1151 lines
564 KiB
Plaintext
1151 lines
564 KiB
Plaintext
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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": 10,
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"id": "b20887f4-26bf-4f39-babe-089e84be7e8e",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"df=pd.read_csv('OrangeQualityData.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": 11,
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"id": "eacde0af-a356-4520-86d3-3f0946e7ba93",
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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>Size (cm)</th>\n",
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" <th>Weight (g)</th>\n",
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" <th>Brix (Sweetness)</th>\n",
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" <th>pH (Acidity)</th>\n",
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" <th>Softness (1-5)</th>\n",
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" <th>HarvestTime (days)</th>\n",
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" <th>Ripeness (1-5)</th>\n",
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" <th>Color</th>\n",
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" <th>Variety</th>\n",
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" <th>Blemishes (Y/N)</th>\n",
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" <th>Quality (1-5)</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>count</th>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241.000000</td>\n",
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" <td>241</td>\n",
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" <td>241</td>\n",
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" <td>241</td>\n",
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" <td>241.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>unique</th>\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>5</td>\n",
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" <td>24</td>\n",
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" <td>12</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>top</th>\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>Deep Orange</td>\n",
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" <td>Cara Cara</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>freq</th>\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>75</td>\n",
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" <td>21</td>\n",
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" <td>149</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>mean</th>\n",
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" <td>7.844813</td>\n",
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" <td>205.128631</td>\n",
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" <td>10.907884</td>\n",
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" <td>3.473900</td>\n",
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" <td>3.072614</td>\n",
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" <td>15.344398</td>\n",
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" <td>3.599585</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>3.817427</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>std</th>\n",
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" <td>1.086002</td>\n",
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" <td>56.461012</td>\n",
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" <td>2.760446</td>\n",
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" <td>0.421007</td>\n",
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" <td>1.323630</td>\n",
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" <td>5.323852</td>\n",
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" <td>1.205214</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>1.014410</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>min</th>\n",
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" <td>6.000000</td>\n",
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" <td>100.000000</td>\n",
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" <td>5.500000</td>\n",
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" <td>2.800000</td>\n",
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" <td>1.000000</td>\n",
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" <td>4.000000</td>\n",
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" <td>1.000000</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>1.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>25%</th>\n",
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" <td>6.900000</td>\n",
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" <td>155.000000</td>\n",
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" <td>8.500000</td>\n",
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" <td>3.200000</td>\n",
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" <td>2.000000</td>\n",
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" <td>11.000000</td>\n",
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" <td>3.000000</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>3.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>50%</th>\n",
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" <td>7.800000</td>\n",
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" <td>205.000000</td>\n",
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" <td>11.000000</td>\n",
