{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Pumpkin Varieties and Color\n", "\n", "Load di libraries wey we need and di dataset. Change di data to dataframe wey get small part of di data:\n", "\n", "Make we check how di color and variety take relate\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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City NameTypePackageVarietySub VarietyGradeDateLow PriceHigh PriceMostly Low...Unit of SaleQualityConditionAppearanceStorageCropRepackTrans ModeUnnamed: 24Unnamed: 25
0BALTIMORENaN24 inch binsNaNNaNNaN4/29/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
1BALTIMORENaN24 inch binsNaNNaNNaN5/6/17270.0280.0270.0...NaNNaNNaNNaNNaNNaNENaNNaNNaN
2BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
3BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN9/24/16160.0160.0160.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
4BALTIMORENaN24 inch binsHOWDEN TYPENaNNaN11/5/1690.0100.090.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
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5 rows × 26 columns

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" ], "text/plain": [ " City Name Type Package Variety Sub Variety Grade Date \\\n", "0 BALTIMORE NaN 24 inch bins NaN NaN NaN 4/29/17 \n", "1 BALTIMORE NaN 24 inch bins NaN NaN NaN 5/6/17 \n", "2 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "3 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 9/24/16 \n", "4 BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN NaN 11/5/16 \n", "\n", " Low Price High Price Mostly Low ... Unit of Sale Quality Condition \\\n", "0 270.0 280.0 270.0 ... NaN NaN NaN \n", "1 270.0 280.0 270.0 ... NaN NaN NaN \n", "2 160.0 160.0 160.0 ... NaN NaN NaN \n", "3 160.0 160.0 160.0 ... NaN NaN NaN \n", "4 90.0 100.0 90.0 ... NaN NaN NaN \n", "\n", " Appearance Storage Crop Repack Trans Mode Unnamed: 24 Unnamed: 25 \n", "0 NaN NaN NaN E NaN NaN NaN \n", "1 NaN NaN NaN E NaN NaN NaN \n", "2 NaN NaN NaN N NaN NaN NaN \n", "3 NaN NaN NaN N NaN NaN NaN \n", "4 NaN NaN NaN N NaN NaN NaN \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "full_pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", "\n", "full_pumpkins.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n\n\n**Disclaimer**: \nDis dokyument don translate wit AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). Even though we dey try make am accurate, abeg sabi say automated translations fit get mistake or no dey correct well. Di original dokyument for im native language na di main source wey you suppose trust. For important information, e good make professional human translation dey use. We no go fit take blame for any misunderstanding or wrong interpretation wey fit happen because you use dis translation.\n\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": "3.11.1" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } }, "orig_nbformat": 2, "coopTranslator": { "original_hash": "dee08c2b49057b0de8b6752c4dbca368", "translation_date": "2025-11-18T19:13:22+00:00", "source_file": "2-Regression/4-Logistic/notebook.ipynb", "language_code": "pcm" } }, "nbformat": 4, "nbformat_minor": 2 }