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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "bfd08331",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "dc96a636",
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"metadata": {},
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"outputs": [],
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"source": [
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"pumpkins = pd.read_csv('C:/Users/admin/Downloads/baltimore_9-24-2016_9-30-2017.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a5e6e008",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Commodity Name</th>\n",
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" <th>City Name</th>\n",
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" <th>Type</th>\n",
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" <th>Package</th>\n",
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" <th>Variety</th>\n",
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" <th>Sub Variety</th>\n",
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" <th>Grade</th>\n",
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" <th>Date</th>\n",
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" <th>Low Price</th>\n",
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" <th>High Price</th>\n",
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" <th>...</th>\n",
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" <th>Color</th>\n",
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" <th>Environment</th>\n",
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" <th>Unit of Sale</th>\n",
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" <th>Quality</th>\n",
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" <th>Condition</th>\n",
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" <th>Appearance</th>\n",
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" <th>Storage</th>\n",
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" <th>Crop</th>\n",
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" <th>Repack</th>\n",
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" <th>Trans Mode</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>PUMPKINS</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>NaN</td>\n",
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" <td>24 inch bins</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>04/29/2017</td>\n",
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" <td>270</td>\n",
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" <td>280.0</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>E</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>PUMPKINS</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>NaN</td>\n",
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" <td>24 inch bins</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>05/06/2017</td>\n",
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" <td>270</td>\n",
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" <td>280.0</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>E</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>PUMPKINS</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>NaN</td>\n",
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" <td>24 inch bins</td>\n",
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" <td>HOWDEN TYPE</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>09/24/2016</td>\n",
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" <td>160</td>\n",
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" <td>160.0</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>N</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>PUMPKINS</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>NaN</td>\n",
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" <td>24 inch bins</td>\n",
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" <td>HOWDEN TYPE</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>09/24/2016</td>\n",
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" <td>160</td>\n",
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" <td>160.0</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>N</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>PUMPKINS</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>NaN</td>\n",
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" <td>24 inch bins</td>\n",
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" <td>HOWDEN TYPE</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>11/05/2016</td>\n",
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" <td>90</td>\n",
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" <td>100.0</td>\n",
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" <td>...</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>N</td>\n",
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" <td>NaN</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>5 rows × 25 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" Commodity Name City Name Type Package Variety Sub Variety \\\n",
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"0 PUMPKINS BALTIMORE NaN 24 inch bins NaN NaN \n",
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"1 PUMPKINS BALTIMORE NaN 24 inch bins NaN NaN \n",
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"2 PUMPKINS BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN \n",
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"3 PUMPKINS BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN \n",
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"4 PUMPKINS BALTIMORE NaN 24 inch bins HOWDEN TYPE NaN \n",
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"\n",
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" Grade Date Low Price High Price ... Color Environment \\\n",
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"0 NaN 04/29/2017 270 280.0 ... NaN NaN \n",
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"1 NaN 05/06/2017 270 280.0 ... NaN NaN \n",
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"2 NaN 09/24/2016 160 160.0 ... NaN NaN \n",
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"3 NaN 09/24/2016 160 160.0 ... NaN NaN \n",
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"4 NaN 11/05/2016 90 100.0 ... NaN NaN \n",
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"\n",
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" Unit of Sale Quality Condition Appearance Storage Crop Repack Trans Mode \n",
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"0 NaN NaN NaN NaN NaN NaN E NaN \n",
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"1 NaN NaN NaN NaN NaN NaN E NaN \n",
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"2 NaN NaN NaN NaN NaN NaN N NaN \n",
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"3 NaN NaN NaN NaN NaN NaN N NaN \n",
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"4 NaN NaN NaN NaN NaN NaN N NaN \n",
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"\n",
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"[5 rows x 25 columns]"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pumpkins.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "7d5eb162",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Month</th>\n",
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" <th>Variety</th>\n",
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" <th>City</th>\n",
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" <th>Package</th>\n",
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" <th>Low Price</th>\n",
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" <th>High Price</th>\n",