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" <td>3.400000</td>\n",
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" <td>3.000000</td>\n",
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" <td>15.000000</td>\n",
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" <td>4.000000</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>4.000000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>75%</th>\n",
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" <td>8.700000</td>\n",
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" <td>252.000000</td>\n",
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" <td>13.400000</td>\n",
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" <td>3.800000</td>\n",
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" <td>4.000000</td>\n",
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" <td>20.000000</td>\n",
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" <td>4.500000</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>4.500000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>max</th>\n",
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" <td>10.000000</td>\n",
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" <td>300.000000</td>\n",
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" <td>16.000000</td>\n",
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" <td>4.400000</td>\n",
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" <td>5.000000</td>\n",
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" <td>25.000000</td>\n",
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" <td>5.000000</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>5.000000</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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" Size (cm) Weight (g) Brix (Sweetness) pH (Acidity) \\\n",
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"count 241.000000 241.000000 241.000000 241.000000 \n",
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"unique NaN NaN NaN NaN \n",
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"top NaN NaN NaN NaN \n",
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"freq NaN NaN NaN NaN \n",
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"mean 7.844813 205.128631 10.907884 3.473900 \n",
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"std 1.086002 56.461012 2.760446 0.421007 \n",
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"min 6.000000 100.000000 5.500000 2.800000 \n",
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"25% 6.900000 155.000000 8.500000 3.200000 \n",
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"50% 7.800000 205.000000 11.000000 3.400000 \n",
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"75% 8.700000 252.000000 13.400000 3.800000 \n",
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"max 10.000000 300.000000 16.000000 4.400000 \n",
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"\n",
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" Softness (1-5) HarvestTime (days) Ripeness (1-5) Color \\\n",
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"count 241.000000 241.000000 241.000000 241 \n",
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"unique NaN NaN NaN 5 \n",
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"top NaN NaN NaN Deep Orange \n",
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"freq NaN NaN NaN 75 \n",
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"mean 3.072614 15.344398 3.599585 NaN \n",
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"std 1.323630 5.323852 1.205214 NaN \n",
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"min 1.000000 4.000000 1.000000 NaN \n",
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"25% 2.000000 11.000000 3.000000 NaN \n",
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"50% 3.000000 15.000000 4.000000 NaN \n",
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"75% 4.000000 20.000000 4.500000 NaN \n",
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"max 5.000000 25.000000 5.000000 NaN \n",
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"\n",
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" Variety Blemishes (Y/N) Quality (1-5) \n",
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"count 241 241 241.000000 \n",
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"unique 24 12 NaN \n",
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"top Cara Cara N NaN \n",
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"freq 21 149 NaN \n",
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"mean NaN NaN 3.817427 \n",
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"std NaN NaN 1.014410 \n",
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"min NaN NaN 1.000000 \n",
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"25% NaN NaN 3.000000 \n",
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"50% NaN NaN 4.000000 \n",
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"75% NaN NaN 4.500000 \n",
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"max NaN NaN 5.000000 "
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]
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},
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"execution_count": 11,
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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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"df.describe(include='all')"
