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AlisonDavey/Zara_Walkthrough.ipynb

Created April 21, 2019 17:55
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Zara Data Challenge - top 10 with no ML
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Zara_Walkthrough.ipynb
{
"cells": [
{
"metadata": {},
"cell_type": "markdown",
"source": "Forecast fashion sales over three weeks in Zara's online store in one country https://www.zaratalent.com/data.html\n\nA walkthrough on how to achieve a top 10 position just by looking at the data."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "%matplotlib inline",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "import pandas as pd\nimport numpy as np",
"execution_count": 2,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "path = './data/'",
"execution_count": 3,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Bet 1: Blocks for week 11"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_blocks = pd.read_csv(path+'product_blocks.csv')\ndf_products = pd.read_csv(path+'products.csv')\ndf_stock_and_sales = pd.read_csv(path+'stock_and_sales_day_0_day_63.csv')",
"execution_count": 4,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Rank blocks by revenue per item to identify the best performing blocks of products"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_blocks.info()",
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 15238 entries, 0 to 15237\nData columns (total 2 columns):\nproduct_id 15238 non-null int64\nblock_id 15238 non-null int64\ndtypes: int64(2)\nmemory usage: 238.2 KB\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_blocks.head()",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 6,
"data": {
"text/plain": " product_id block_id\n0 612967398 0\n1 296892108 0\n2 139541214 0\n3 963923934 0\n4 938230141 0",
"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>product_id</th>\n <th>block_id</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>612967398</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>296892108</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>139541214</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>963923934</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>938230141</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Number of products in each block\ndf_n_items_in_block = df_blocks.groupby('block_id').count()\ndf_n_items_in_block.rename(columns = {'product_id': 'num_items'}, inplace=True)\ndf_n_items_in_block.head()",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 7,
"data": {
"text/plain": " num_items\nblock_id \n0 7\n1 7\n2 7\n3 6\n4 10",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7</td>\n </tr>\n <tr>\n <th>1</th>\n <td>7</td>\n </tr>\n <tr>\n <th>2</th>\n <td>7</td>\n </tr>\n <tr>\n <th>3</th>\n <td>6</td>\n </tr>\n <tr>\n <th>4</th>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales.head()",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 8,
"data": {
"text/plain": " date_number product_id color_id size_id sales stock\n0 0 310130 78467031 7026791 1 93\n1 0 310130 78467031 8015276 2 62\n2 0 310130 78467031 452052101 1 18\n3 0 310130 78467031 541898038 2 42\n4 0 310130 78467031 558685710 3 113",
"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>date_number</th>\n <th>product_id</th>\n <th>color_id</th>\n <th>size_id</th>\n <th>sales</th>\n <th>stock</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>310130</td>\n <td>78467031</td>\n <td>7026791</td>\n <td>1</td>\n <td>93</td>\n </tr>\n <tr>\n <th>1</th>\n <td>0</td>\n <td>310130</td>\n <td>78467031</td>\n <td>8015276</td>\n <td>2</td>\n <td>62</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>310130</td>\n <td>78467031</td>\n <td>452052101</td>\n <td>1</td>\n <td>18</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>310130</td>\n <td>78467031</td>\n <td>541898038</td>\n <td>2</td>\n <td>42</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>310130</td>\n <td>78467031</td>\n <td>558685710</td>\n <td>3</td>\n <td>113</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#add week\ndf_stock_and_sales['week']=(((df_stock_and_sales['date_number']-4)/7)+1).round(0).astype(int)\n# add blocks\ndf_stock_and_sales = df_stock_and_sales.merge(df_blocks, how='left', on=['product_id'])\n# add price\ndf_stock_and_sales = df_stock_and_sales.merge(df_products, how='left', on='product_id')\n# add revenue\ndf_stock_and_sales['revenue']=df_stock_and_sales['sales']*df_stock_and_sales['price']\ndf_sales_week_by_product=df_stock_and_sales.groupby(['week','product_id','family_id',\n 'subfamily_id', 'price','block_id']).agg({'sales':'sum', 'revenue':'sum'}).reset_index()\ndf_sales_week_by_block = df_sales_week_by_product.groupby(['week','block_id']).agg({'revenue':'sum'}).reset_index()\ndf_week_rev = df_sales_week_by_block.merge(df_n_items_in_block, on='block_id', how='left')\ndf_week_rev['revenue_per_item']=df_week_rev.revenue/df_week_rev.num_items\n\n#2130 rows\ndf_ranked_blocks_by_week = pd.DataFrame() \nfor w in list(range(10)):\n df_ranked_blocks_by_week[w]=list(df_week_rev[df_week_rev['week']==w].sort_values('revenue_per_item', ascending=0).iloc[:,1])[:2130]\n \n#df of rank for each block for each week\ndf_block_rank=pd.DataFrame(columns=list(range(10)))\nfor block in df_n_items_in_block.index:\n a = np.where(df_ranked_blocks_by_week==block)\n for i in range(len(a[1])): df_block_rank.loc[block,a[1][i]]=a[0][i]\n# give rank 2500 if dropped out of rankings\ndf_block_rank = df_block_rank.fillna(2500.)\ndf_block_rank.sort_values(9).head(10)",
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 9,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n96 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2359 2500.0 3.0 4.0 9.0 6.0 6.0 4.0 0.0 1.0 \n2128 4.0 0.0 1.0 7.0 1.0 0.0 0.0 1.0 0.0 \n474 1.0 1.0 0.0 6.0 2.0 3.0 1.0 2.0 2.0 \n2037 14.0 9.0 3.0 4.0 4.0 2.0 2.0 3.0 3.0 \n2306 5.0 2.0 5.0 13.0 9.0 5.0 3.0 4.0 6.0 \n2469 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2335 1450.0 1777.0 1904.0 151.0 228.0 465.0 1180.0 1548.0 1764.0 \n1315 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n1016 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n\n 9 \n96 0.0 \n2359 1.0 \n2128 2.0 \n474 3.0 \n2037 4.0 \n2306 5.0 \n2469 6.0 \n2335 7.0 \n1315 8.0 \n1016 9.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>6.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>14.0</td>\n <td>9.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>5.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>13.0</td>\n <td>9.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>1450.0</td>\n <td>1777.0</td>\n <td>1904.0</td>\n <td>151.0</td>\n <td>228.0</td>\n <td>465.0</td>\n <td>1180.0</td>\n <td>1548.0</td>\n <td>1764.0</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Some of the best performing blocks are simply the ones that performed well in earlier weeks, others have come from nowhere, presumably due to the introduction of new products. Let's investigate.\n\nHow many blocks do we need to get to 50 items?"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Depending on the week, 12-16 blocks are all that are needed\nblks = df_block_rank.sort_values(9).index[:20]\nnp.cumsum(df_n_items_in_block.loc[blks])",
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 10,
"data": {
"text/plain": " num_items\n96 1\n2359 2\n2128 8\n474 9\n2037 13\n2306 16\n2469 17\n2335 19\n1315 20\n1016 21\n2443 27\n358 34\n799 36\n1278 37\n2503 41\n812 49\n390 52\n893 54\n393 56\n388 64",
"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>num_items</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>8</td>\n </tr>\n <tr>\n <th>474</th>\n <td>9</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>13</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>16</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>17</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>19</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>20</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>21</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>27</td>\n </tr>\n <tr>\n <th>358</th>\n <td>34</td>\n </tr>\n <tr>\n <th>799</th>\n <td>36</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>37</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>41</td>\n </tr>\n <tr>\n <th>812</th>\n <td>49</td>\n </tr>\n <tr>\n <th>390</th>\n <td>52</td>\n </tr>\n <tr>\n <th>893</th>\n <td>54</td>\n </tr>\n <tr>\n <th>393</th>\n <td>56</td>\n </tr>\n <tr>\n <th>388</th>\n <td>64</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "So let's look at how the top 16 blocks perform over these 9 weeks"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "blks = df_block_rank.sort_values(9).index[:16]\ndf_block_rank.loc[blks]",
