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https://github.com/yannbouyeron/koala

Un compagnon à Pandas pour les SVT
https://github.com/yannbouyeron/koala

pandas svt

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Un compagnon à Pandas pour les SVT

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{
"cells": [
{
"cell_type": "markdown",
"id": "9f6af306-a609-43f3-9172-d99a21372d23",
"metadata": {},
"source": [
"# Koala"
]
},
{
"cell_type": "markdown",
"id": "ff1b5348-e2a8-48a0-979a-800a6f2c2a3b",
"metadata": {},
"source": [
"Testez facilement et rapidement des régressions linéaires, puissances, et exponentielles, avec affichage graphique."
]
},
{
"cell_type": "markdown",
"id": "23892820-6f18-4c62-a44e-241a952601ba",
"metadata": {
"tags": []
},
"source": [
"### Importer matplotlib.pyplot\n",
"\n",
"Si vous êtes dans jupyter:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d17a9b14-84fb-4e70-a7fb-e87423706d58",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"%config Completer.use_jedi = False\n",
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "29d29ac5-5cf0-4348-bfc6-e835aadf430b",
"metadata": {
"tags": []
},
"source": [
"Si vous travaillez en console linux sans interface graphique:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "b4b17585-f996-4835-a49a-23e82b734717",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"#import matplotlib\n",
"#import os\n",
"\n",
"#if \"DISPLAY\" not in os.environ:\n",
" \n",
" #matplotlib.use(\"Agg\")\n",
"\n",
"#import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "2b8e0dc3-d3d6-4a77-a904-6167f7f07499",
"metadata": {},
"source": [
"Modification de l´apparence des graphiques pour permettre l´export depuis Jupyter en html / markdown dark theme, tout en conservant les axes visibles :"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "c7df6dec-4134-4ee9-af99-fdb2067e9c63",
"metadata": {},
"outputs": [],
"source": [
"plt.style.use('seaborn-white') # blanc\n",
"\n",
"#plt.style.use('dark_background') # noir"
]
},
{
"cell_type": "markdown",
"id": "bfcefd90-9687-4a7b-85e9-bb59574e6d1f",
"metadata": {},
"source": [
"On importe ensuite pandas, numpy et koala"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "fd6266ef-3e5c-47e2-9b8e-f8e6bdc1ab9f",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",
"from koala import Koala"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "3b8ac2e0-e7ea-499b-9885-bab8dfde88fc",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" x y\n",
"0 1 6\n",
"1 2 8\n",
"2 3 10\n",
"3 4 12\n",
"4 5 14\n",
"5 6 16\n",
"6 7 18\n",
"7 8 20\n",
"8 9 22\n"
]
}
],
"source": [
"x = np.arange(10)\n",
"\n",
"y = 2*x + 4\n",
"\n",
"kf = Koala({\"x\":x[1:], \"y\":y[1:]})\n",
"\n",
"print(kf)"
]
},
{
"cell_type": "markdown",
"id": "80e9070c-00a8-4276-a000-10348e962ebd",
"metadata": {},
"source": [
"## Régression linéaire\n",
"\n",
"On utilise la méthode 'lin' pour effectuer une régression linéaire:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bce143d2-169c-4e44-9237-99418b4ba7c6",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lin = kf.lin(\"x\", \"y\")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a4a3de64-d8fd-4560-a9d3-e421f9ed620a",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AttrDict({'a': 2.000000000000001, 'b': 4.0, 'r': 1.0, 'equation': 'y = 2.0x + 4.0', 'fig': , 'graph': })"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lin"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "7b1d8107-92a6-42ff-834d-c2b744819053",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lin.graph.set_xlabel(\"abscisses\")\n",
"lin.graph.set_ylabel(\"ordonnées\")\n",
"lin.graph.set_title(\"mon titre\")\n",
"lin.graph.legend(fontsize=10)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "b955beaa-3b6c-4496-a90c-b0ca14ce00fe",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lin.fig"
]
},
{
"cell_type": "markdown",
"id": "28dd3448-82a2-4d9f-8d9b-50d53f2a439c",
"metadata": {},
"source": [
"## Régressions puissance et exponentielle\n",
"\n",
"Pour réaliser une régression puissance, utilisez la méthode 'pwr':\n",
"\n",
" pwr = kf.pwr(\"x\", \"y\")\n",
"\n",
"Pour réaliser une régression exponentielle, utilisez la méthode 'exp':\n",
"\n",
" exp = kf.exp(\"x\", \"y\")\n",
" \n",
"La méthode 'reg' permet de réaliser les 3 régressions (lin, pwr et exp) en une seule fois:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "4fd8d07d-a80b-43bc-b17d-796ea9abdb6c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"r = kf.reg(\"x\",\"y\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "a1d9473e-a1db-4a9b-bd1f-b4fdec6c315c",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"AttrDict({'fig': , 'lin': AttrDict({'a': 2.000000000000001, 'b': 4.0, 'r': 1.0, 'equation': 'y = 2.0x + 4.0', 'fig': , 'graph': }), 'exp': AttrDict({'a': 5.910478175636692, 'b': 0.15682099223999219, 'r': 0.9835038872887962, 'equation': 'y = 5.91 *exp(0.16x)', 'fig': , 'graph': }), 'pwr': AttrDict({'a': 5.495458089762599, 'b': 0.6024286578942702, 'r': 0.9922794506535199, 'equation': 'y = 5.5 * x^0.6', 'fig': , 'graph': }), 'g1': , 'g2': , 'g3': , 'g4': })"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r"
]
},
{
"cell_type": "markdown",
"id": "a5f01ba4-ffce-4cee-be04-1f4cfb35db48",
"metadata": {},
"source": [
"La méthode 'reg' renvoie un dictionnaire au format attrdict qui permet de modifier les graphiques et de récupérer les coefficients de corrélations et les équations:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "74f850dc-4cbb-4a5e-8a72-3514b63f1325",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
">"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.g2.set_xlabel(\"abscisses\")\n",
"r.g2.set_ylabel(\"ordonnées\")\n",
"r.g2.set"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "029270bb-0601-49de-861a-8233ad2879f9",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.fig"
]
},
{
"cell_type": "markdown",
"id": "13e8a4f7-5c1b-4dcb-936c-bca9c2f99e94",
"metadata": {},
"source": [
"On récupère l'équation de la régression linéaire:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "2a52ff57-f748-4fb8-91cb-4b2d7a7aba87",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"'y = 2.0x + 4.0'"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.lin.equation"
]
},
{
"cell_type": "markdown",
"id": "20fc7353-0c05-40c9-a4fb-d79b8d144ad5",
"metadata": {
"tags": []
},
"source": [
"On récupère le coefficient de corrélation de la régression linéaire:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "27b588c1-eee8-4732-984c-c9b512c107b9",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"1.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.lin.r"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}