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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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"## かぼちゃの価格設定\n",
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"\n",
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"必要なライブラリとデータセットを読み込みます。データを以下の条件を満たすデータフレームに変換します：\n",
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"\n",
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"- ブッシェル単位で価格が設定されているかぼちゃのみを取得する\n",
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"- 日付を月に変換する\n",
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"- 高値と安値の平均を計算して価格を求める\n",
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"- 価格をブッシェル単位の数量に基づいた価格に変換する\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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"基本的な散布図は、8月から12月までの月データしかないことを思い出させてくれます。結論を線形的に導き出すには、もっと多くのデータが必要かもしれません。\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**免責事項**: \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な部分が含まれる可能性があることをご承知ください。元の言語で記載された文書が正式な情報源とみなされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤った解釈について、当方は責任を負いません。\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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"translation_date": "2025-09-04T01:01:40+00:00",
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"source_file": "2-Regression/3-Linear/notebook.ipynb",
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