{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Pumpkin Varieties and Color\n", "\n", "Load the necessary libraries and dataset. Transform the data into a dataframe containing a subset of the information:\n", "\n", "Let's examine the connection between color and variety\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
\n", "

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**: \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we aim for accuracy, please note that automated translations may include errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is advised. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\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": "2025-09-06T15:29:47+00:00", "source_file": "2-Regression/4-Logistic/notebook.ipynb", "language_code": "en" } }, "nbformat": 4, "nbformat_minor": 2 }