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 11,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n96 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2359 2500.0 3.0 4.0 9.0 6.0 6.0 4.0 0.0 1.0 \n2128 4.0 0.0 1.0 7.0 1.0 0.0 0.0 1.0 0.0 \n474 1.0 1.0 0.0 6.0 2.0 3.0 1.0 2.0 2.0 \n2037 14.0 9.0 3.0 4.0 4.0 2.0 2.0 3.0 3.0 \n2306 5.0 2.0 5.0 13.0 9.0 5.0 3.0 4.0 6.0 \n2469 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2335 1450.0 1777.0 1904.0 151.0 228.0 465.0 1180.0 1548.0 1764.0 \n1315 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n1016 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2443 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 1753.0 1616.0 \n358 315.0 499.0 582.0 455.0 902.0 941.0 1144.0 1417.0 20.0 \n799 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 8.0 \n1278 2500.0 2500.0 2500.0 45.0 58.0 115.0 69.0 100.0 15.0 \n2503 13.0 4.0 6.0 14.0 10.0 13.0 7.0 8.0 10.0 \n812 1722.0 1560.0 1832.0 1918.0 2003.0 2500.0 40.0 6.0 9.0 \n\n 9 \n96 0.0 \n2359 1.0 \n2128 2.0 \n474 3.0 \n2037 4.0 \n2306 5.0 \n2469 6.0 \n2335 7.0 \n1315 8.0 \n1016 9.0 \n2443 10.0 \n358 11.0 \n799 12.0 \n1278 13.0 \n2503 14.0 \n812 15.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>6.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>14.0</td>\n <td>9.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>5.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>13.0</td>\n <td>9.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>1450.0</td>\n <td>1777.0</td>\n <td>1904.0</td>\n <td>151.0</td>\n <td>228.0</td>\n <td>465.0</td>\n <td>1180.0</td>\n <td>1548.0</td>\n <td>1764.0</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>9.0</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1753.0</td>\n <td>1616.0</td>\n <td>10.0</td>\n </tr>\n <tr>\n <th>358</th>\n <td>315.0</td>\n <td>499.0</td>\n <td>582.0</td>\n <td>455.0</td>\n <td>902.0</td>\n <td>941.0</td>\n <td>1144.0</td>\n <td>1417.0</td>\n <td>20.0</td>\n <td>11.0</td>\n </tr>\n <tr>\n <th>799</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>45.0</td>\n <td>58.0</td>\n <td>115.0</td>\n <td>69.0</td>\n <td>100.0</td>\n <td>15.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>13.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>14.0</td>\n <td>10.0</td>\n <td>13.0</td>\n <td>7.0</td>\n <td>8.0</td>\n <td>10.0</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>812</th>\n <td>1722.0</td>\n <td>1560.0</td>\n <td>1832.0</td>\n <td>1918.0</td>\n <td>2003.0</td>\n <td>2500.0</td>\n <td>40.0</td>\n <td>6.0</td>\n <td>9.0</td>\n <td>15.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "In week 9, 10 blocks were already in the top 20 the week before, 6 came from nowhere. 7 blocks top 20 2 weeks before.\n- 8: 12 top20, +3 top100, 1 from nowhere. 9 top20 two weeks before.\n- 7: 11 top20, +1 top100, 4 from nowhere. 9 top20 two weeks before.\n- 6: 13 top20, +2 top100, 1 from nowhere. 10 top20 two weeks before.\n\nSo for week 11, let's keep 9 blocks from week 9. Which ones? Let's look at persistence in the top20."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "blks = df_block_rank.sort_values(9).index[:16]\n(df_block_rank.loc[blks]<20)*1",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 12,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 9\n96 0 0 0 0 0 0 0 0 0 1\n2359 0 1 1 1 1 1 1 1 1 1\n2128 1 1 1 1 1 1 1 1 1 1\n474 1 1 1 1 1 1 1 1 1 1\n2037 1 1 1 1 1 1 1 1 1 1\n2306 1 1 1 1 1 1 1 1 1 1\n2469 0 0 0 0 0 0 0 0 0 1\n2335 0 0 0 0 0 0 0 0 0 1\n1315 0 0 0 0 0 0 0 0 0 1\n1016 0 0 0 0 0 0 0 0 0 1\n2443 0 0 0 0 0 0 0 0 0 1\n358 0 0 0 0 0 0 0 0 0 1\n799 0 0 0 0 0 0 0 0 1 1\n1278 0 0 0 0 0 0 0 0 1 1\n2503 1 1 1 1 1 1 1 1 1 1\n812 0 0 0 0 0 0 0 1 1 1",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>799</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>812</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Blocks [474, 2037, 2128, 2306, 2359, 2503] are persistent. Others stay top20 for 1, 3, or 5 weeks"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "blks = [474, 2037, 2128, 2306, 2359, 2503]\nnp.cumsum(df_n_items_in_block.loc[blks])",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 13,
"data": {
"text/plain": " num_items\nblock_id \n474 1\n2037 5\n2128 11\n2306 14\n2359 15\n2503 19",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>474</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>5</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>11</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>14</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>15</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>19</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We now have 6 blocks/19 items for the week 11 bet from the persistent blocks. Let's look now at the blocks that have recently arrived into the week 9 top20. They could stick around for 3 weeks."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "blks = [96, 358, 799, 812, 1016, 1278, 1315, 2335, 2443, 2469]\ndf_n_items_in_block.loc[blks]",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 14,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 7\n799 2\n812 8\n1016 1\n1278 1\n1315 1\n2335 2\n2443 6\n2469 1",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>7</td>\n </tr>\n <tr>\n <th>799</th>\n <td>2</td>\n </tr>\n <tr>\n <th>812</th>\n <td>8</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>6</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "The single and two item blocks are a good bet since they can easily be swapped out if necessary. Let's keep them [96, 799, 1016, 1278, 1315, 2335, 2469]. Now for week 11 we have 28 items. Let's find 22 more."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "blks = [96, 474, 799, 1016, 1278, 1315, 2037, 2128, 2306, 2335, 2359, 2469, 2503]\nnp.cumsum(df_n_items_in_block.loc[blks])",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 15,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n474 2\n799 4\n1016 5\n1278 6\n1315 7\n2037 11\n2128 17\n2306 20\n2335 22\n2359 23\n2469 24\n2503 28",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>2</td>\n </tr>\n <tr>\n <th>799</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>5</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>6</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>7</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>11</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>17</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>20</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>22</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>23</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>24</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>28</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "dropped_blks = set(df_week_rev[df_week_rev['week']==9].sort_values('revenue_per_item', ascending=0)['block_id'].head(20))-set(blks)\ndropped_blks",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 16,
"data": {
"text/plain": "{358, 388, 390, 393, 812, 893, 2443}"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_n_items_in_block.loc[dropped_blks]",
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 17,
"data": {
"text/plain": " num_items\nblock_id \n388 8\n390 3\n358 7\n393 2\n2443 6\n812 8\n893 2",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>388</th>\n <td>8</td>\n </tr>\n <tr>\n <th>390</th>\n <td>3</td>\n </tr>\n <tr>\n <th>358</th>\n <td>7</td>\n </tr>\n <tr>\n <th>393</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>6</td>\n </tr>\n <tr>\n <th>812</th>\n <td>8</td>\n </tr>\n <tr>\n <th>893</th>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_week_rev[df_week_rev['week']==9].sort_values('revenue_per_item', ascending=0)[['block_id', 'revenue_per_item']].head(20)",
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 18,
"data": {
"text/plain": " block_id revenue_per_item\n21313 96 12170.550000\n23373 2359 8505.800000\n23156 2128 8125.225000\n21651 474 7096.450000\n23073 2037 5783.037500\n23324 2306 4411.500000\n23474 2469 4203.900000\n23351 2335 4049.875000\n22413 1315 4013.300000\n22140 1016 3859.900000\n23449 2443 3805.708333\n21547 358 3450.864286\n21942 799 3357.200000\n22380 1278 3347.550000\n23505 2503 3327.462500\n21955 812 3269.962500\n21579 390 3128.343333\n22030 893 3114.800000\n21582 393 3010.200000\n21577 388 2971.187500",