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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": 12,
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"id": "a6b24d80-a8d1-460a-b723-ee3ad2573f49",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Variety\n",
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"Cara Cara 21\n",
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"Temple 18\n",
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"Star Ruby 18\n",
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"Navel 16\n",
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"Moro (Blood) 16\n",
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"Tangerine 14\n",
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"Clementine 14\n",
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"Washington Navel 14\n",
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"Satsuma Mandarin 13\n",
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"Ortanique (Hybrid) 13\n",
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"Minneola (Hybrid) 12\n",
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"Jaffa 11\n",
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"Ambiance 11\n",
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"Valencia 11\n",
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"California Valencia 7\n",
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"Honey Tangerine 7\n",
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"Hamlin 5\n",
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"Midsweet (Hybrid) 5\n",
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"Clementine (Seedless) 4\n",
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"Murcott (Hybrid) 3\n",
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"Navel (Late Season) 3\n",
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"Blood Orange 2\n",
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"Navel (Early Season) 2\n",
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"Tangelo (Hybrid) 1\n",
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"Name: count, dtype: int64"
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]
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},
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"execution_count": 12,
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||
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"metadata": {},
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||
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"output_type": "execute_result"
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||
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}
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||
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],
|
||
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"source": [
|
||
|
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"df[\"Variety\"].value_counts()"
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]
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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": 13,
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||
|
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"id": "69623b00-d707-48ad-b639-cea488ff7004",
|
||
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"metadata": {},
|
||
|
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"outputs": [
|
||
|
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{
|
||
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"data": {
|
||
|
|
"text/plain": [
|
||
|
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"<Axes: xlabel='Variety'>"
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]
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||
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},
|
||
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"execution_count": 13,
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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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"data": {
|
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df[\"Variety\"].value_counts().plot(kind=\"bar\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"id": "c18af40e-2b27-4223-871f-840fc2ddec38",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Size (cm)</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Variety</th>\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Ambiance</th>\n",
|
||
|
|
" <td>7.827273</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Blood Orange</th>\n",
|
||
|
|
" <td>9.500000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>California Valencia</th>\n",
|
||
|
|
" <td>7.885714</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Cara Cara</th>\n",
|
||
|
|
" <td>8.419048</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Clementine</th>\n",
|
||
|
|
" <td>7.578571</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Clementine (Seedless)</th>\n",
|
||
|
|
" <td>6.500000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Hamlin</th>\n",
|
||
|
|
" <td>8.160000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Honey Tangerine</th>\n",
|
||
|
|
" <td>7.742857</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Jaffa</th>\n",
|
||
|
|
" <td>7.090909</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Midsweet (Hybrid)</th>\n",
|
||
|
|
" <td>8.660000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Minneola (Hybrid)</th>\n",
|
||
|
|
" <td>7.683333</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Moro (Blood)</th>\n",
