239 lines
17 KiB
Plaintext
239 lines
17 KiB
Plaintext
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{
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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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|
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],
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"source": [
|
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|
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"data"
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|
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]
|
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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": 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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|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
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},
|
||
|
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"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
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},
|
||
|
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"output_type": "display_data"
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|
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}
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|
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],
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"source": [
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"%matplotlib inline\n",
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"plt.bar(data['species'], height=data['n_appearence'])\n",
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"plt.title(\"Nombre d'apparitions des espèces devant le PiCameraTrap\")\n",
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"plt.savefig(\"../img/n_appearences_species.png\")\n",
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|
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"plt.show()"
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|
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]
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|
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},
|
||
|
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{
|
||
|
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"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
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"id": "informed-truth",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
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"display_name": "tb-venv",
|
||
|
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"language": "python",
|
||
|
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"name": "tb-venv"
|
||
|
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},
|
||
|
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"language_info": {
|
||
|
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"codemirror_mode": {
|
||
|
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"name": "ipython",
|
||
|
|
"version": 3
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||
|
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},
|
||
|
|
"file_extension": ".py",
|
||
|
|
"mimetype": "text/x-python",
|
||
|
|
"name": "python",
|
||
|
|
"nbconvert_exporter": "python",
|
||
|
|
"pygments_lexer": "ipython3",
|
||
|
|
"version": "3.8.5"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|