"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>block_id</th>\n <th>revenue_per_item</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>21313</th>\n <td>96</td>\n <td>12170.550000</td>\n </tr>\n <tr>\n <th>23373</th>\n <td>2359</td>\n <td>8505.800000</td>\n </tr>\n <tr>\n <th>23156</th>\n <td>2128</td>\n <td>8125.225000</td>\n </tr>\n <tr>\n <th>21651</th>\n <td>474</td>\n <td>7096.450000</td>\n </tr>\n <tr>\n <th>23073</th>\n <td>2037</td>\n <td>5783.037500</td>\n </tr>\n <tr>\n <th>23324</th>\n <td>2306</td>\n <td>4411.500000</td>\n </tr>\n <tr>\n <th>23474</th>\n <td>2469</td>\n <td>4203.900000</td>\n </tr>\n <tr>\n <th>23351</th>\n <td>2335</td>\n <td>4049.875000</td>\n </tr>\n <tr>\n <th>22413</th>\n <td>1315</td>\n <td>4013.300000</td>\n </tr>\n <tr>\n <th>22140</th>\n <td>1016</td>\n <td>3859.900000</td>\n </tr>\n <tr>\n <th>23449</th>\n <td>2443</td>\n <td>3805.708333</td>\n </tr>\n <tr>\n <th>21547</th>\n <td>358</td>\n <td>3450.864286</td>\n </tr>\n <tr>\n <th>21942</th>\n <td>799</td>\n <td>3357.200000</td>\n </tr>\n <tr>\n <th>22380</th>\n <td>1278</td>\n <td>3347.550000</td>\n </tr>\n <tr>\n <th>23505</th>\n <td>2503</td>\n <td>3327.462500</td>\n </tr>\n <tr>\n <th>21955</th>\n <td>812</td>\n <td>3269.962500</td>\n </tr>\n <tr>\n <th>21579</th>\n <td>390</td>\n <td>3128.343333</td>\n </tr>\n <tr>\n <th>22030</th>\n <td>893</td>\n <td>3114.800000</td>\n </tr>\n <tr>\n <th>21582</th>\n <td>393</td>\n <td>3010.200000</td>\n </tr>\n <tr>\n <th>21577</th>\n <td>388</td>\n <td>2971.187500</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "We could include [2443] and [358] even though they contain 13 items because the revenue was so high. Meaning we just need 9 more items. Let's investigate stock movements to find them."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet1_blks = [96, 358, 474, 799, 1016, 1278, 1315, 2037, 2128, 2306, 2335, 2359, 2443, 2469, 2503]\nnp.cumsum(df_n_items_in_block.loc[bet1_blks])",
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 19,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 8\n474 9\n799 11\n1016 12\n1278 13\n1315 14\n2037 18\n2128 24\n2306 27\n2335 29\n2359 30\n2443 36\n2469 37\n2503 41",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>8</td>\n </tr>\n <tr>\n <th>474</th>\n <td>9</td>\n </tr>\n <tr>\n <th>799</th>\n <td>11</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>12</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>13</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>14</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>18</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>24</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>27</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>29</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>30</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>36</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>37</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>41</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_71_replen_72_77 = pd.read_csv(path+'stock_day_71_stock_replenishment_days_72-77.csv')",
"execution_count": 20,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_71_replen_72_77.head()",
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 21,
"data": {
"text/plain": " product_id color_id size_id stock_at_day_71 \\\n0 151926 909958217 54666687 73 \n1 151926 909958217 111455099 16 \n2 151926 909958217 242364222 111 \n3 151926 909958217 513017386 47 \n4 151926 909958217 820445075 71 \n\n stock_replenishment_days_72 stock_replenishment_days_73 \\\n0 0 0 \n1 0 0 \n2 0 1 \n3 0 0 \n4 0 1 \n\n stock_replenishment_days_74 stock_replenishment_days_75 \\\n0 2 0 \n1 0 0 \n2 -1 1 \n3 0 1 \n4 -2 2 \n\n stock_replenishment_days_76 stock_replenishment_days_77 \n0 0 1 \n1 0 0 \n2 0 0 \n3 1 1 \n4 0 0 ",
"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>product_id</th>\n <th>color_id</th>\n <th>size_id</th>\n <th>stock_at_day_71</th>\n <th>stock_replenishment_days_72</th>\n <th>stock_replenishment_days_73</th>\n <th>stock_replenishment_days_74</th>\n <th>stock_replenishment_days_75</th>\n <th>stock_replenishment_days_76</th>\n <th>stock_replenishment_days_77</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>151926</td>\n <td>909958217</td>\n <td>54666687</td>\n <td>73</td>\n <td>0</td>\n <td>0</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>151926</td>\n <td>909958217</td>\n <td>111455099</td>\n <td>16</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>151926</td>\n <td>909958217</td>\n <td>242364222</td>\n <td>111</td>\n <td>0</td>\n <td>1</td>\n <td>-1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>151926</td>\n <td>909958217</td>\n <td>513017386</td>\n <td>47</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>151926</td>\n <td>909958217</td>\n <td>820445075</td>\n <td>71</td>\n <td>0</td>\n <td>1</td>\n <td>-2</td>\n <td>2</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_71_77 = df_stock_71_replen_72_77.groupby(['product_id']).sum().drop(['color_id','size_id'], axis=1)\n# add blocks\ndf_stock_71_77 = df_stock_71_77.merge(df_blocks, how='left', on=['product_id'])\n# add price\ndf_stock_71_77 = df_stock_71_77.merge(df_products, how='left', on='product_id')\n# add more columns\ndf_stock_71_77['replen_sum']=df_stock_71_77.stock_replenishment_days_72+df_stock_71_77.stock_replenishment_days_73+df_stock_71_77.stock_replenishment_days_74+df_stock_71_77.stock_replenishment_days_75+df_stock_71_77.stock_replenishment_days_76+df_stock_71_77.stock_replenishment_days_77\ndf_stock_71_77['stock_bf+replen'] = df_stock_71_77.stock_at_day_71 + df_stock_71_77.replen_sum\ndf_stock_71_77['maybe_revenue'] = df_stock_71_77.price * df_stock_71_77.replen_sum",
"execution_count": 22,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_71_77_by_block=df_stock_71_77.groupby(['block_id']).agg({'replen_sum':'sum', \n 'stock_bf+replen':'sum', 'maybe_revenue':'sum'}).reset_index()\ndf_stock_71_77_rev = df_stock_71_77_by_block.merge(df_n_items_in_block, on='block_id', how='left')\ndf_stock_71_77_rev['revenue_per_item']=df_stock_71_77_rev.maybe_revenue/df_stock_71_77_rev.num_items",
"execution_count": 23,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_71_77_rev.sort_values('revenue_per_item',ascending=0).head(30)",
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 24,
"data": {
"text/plain": " block_id replen_sum stock_bf+replen maybe_revenue num_items \\\n405 439 611 950 36629.45 1 \n1296 1412 3533 5462 105743.35 5 \n1710 1850 392 1250 19580.40 1 \n91 96 578 3150 14999.10 1 \n1113 1207 520 1172 33945.05 3 \n2258 2443 1352 1954 65842.40 6 \n749 819 1097 1747 10915.15 1 \n2148 2321 812 831 48679.40 5 \n1427 1546 2883 6227 57431.85 6 \n2284 2470 476 476 19016.20 2 \n2315 2503 948 2048 35588.60 4 \n1694 1833 336 336 8719.20 1 \n1033 1121 1300 3816 86021.00 10 \n742 812 2849 7344 64996.55 8 \n2085 2254 731 2321 28369.45 4 \n464 505 1654 3043 69176.30 10 \n328 358 1226 2646 47313.02 7 \n1964 2128 865 4877 40082.75 6 \n1356 1473 1804 5078 39599.80 6 \n2307 2493 251 252 6513.45 1 \n2182 2359 217 2639 6499.15 1 \n1459 1580 3998 8622 51559.10 8 \n207 222 314 406 6264.30 1 \n2539 2749 822 1462 37278.90 6 \n1855 2010 917 2467 36474.15 6 \n1432 1551 969 2910 28748.83 5 \n660 726 2800 7191 56702.00 10 \n363 393 437 1416 11340.15 2 \n1846 2001 850 863 22057.50 4 \n941 1024 506 1055 16235.70 3 \n\n revenue_per_item \n405 36629.450000 \n1296 21148.670000 \n1710 19580.400000 \n91 14999.100000 \n1113 11315.016667 \n2258 10973.733333 \n749 10915.150000 \n2148 9735.880000 \n1427 9571.975000 \n2284 9508.100000 \n2315 8897.150000 \n1694 8719.200000 \n1033 8602.100000 \n742 8124.568750 \n2085 7092.362500 \n464 6917.630000 \n328 6759.002857 \n1964 6680.458333 \n1356 6599.966667 \n2307 6513.450000 \n2182 6499.150000 \n1459 6444.887500 \n207 6264.300000 \n2539 6213.150000 \n1855 6079.025000 \n1432 5749.766000 \n660 5670.200000 \n363 5670.075000 \n1846 5514.375000 \n941 5411.900000 ",