|
||
|
|
" <td>8.475000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Murcott (Hybrid)</th>\n",
|
||
|
|
" <td>7.733333</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Navel</th>\n",
|
||
|
|
" <td>7.662500</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Navel (Early Season)</th>\n",
|
||
|
|
" <td>7.850000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Navel (Late Season)</th>\n",
|
||
|
|
" <td>8.400000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Ortanique (Hybrid)</th>\n",
|
||
|
|
" <td>7.215385</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Satsuma Mandarin</th>\n",
|
||
|
|
" <td>7.038462</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Star Ruby</th>\n",
|
||
|
|
" <td>8.444444</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Tangelo (Hybrid)</th>\n",
|
||
|
|
" <td>7.200000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Tangerine</th>\n",
|
||
|
|
" <td>7.607143</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Temple</th>\n",
|
||
|
|
" <td>7.638889</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Valencia</th>\n",
|
||
|
|
" <td>7.927273</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>Washington Navel</th>\n",
|
||
|
|
" <td>8.207143</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Size (cm)\n",
|
||
|
|
"Variety \n",
|
||
|
|
"Ambiance 7.827273\n",
|
||
|
|
"Blood Orange 9.500000\n",
|
||
|
|
"California Valencia 7.885714\n",
|
||
|
|
"Cara Cara 8.419048\n",
|
||
|
|
"Clementine 7.578571\n",
|
||
|
|
"Clementine (Seedless) 6.500000\n",
|
||
|
|
"Hamlin 8.160000\n",
|
||
|
|
"Honey Tangerine 7.742857\n",
|
||
|
|
"Jaffa 7.090909\n",
|
||
|
|
"Midsweet (Hybrid) 8.660000\n",
|
||
|
|
"Minneola (Hybrid) 7.683333\n",
|
||
|
|
"Moro (Blood) 8.475000\n",
|
||
|
|
"Murcott (Hybrid) 7.733333\n",
|
||
|
|
"Navel 7.662500\n",
|
||
|
|
"Navel (Early Season) 7.850000\n",
|
||
|
|
"Navel (Late Season) 8.400000\n",
|
||
|
|
"Ortanique (Hybrid) 7.215385\n",
|
||
|
|
"Satsuma Mandarin 7.038462\n",
|
||
|
|
"Star Ruby 8.444444\n",
|
||
|
|
"Tangelo (Hybrid) 7.200000\n",
|
||
|
|
"Tangerine 7.607143\n",
|
||
|
|
"Temple 7.638889\n",
|
||
|
|
"Valencia 7.927273\n",
|
||
|
|
"Washington Navel 8.207143"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df[[\"Variety\",\"Size (cm)\"]].groupby(\"Variety\").mean()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 18,
|
||
|
|
"id": "36a1a169-21c6-4fc3-a9ec-0ac718b88df0",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Axes: xlabel='Variety'>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 18,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df[[\"Variety\",\"Size (cm)\"]].groupby(\"Variety\").mean().plot(kind=\"bar\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "f07a55ff-c3fd-4c74-8826-8b874e4c1285",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x2ab15b4f150>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 19,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 709.625x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 709.625x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import seaborn as sns\n",
|
||
|
|
"sns.set_theme()\n",
|
||
|
|
"sns.relplot(data=df, x=\"Size (cm)\", y=\"Weight (g)\", hue=\"Variety\")\n",
|
||
|
|
"sns.relplot(data=df, x=\"Size (cm)\", y=\"Weight (g)\", hue=\"Variety\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 21,
|
||
|
|
"id": "84984fe1-8a1d-43ec-9dff-44251cb03d76",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<seaborn.axisgrid.FacetGrid at 0x2ab180d9610>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 21,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 709.625x500 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"dfv = df[df[\"Variety\"] != \"Cara Cara\"]\n",
|
||
|
|
"sns.relplot(data=dfv, x=\"Size (cm)\", y=\"Weight (g)\", hue=\"Variety\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "c707980c-a2ff-4454-ad93-4ae3f7eb9495",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Variety\n",
|
||
|
|
"Star Ruby 16\n",
|
||
|
|
"Moro (Blood) 15\n",
|
||
|
|
"Cara Cara 15\n",
|
||
|
|
"Navel 14\n",
|
||
|
|
"Temple 14\n",
|
||
|
|
"Clementine 11\n",
|
||
|
|
"Washington Navel 11\n",
|
||
|
|
"Tangerine 11\n",
|
||
|
|
"Ortanique (Hybrid) 10\n",
|
||
|
|
"Ambiance 9\n",
|
||
|
|
"Satsuma Mandarin 9\n",
|
||
|
|
"Jaffa 8\n",
|
||
|
|
"Minneola (Hybrid) 8\n",
|
||
|
|
"California Valencia 7\n",
|
||
|
|
"Valencia 7\n",
|
||
|
|
"Honey Tangerine 6\n",
|
||
|
|
"Midsweet (Hybrid) 4\n",
|
||
|
|
"Navel (Late Season) 3\n",
|
||
|
|
"Hamlin 3\n",
|
||
|
|
"Murcott (Hybrid) 3\n",
|
||
|
|
"Clementine (Seedless) 3\n",
|
||
|
|
"Blood Orange 2\n",
|
||
|
|
"Navel (Early Season) 1\n",
|
||
|
|
"Tangelo (Hybrid) 1\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import sklearn\n",
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"df_train, df_test = sklearn.model_selection.train_test_split(df, test_size=50, random_state=1)\n",
|
||
|
|
"df_train[\"Variety\"].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "03c5cfbe-26dd-4549-89e1-cecbb8629463",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Variety\n",
|
||
|
|
"Cara Cara 6\n",
|
||
|
|
"Minneola (Hybrid) 4\n",
|
||
|
|
"Satsuma Mandarin 4\n",
|
||
|
|
"Valencia 4\n",
|
||
|
|
"Temple 4\n",
|
||
|
|
"Tangerine 3\n",
|
||
|
|
"Ortanique (Hybrid) 3\n",
|
||
|
|
"Washington Navel 3\n",
|
||
|
|
"Clementine 3\n",
|
||
|
|
"Jaffa 3\n",
|
||
|
|
"Ambiance 2\n",
|
||
|
|
"Star Ruby 2\n",
|
||
|
|
"Hamlin 2\n",
|
||
|
|
"Navel 2\n",
|
||
|
|
