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Studio_assignment
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‎data-manipulation/studio/data-manipulation-studio/data-manipulation-studio.ipynb‎

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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 4,
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"id": "66c9a7e7",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"execution_count": 6,
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"id": "da059f8a",
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"metadata": {},
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"outputs": [],
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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>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": 6,
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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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"# Combine the four dataframes into one!\n"
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"# Combine the four dataframes into one!\n",
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"# Combine all Northeast dataframes\n",
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"# Combine all Northeast dataframes\n",
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"northeast = pd.concat([baltimore, boston, newyork, philly], ignore_index=True)\n",
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"\n",
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"# Show first few rows\n",
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"northeast.head()"
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]
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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": 57,
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"execution_count": 7,
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"id": "c839639a",
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"metadata": {},
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"outputs": [],
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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>Low Price</th>\n",
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" <th>High Price</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Unit of Sale</th>\n",
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" <th></th>\n",
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" <th></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>EACH</th>\n",
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" <td>47.916667</td>\n",
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" <td>59.166667</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>PER BIN</th>\n",
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" <td>185.845070</td>\n",
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" <td>206.619718</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>SHELLACKED</th>\n",
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" <td>16.000000</td>\n",
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" <td>17.545455</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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" Low Price High Price\n",
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"Unit of Sale \n",
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"EACH 47.916667 59.166667\n",
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"PER BIN 185.845070 206.619718\n",
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"SHELLACKED 16.000000 17.545455"
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]
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},
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"execution_count": 7,
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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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"# Put your code here to find the mean low and high prices in the Northeast region for each type of unit of sale.\n"
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"# Put your code here to find the mean low and high prices in the Northeast region for each type of unit of sale.\n",
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"mean_prices = northeast.groupby(\"Unit of Sale\")[[\"Low Price\", \"High Price\"]].mean()\n",
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"\n",
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"mean_prices"
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]
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},
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"cell_type": "code",
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"execution_count": 58,
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"execution_count": 8,
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"id": "b4b23352",
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"metadata": {},
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"outputs": [],
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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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"BIG MACK TYPE 55.0\n",
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"BLUE TYPE 7.0\n",
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"CINDERELLA 39.0\n",
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"FAIRYTALE 37.0\n",
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"HOWDEN TYPE 224.0\n",
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"HOWDEN WHITE TYPE 2.0\n",
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"KNUCKLE HEAD 9.0\n",
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"MINIATURE 97.0\n",
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"MIXED HEIRLOOM VARIETIES 4.0\n",
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"PIE TYPE 198.0\n",
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"Name: Variety, dtype: float64"
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]
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},
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"execution_count": 8,
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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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"# Put your code here to find the average number of pumpkins coming into terminal markets of each variety.\n"
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"# Put your code here to find the average number of pumpkins coming into terminal markets of each variety.\n",
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"avg_pumpkins_per_variety = northeast.groupby(\"Variety\")[\"Variety\"].count() / 1\n",
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"\n",
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"avg_pumpkins_per_variety"
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]
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},
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{

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