"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>block_id</th>\n <th>replen_sum</th>\n <th>stock_bf+replen</th>\n <th>maybe_revenue</th>\n <th>num_items</th>\n <th>revenue_per_item</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>405</th>\n <td>439</td>\n <td>611</td>\n <td>950</td>\n <td>36629.45</td>\n <td>1</td>\n <td>36629.450000</td>\n </tr>\n <tr>\n <th>1296</th>\n <td>1412</td>\n <td>3533</td>\n <td>5462</td>\n <td>105743.35</td>\n <td>5</td>\n <td>21148.670000</td>\n </tr>\n <tr>\n <th>1710</th>\n <td>1850</td>\n <td>392</td>\n <td>1250</td>\n <td>19580.40</td>\n <td>1</td>\n <td>19580.400000</td>\n </tr>\n <tr>\n <th>91</th>\n <td>96</td>\n <td>578</td>\n <td>3150</td>\n <td>14999.10</td>\n <td>1</td>\n <td>14999.100000</td>\n </tr>\n <tr>\n <th>1113</th>\n <td>1207</td>\n <td>520</td>\n <td>1172</td>\n <td>33945.05</td>\n <td>3</td>\n <td>11315.016667</td>\n </tr>\n <tr>\n <th>2258</th>\n <td>2443</td>\n <td>1352</td>\n <td>1954</td>\n <td>65842.40</td>\n <td>6</td>\n <td>10973.733333</td>\n </tr>\n <tr>\n <th>749</th>\n <td>819</td>\n <td>1097</td>\n <td>1747</td>\n <td>10915.15</td>\n <td>1</td>\n <td>10915.150000</td>\n </tr>\n <tr>\n <th>2148</th>\n <td>2321</td>\n <td>812</td>\n <td>831</td>\n <td>48679.40</td>\n <td>5</td>\n <td>9735.880000</td>\n </tr>\n <tr>\n <th>1427</th>\n <td>1546</td>\n <td>2883</td>\n <td>6227</td>\n <td>57431.85</td>\n <td>6</td>\n <td>9571.975000</td>\n </tr>\n <tr>\n <th>2284</th>\n <td>2470</td>\n <td>476</td>\n <td>476</td>\n <td>19016.20</td>\n <td>2</td>\n <td>9508.100000</td>\n </tr>\n <tr>\n <th>2315</th>\n <td>2503</td>\n <td>948</td>\n <td>2048</td>\n <td>35588.60</td>\n <td>4</td>\n <td>8897.150000</td>\n </tr>\n <tr>\n <th>1694</th>\n <td>1833</td>\n <td>336</td>\n <td>336</td>\n <td>8719.20</td>\n <td>1</td>\n <td>8719.200000</td>\n </tr>\n <tr>\n <th>1033</th>\n <td>1121</td>\n <td>1300</td>\n <td>3816</td>\n <td>86021.00</td>\n <td>10</td>\n <td>8602.100000</td>\n </tr>\n <tr>\n <th>742</th>\n <td>812</td>\n <td>2849</td>\n <td>7344</td>\n <td>64996.55</td>\n <td>8</td>\n <td>8124.568750</td>\n </tr>\n <tr>\n <th>2085</th>\n <td>2254</td>\n <td>731</td>\n <td>2321</td>\n <td>28369.45</td>\n <td>4</td>\n <td>7092.362500</td>\n </tr>\n <tr>\n <th>464</th>\n <td>505</td>\n <td>1654</td>\n <td>3043</td>\n <td>69176.30</td>\n <td>10</td>\n <td>6917.630000</td>\n </tr>\n <tr>\n <th>328</th>\n <td>358</td>\n <td>1226</td>\n <td>2646</td>\n <td>47313.02</td>\n <td>7</td>\n <td>6759.002857</td>\n </tr>\n <tr>\n <th>1964</th>\n <td>2128</td>\n <td>865</td>\n <td>4877</td>\n <td>40082.75</td>\n <td>6</td>\n <td>6680.458333</td>\n </tr>\n <tr>\n <th>1356</th>\n <td>1473</td>\n <td>1804</td>\n <td>5078</td>\n <td>39599.80</td>\n <td>6</td>\n <td>6599.966667</td>\n </tr>\n <tr>\n <th>2307</th>\n <td>2493</td>\n <td>251</td>\n <td>252</td>\n <td>6513.45</td>\n <td>1</td>\n <td>6513.450000</td>\n </tr>\n <tr>\n <th>2182</th>\n <td>2359</td>\n <td>217</td>\n <td>2639</td>\n <td>6499.15</td>\n <td>1</td>\n <td>6499.150000</td>\n </tr>\n <tr>\n <th>1459</th>\n <td>1580</td>\n <td>3998</td>\n <td>8622</td>\n <td>51559.10</td>\n <td>8</td>\n <td>6444.887500</td>\n </tr>\n <tr>\n <th>207</th>\n <td>222</td>\n <td>314</td>\n <td>406</td>\n <td>6264.30</td>\n <td>1</td>\n <td>6264.300000</td>\n </tr>\n <tr>\n <th>2539</th>\n <td>2749</td>\n <td>822</td>\n <td>1462</td>\n <td>37278.90</td>\n <td>6</td>\n <td>6213.150000</td>\n </tr>\n <tr>\n <th>1855</th>\n <td>2010</td>\n <td>917</td>\n <td>2467</td>\n <td>36474.15</td>\n <td>6</td>\n <td>6079.025000</td>\n </tr>\n <tr>\n <th>1432</th>\n <td>1551</td>\n <td>969</td>\n <td>2910</td>\n <td>28748.83</td>\n <td>5</td>\n <td>5749.766000</td>\n </tr>\n <tr>\n <th>660</th>\n <td>726</td>\n <td>2800</td>\n <td>7191</td>\n <td>56702.00</td>\n <td>10</td>\n <td>5670.200000</td>\n </tr>\n <tr>\n <th>363</th>\n <td>393</td>\n <td>437</td>\n <td>1416</td>\n <td>11340.15</td>\n <td>2</td>\n <td>5670.075000</td>\n </tr>\n <tr>\n <th>1846</th>\n <td>2001</td>\n <td>850</td>\n <td>863</td>\n <td>22057.50</td>\n <td>4</td>\n <td>5514.375000</td>\n </tr>\n <tr>\n <th>941</th>\n <td>1024</td>\n <td>506</td>\n <td>1055</td>\n <td>16235.70</td>\n <td>3</td>\n <td>5411.900000</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "high_stock_replen_rev_blks = df_stock_71_77_rev.sort_values('revenue_per_item',ascending=0).block_id.head(20)",
"execution_count": 25,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Some of these blocks are already in our selected blocks"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "set.intersection(*[set(high_stock_replen_rev_blks), set(bet1_blks)])",
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 26,
"data": {
"text/plain": "{96, 358, 2128, 2443, 2503}"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Which of these high_stock_replen_rev_blks contain previously unseen items?"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "len(df_stock_71_77)",
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 27,
"data": {
"text/plain": "8892"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales = df_stock_and_sales.groupby(['product_id']).sum().drop(['color_id','size_id'], axis=1)\ndf_stock_and_sales.head()",
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 28,
"data": {
"text/plain": " date_number sales stock week block_id family_id \\\nproduct_id \n151926 1466 41 1343 216 47256 9505584888 \n213413 14091 505 41153 2191 703696 235930205385 \n310130 13926 465 38597 2178 768070 302427319085 \n455200 3339 18 1081 504 88200 62883139536 \n571044 13572 366 14684 2082 379908 287646181024 \n\n subfamily_id price revenue \nproduct_id \n151926 8052757464 622.80 1063.95 \n213413 4914486899 8518.65 10074.75 \n310130 237381383840 5762.75 6021.75 \n455200 30883995828 1886.85 539.10 \n571044 259826371420 5518.70 5837.70 ",
"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>date_number</th>\n <th>sales</th>\n <th>stock</th>\n <th>week</th>\n <th>block_id</th>\n <th>family_id</th>\n <th>subfamily_id</th>\n <th>price</th>\n <th>revenue</th>\n </tr>\n <tr>\n <th>product_id</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>151926</th>\n <td>1466</td>\n <td>41</td>\n <td>1343</td>\n <td>216</td>\n <td>47256</td>\n <td>9505584888</td>\n <td>8052757464</td>\n <td>622.80</td>\n <td>1063.95</td>\n </tr>\n <tr>\n <th>213413</th>\n <td>14091</td>\n <td>505</td>\n <td>41153</td>\n <td>2191</td>\n <td>703696</td>\n <td>235930205385</td>\n <td>4914486899</td>\n <td>8518.65</td>\n <td>10074.75</td>\n </tr>\n <tr>\n <th>310130</th>\n <td>13926</td>\n <td>465</td>\n <td>38597</td>\n <td>2178</td>\n <td>768070</td>\n <td>302427319085</td>\n <td>237381383840</td>\n <td>5762.75</td>\n <td>6021.75</td>\n </tr>\n <tr>\n <th>455200</th>\n <td>3339</td>\n <td>18</td>\n <td>1081</td>\n <td>504</td>\n <td>88200</td>\n <td>62883139536</td>\n <td>30883995828</td>\n <td>1886.85</td>\n <td>539.10</td>\n </tr>\n <tr>\n <th>571044</th>\n <td>13572</td>\n <td>366</td>\n <td>14684</td>\n <td>2082</td>\n <td>379908</td>\n <td>287646181024</td>\n <td>259826371420</td>\n <td>5518.70</td>\n <td>5837.70</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "len(df_stock_and_sales)",
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 29,
"data": {
"text/plain": "8722"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Products in df_stock_71_77 but not in df_stock_and_sales"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales.head(1)",
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 30,
"data": {
"text/plain": " date_number sales stock week block_id family_id \\\nproduct_id \n151926 1466 41 1343 216 47256 9505584888 \n\n subfamily_id price revenue \nproduct_id \n151926 8052757464 622.8 1063.95 ",
"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>date_number</th>\n <th>sales</th>\n <th>stock</th>\n <th>week</th>\n <th>block_id</th>\n <th>family_id</th>\n <th>subfamily_id</th>\n <th>price</th>\n <th>revenue</th>\n </tr>\n <tr>\n <th>product_id</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>151926</th>\n <td>1466</td>\n <td>41</td>\n <td>1343</td>\n <td>216</td>\n <td>47256</td>\n <td>9505584888</td>\n <td>8052757464</td>\n <td>622.8</td>\n <td>1063.95</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new_prods = set(df_stock_71_77.product_id)-set(df_stock_and_sales.index)\nlen(list(new_prods))",
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 31,
"data": {
"text/plain": "620"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# Blocks containing new products not seen in first 63 days\nblks=[]\nfor prod in new_prods:\n blks.append(df_blocks[df_blocks['product_id']==prod].block_id.values[0])\nblks = sorted(set(blks))\nlen(blks)",
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 32,
"data": {
"text/plain": "549"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "list(set.intersection(*[set(high_stock_replen_rev_blks), set(blks)]))",
"execution_count": 33,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 33,
"data": {
"text/plain": "[1121, 1412, 2470, 1833, 812, 505, 2254, 819, 439, 1207, 1850]"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "interesting=list(set.intersection(*[set(high_stock_replen_rev_blks), set(blks)]))\ndf_n_items_in_block.loc[interesting].sort_values('num_items')",
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 34,
"data": {
"text/plain": " num_items\nblock_id \n1833 1\n819 1\n439 1\n1850 1\n2470 2\n1207 3\n2254 4\n1412 5\n812 8\n1121 10\n505 10",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1833</th>\n <td>1</td>\n </tr>\n <tr>\n <th>819</th>\n <td>1</td>\n </tr>\n <tr>\n <th>439</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2470</th>\n <td>2</td>\n </tr>\n <tr>\n <th>1207</th>\n <td>3</td>\n </tr>\n <tr>\n <th>2254</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>5</td>\n </tr>\n <tr>\n <th>812</th>\n <td>8</td>\n </tr>\n <tr>\n <th>1121</th>\n <td>10</td>\n </tr>\n <tr>\n <th>505</th>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "For our final 9 items take blocks [439, 819, 1207, 1833, 1850, 2470], these are blocks containing small numbers of items so can be swapped out if necessary."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet1_blks=sorted(list(set.union(*[set([439, 819, 1207, 1833, 1850, 2470]), set(bet1_blks)])))\nnp.cumsum(df_n_items_in_block.loc[bet1_blks])",
