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128 lines
4.2 KiB
128 lines
4.2 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Tarification des citrouilles\n",
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"\n",
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"Chargez les bibliothèques nécessaires et le jeu de données. Convertissez les données en un dataframe contenant un sous-ensemble des données :\n",
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"\n",
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"- Ne conserver que les citrouilles dont le prix est indiqué par boisseau\n",
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"- Convertir la date en mois\n",
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"- Calculer le prix comme une moyenne des prix haut et bas\n",
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"- Convertir le prix pour refléter la tarification par quantité de boisseaux\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from datetime import datetime\n",
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"\n",
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"pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n",
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"\n",
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"pumpkins.head()\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
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"\n",
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"columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n",
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"pumpkins = pumpkins.loc[:, columns_to_select]\n",
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"\n",
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"price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
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"\n",
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"month = pd.DatetimeIndex(pumpkins['Date']).month\n",
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"day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n",
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"\n",
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"new_pumpkins = pd.DataFrame(\n",
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" {'Month': month, \n",
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" 'DayOfYear' : day_of_year, \n",
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" 'Variety': pumpkins['Variety'], \n",
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" 'City': pumpkins['City Name'], \n",
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" 'Package': pumpkins['Package'], \n",
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" 'Low Price': pumpkins['Low Price'],\n",
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" 'High Price': pumpkins['High Price'], \n",
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" 'Price': price})\n",
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"\n",
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"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n",
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"new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n",
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"\n",
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"new_pumpkins.head()\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Un nuage de points de base nous rappelle que nous n'avons des données mensuelles que d'août à décembre. Nous avons probablement besoin de plus de données pour pouvoir tirer des conclusions de manière linéaire.\n"
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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": null,
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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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"plt.scatter('Month','Price',data=new_pumpkins)"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"plt.scatter('DayOfYear','Price',data=new_pumpkins)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**Avertissement** : \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de faire appel à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\n"
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]
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