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" <th>Price</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>70</th>\n",
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" <td>9</td>\n",
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" <td>PIE TYPE</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>1 1/9 bushel cartons</td>\n",
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" <td>15</td>\n",
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" <td>15.0</td>\n",
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" <td>13.636364</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>71</th>\n",
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" <td>9</td>\n",
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" <td>PIE TYPE</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>1 1/9 bushel cartons</td>\n",
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" <td>18</td>\n",
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" <td>18.0</td>\n",
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" <td>16.363636</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>72</th>\n",
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" <td>10</td>\n",
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" <td>PIE TYPE</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>1 1/9 bushel cartons</td>\n",
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" <td>18</td>\n",
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" <td>18.0</td>\n",
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" <td>16.363636</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>73</th>\n",
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" <td>10</td>\n",
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" <td>PIE TYPE</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>1 1/9 bushel cartons</td>\n",
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" <td>17</td>\n",
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" <td>17.0</td>\n",
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" <td>15.454545</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>74</th>\n",
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" <td>10</td>\n",
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" <td>PIE TYPE</td>\n",
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" <td>BALTIMORE</td>\n",
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" <td>1 1/9 bushel cartons</td>\n",
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" <td>15</td>\n",
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|
|
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" <td>15.0</td>\n",
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|
|
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" <td>13.636364</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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|
" Month Variety City Package Low Price High Price \\\n",
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|
"70 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15 15.0 \n",
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|
"71 9 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18 18.0 \n",
|
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|
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|
"72 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 18 18.0 \n",
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|
"73 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 17 17.0 \n",
|
|
|
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|
"74 10 PIE TYPE BALTIMORE 1 1/9 bushel cartons 15 15.0 \n",
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"\n",
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" Price \n",
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|
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|
"70 13.636364 \n",
|
|
|
|
|
"71 16.363636 \n",
|
|
|
|
|
"72 16.363636 \n",
|
|
|
|
|
"73 15.454545 \n",
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|
"74 13.636364 "
|
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|
|
|
]
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|
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|
},
|
|
|
|
|
"execution_count": 4,
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|
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"metadata": {},
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"output_type": "execute_result"
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}
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|
],
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|
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"source": [
|
|
|
|
|
"pumpkins = pumpkins[pumpkins['Package'].str.contains('bushel', case=True, regex=True)]\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"new_columns = ['Package', 'Variety', 'City Name', 'Month', 'Low Price', 'High Price', 'Date', 'City Num', 'Variety Num']\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"pumpkins = pumpkins.drop([c for c in pumpkins.columns if c not in new_columns], axis=1)\n",
|
|
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|
"\n",
|
|
|
|
|
"price = (pumpkins['Low Price'] + pumpkins['High Price']) / 2\n",
|
|
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|
"\n",
|
|
|
|
|
"month = pd.DatetimeIndex(pumpkins['Date']).month\n",
|
|
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|
|
"\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"new_pumpkins = pd.DataFrame({'Month': month, 'Variety': pumpkins['Variety'], 'City': pumpkins['City Name'], 'Package': pumpkins['Package'], 'Low Price': pumpkins['Low Price'],'High Price': pumpkins['High Price'], 'Price': price})\n",
|
|
|
|
|
"\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",
|
|
|
|
|
"new_pumpkins.head()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 5,
|
|
|
|
|
"id": "bde5818a",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"X = new_pumpkins.copy()\n",
|
|
|
|
|
"y = X.pop('Price')"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 6,
|
|
|
|
|
"id": "5ce08713",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"from sklearn.model_selection import train_test_split\n",
|
|
|
|
|
"xtrain, xtest, ytrain, ytest = train_test_split(X, y, test_size = 0.25, random_state = 0)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 7,
|
|
|
|
|
"id": "efad6351",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": [
|
|
|
|
|
"from sklearn.preprocessing import OrdinalEncoder\n",
|
|
|
|
|
"ordinal_encoder = OrdinalEncoder()\n",
|
|
|
|
|
"s = (xtrain.dtypes == 'object')\n",
|
|
|
|
|
"object_cols = list(s[s].index)\n",
|
|
|
|
|
"label_x_train = xtrain.copy()\n",
|
|
|
|
|
"label_x_test = xtest.copy()\n",
|
|
|
|
|
"label_x_train[object_cols] = ordinal_encoder.fit_transform(xtrain[object_cols])\n",
|
|
|
|
|
"label_x_test[object_cols] = ordinal_encoder.transform(xtest[object_cols])"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 8,
|
|
|
|
|
"id": "7f8943bc",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"0.9791305564379404\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"print(label_x_train['Package'].corr(ytrain))"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 9,
|
|
|
|
|
"id": "1c5c2b3c",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"0.9759780821029631\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"print(label_x_test['Package'].corr(ytest))"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"metadata": {
|
|
|
|
|
"kernelspec": {
|
|
|
|
|
"display_name": "Python 3 (ipykernel)",
|
|
|
|
|
"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": "3.7.6"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 5
|
|
|
|
|
}
|