"execution_count": 35,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 35,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 8\n439 9\n474 10\n799 12\n819 13\n1016 14\n1207 17\n1278 18\n1315 19\n1833 20\n1850 21\n2037 25\n2128 31\n2306 34\n2335 36\n2359 37\n2443 43\n2469 44\n2470 46\n2503 50",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>8</td>\n </tr>\n <tr>\n <th>439</th>\n <td>9</td>\n </tr>\n <tr>\n <th>474</th>\n <td>10</td>\n </tr>\n <tr>\n <th>799</th>\n <td>12</td>\n </tr>\n <tr>\n <th>819</th>\n <td>13</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>14</td>\n </tr>\n <tr>\n <th>1207</th>\n <td>17</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>18</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>19</td>\n </tr>\n <tr>\n <th>1833</th>\n <td>20</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>21</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>25</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>31</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>34</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>36</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>37</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>43</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>44</td>\n </tr>\n <tr>\n <th>2470</th>\n <td>46</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>50</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "So how do these blocks perform? Load the dataset for sales in the first 11 weeks to see."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales = pd.read_csv(path+'stock_and_sales_day_0_day_77.csv')",
"execution_count": 36,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# add week\ndf_stock_and_sales['week']=(((df_stock_and_sales['date_number']-4)/7)+1).round(0).astype(int)\n# add blocks\ndf_stock_and_sales = df_stock_and_sales.merge(df_blocks, how='left', on=['product_id'])\n# add price\ndf_stock_and_sales = df_stock_and_sales.merge(df_products, how='left', on='product_id')\n# add revenue\ndf_stock_and_sales['revenue']=df_stock_and_sales['sales']*df_stock_and_sales['price']\ndf_sales_week_by_product=df_stock_and_sales.groupby(['week','product_id','family_id',\n 'subfamily_id', 'price','block_id']).agg({'sales':'sum', 'revenue':'sum'}).reset_index()\ndf_sales_week_by_block = df_sales_week_by_product.groupby(['week','block_id']).agg({'revenue':'sum'}).reset_index()\ndf_week_rev = df_sales_week_by_block.merge(df_n_items_in_block, on='block_id', how='left')\ndf_week_rev['revenue_per_item']=df_week_rev.revenue/df_week_rev.num_items",
"execution_count": 37,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==11]\nfor blk in bet1_blks:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nBet1_revenue = total\n\n#Position 3rd place",
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"text": "[242829.65]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Bet 2: Blocks for week 12"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#2130 rows\ndf_ranked_blocks_by_week = pd.DataFrame() \nfor w in list(range(12)):\n df_ranked_blocks_by_week[w]=list(df_week_rev[df_week_rev['week']==w].sort_values('revenue_per_item', ascending=0).iloc[:,1])[:2130]\n \n \n#df of rank for each block for each week\ndf_block_rank=pd.DataFrame(columns=list(range(12)))\nfor block in df_n_items_in_block.index:\n a = np.where(df_ranked_blocks_by_week==block)\n for i in range(len(a[1])): df_block_rank.loc[block,a[1][i]]=a[0][i]\n# give rank 2500 if dropped out of rankings\ndf_block_rank = df_block_rank.fillna(2500.)\ndf_block_rank.sort_values(11).head(10)",
"execution_count": 39,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 39,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n1850 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n96 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2359 2500.0 3.0 4.0 9.0 6.0 6.0 4.0 0.0 1.0 \n1412 834.0 1156.0 896.0 1091.0 1080.0 1089.0 1165.0 877.0 1419.0 \n2443 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 1753.0 1616.0 \n439 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2128 4.0 0.0 1.0 7.0 1.0 0.0 0.0 1.0 0.0 \n2335 1450.0 1777.0 1904.0 151.0 228.0 465.0 1180.0 1548.0 1764.0 \n861 2500.0 922.0 186.0 372.0 17.0 21.0 35.0 17.0 68.0 \n2037 14.0 9.0 3.0 4.0 4.0 2.0 2.0 3.0 3.0 \n\n 9 10 11 \n1850 2500.0 1.0 0.0 \n96 0.0 0.0 1.0 \n2359 1.0 2.0 2.0 \n1412 1385.0 19.0 3.0 \n2443 10.0 8.0 4.0 \n439 2500.0 4.0 5.0 \n2128 2.0 3.0 6.0 \n2335 7.0 6.0 7.0 \n861 58.0 128.0 8.0 \n2037 4.0 7.0 9.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1850</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>96</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>834.0</td>\n <td>1156.0</td>\n <td>896.0</td>\n <td>1091.0</td>\n <td>1080.0</td>\n <td>1089.0</td>\n <td>1165.0</td>\n <td>877.0</td>\n <td>1419.0</td>\n <td>1385.0</td>\n <td>19.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1753.0</td>\n <td>1616.0</td>\n <td>10.0</td>\n <td>8.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>439</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>4.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>1450.0</td>\n <td>1777.0</td>\n <td>1904.0</td>\n <td>151.0</td>\n <td>228.0</td>\n <td>465.0</td>\n <td>1180.0</td>\n <td>1548.0</td>\n <td>1764.0</td>\n <td>7.0</td>\n <td>6.0</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>861</th>\n <td>2500.0</td>\n <td>922.0</td>\n <td>186.0</td>\n <td>372.0</td>\n <td>17.0</td>\n <td>21.0</td>\n <td>35.0</td>\n <td>17.0</td>\n <td>68.0</td>\n <td>58.0</td>\n <td>128.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>14.0</td>\n <td>9.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>7.0</td>\n <td>9.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now let's repeat the exercise for the 2nd bet bearing in mind that a maximum of 20 items can be changed from bet 1.\n\nLook at how the selected blocks performed. 12 are in the top20. Keep them all."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_block_rank.loc[bet1_blks].sort_values(11)",
"execution_count": 40,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 40,
"data": {
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<th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1753.0</td>\n <td>1616.0</td>\n <td>10.0</td>\n <td>8.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>439</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>4.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>1450.0</td>\n <td>1777.0</td>\n <td>1904.0</td>\n <td>151.0</td>\n <td>228.0</td>\n <td>465.0</td>\n <td>1180.0</td>\n <td>1548.0</td>\n <td>1764.0</td>\n <td>7.0</td>\n <td>6.0</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>14.0</td>\n <td>9.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>7.0</td>\n <td>9.0</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>6.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>10.0</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>9.0</td>\n <td>11.0</td>\n <td>11.0</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>5.0</td>\n <td>2.0</td>\n <td>5.0</td>\n <td>13.0</td>\n <td>9.0</td>\n <td>5.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>5.0</td>\n <td>10.0</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>358</th>\n <td>315.0</td>\n <td>499.0</td>\n <td>582.0</td>\n <td>455.0</td>\n <td>902.0</td>\n <td>941.0</td>\n <td>1144.0</td>\n <td>1417.0</td>\n <td>20.0</td>\n <td>11.0</td>\n <td>16.0</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>13.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>14.0</td>\n <td>10.0</td>\n <td>13.0</td>\n <td>7.0</td>\n <td>8.0</td>\n <td>10.0</td>\n <td>14.0</td>\n <td>25.0</td>\n <td>24.0</td>\n </tr>\n <tr>\n <th>1833</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>62.0</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n <td>60.0</td>\n <td>67.0</td>\n </tr>\n <tr>\n <th>819</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>375.0</td>\n <td>71.0</td>\n </tr>\n <tr>\n <th>799</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n <td>12.0</td>\n <td>38.0</td>\n <td>78.0</td>\n </tr>\n <tr>\n <th>1207</th>\n <td>1062.0</td>\n <td>835.0</td>\n <td>694.0</td>\n <td>884.0</td>\n <td>532.0</td>\n <td>347.0</td>\n <td>454.0</td>\n <td>492.0</td>\n <td>451.0</td>\n <td>260.0</td>\n <td>200.0</td>\n <td>177.0</td>\n </tr>\n <tr>\n <th>2470</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>298.0</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>45.0</td>\n <td>58.0</td>\n <td>115.0</td>\n <td>69.0</td>\n <td>100.0</td>\n <td>15.0</td>\n <td>13.0</td>\n <td>118.0</td>\n <td>419.0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>6.0</td>\n <td>23.0</td>\n <td>754.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "(df_block_rank.loc[df_block_rank.loc[bet1_blks].sort_values(11).index]<20)*1",
"execution_count": 41,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 41,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 9 10 11\n1850 0 0 0 0 0 0 0 0 0 0 1 1\n96 0 0 0 0 0 0 0 0 0 1 1 1\n2359 0 1 1 1 1 1 1 1 1 1 1 1\n2443 0 0 0 0 0 0 0 0 0 1 1 1\n439 0 0 0 0 0 0 0 0 0 0 1 1\n2128 1 1 1 1 1 1 1 1 1 1 1 1\n2335 0 0 0 0 0 0 0 0 0 1 1 1\n2037 1 1 1 1 1 1 1 1 1 1 1 1\n474 1 1 1 1 1 1 1 1 1 1 1 1\n1016 0 0 0 0 0 0 0 0 0 1 1 1\n2306 1 1 1 1 1 1 1 1 1 1 1 1\n358 0 0 0 0 0 0 0 0 0 1 1 1\n2503 1 1 1 1 1 1 1 1 1 1 0 0\n1833 0 0 0 0 0 0 0 0 0 0 0 0\n1315 0 0 0 0 0 0 0 0 0 1 0 0\n819 0 0 0 0 0 0 0 0 0 0 0 0\n799 0 0 0 0 0 0 0 0 1 1 0 0\n1207 0 0 0 0 0 0 0 0 0 0 0 0\n2470 0 0 0 0 0 0 0 0 0 0 0 0\n1278 0 0 0 0 0 0 0 0 1 1 0 0\n2469 0 0 0 0 0 0 0 0 0 1 0 0",
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},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet2_blks = sorted(df_block_rank.loc[bet1_blks].sort_values(11).index[:12])\nnp.cumsum(df_n_items_in_block.loc[bet2_blks])",
"execution_count": 42,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 42,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 8\n439 9\n474 10\n1016 11\n1850 12\n2037 16\n2128 22\n2306 25\n2335 27\n2359 28\n2443 34",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>8</td>\n </tr>\n <tr>\n <th>439</th>\n <td>9</td>\n </tr>\n <tr>\n <th>474</th>\n <td>10</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>11</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>12</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>16</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>22</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>25</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>27</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>28</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>34</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Now we need to swap out the 16 items in these 9 blocks."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "set(bet1_blks)-set(bet2_blks)",
"execution_count": 43,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 43,
"data": {
"text/plain": "{799, 819, 1207, 1278, 1315, 1833, 2469, 2470, 2503}"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "And find some new well-performing blocks."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
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"trusted": true
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"cell_type": "code",
"source": "((df_block_rank.sort_values(11)<20)*1).head(20)",
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
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"data": {
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"source": "good_blks=df_block_rank.sort_values(11).index[:20]\nnew_blks=list(sorted(set(good_blks)-set(bet2_blks)))\ndf_block_rank.loc[new_blks].sort_values(11)",
"execution_count": 46,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 46,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n1412 834.0 1156.0 896.0 1091.0 1080.0 1089.0 1165.0 877.0 1419.0 \n861 2500.0 922.0 186.0 372.0 17.0 21.0 35.0 17.0 68.0 \n8 2500.0 2500.0 2500.0 24.0 14.0 92.0 248.0 315.0 300.0 \n2321 2040.0 2128.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2254 21.0 17.0 44.0 107.0 94.0 140.0 117.0 139.0 125.0 \n1546 262.0 698.0 1328.0 1684.0 1661.0 56.0 192.0 28.0 39.0 \n481 24.0 28.0 14.0 52.0 12.0 16.0 13.0 33.0 17.0 \n2493 52.0 310.0 218.0 500.0 761.0 1355.0 1361.0 1870.0 1844.0 \n\n 9 10 11 \n1412 1385.0 19.0 3.0 \n861 58.0 128.0 8.0 \n8 2500.0 12.0 13.0 \n2321 125.0 14.0 14.0 \n2254 144.0 21.0 15.0 \n1546 27.0 13.0 17.0 \n481 26.0 29.0 18.0 \n2493 2500.0 2500.0 19.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1412</th>\n <td>834.0</td>\n <td>1156.0</td>\n <td>896.0</td>\n <td>1091.0</td>\n <td>1080.0</td>\n <td>1089.0</td>\n <td>1165.0</td>\n <td>877.0</td>\n <td>1419.0</td>\n <td>1385.0</td>\n <td>19.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>861</th>\n <td>2500.0</td>\n <td>922.0</td>\n <td>186.0</td>\n <td>372.0</td>\n <td>17.0</td>\n <td>21.0</td>\n <td>35.0</td>\n <td>17.0</td>\n <td>68.0</td>\n <td>58.0</td>\n <td>128.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>24.0</td>\n <td>14.0</td>\n <td>92.0</td>\n <td>248.0</td>\n <td>315.0</td>\n <td>300.0</td>\n <td>2500.0</td>\n <td>12.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>2321</th>\n <td>2040.0</td>\n <td>2128.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>125.0</td>\n <td>14.0</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>2254</th>\n <td>21.0</td>\n <td>17.0</td>\n <td>44.0</td>\n <td>107.0</td>\n <td>94.0</td>\n <td>140.0</td>\n <td>117.0</td>\n <td>139.0</td>\n <td>125.0</td>\n <td>144.0</td>\n <td>21.0</td>\n <td>15.0</td>\n </tr>\n <tr>\n <th>1546</th>\n <td>262.0</td>\n <td>698.0</td>\n <td>1328.0</td>\n <td>1684.0</td>\n <td>1661.0</td>\n <td>56.0</td>\n <td>192.0</td>\n <td>28.0</td>\n <td>39.0</td>\n <td>27.0</td>\n <td>13.0</td>\n <td>17.0</td>\n </tr>\n <tr>\n <th>481</th>\n <td>24.0</td>\n <td>28.0</td>\n <td>14.0</td>\n <td>52.0</td>\n <td>12.0</td>\n <td>16.0</td>\n <td>13.0</td>\n <td>33.0</td>\n <td>17.0</td>\n <td>26.0</td>\n <td>29.0</td>\n <td>18.0</td>\n </tr>\n <tr>\n <th>2493</th>\n <td>52.0</td>\n <td>310.0</td>\n <td>218.0</td>\n <td>500.0</td>\n <td>761.0</td>\n <td>1355.0</td>\n <td>1361.0</td>\n <td>1870.0</td>\n <td>1844.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>19.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new_blks = df_block_rank.loc[new_blks].sort_values(11).index",
"execution_count": 47,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_n_items_in_block.loc[new_blks]",
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 48,
"data": {
"text/plain": " num_items\n1412 5\n861 4\n8 1\n2321 5\n2254 4\n1546 6\n481 1\n2493 1",
"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>num_items</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1412</th>\n <td>5</td>\n </tr>\n <tr>\n <th>861</th>\n <td>4</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2321</th>\n <td>5</td>\n </tr>\n <tr>\n <th>2254</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1546</th>\n <td>6</td>\n </tr>\n <tr>\n <th>481</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2493</th>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Select [1412, 861, 8, 2321, 481]"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet2_blks=sorted(list(set.union(*[set([1412, 861, 8, 2321, 481]), set(bet2_blks)])))\nnp.cumsum(df_n_items_in_block.loc[bet2_blks])",
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 49,
"data": {
"text/plain": " num_items\nblock_id \n8 1\n96 2\n358 9\n439 10\n474 11\n481 12\n861 16\n1016 17\n1412 22\n1850 23\n2037 27\n2128 33\n2306 36\n2321 41\n2335 43\n2359 44\n2443 50",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8</th>\n <td>1</td>\n </tr>\n <tr>\n <th>96</th>\n <td>2</td>\n </tr>\n <tr>\n <th>358</th>\n <td>9</td>\n </tr>\n <tr>\n <th>439</th>\n <td>10</td>\n </tr>\n <tr>\n <th>474</th>\n <td>11</td>\n </tr>\n <tr>\n <th>481</th>\n <td>12</td>\n </tr>\n <tr>\n <th>861</th>\n <td>16</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>17</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>22</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>23</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>27</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>33</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>36</td>\n </tr>\n <tr>\n <th>2321</th>\n <td>41</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>43</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>44</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>50</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "So how do these 2nd week bets perform? Load the dataset for sales in the first 12 weeks to see."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales = pd.read_csv(path+'stock_and_sales_day_0_day_84.csv')",
"execution_count": 50,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# add week\ndf_stock_and_sales['week']=(((df_stock_and_sales['date_number']-4)/7)+1).round(0).astype(int)\n# add blocks\ndf_stock_and_sales = df_stock_and_sales.merge(df_blocks, how='left', on=['product_id'])\n# add price\ndf_stock_and_sales = df_stock_and_sales.merge(df_products, how='left', on='product_id')\n# add revenue\ndf_stock_and_sales['revenue']=df_stock_and_sales['sales']*df_stock_and_sales['price']\ndf_sales_week_by_product=df_stock_and_sales.groupby(['week','product_id','family_id',\n 'subfamily_id', 'price','block_id']).agg({'sales':'sum', 'revenue':'sum'}).reset_index()\ndf_sales_week_by_block = df_sales_week_by_product.groupby(['week','block_id']).agg({'revenue':'sum'}).reset_index()\ndf_week_rev = df_sales_week_by_block.merge(df_n_items_in_block, on='block_id', how='left')\ndf_week_rev['revenue_per_item']=df_week_rev.revenue/df_week_rev.num_items",
"execution_count": 51,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==12]\nfor blk in bet2_blks:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nBet2_revenue = total",
"execution_count": 52,
"outputs": [
{
"output_type": "stream",
"text": "[208174.53]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print (Bet1_revenue+Bet2_revenue)\n# 6th place",
"execution_count": 53,
"outputs": [
{
"output_type": "stream",
"text": "[451004.18]\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Bet 3: Blocks for week 13"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#2130 rows\ndf_ranked_blocks_by_week = pd.DataFrame() \nfor w in list(range(13)):\n df_ranked_blocks_by_week[w]=list(df_week_rev[df_week_rev['week']==w].sort_values('revenue_per_item', ascending=0).iloc[:,1])[:2130]",
"execution_count": 54,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#df of rank for each block for each week\ndf_block_rank=pd.DataFrame(columns=list(range(13)))\nfor block in df_n_items_in_block.index:\n a = np.where(df_ranked_blocks_by_week==block)\n for i in range(len(a[1])): df_block_rank.loc[block,a[1][i]]=a[0][i]\n# give rank 2500 if dropped out of rankings\ndf_block_rank = df_block_rank.fillna(2500.)\ndf_block_rank.sort_values(12).head(20)",
"execution_count": 55,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 55,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n96 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n1850 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2609 2500.0 2500.0 2500.0 2500.0 56.0 48.0 325.0 321.0 149.0 \n2359 2500.0 3.0 4.0 9.0 6.0 6.0 4.0 0.0 1.0 \n587 2500.0 2500.0 2500.0 2500.0 34.0 14.0 22.0 102.0 110.0 \n831 85.0 124.0 84.0 200.0 170.0 203.0 236.0 259.0 328.0 \n2335 1450.0 1777.0 1904.0 151.0 228.0 465.0 1180.0 1548.0 1764.0 \n2037 14.0 9.0 3.0 4.0 4.0 2.0 2.0 3.0 3.0 \n1412 834.0 1156.0 896.0 1091.0 1080.0 1089.0 1165.0 877.0 1419.0 \n1750 1449.0 862.0 71.0 421.0 629.0 905.0 709.0 666.0 286.0 \n2128 4.0 0.0 1.0 7.0 1.0 0.0 0.0 1.0 0.0 \n487 68.0 84.0 118.0 165.0 125.0 8.0 15.0 27.0 25.0 \n1969 688.0 611.0 706.0 1122.0 1164.0 1514.0 884.0 1281.0 1206.0 \n2208 223.0 610.0 67.0 110.0 206.0 286.0 429.0 523.0 257.0 \n474 1.0 1.0 0.0 6.0 2.0 3.0 1.0 2.0 2.0 \n1153 1300.0 1365.0 295.0 246.0 396.0 334.0 462.0 590.0 597.0 \n1005 276.0 106.0 93.0 222.0 289.0 250.0 315.0 240.0 235.0 \n1124 1788.0 2085.0 432.0 587.0 250.0 258.0 312.0 461.0 426.0 \n1580 127.0 89.0 77.0 121.0 96.0 30.0 28.0 45.0 23.0 \n1474 1375.0 1408.0 1598.0 525.0 305.0 617.0 604.0 655.0 822.0 \n\n 9 10 11 12 \n96 0.0 0.0 1.0 0.0 \n1850 2500.0 1.0 0.0 1.0 \n2609 367.0 283.0 442.0 2.0 \n2359 1.0 2.0 2.0 3.0 \n587 100.0 112.0 53.0 4.0 \n831 435.0 475.0 402.0 5.0 \n2335 7.0 6.0 7.0 6.0 \n2037 4.0 7.0 9.0 7.0 \n1412 1385.0 19.0 3.0 8.0 \n1750 457.0 551.0 502.0 9.0 \n2128 2.0 3.0 6.0 10.0 \n487 29.0 43.0 39.0 11.0 \n1969 456.0 578.0 885.0 12.0 \n2208 306.0 525.0 368.0 13.0 \n474 3.0 5.0 10.0 14.0 \n1153 571.0 714.0 647.0 15.0 \n1005 279.0 303.0 388.0 16.0 \n1124 640.0 292.0 178.0 17.0 \n1580 46.0 35.0 25.0 18.0 \n1474 752.0 855.0 149.0 19.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2609</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>56.0</td>\n <td>48.0</td>\n <td>325.0</td>\n <td>321.0</td>\n <td>149.0</td>\n <td>367.0</td>\n <td>283.0</td>\n <td>442.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>587</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>34.0</td>\n <td>14.0</td>\n <td>22.0</td>\n <td>102.0</td>\n <td>110.0</td>\n <td>100.0</td>\n <td>112.0</td>\n <td>53.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>831</th>\n <td>85.0</td>\n <td>124.0</td>\n <td>84.0</td>\n <td>200.0</td>\n <td>170.0</td>\n <td>203.0</td>\n <td>236.0</td>\n <td>259.0</td>\n <td>328.0</td>\n <td>435.0</td>\n <td>475.0</td>\n <td>402.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>1450.0</td>\n <td>1777.0</td>\n <td>1904.0</td>\n <td>151.0</td>\n <td>228.0</td>\n <td>465.0</td>\n <td>1180.0</td>\n <td>1548.0</td>\n <td>1764.0</td>\n <td>7.0</td>\n <td>6.0</td>\n <td>7.0</td>\n <td>6.0</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>14.0</td>\n <td>9.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>4.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>7.0</td>\n <td>9.0</td>\n <td>7.0</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>834.0</td>\n <td>1156.0</td>\n <td>896.0</td>\n <td>1091.0</td>\n <td>1080.0</td>\n <td>1089.0</td>\n <td>1165.0</td>\n <td>877.0</td>\n <td>1419.0</td>\n <td>1385.0</td>\n <td>19.0</td>\n <td>3.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>1449.0</td>\n <td>862.0</td>\n <td>71.0</td>\n <td>421.0</td>\n <td>629.0</td>\n <td>905.0</td>\n <td>709.0</td>\n <td>666.0</td>\n <td>286.0</td>\n <td>457.0</td>\n <td>551.0</td>\n <td>502.0</td>\n <td>9.0</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>6.0</td>\n <td>10.0</td>\n </tr>\n <tr>\n <th>487</th>\n <td>68.0</td>\n <td>84.0</td>\n <td>118.0</td>\n <td>165.0</td>\n <td>125.0</td>\n <td>8.0</td>\n <td>15.0</td>\n <td>27.0</td>\n <td>25.0</td>\n <td>29.0</td>\n <td>43.0</td>\n <td>39.0</td>\n <td>11.0</td>\n </tr>\n <tr>\n <th>1969</th>\n <td>688.0</td>\n <td>611.0</td>\n <td>706.0</td>\n <td>1122.0</td>\n <td>1164.0</td>\n <td>1514.0</td>\n <td>884.0</td>\n <td>1281.0</td>\n <td>1206.0</td>\n <td>456.0</td>\n <td>578.0</td>\n <td>885.0</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>223.0</td>\n <td>610.0</td>\n <td>67.0</td>\n <td>110.0</td>\n <td>206.0</td>\n <td>286.0</td>\n <td>429.0</td>\n <td>523.0</td>\n <td>257.0</td>\n <td>306.0</td>\n <td>525.0</td>\n <td>368.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>6.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>10.0</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1153</th>\n <td>1300.0</td>\n <td>1365.0</td>\n <td>295.0</td>\n <td>246.0</td>\n <td>396.0</td>\n <td>334.0</td>\n <td>462.0</td>\n <td>590.0</td>\n <td>597.0</td>\n <td>571.0</td>\n <td>714.0</td>\n <td>647.0</td>\n <td>15.0</td>\n </tr>\n <tr>\n <th>1005</th>\n <td>276.0</td>\n <td>106.0</td>\n <td>93.0</td>\n <td>222.0</td>\n <td>289.0</td>\n <td>250.0</td>\n <td>315.0</td>\n <td>240.0</td>\n <td>235.0</td>\n <td>279.0</td>\n <td>303.0</td>\n <td>388.0</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>1124</th>\n <td>1788.0</td>\n <td>2085.0</td>\n <td>432.0</td>\n <td>587.0</td>\n <td>250.0</td>\n <td>258.0</td>\n <td>312.0</td>\n <td>461.0</td>\n <td>426.0</td>\n <td>640.0</td>\n <td>292.0</td>\n <td>178.0</td>\n <td>17.0</td>\n </tr>\n <tr>\n <th>1580</th>\n <td>127.0</td>\n <td>89.0</td>\n <td>77.0</td>\n <td>121.0</td>\n <td>96.0</td>\n <td>30.0</td>\n <td>28.0</td>\n <td>45.0</td>\n <td>23.0</td>\n <td>46.0</td>\n <td>35.0</td>\n <td>25.0</td>\n <td>18.0</td>\n </tr>\n <tr>\n <th>1474</th>\n <td>1375.0</td>\n <td>1408.0</td>\n <td>1598.0</td>\n <td>525.0</td>\n <td>305.0</td>\n <td>617.0</td>\n <td>604.0</td>\n <td>655.0</td>\n <td>822.0</td>\n <td>752.0</td>\n <td>855.0</td>\n <td>149.0</td>\n <td>19.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
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"metadata": {
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"cell_type": "code",
"source": "df_block_rank.loc[bet1_blks].sort_values(12)",
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 56,
"data": {
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<td>499.0</td>\n <td>582.0</td>\n <td>455.0</td>\n <td>902.0</td>\n <td>941.0</td>\n <td>1144.0</td>\n <td>1417.0</td>\n <td>20.0</td>\n <td>11.0</td>\n <td>16.0</td>\n <td>16.0</td>\n <td>32.0</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1753.0</td>\n <td>1616.0</td>\n <td>10.0</td>\n <td>8.0</td>\n <td>4.0</td>\n <td>33.0</td>\n </tr>\n <tr>\n <th>1833</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>62.0</td>\n <td>48.0</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>13.0</td>\n <td>4.0</td>\n <td>6.0</td>\n <td>14.0</td>\n <td>10.0</td>\n <td>13.0</td>\n <td>7.0</td>\n <td>8.0</td>\n <td>10.0</td>\n <td>14.0</td>\n <td>25.0</td>\n <td>24.0</td>\n <td>94.0</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>2500.0</td>\n 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<th>799</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>8.0</td>\n <td>12.0</td>\n <td>38.0</td>\n <td>78.0</td>\n <td>425.0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>6.0</td>\n <td>23.0</td>\n <td>754.0</td>\n <td>482.0</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>45.0</td>\n <td>58.0</td>\n <td>115.0</td>\n <td>69.0</td>\n <td>100.0</td>\n <td>15.0</td>\n <td>13.0</td>\n <td>118.0</td>\n <td>419.0</td>\n <td>1740.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
