIh921TtWJk2gEPbtHMWcFaPojMzs0HjX1KbmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVU5QZiZWZUThJmZVTlBmJlZlROEmZlVOUGYmVmVE4SZmVUNH+wAbLFJR18y2CEsYfZJewx2CGY2iPwEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlVOEGZmVuUEYWZmVf4dhP3b8e9NzLrHTxBmZlblBGFmZlVOEGZmVuUEYWZmVU4QZmZW5QRhZmZVThBmZlblBGFmZlX+oZzZcmAo/bjPP+z79+EnCDMzqxrwBCFpV0l3S5ol6eiBXr6ZmXXPgCYIScOA7wC7ARsD+0raeCBjMDOz7hnodxBbArMi4h4ASecBewJ3DHAcZmZLGErveWBovOtRRAzcwqS9gV0j4uAc3w/YKiI+3pjmEOCQHH0lcPeABVg3FnhkkGPoKcc8MJa3mJe3eMEx99Z6ETGur40Mua+YIuJ04PTBjqODpBkRMXmw4+gJxzwwlreYl7d4wTEPtoF+SX0/MLExvk6WmZnZEDPQCeIGYENJ60taCdgHmDrAMZiZWTcMaBdTRDwr6ePAZcAw4KyImDmQMfTCkOnu6gHHPDCWt5iXt3jBMQ+qAX1JbWZmyw//ktrMzKqcIMzMrMoJogckzZY0drDjaCXpjJ7+Il3SsZKOXFYxDSRJl0oaNdhxDDRJh0m6U9K5beq3l3RxL9s+QtJL+hbhUm1W45U0WdKp/bms5Z2kSZJuH+w4htzvIKxnJA3r+OFhm7rnBjqmnpA0PCKe7UsbEbF7f8WznPkYsFNEzF0GbR8B/Aj4Rz+2uVS8uf9nADP6cTnLzPJwTvWnF+0TRGbguySdm3ctP5f0EklflHSDpNslnS5JOf1hku6QdGv+CRAkrSnpt5JmSjoDUKP9T2Ybt0s6omWZUyT9JZe9k6RrJP1V0pa9WI/3S7pe0s2S/lfSMElPSvq6pFuAbSRNlzQ5p2+te+GpJ+/Upjeaf42kP2VsH+rdlu7Vtp4u6ZuSZgCH5/gpkmbk/K+XdEHGdUJjORdKujH3xyGN8l492UnaP/f3LZLOkfR2SddJuknS7yStJWmFjGNczrOCyh+aHJf1v8z5b5H0hi7i3Dm3958l/UzSan3Y5t8DXgb8WtLnJZ2Vx8lNkvasTL+apB9Iui3X+Z1Zflpu95mSvpRlhwHjgSslXdnbGDuJd1Fu72uAc9R40ukkzn2z7HZJJzfa7c9t2u44ni3pZEl/Bt7Vcr6NlTQ7hw/MfT8t5/m4ynXiJknXShqT022e47fm8TM6y1/XcSwBh/Z2PfpVRLwo/wGTgAC2zfGzgCOBMY1pzgHensMPACvn8Kj876nAF3N4j2xvLPA64DZgVWA1YCawRS7zWWAzSvK9MZcryt+curCH6/Aq4FfAijn+XWD/jOPdjemmA5NzuLVuNjA2hycD03P4WOAWYJVcpznA+AHa1tOB77bEf3IOH577Ym1gZWAusGbWjcn/rgLc3ih/YR17EPMmwF8a22YMMJrFX/YdDHw9h48BjsjhnYFf5PBPG+XDgJHt4sxtfDWwatYd1XFs9eEYn53tfhl4f8exm+u1KrA9cHGWnwx8szHv6JZYh+V+eHVvt2kP4j2Wcm6skuWdxklJVvcB4yi9HlcAe/X3NqX9cTwb+Eyb820sMDuHDwRmAatnrIuAj2TdKY1j5VbgzTl8XMf6Zvl2OfxV4Pb+3P69+fdi72KaExHX5PCPgMOAeyV9BngJ5aIwk3IRvhU4V9KFwIU5z3bAfwFExCWSFmb5G4FfRsRTAJIuAN5E+dHfvRFxW5bPBC6PiJB0G+UA7IkdKcnohrz5XgWYBzwH/KLNPJ3VtbooIp4Gns47xS1ZvO491