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datadersdataders
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add jupyterlab drop notebook outputs
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‎fuzzer.ipynb

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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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"execution_count": null,
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"id": "ce99a589-7a2e-4b6b-a13b-1dc4a17a6fc0",
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"metadata": {},
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"outputs": [],
@@ -13,7 +13,7 @@
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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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"execution_count": null,
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"id": "3827f60f-0b87-4642-bc07-8c2625f3377a",
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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": 3,
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"execution_count": null,
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"id": "17773922-39ca-4780-a001-ea731ff86f80",
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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>fruit_name</th>\n",
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" <th>cost</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>banana</td>\n",
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" <td>1</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>apple</td>\n",
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" <td>3</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>kiwi</td>\n",
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" <td>5</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>pear</td>\n",
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" <td>4</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>kiwi</td>\n",
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" <td>5</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>pawpaw</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>watermelon</td>\n",
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" <td>12</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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" fruit_name cost\n",
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"0 banana 1\n",
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"1 apple 3\n",
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"2 kiwi 5\n",
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"3 pear 4\n",
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"4 kiwi 5\n",
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"5 pawpaw 7\n",
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"6 watermelon 12"
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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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"outputs": [],
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"source": [
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"df_price = pd.read_csv('seeds/fruit_prices_fact.csv')\n",
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"df_price"
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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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"execution_count": null,
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"id": "2a140bcf-925b-440a-8d25-e08ba9391b9c",
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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>fruit_user_input</th>\n",
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" <th>quantity</th>\n",
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" <th>user_name</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>apple</td>\n",
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" <td>2</td>\n",
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" <td>doug</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>anana</td>\n",
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" <td>1</td>\n",
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" <td>doug</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>kiwi</td>\n",
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" <td>1</td>\n",
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" <td>amy</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>a pull</td>\n",
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" <td>1</td>\n",
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" <td>amy</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>kiwee</td>\n",
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" <td>2</td>\n",
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" <td>anders</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>papaw</td>\n",
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" <td>2</td>\n",
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" <td>anders</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>pumpkin</td>\n",
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" <td>1</td>\n",
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" <td>anders</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>watrmln</td>\n",
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" <td>1</td>\n",
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" <td>ripu</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>apple</td>\n",
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" <td>5</td>\n",
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" <td>ripu</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>pere</td>\n",
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" <td>1</td>\n",
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" <td>azzam</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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" fruit_user_input quantity user_name\n",
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"0 apple 2 doug\n",
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"1 anana 1 doug\n",
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"2 kiwi 1 amy\n",
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"3 a pull 1 amy\n",
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"4 kiwee 2 anders\n",
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"5 papaw 2 anders\n",
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"6 pumpkin 1 anders\n",
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"7 watrmln 1 ripu\n",
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"8 apple 5 ripu\n",
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"9 pere 1 azzam"
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]
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},
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"execution_count": 4,
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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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"outputs": [],
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"source": [
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"df_input = pd.read_csv('seeds/fruit_user_input.csv')\n",
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"df_input"
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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": 5,
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"execution_count": null,
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"id": "ea1f2ba8-dd07-4c25-9598-c549742d8e0a",
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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": 6,
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"execution_count": null,
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"id": "cc221d15-b413-4917-be06-3b22b1c1dad6",
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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>user_name</th>\n",
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" <th>total</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>1</th>\n",
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" <td>anders</td>\n",
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" <td>34</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>ripu</td>\n",
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" <td>27</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>amy</td>\n",
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" <td>10</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>doug</td>\n",
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" <td>7</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>azzam</td>\n",
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" <td>4</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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" user_name total\n",
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"1 anders 34\n",
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"4 ripu 27\n",
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"0 amy 10\n",
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"3 doug 7\n",
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"2 azzam 4"
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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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"outputs": [],
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"source": [
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"df_final = (df_input\n",
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" # make new col, `fruit_name`, with best match against actual table\n",
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" )\n",
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"df_final"
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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": null,
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"id": "7eb5d9dc",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.13 ('dbtpy')",
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"display_name": "Python 3.9.13 64-bit",
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"language": "python",
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"name": "python3"
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},
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},
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"vscode": {
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"interpreter": {
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"hash": "a75c8262f55743daafc6098d7b768e88187e06d46ce627852e74c04894e9aa89"
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"hash": "949777d72b0d2535278d3dc13498b2535136f6dfe0678499012e853ee9abcab1"
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}
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}
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},

‎requirements.txt

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dbt-snowflake==1.3.0b2
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jupyterlab==3.4.7
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# extra features
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sqlfluff~=1.2.1

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