{ "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": "3.7.0" }, "orig_nbformat": 2, "kernelspec": { "name": "python37364bit8d3b438fb5fc4430a93ac2cb74d693a7", "display_name": "Python 3.7.0 64-bit ('3.7')" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } } }, "nbformat": 4, "nbformat_minor": 2, "cells": [ { "source": [ "## Linear Regression for Pumpkins - Lesson 2" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " City Name Type Package Variety Sub Variety Grade \\\n", "70 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n", "71 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n", "72 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n", "73 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n", "74 BALTIMORE NaN 1 1/9 bushel cartons PIE TYPE NaN NaN \n", "\n", " Date Low Price High Price Mostly Low ... Unit of Sale Quality \\\n", "70 9/24/16 15.0 15.0 15.0 ... NaN NaN \n", "71 9/24/16 18.0 18.0 18.0 ... NaN NaN \n", "72 10/1/16 18.0 18.0 18.0 ... NaN NaN \n", "73 10/1/16 17.0 17.0 17.0 ... NaN NaN \n", "74 10/8/16 15.0 15.0 15.0 ... NaN NaN \n", "\n", " Condition Appearance Storage Crop Repack Trans Mode Unnamed: 24 \\\n", "70 NaN NaN NaN NaN N NaN NaN \n", "71 NaN NaN NaN NaN N NaN NaN \n", "72 NaN NaN NaN NaN N NaN NaN \n", "73 NaN NaN NaN NaN N NaN NaN \n", "74 NaN NaN NaN NaN N NaN NaN \n", "\n", " Unnamed: 25 \n", "70 NaN \n", "71 NaN \n", "72 NaN \n", "73 NaN \n", "74 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
70BALTIMORENaN1 1/9 bushel cartonsPIE TYPENaNNaN9/24/1615.015.015.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
71BALTIMORENaN1 1/9 bushel cartonsPIE TYPENaNNaN9/24/1618.018.018.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
72BALTIMORENaN1 1/9 bushel cartonsPIE TYPENaNNaN10/1/1618.018.018.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
73BALTIMORENaN1 1/9 bushel cartonsPIE TYPENaNNaN10/1/1617.017.017.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
74BALTIMORENaN1 1/9 bushel cartonsPIE TYPENaNNaN10/8/1615.015.015.0...NaNNaNNaNNaNNaNNaNNNaNNaNNaN
\n

5 rows × 26 columns

\n
" }, "metadata": {}, "execution_count": 1 } ], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "pumpkins = pd.read_csv('../../data/US-pumpkins.csv')\n", "\n", "pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n", "\n", "pumpkins.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "City Name 0\n", "Type 406\n", "Package 0\n", "Variety 0\n", "Sub Variety 167\n", "Grade 415\n", "Date 0\n", "Low Price 0\n", "High Price 0\n", "Mostly Low 24\n", "Mostly High 24\n", "Origin 0\n", "Origin District 396\n", "Item Size 114\n", "Color 145\n", "Environment 415\n", "Unit of Sale 404\n", "Quality 415\n", "Condition 415\n", "Appearance 415\n", "Storage 415\n", "Crop 415\n", "Repack 0\n", "Trans Mode 415\n", "Unnamed: 24 415\n", "Unnamed: 25 391\n", "dtype: int64" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "pumpkins.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Month Package Low Price High Price Price\n70 9 1 1/9 bushel cartons 15.00 15.0 13.50\n71 9 1 1/9 bushel cartons 18.00 18.0 16.20\n72 10 1 1/9 bushel cartons 18.00 18.0 16.20\n73 10 1 1/9 bushel cartons 17.00 17.0 15.30\n74 10 1 1/9 bushel cartons 15.00 15.0 13.50\n... ... ... ... ... ...\n1738 9 1/2 bushel cartons 15.00 15.0 30.00\n1739 9 1/2 bushel cartons 13.75 15.0 28.75\n1740 9 1/2 bushel cartons 10.75 15.0 25.75\n1741 9 1/2 bushel cartons 12.00 12.0 24.00\n1742 9 1/2 bushel cartons 12.00 12.0 24.00\n\n[415 rows x 5 columns]\n" ] } ], "source": [ "\n", "# A set of new columns for a new dataframe. Filter out nonmatching columns\n", "new_columns = ['Package', 'Month', 'Low Price', 'High Price', 'Date']\n", "pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n", "\n", "# Get an average between low and high price for the base pumpkin price\n", "price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n", "\n", "# Convert the date to its month only\n", "month = pd.DatetimeIndex(pumpkins['Date']).month\n", "\n", "# Create a new dataframe with this basic data\n", "new_pumpkins = pd.DataFrame({'Month': month, 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n", "\n", "# Convert the price if the Package contains fractional bushel values\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1 1/9'), 'Price'] = price/(1 + 1/9)\n", "\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price/(1/2)\n", "\n", "print(new_pumpkins)\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "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", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "\n", "price = new_pumpkins.Price\n", "month = new_pumpkins.Month\n", "plt.scatter(price, month)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'Pumpkin Price')" ] }, "metadata": {}, "execution_count": 5 }, { "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", "image/png": "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\n" }, "metadata": { "needs_background": "light" } } ], "source": [ "\n", "new_pumpkins.groupby(['Month'])['Price'].mean().plot(kind='bar')\n", "plt.ylabel(\"Pumpkin Price\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ] }