ZNtDeXi2tTxg8nbgJkR8SCApHsov75/FDhM0n/mdBOBDbO8N3YAfhYRjwBExAJJmwE/lbQ2sBJwb057FnAR8E3gg8APGm3sn/M/R7kg0CbOsZS/YHxN7suVgD/1MvZWOwPv0OJ3SiOAdVum2Ynyw1Qy3o5j+d35lDOckpQ3ppwLy9rUPPZaLRWnpO0oNzbzAVTeYWxHuRnr721aO45h6eO1nSsj4gngCUmLWHy83wa8WtJIyg3oVVl+NvAzlXdooyLi6iw/h/JXrwfViz1BtP7IIyh34ZMjYo6kYyknE5QnhO2AtwOfz4tFb/yzMfx8Y/x5er69BZwdEZ9dolA6Mtr3gz7TUvcsi7sSR7RMW9s+vdWTbQ3wVMv0ze3Uug2HS9qecvHYJiL+odJV1ro+ffV/gW9ExNRc3rEAGf/DknagJNH3tWugkzgFTIuIffs5ZrLtd0bEEn/YUtJanc4krU+5Q359Xoin0P/btJ3W/d8by2KbtjsnmvF2dk715/k/6F607yDSupK2yeH3An/I4Ueyr3JvKP3KwMSIuJLymDqS0nV0dc6HpN0oj7sAvwf2yv7JVYH/zLL+djmwt6SXZgxjJK3XwzZmU55CAN7ZUrenpBGS1qQ85t/Qh1i7ta37YCSwMC+6GwFb97G9Kyj9yWtC2ba5jI6/DXZAy/RnUO4of9ZIwJcDH835h+XdYbs4rwW2lbRBTr+qpFf0cR06XAZ8QnrhHc8WlWmm0ejXzn7vNSgXvkWZTJp3rE9QukoGWi3O64E3Z3//MGBf4CqWzTZtdxw3zWbxOdWj4zoiFgELJb0pi/YDroqIx4DHJL0xy9vehAykF3uCuBs4VNKdlIv7acD3Kf3Cl7H4gjgM+FF2A90EnJo77EvAdtlV9F+UflAi4s/AFMqBex1wRkTc1N/BR8QdwBeA30q6lXLyrN3DZr4EfEvlhXDrU8etwJWUE+34iHigD+F2d1v31m8oTxJ3AidRYm7q0dNPlD/xciJwVb4U/AblieFnkm5k6T/XPJVy0/CDRtnhwFvyuLmR0t1RjTO7Rw4EfpL78k/ARj2JuRPHAysCt+axenxlmhOA0SoveW8B3hIRt1CO97uAHwPXNKY/HfiN+ukldQ/U4nwQOJpyrN4C3BgRFy2jbVo7jlt9DfiopJsoXYc9dQDw1Yx5c8p7CIAPAN+RdDOND2IG04v2T21ImkR58bXpYMfyYjeY2zrvKOcB/xER/1qGy5kMnBIRb+pyYlsu+ZqxtOWuT8ysxUzKE9yyTA5HU7qShsRjv9lAedE+QZiZWd+82N9BmJlZLzlBmJlZlROEmZlVOUGYmVmVE4SZmVX9f2wTYfH3FK+8AAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"%matplotlib inline\n",
|
|
"plt.bar(data['species'], height=data['n_appearence'])\n",
|
|
"plt.title(\"Nombre d'apparitions des espèces devant le PiCameraTrap\")\n",
|
|
"plt.savefig(\"../img/n_appearences_species.png\")\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "informed-truth",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "tb-venv",
|
|
"language": "python",
|
|
"name": "tb-venv"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.8.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|