{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Harga Labu\n", "\n", "Muatkan perpustakaan dan set data yang diperlukan. Tukarkan data kepada dataframe yang mengandungi subset data:\n", "\n", "- Hanya ambil labu yang harganya berdasarkan bushel\n", "- Tukarkan tarikh kepada bulan\n", "- Kira harga sebagai purata daripada harga tinggi dan rendah\n", "- Tukarkan harga untuk mencerminkan harga berdasarkan kuantiti bushel\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from datetime import datetime\n", "\n", "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", "\n", "pumpkins.head()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", "\n", "columns_to_select = ['Package', 'Variety', 'City Name', 'Low Price', 'High Price', 'Date']\n", "pumpkins = pumpkins.loc[:, columns_to_select]\n", "\n", "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n", "\n", "month = pd.DatetimeIndex(pumpkins['Date']).month\n", "day_of_year = pd.to_datetime(pumpkins['Date']).apply(lambda dt: (dt-datetime(dt.year,1,1)).days)\n", "\n", "new_pumpkins = pd.DataFrame(\n", " {'Month': month, \n", " 'DayOfYear' : day_of_year, \n", " 'Variety': pumpkins['Variety'], \n", " 'City': pumpkins['City Name'], \n", " 'Package': pumpkins['Package'], \n", " 'Low Price': pumpkins['Low Price'],\n", " 'High Price': pumpkins['High Price'], \n", " 'Price': price})\n", "\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/1.1\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n", "\n", "new_pumpkins.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Plot pencar asas mengingatkan kita bahawa kita hanya mempunyai data bulan dari Ogos hingga Disember. Kita mungkin memerlukan lebih banyak data untuk dapat membuat kesimpulan secara linear.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "plt.scatter('Month','Price',data=new_pumpkins)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "plt.scatter('DayOfYear','Price',data=new_pumpkins)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**Penafian**: \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil perhatian bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat yang kritikal, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\n" ] } ], "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": "3.8.3-final" }, "orig_nbformat": 2, "coopTranslator": { "original_hash": "b032d371c75279373507f003439a577e", "translation_date": "2025-09-06T11:29:19+00:00", "source_file": "2-Regression/3-Linear/notebook.ipynb", "language_code": "ms" } }, "nbformat": 4, "nbformat_minor": 2 }