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"source": [
"product_hours"
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},
{
"cell_type": "code",
"execution_count": 43,
"id": "232cf008",
"metadata": {
"ExecuteTime": {
"end_time": "2022-10-22T17:21:28.290430Z",
"start_time": "2022-10-22T17:21:28.189620Z"
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"source": [
"sales_list = []\n",
"sales_list_sum = []\n",
"sales_list_avg = []\n",
"for i in hours_list:\n",
" sales_list.append(product_hours.loc[i, :].sum())\n",
" counter_val = dataset[dataset.Hour == i]\n",
" sales_list_sum.append(counter_val.Item.value_counts())\n",
" sales_list_avg.append(((counter_val.Item.value_counts())/len(counter_val.Hour)*100))\n",
"sales_list = (pd.DataFrame(sales_list)).set_index([hours_list])\n",
"sales_list['Hours'] = sales_list.index\n",
"sales_list.columns = \"Number of sales\", \"Hours\"\n",
"sales_list_sum = (pd.DataFrame(sales_list_sum)).set_index([hours_list])\n",
"sales_list_percent = (pd.DataFrame(sales_list_avg)).set_index([hours_list])"
]
},
{
"cell_type": "code",
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"end_time": "2022-10-22T17:21:35.181170Z",
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NaN
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NaN
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NaN
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23
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NaN
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NaN
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NaN
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NaN
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NaN
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NaN
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NaN
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"
...
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NaN
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NaN
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NaN
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NaN
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" \n",
"
\n",
"
18 rows × 95 columns
\n",
"
"
],
"text/plain": [
" Bread Coffee Medialuna Pastry NONE Toast Tea \\\n",
"01 100.000000 NaN NaN NaN NaN NaN NaN \n",
"07 8.000000 52.000000 24.000000 8.000000 4.000000 4.000000 NaN \n",
"08 25.560538 29.745889 6.427504 8.520179 3.587444 3.437967 3.139013 \n",
"09 19.559902 28.508557 5.867971 9.339853 3.863081 3.129584 5.036675 \n",
"10 18.385813 29.677886 4.524068 7.347087 3.510677 2.207745 5.646037 \n",
"11 16.417910 29.415423 3.358209 4.695274 3.544776 1.710199 5.472637 \n",
"12 15.690169 24.495200 1.820589 3.210857 5.527971 1.489573 6.057597 \n",
"13 12.247839 21.865994 1.296830 1.729107 5.727666 0.828530 6.520173 \n",
"14 12.463450 23.245614 1.754386 1.790936 3.508772 0.804094 8.516082 \n",
"15 14.438752 24.173265 1.583605 1.490452 1.490452 0.745226 9.641360 \n",
"16 14.454277 23.672566 1.843658 1.179941 0.958702 0.589971 9.292035 \n",
"17 12.365591 18.548387 2.956989 2.150538 1.075269 NaN 11.021505 \n",
"18 7.317073 13.414634 4.878049 2.439024 NaN NaN 6.097561 \n",
"19 4.166667 12.500000 2.083333 NaN NaN NaN 6.250000 \n",
"20 NaN 4.545455 NaN NaN NaN NaN NaN \n",
"21 NaN NaN NaN NaN NaN NaN NaN \n",
"22 NaN NaN NaN NaN NaN NaN NaN \n",
"23 NaN NaN NaN NaN NaN NaN NaN \n",
"\n",
" Farm House Cookies Hot chocolate ... Bare Popcorn Spread \\\n",
"01 NaN NaN NaN ... NaN NaN \n",
"07 NaN NaN NaN ... NaN NaN \n",
"08 1.943199 1.793722 1.345291 ... NaN NaN \n",
"09 3.129584 1.907090 2.738386 ... NaN NaN \n",
"10 1.990590 2.786826 2.750633 ... NaN NaN \n",
"11 2.425373 2.425373 2.829602 ... NaN NaN \n",
"12 1.919894 2.217809 1.853691 ... NaN NaN \n",
"13 1.332853 2.197406 1.837176 ... 0.036023 0.036023 \n",
"14 0.767544 1.790936 2.485380 ... 0.073099 0.036550 \n",
"15 1.164415 3.586400 4.145319 ... NaN NaN \n",
"16 1.032448 4.351032 5.235988 ... 0.073746 NaN \n",
"17 1.612903 5.376344 3.763441 ... 0.268817 NaN \n",
"18 3.658537 1.219512 4.878049 ... NaN NaN \n",
"19 NaN NaN 6.250000 ... NaN NaN \n",
"20 NaN NaN NaN ... NaN NaN \n",
"21 NaN NaN 66.666667 ... NaN NaN \n",
"22 NaN NaN NaN ... NaN NaN \n",
"23 NaN NaN NaN ... NaN NaN \n",
"\n",
" Raw bars Polenta Olum & polenta Hack the stack Tshirt Postcard \\\n",
"01 NaN NaN NaN NaN NaN NaN \n",
"07 NaN NaN NaN NaN NaN NaN \n",
"08 NaN NaN NaN NaN NaN NaN \n",
"09 NaN NaN NaN NaN NaN NaN \n",
"10 NaN NaN NaN NaN NaN NaN \n",
"11 NaN NaN NaN NaN NaN NaN \n",
"12 NaN NaN NaN NaN NaN NaN \n",
"13 NaN NaN NaN NaN NaN NaN \n",
"14 0.03655 0.03655 0.03655 NaN NaN NaN \n",
"15 NaN NaN NaN NaN NaN NaN \n",
"16 NaN NaN NaN 0.073746 NaN NaN \n",
"17 NaN NaN NaN 0.268817 NaN NaN \n",
"18 NaN NaN NaN NaN 6.097561 NaN \n",
"19 NaN NaN NaN NaN 22.916667 6.250000 \n",
"20 NaN NaN NaN NaN 22.727273 31.818182 \n",
"21 NaN NaN NaN NaN NaN NaN \n",
"22 NaN NaN NaN NaN NaN NaN \n",
"23 NaN NaN NaN NaN NaN NaN \n",
"\n",
" Pintxos Adjustment \n",
"01 NaN NaN \n",
"07 NaN NaN \n",
"08 NaN NaN \n",
"09 NaN NaN \n",
"10 NaN NaN \n",
"11 NaN NaN \n",
"12 NaN NaN \n",
"13 NaN NaN \n",
"14 NaN NaN \n",
"15 NaN NaN \n",
"16 NaN NaN \n",
"17 NaN NaN \n",
"18 NaN NaN \n",
"19 4.166667 2.083333 \n",
"20 18.181818 NaN \n",
"21 NaN NaN \n",
"22 NaN NaN \n",
"23 NaN NaN \n",
"\n",
"[18 rows x 95 columns]"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"sales_list_percent"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "70f54c31",
"metadata": {
"ExecuteTime": {
"end_time": "2022-10-22T17:28:31.309541Z",
"start_time": "2022-10-22T17:28:31.083206Z"
}
},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 50,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"f, ax = plt.subplots(figsize=(15, 7))\n",
"sns.set(style='ticks')\n",
"sns.barplot(\n",
" x='Hours',\n",
" y=\"Number of sales\",\n",
" data=sales_list,\n",
" label=\"Sales Frequency based upon Hours\",\n",
" color='b',\n",
")\n",
"sns.set_context('paper')\n",
"ax.legend(ncol=2, loc=\"upper right\", frameon=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "dcbf6bc7",
"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": "3.8.8"
}
},
"nbformat": 4,
"nbformat_minor": 5
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