{ "metadata": { "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, "kernelspec": { "name": "python3", "display_name": "Python 3", "language": "python" } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "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": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "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]" ], "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
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

\n
" }, "metadata": {}, "execution_count": 44 } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "pumpkins = pd.read_csv('../data/US-pumpkins.csv')\n", "\n", "pumpkins.head()\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\nInt64Index: 716 entries, 23 to 1752\nData columns (total 2 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 Color 716 non-null object\n 1 Variety 716 non-null object\ndtypes: object(2)\nmemory usage: 16.8+ KB\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "Color 716\n", "Variety 716\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 45 } ], "source": [ "\n", "new_columns = ['Variety', 'Color']\n", "\n", "\n", "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", "\n", "\n", "new_pumpkins = pd.DataFrame({'Color': pumpkins['Color'],'Variety': pumpkins['Variety']})\n", "\n", "new_pumpkins.dropna(inplace=True)\n", "new_pumpkins.info()\n", "\n", "\n", "\n", "new_pumpkins.count()\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "" ] }, "metadata": {}, "execution_count": 46 }, { "output_type": "display_data", "data": { "text/plain": "
", "image/svg+xml": "\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "import matplotlib.pyplot as plt\n", "plt.bar('Color','Variety',data=new_pumpkins)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Color Variety\n", "23 0 HOWDEN TYPE\n", "24 0 HOWDEN TYPE\n", "25 0 HOWDEN TYPE\n", "26 0 HOWDEN TYPE\n", "87 0 PIE TYPE\n", "... ... ...\n", "1748 0 MINIATURE\n", "1749 0 MINIATURE\n", "1750 2 MINIATURE\n", "1751 0 MINIATURE\n", "1752 2 MINIATURE\n", "\n", "[716 rows x 2 columns]" ], "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
ColorVariety
230HOWDEN TYPE
240HOWDEN TYPE
250HOWDEN TYPE
260HOWDEN TYPE
870PIE TYPE
.........
17480MINIATURE
17490MINIATURE
17502MINIATURE
17510MINIATURE
17522MINIATURE
\n

716 rows × 2 columns

\n
" }, "metadata": {}, "execution_count": 48 } ], "source": [ "from sklearn.preprocessing import LabelEncoder\n", "\n", "new_pumpkins.iloc[:, 0:-1] = new_pumpkins.iloc[:, 0:-1].apply(LabelEncoder().fit_transform)\n", "\n", "new_pumpkins\n", "\n", "#print(new_pumpkins['Color'].corr(new_pumpkins['Variety']))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }