{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Pumpkin varieties and color\n", "\n", "Load up required libraries and dataset. Convert the data to a dataframe containing a subset of the data: \n", "\n", "Let's look at the relationship between color and variety" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "pumpkins = pd.read_csv('../../assets/data/us-pumpkins.csv')\n", "\n", "pumpkins.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Acknowledgments\n", "\n", "Thanks to Microsoft for creating the open-source course [ML-For-Beginners](https://github.com/microsoft/ML-For-Beginners). It inspires the majority of the content in this chapter." ] } ], "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": "3.9.13 (main, May 24 2022, 21:28:31) \n[Clang 13.1.6 (clang-1316.0.21.2)]" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } }, "orig_nbformat": 2, "vscode": { "interpreter": { "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49" } } }, "nbformat": 4, "nbformat_minor": 2 }