"Navel (Early Season) 1\n",
|
||
|
|
"Clementine (Seedless) 1\n",
|
||
|
|
"Honey Tangerine 1\n",
|
||
|
|
"Moro (Blood) 1\n",
|
||
|
|
"Midsweet (Hybrid) 1\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df_test[\"Variety\"].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"id": "63bb94e9-1685-4441-ad3a-4b84537f329b",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Color\n",
|
||
|
|
"Deep Orange 67\n",
|
||
|
|
"Light Orange 58\n",
|
||
|
|
"Orange-Red 49\n",
|
||
|
|
"Orange 34\n",
|
||
|
|
"Yellow-Orange 8\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 36,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import sklearn\n",
|
||
|
|
"from sklearn.model_selection import train_test_split\n",
|
||
|
|
"df_train, df_test = train_test_split(df, test_size=0.1, random_state=1, stratify=df[\"Color\"])\n",
|
||
|
|
"df_train[\"Color\"].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "a713b480-a260-4ecf-8021-ba42b60e059c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Color\n",
|
||
|
|
"Deep Orange 8\n",
|
||
|
|
"Light Orange 6\n",
|
||
|
|
"Orange-Red 6\n",
|
||
|
|
"Orange 4\n",
|
||
|
|
"Yellow-Orange 1\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 37,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df_test[\"Color\"].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 38,
|
||
|
|
"id": "d9ed0c34-df90-4723-bf6b-9d0379d38b62",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(241, 11)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 38,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df = df.dropna()\n",
|
||
|
|
"df.shape"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 39,
|
||
|
|
"id": "241b2de1-b3e2-473f-905a-54a9f8beac37",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Size (cm) 0\n",
|
||
|
|
"Weight (g) 0\n",
|
||
|
|
"Brix (Sweetness) 0\n",
|
||
|
|
"pH (Acidity) 0\n",
|
||
|
|
"Softness (1-5) 0\n",
|
||
|
|
"HarvestTime (days) 0\n",
|
||
|
|
"Ripeness (1-5) 0\n",
|
||
|
|
"Color 0\n",
|
||
|
|
"Variety 0\n",
|
||
|
|
"Blemishes (Y/N) 0\n",
|
||
|
|
"Quality (1-5) 0\n",
|
||
|
|
"dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 39,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df.isna().sum()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 41,
|
||
|
|
"id": "f85b1df7-165b-4f48-96f6-7ea0461bf46f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from sklearn.preprocessing import MinMaxScaler\n",
|
||
|
|
"\n",
|
||
|
|
"numeric_columns = df.select_dtypes(include=['int', 'float']).columns\n",
|
||
|
|
"scaler = MinMaxScaler(feature_range=(0, 1))\n",
|
||
|
|
"\n",
|
||
|
|
"df_scaled = df.copy()\n",
|
||
|
|
"df_scaled[numeric_columns] = scaler.fit_transform(df[numeric_columns])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 42,
|
||
|
|
"id": "e6c850eb-97dd-44e1-a257-154b9fdb68ec",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>Size (cm)</th>\n",
|
||
|
|
" <th>Weight (g)</th>\n",
|
||
|
|
" <th>Brix (Sweetness)</th>\n",
|
||
|
|
" <th>pH (Acidity)</th>\n",
|
||
|
|
" <th>Softness (1-5)</th>\n",
|
||
|
|
" <th>HarvestTime (days)</th>\n",
|
||
|
|
" <th>Ripeness (1-5)</th>\n",
|
||
|
|
" <th>Color</th>\n",
|
||
|
|
" <th>Variety</th>\n",
|
||
|
|
" <th>Blemishes (Y/N)</th>\n",
|
||
|
|
" <th>Quality (1-5)</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>0.375</td>\n",
|
||
|
|
" <td>0.40</td>\n",
|
||
|
|
" <td>0.619048</td>\n",
|
||
|
|
" <td>0.2500</td>\n",
|
||
|
|
" <td>0.250</td>\n",
|
||
|
|
" <td>0.285714</td>\n",
|
||
|
|
" <td>0.750</td>\n",
|
||
|
|
" <td>Orange</td>\n",
|
||
|
|
" <td>Valencia</td>\n",
|
||
|
|
" <td>N</td>\n",
|
||
|
|
" <td>0.750</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>0.550</td>\n",
|
||
|
|
" <td>0.60</td>\n",
|
||
|
|
" <td>0.476190</td>\n",
|
||
|
|
" <td>0.3750</td>\n",
|
||
|
|
" <td>0.500</td>\n",
|
||
|
|
" <td>0.476190</td>\n",
|
||
|
|
" <td>0.875</td>\n",
|
||
|
|
" <td>Deep Orange</td>\n",
|
||
|
|
" <td>Navel</td>\n",
|
||
|
|
" <td>N</td>\n",
|
||
|
|
" <td>0.875</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>0.200</td>\n",
|
||
|
|
" <td>0.25</td>\n",
|
||
|
|
" <td>0.809524</td>\n",
|
||
|
|
" <td>0.1250</td>\n",
|
||
|
|
" <td>0.000</td>\n",
|
||
|
|
" <td>0.142857</td>\n",
|
||
|
|
" <td>1.000</td>\n",
|
||
|
|
" <td>Light Orange</td>\n",
|
||
|
|
" <td>Cara Cara</td>\n",
|
||
|
|
" <td>N</td>\n",
|
||
|
|
" <td>1.000</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>0.750</td>\n",
|
||
|
|
" <td>0.75</td>\n",
|
||
|
|
" <td>0.285714</td>\n",
|
||
|
|
" <td>0.6250</td>\n",
|
||
|
|
" <td>0.750</td>\n",
|
||
|
|
" <td>0.809524</td>\n",
|
||
|
|
" <td>0.625</td>\n",
|
||
|
|
" <td>Orange-Red</td>\n",
|
||
|
|
" <td>Blood Orange</td>\n",
|
||
|
|
" <td>N</td>\n",
|
||
|
|
" <td>0.625</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>0.625</td>\n",
|
||
|
|
" <td>0.55</td>\n",
|
||
|
|
" <td>0.571429</td>\n",
|
||
|
|
" <td>0.3125</td>\n",
|
||
|
|
" <td>0.375</td>\n",
|
||
|
|
" <td>0.380952</td>\n",
|
||
|
|
" <td>1.000</td>\n",
|
||
|
|
" <td>Orange</td>\n",
|
||
|
|
" <td>Hamlin</td>\n",
|
||
|
|
" <td>Y (Minor)</td>\n",
|
||
|
|
" <td>0.875</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" Size (cm) Weight (g) Brix (Sweetness) pH (Acidity) Softness (1-5) \\\n",
|
||
|
|
"0 0.375 0.40 0.619048 0.2500 0.250 \n",
|
||
|
|
"1 0.550 0.60 0.476190 0.3750 0.500 \n",
|
||
|
|
"2 0.200 0.25 0.809524 0.1250 0.000 \n",
|
||
|
|
"3 0.750 0.75 0.285714 0.6250 0.750 \n",
|
||
|
|
"4 0.625 0.55 0.571429 0.3125 0.375 \n",
|
||
|
|
"\n",
|
||
|
|
