{ "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": [ "## Linear Regression for Pumpkins - Lesson 2" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "code", "execution_count": 22, "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": 22 } ], "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": 23, "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 180\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": 23 } ], "source": [ "pumpkins.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 24, "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 13.636364\n71 9 1 1/9 bushel cartons 18.00 18.0 16.363636\n72 10 1 1/9 bushel cartons 18.00 18.0 16.363636\n73 10 1 1/9 bushel cartons 17.00 17.0 15.454545\n74 10 1 1/9 bushel cartons 15.00 15.0 13.636364\n... ... ... ... ... ...\n1738 9 1/2 bushel cartons 15.00 15.0 30.000000\n1739 9 1/2 bushel cartons 13.75 15.0 28.750000\n1740 9 1/2 bushel cartons 10.75 15.0 25.750000\n1741 9 1/2 bushel cartons 12.00 12.0 24.000000\n1742 9 1/2 bushel cartons 12.00 12.0 24.000000\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\n", "\n", "new_pumpkins.loc[new_pumpkins['Package'].str.contains('1/2'), 'Price'] = price*2\n", "\n", "print(new_pumpkins)\n", "\n" ] }, { "cell_type": "code", "execution_count": 45, "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": 48, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "Text(0, 0.5, 'Pumpkin Price')" ] }, "metadata": {}, "execution_count": 48 }, { "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", "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAENCAYAAAAIbA6TAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAR7ElEQVR4nO3dfbAddX3H8fdHoEV5ENALjUqIUkZEi8FG6hTHokiL4AM+tBU7mlY70bE+UB1rlLZi+0+sqNOxahsLGh3QYn2Agk8IKvUJDBohNjgogxaJEBQEa0sNfPvH2ZhLcu/l5DZ79ia/92vmzNn97Z7db1b8nL2/89vdVBWSpHbcb+gCJEmTZfBLUmMMfklqjMEvSY0x+CWpMQa/JDWmt+BPsneSK5N8K8m3k7y5az8oySVJruveD+yrBknS9tLXOP4kAfapqp8l2Qv4EvBq4DnAT6pqVZKVwIFV9fpeipAkbae3M/4a+Vk3u1f3KuBZwJqufQ1wal81SJK212sff5I9kqwDbgEuqaorgEOqaiNA935wnzVIku5tzz43XlV3A0uTHAB8PMljxv1skhXACoB99tnnN4888sieqpSk3dNVV111a1VNbdvea/BvUVW3J/kCcBJwc5JFVbUxySJGfw3M9JnVwGqAZcuW1dq1aydRqiTtNpJ8f6b2Pkf1THVn+iS5P/BU4FrgQmB5t9py4IK+apAkba/PM/5FwJokezD6gjm/qi5K8lXg/CQvAX4A/H6PNUiSttFb8FfV1cAxM7T/GDihr/1KkubmlbuS1BiDX5IaY/BLUmMMfklqjMEvSY2ZyAVcWliWrLx46BK4YdUpQ5cgNcvgV9P8ElSL7OqRpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1Jjegv+JIcm+XySDUm+neTVXfuZSX6YZF33OrmvGiRJ29uzx21vBl5bVd9Ish9wVZJLumXvqKqzety3JGkWvQV/VW0ENnbTdybZADy0r/1JksbT5xn/LyVZAhwDXAEcB7wiyYuAtYz+KrhtEnVImt2SlRcPXQI3rDpl6BKa0PuPu0n2BT4KnF5VdwDvAQ4HljL6i+Bts3xuRZK1SdZu2rSp7zIlqRm9Bn+SvRiF/rlV9TGAqrq5qu6uqnuA9wLHzvTZqlpdVcuqatnU1FSfZUpSU/oc1RPgbGBDVb19Wvuiaas9G1jfVw2SpO312cd/HPBC4Jok67q2NwKnJVkKFHAD8NIea5AkbaPPUT1fAjLDok/2tc+5+MOVJI145a4kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5Ia01vwJzk0yeeTbEjy7SSv7toPSnJJkuu69wP7qkGStL0+z/g3A6+tqkcBTwD+LMlRwErg0qo6Ari0m5ckTUhvwV9VG6vqG930ncAG4KHAs4A13WprgFP7qkGStL2J9PEnWQIcA1wBHFJVG2H05QAcPMtnViRZm2Ttpk2bJlGmJDWh9+BPsi/wUeD0qrpj3M9V1eqqWlZVy6ampvorUJIaM1bwJ7l/kkfu6MaT7MUo9M+tqo91zTcnWdQtXwTcsqPblSTN330Gf5JnAOuAT3fzS5NcOMbnApwNbKiqt09bdCGwvJteDlywo0VLkuZvnDP+M4FjgdsBqmodsGSMzx0HvBB4SpJ13etkYBVwYpLrgBO7eUnShOw5xjqbq+qnoxP48VXVl4DZPnTCDm1MkrTTjBP865O8ANgjyRHAq4Cv9FuWJKkv43T1vBJ4NHAXcB7wU+D0PouSJPXnPs/4q+rnwBndS5K0ixtnVM8lSQ6YNn9gks/0W5YkqS/jdPU8uKpu3zJTVbcxy9W2kqSFb5zgvyfJ4i0zSQ4Dqr+SJEl9GmdUzxnAl5J8sZt/ErCiv5IkSX0a58fdTyd5HKNbKwf486q6tffKJEm9mLWrJ8mR3fvjgMXATcAPgcVdmyRpFzTXGf9rGHXpvG2GZQU8pZeKJEm9mjX4q2pFkvsBf1lVX55gTZKkHs05qqeq7gHOmlAtkqQJGGc452eTPDc7epc2SdKCNM5wztcA+wCbk/wPo5E9VVX791qZJKkX4wzn3G8ShUiSJmOu4ZxHJLkgyfok5yV56CQLkyT1Y64+/nOAi4DnAt8E3jmRiiRJvZqrq2e/qnpvN/3WJN+YREGSpH7NFfx7JzmGrY9PvP/0+aryi0CSdkFzBf9G4O3T5n80bd4rdyVpFzXXlbtPnmQhkqTJGOcCLknSbsTgl6TGGPyS1JhxbtlAd/HWYdPXr6rL+ypKktSf+wz+JG8B/hD4D+DurrkAg1+SdkHjnPGfCjyyqu7quxhJWgiWrLx46BK4YdUpvW17nD7+64G9dnTDSc5JckuS9dPazkzywyTrutfJO7pdSdL/zzhn/D8H1iW5FPjlWX9Vveo+Pvd+4B+AD2zT/o6q8uEukjSQcYL/wu61Q6rq8iRLdvRzkqR+jXM//jU7eZ+vSPIiYC3w2qq6baaVkqxg9LB3Fi9evJNLkKR2zXU//vO792uSXL3ta577ew9wOLCU0b2A3jbbilW1uqqWVdWyqampee5OkrStuc74X929P31n7ayqbt4yneS9jO73L0maoFnP+KtqYzd5VFV9f/oLeNp8dpZk0bTZZwPrZ1tXktSPcX7c/askd1XVZQBJXg8cD/zjXB9K8qFuvQcnuRF4E3B8kqWMLgC7AXjpvCuXJM3LOMH/TOCiJK8DTgKO7NrmVFWnzdB89o6VJ0na2cYZ1XNrkmcCnwOuAp5XVdV7ZZKkXswa/EnuZNQls8WvAI8Anpekqmr/vouTJO18cz2Ba79JFiJJmoxxb8v8HOCJjP4C+Peq+kSvVUmSenOfN2lL8m7gZcA1jIZfvizJu/ouTJLUj3HO+H8HeMyWH3STrGH0JSBJ2gWNc1vm7wDTb5ZzKDDfWzZIkgY2zhn/g4ANSa7s5h8PfDXJhQBVdZ9j+iVJC8c4wf/XvVchSZqYcS7g+iJAkv2598PWf9JjXZKknozzsPUVwN8C/w3cA4TRsM5H9FuaJKkP43T1vA54dFXd2ncxkqT+jTOq53uMnrsrSdoNjHPG/wbgK0muYMceti5JWoDGCf5/Ai5jdNHWPf2WI0nq2zjBv7mqXtN7JZKkiRinj//zSVYkWZTkoC2v3iuTJPVinDP+F3Tvb5jW5nBOSdpFjXMB18MnUYgkaTLGuYDrRTO1V9UHdn45kqS+jdPV8/hp03sDJwDfAAx+SdoFjdPV88rp80keCHywt4okSb0aZ1TPtn4OHLGzC5EkTcY4ffz/xmgUD4y+KI4Czu+zKElSf8bp4z9r2vRm4PtVdWNP9UiSejZr8CfZm9FD1n+d0e0azq6qzZMqTJLUj7n6+NcAyxiF/tOAt02kIklSr+bq6jmqqn4DIMnZwJVzrLudJOcATwduqarHdG0HAf8CLAFuAP6gqm7b8bIlSfM11xn/L7ZMzLOL5/3ASdu0rQQuraojgEu7eUnSBM0V/I9Nckf3uhM4est0kjvua8NVdTmw7XN5n8WoC4nu/dR5VS1JmrdZu3qqao8e9ndIVW3str8xycE97EOSNIf5XMA1Ed2toNcmWbtp06ahy5Gk3cakg//mJIsAuvdbZluxqlZX1bKqWjY1NTWxAiVpdzfp4L8QWN5NLwcumPD+Jal5vQV/kg8BXwUemeTGJC8BVgEnJrkOOLGblyRN0Di3bJiXqjptlkUn9LVPSdJ9W7A/7kqS+mHwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JasyeQ+w0yQ3AncDdwOaqWjZEHZLUokGCv/Pkqrp1wP1LUpPs6pGkxgwV/AV8NslVSVYMVIMkNWmorp7jquqmJAcDlyS5tqoun75C94WwAmDx4sVD1ChJu6VBzvir6qbu/Rbg48CxM6yzuqqWVdWyqampSZcoSbutiQd/kn2S7LdlGvhdYP2k65CkVg3R1XMI8PEkW/Z/XlV9eoA6JKlJEw/+qroeeOyk9ytJGnE4pyQ1xuCXpMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhozSPAnOSnJd5J8N8nKIWqQpFZNPPiT7AG8C3gacBRwWpKjJl2HJLVqiDP+Y4HvVtX1VfW/wIeBZw1QhyQ1KVU12R0mzwNOqqo/7eZfCPxWVb1im/VWACu62UcC35loodt7MHDrwDUsFB6LrTwWW3kstloox+KwqpratnHPAQrJDG3bfftU1Wpgdf/ljCfJ2qpaNnQdC4HHYiuPxVYei60W+rEYoqvnRuDQafMPA24aoA5JatIQwf914IgkD0/yK8DzgQsHqEOSmjTxrp6q2pzkFcBngD2Ac6rq25OuYx4WTLfTAuCx2MpjsZXHYqsFfSwm/uOuJGlYXrkrSY0x+CWpMQa/JDVmiHH8C9600UY3VdXnkrwA+G1gA7C6qn4xaIETluRw4NmMhuFuBq4DPlRVPx20MEnz4o+7M0hyLqMvxQcAtwP7Ah8DTmB0zJYPWN5EJXkV8Azgi8DJwDrgNkZfBC+vqi8MV52k+TD4Z5Dk6qo6OsmewA+Bh1TV3UkCfKuqjh64xIlJcg2wtPv3PwD4ZFUdn2QxcEFVHTNwiROT5IHAG4BTgS2Xwd8CXACsqqrbh6ptIUnyqap62tB1TEqS/Rn9d/Ew4FNVdd60Ze+uqpcPVtws7OqZ2f267p59GJ31PxD4CfCrwF5DFjaQPYG7Gf379wOoqh8kae1YnA9cBhxfVT8CSPJrwHLgI8CJA9Y2UUkeN9siYOkka1kA3seo+/OjwIuTPBd4QVXdBTxh0MpmYfDP7GzgWkYXmJ0BfCTJ9Yz+R/zwkIUN4J+Bryf5GvAk4C0ASaYYfRm2ZElVvWV6Q/cF8JYkLx6opqF8nVH330z33jpgwrUM7fCqem43/YkkZwCXJXnmkEXNxa6eWSR5CEBV3ZTkAOCpwA+q6sphK5u8JI8GHgWsr6prh65nKEk+C3wOWFNVN3dthwB/DJxYVU8dsLyJSrIeeHZVXTfDsv+sqkNn+NhuKckG4NFVdc+0tuXAXwD7VtVhgxU3C4NfGlOSA4GVjJ4fcXDXfDOje02tqqrbhqpt0rrbq19TVdvdLj3JqVX1iQHKGkSSvwM+W1Wf26b9JOCdVXXEMJXNzuCXdoIkf1JV7xu6joXAY7HVQj0WBr+0EyT5QVUtHrqOhcBjsdVCPRb+uCuNKcnVsy0CDplkLUPzWGy1Kx4Lg18a3yHA7zG6gG26AF+ZfDmD8lhstcsdC4NfGt9FjEZprNt2QZIvTL6cQXksttrljoV9/JLUGO/OKUmNMfglqTEGvwQkqSQfnDa/Z5JNSS6a5/YOSPLyafPHz3db0s5m8Esj/wU8Jsn9u/kTGd2Zdb4OABbcXRklMPil6T4FnNJNnwZ8aMuCJAcl+USSq5N8LcnRXfuZSc5J8oUk13fPLwBYBRyeZF2St3Zt+yb51yTXJjm3u823NHEGv7TVh4HnJ9kbOBq4YtqyNwPf7J7F8EbgA9OWHcloHPexwJu621WvBL5XVUur6nXdescApwNHAY8AjuvzHyPNxuCXOlV1NbCE0dn+J7dZ/ETgg916lwEP6h7MAnBxVd1VVbcyejDLbFdrXllVN3Z3cVzX7UuaOC/gku7tQuAs4HjgQdPaZ+qW2XIRzF3T2u5m9v9fjbue1CvP+KV7Owf4m6q6Zpv2y4E/gtEIHeDWqrpjju3cSfe0Mmmh8YxDmqaqbgT+foZFZwLv627I9XNGj1ucazs/TvLl7oElnwIu3tm1SvPlLRskqTF29UhSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5Ia83/e+aglZ1t96wAAAABJRU5ErkJggg==\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": [] } ] }