" HarvestTime (days) Ripeness (1-5) Color Variety \\\n",
|
||
|
|
"0 0.285714 0.750 Orange Valencia \n",
|
||
|
|
"1 0.476190 0.875 Deep Orange Navel \n",
|
||
|
|
"2 0.142857 1.000 Light Orange Cara Cara \n",
|
||
|
|
"3 0.809524 0.625 Orange-Red Blood Orange \n",
|
||
|
|
"4 0.380952 1.000 Orange Hamlin \n",
|
||
|
|
"\n",
|
||
|
|
" Blemishes (Y/N) Quality (1-5) \n",
|
||
|
|
"0 N 0.750 \n",
|
||
|
|
"1 N 0.875 \n",
|
||
|
|
"2 N 1.000 \n",
|
||
|
|
"3 N 0.625 \n",
|
||
|
|
"4 Y (Minor) 0.875 "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 42,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df_scaled.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 44,
|
||
|
|
"id": "2daba6a5-6ff9-4c02-99cb-75aa437eaa3f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Quality (1-5)\n",
|
||
|
|
"4.0 76\n",
|
||
|
|
"5.0 52\n",
|
||
|
|
"4.5 29\n",
|
||
|
|
"3.0 26\n",
|
||
|
|
"3.5 23\n",
|
||
|
|
"2.0 14\n",
|
||
|
|
"2.5 12\n",
|
||
|
|
"1.0 9\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 44,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['Quality (1-5)'].value_counts().head(10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 45,
|
||
|
|
"id": "bd75c6b7-8faf-4a94-af2c-d34161476aa8",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"Ripeness (1-5)\n",
|
||
|
|
"5.0 58\n",
|
||
|
|
"4.0 52\n",
|
||
|
|
"3.0 46\n",
|
||
|
|
"2.0 27\n",
|
||
|
|
"4.5 23\n",
|
||
|
|
"1.0 17\n",
|
||
|
|
"3.5 12\n",
|
||
|
|
"2.5 6\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 45,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['Ripeness (1-5)'].value_counts().head(10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 46,
|
||
|
|
"id": "243cfbf0-a108-4459-8bd8-a070fbfa368d",
|
||
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|
"metadata": {},
|
||
|
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"outputs": [
|
||
|
|
{
|
||
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"data": {
|
||
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|
"text/plain": [
|
||
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|
"HarvestTime (days)\n",
|
||
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|
"11 23\n",
|
||
|
|
"15 16\n",
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||
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"12 16\n",
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||
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"10 15\n",
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||
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"22 14\n",
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||
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"20 14\n",
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||
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"16 13\n",
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"17 13\n",
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||
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"14 13\n",
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||
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"13 13\n",
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||
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"Name: count, dtype: int64"
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||
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|
]
|
||
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|
},
|
||
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|
"execution_count": 46,
|
||
|
|
"metadata": {},
|
||
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|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['HarvestTime (days)'].value_counts().head(10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 47,
|
||
|
|
"id": "a3f0bb30-9f2f-4c47-a5f4-850e91b870c0",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"HarvestTime (days)\n",
|
||
|
|
"11 23\n",
|
||
|
|
"15 16\n",
|
||
|
|
"12 16\n",
|
||
|
|
"10 15\n",
|
||
|
|
"22 14\n",
|
||
|
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"20 14\n",
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||
|
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"16 13\n",
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||
|
|
"17 13\n",
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||
|
|
"14 13\n",
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||
|
|
"13 13\n",
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||
|
|
"18 12\n",
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||
|
|
"23 12\n",
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||
|
|
"21 12\n",
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||
|
|
"19 12\n",
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||
|
|
"25 7\n",
|
||
|
|
"24 6\n",
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||
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|
"5 6\n",
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||
|
|
"9 6\n",
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||
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|
"7 6\n",
|
||
|
|
"8 5\n",
|
||
|
|
"6 5\n",
|
||
|
|
"4 2\n",
|
||
|
|
"Name: count, dtype: int64"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 47,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"df['HarvestTime (days)'].value_counts()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "0996b978-3471-4d87-9319-8956fc96311a",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"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": "3.11.7"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|