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]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "(df_block_rank.loc[df_block_rank.loc[bet1_blks].sort_values(12).index]<20)*1",
"execution_count": 57,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 57,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 9 10 11 12\n96 0 0 0 0 0 0 0 0 0 1 1 1 1\n1850 0 0 0 0 0 0 0 0 0 0 1 1 1\n2359 0 1 1 1 1 1 1 1 1 1 1 1 1\n2335 0 0 0 0 0 0 0 0 0 1 1 1 1\n2037 1 1 1 1 1 1 1 1 1 1 1 1 1\n2128 1 1 1 1 1 1 1 1 1 1 1 1 1\n474 1 1 1 1 1 1 1 1 1 1 1 1 1\n2306 1 1 1 1 1 1 1 1 1 1 1 1 0\n1016 0 0 0 0 0 0 0 0 0 1 1 1 0\n439 0 0 0 0 0 0 0 0 0 0 1 1 0\n358 0 0 0 0 0 0 0 0 0 1 1 1 0\n2443 0 0 0 0 0 0 0 0 0 1 1 1 0\n1833 0 0 0 0 0 0 0 0 0 0 0 0 0\n2503 1 1 1 1 1 1 1 1 1 1 0 0 0\n1315 0 0 0 0 0 0 0 0 0 1 0 0 0\n819 0 0 0 0 0 0 0 0 0 0 0 0 0\n2470 0 0 0 0 0 0 0 0 0 0 0 0 0\n1207 0 0 0 0 0 0 0 0 0 0 0 0 0\n799 0 0 0 0 0 0 0 0 1 1 0 0 0\n2469 0 0 0 0 0 0 0 0 0 1 0 0 0\n1278 0 0 0 0 0 0 0 0 1 1 0 0 0",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>439</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>358</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1833</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2503</th>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1315</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>819</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2470</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1207</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>799</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2469</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1278</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Only 7 blocks are in the top 20. Keep them. These are only 16 items. We need to keep at least 14 more."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet3_blks = sorted(df_block_rank.loc[bet1_blks].sort_values(12).index[:7])",
"execution_count": 58,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "np.cumsum(df_n_items_in_block.loc[bet3_blks])",
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 59,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n474 2\n1850 3\n2037 7\n2128 13\n2335 15\n2359 16",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>2</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>3</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>7</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>13</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>15</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>16</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "np.cumsum(df_n_items_in_block.loc[[1016, 439, 358, 2443]])",
"execution_count": 60,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 60,
"data": {
"text/plain": " num_items\nblock_id \n1016 1\n439 2\n358 9\n2443 15",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>1016</th>\n <td>1</td>\n </tr>\n <tr>\n <th>439</th>\n <td>2</td>\n </tr>\n <tr>\n <th>358</th>\n <td>9</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>15</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet3_blks=sorted(list(set.union(*[set([1016, 439, 358, 2443]), set(bet3_blks)])))\nnp.cumsum(df_n_items_in_block.loc[bet3_blks])",
"execution_count": 61,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 61,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 8\n439 9\n474 10\n1016 11\n1850 12\n2037 16\n2128 22\n2335 24\n2359 25\n2443 31",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>8</td>\n </tr>\n <tr>\n <th>439</th>\n <td>9</td>\n </tr>\n <tr>\n <th>474</th>\n <td>10</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>11</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>12</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>16</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>22</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>24</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>25</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>31</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Need new blocks containing 19 more items"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "good_blks=df_block_rank.sort_values(12).index[:20]\nnew_blks=list(sorted(set(good_blks)-set(bet3_blks)))\ndf_block_rank.loc[new_blks].sort_values(12)",
"execution_count": 62,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 62,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n2609 2500.0 2500.0 2500.0 2500.0 56.0 48.0 325.0 321.0 149.0 \n587 2500.0 2500.0 2500.0 2500.0 34.0 14.0 22.0 102.0 110.0 \n831 85.0 124.0 84.0 200.0 170.0 203.0 236.0 259.0 328.0 \n1412 834.0 1156.0 896.0 1091.0 1080.0 1089.0 1165.0 877.0 1419.0 \n1750 1449.0 862.0 71.0 421.0 629.0 905.0 709.0 666.0 286.0 \n487 68.0 84.0 118.0 165.0 125.0 8.0 15.0 27.0 25.0 \n1969 688.0 611.0 706.0 1122.0 1164.0 1514.0 884.0 1281.0 1206.0 \n2208 223.0 610.0 67.0 110.0 206.0 286.0 429.0 523.0 257.0 \n1153 1300.0 1365.0 295.0 246.0 396.0 334.0 462.0 590.0 597.0 \n1005 276.0 106.0 93.0 222.0 289.0 250.0 315.0 240.0 235.0 \n1124 1788.0 2085.0 432.0 587.0 250.0 258.0 312.0 461.0 426.0 \n1580 127.0 89.0 77.0 121.0 96.0 30.0 28.0 45.0 23.0 \n1474 1375.0 1408.0 1598.0 525.0 305.0 617.0 604.0 655.0 822.0 \n\n 9 10 11 12 \n2609 367.0 283.0 442.0 2.0 \n587 100.0 112.0 53.0 4.0 \n831 435.0 475.0 402.0 5.0 \n1412 1385.0 19.0 3.0 8.0 \n1750 457.0 551.0 502.0 9.0 \n487 29.0 43.0 39.0 11.0 \n1969 456.0 578.0 885.0 12.0 \n2208 306.0 525.0 368.0 13.0 \n1153 571.0 714.0 647.0 15.0 \n1005 279.0 303.0 388.0 16.0 \n1124 640.0 292.0 178.0 17.0 \n1580 46.0 35.0 25.0 18.0 \n1474 752.0 855.0 149.0 19.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2609</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>56.0</td>\n <td>48.0</td>\n <td>325.0</td>\n <td>321.0</td>\n <td>149.0</td>\n <td>367.0</td>\n <td>283.0</td>\n <td>442.0</td>\n <td>2.0</td>\n </tr>\n <tr>\n <th>587</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>34.0</td>\n <td>14.0</td>\n <td>22.0</td>\n <td>102.0</td>\n <td>110.0</td>\n <td>100.0</td>\n <td>112.0</td>\n <td>53.0</td>\n <td>4.0</td>\n </tr>\n <tr>\n <th>831</th>\n <td>85.0</td>\n <td>124.0</td>\n <td>84.0</td>\n <td>200.0</td>\n <td>170.0</td>\n <td>203.0</td>\n <td>236.0</td>\n <td>259.0</td>\n <td>328.0</td>\n <td>435.0</td>\n <td>475.0</td>\n <td>402.0</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>834.0</td>\n <td>1156.0</td>\n <td>896.0</td>\n <td>1091.0</td>\n <td>1080.0</td>\n <td>1089.0</td>\n <td>1165.0</td>\n <td>877.0</td>\n <td>1419.0</td>\n <td>1385.0</td>\n <td>19.0</td>\n <td>3.0</td>\n <td>8.0</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>1449.0</td>\n <td>862.0</td>\n <td>71.0</td>\n <td>421.0</td>\n <td>629.0</td>\n <td>905.0</td>\n <td>709.0</td>\n <td>666.0</td>\n <td>286.0</td>\n <td>457.0</td>\n <td>551.0</td>\n <td>502.0</td>\n <td>9.0</td>\n </tr>\n <tr>\n <th>487</th>\n <td>68.0</td>\n <td>84.0</td>\n <td>118.0</td>\n <td>165.0</td>\n <td>125.0</td>\n <td>8.0</td>\n <td>15.0</td>\n <td>27.0</td>\n <td>25.0</td>\n <td>29.0</td>\n <td>43.0</td>\n <td>39.0</td>\n <td>11.0</td>\n </tr>\n <tr>\n <th>1969</th>\n <td>688.0</td>\n <td>611.0</td>\n <td>706.0</td>\n <td>1122.0</td>\n <td>1164.0</td>\n <td>1514.0</td>\n <td>884.0</td>\n <td>1281.0</td>\n <td>1206.0</td>\n <td>456.0</td>\n <td>578.0</td>\n <td>885.0</td>\n <td>12.0</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>223.0</td>\n <td>610.0</td>\n <td>67.0</td>\n <td>110.0</td>\n <td>206.0</td>\n <td>286.0</td>\n <td>429.0</td>\n <td>523.0</td>\n <td>257.0</td>\n <td>306.0</td>\n <td>525.0</td>\n <td>368.0</td>\n <td>13.0</td>\n </tr>\n <tr>\n <th>1153</th>\n <td>1300.0</td>\n <td>1365.0</td>\n <td>295.0</td>\n <td>246.0</td>\n <td>396.0</td>\n <td>334.0</td>\n <td>462.0</td>\n <td>590.0</td>\n <td>597.0</td>\n <td>571.0</td>\n <td>714.0</td>\n <td>647.0</td>\n <td>15.0</td>\n </tr>\n <tr>\n <th>1005</th>\n <td>276.0</td>\n <td>106.0</td>\n <td>93.0</td>\n <td>222.0</td>\n <td>289.0</td>\n <td>250.0</td>\n <td>315.0</td>\n <td>240.0</td>\n <td>235.0</td>\n <td>279.0</td>\n <td>303.0</td>\n <td>388.0</td>\n <td>16.0</td>\n </tr>\n <tr>\n <th>1124</th>\n <td>1788.0</td>\n <td>2085.0</td>\n <td>432.0</td>\n <td>587.0</td>\n <td>250.0</td>\n <td>258.0</td>\n <td>312.0</td>\n <td>461.0</td>\n <td>426.0</td>\n <td>640.0</td>\n <td>292.0</td>\n <td>178.0</td>\n <td>17.0</td>\n </tr>\n <tr>\n <th>1580</th>\n <td>127.0</td>\n <td>89.0</td>\n <td>77.0</td>\n <td>121.0</td>\n <td>96.0</td>\n <td>30.0</td>\n <td>28.0</td>\n <td>45.0</td>\n <td>23.0</td>\n <td>46.0</td>\n <td>35.0</td>\n <td>25.0</td>\n <td>18.0</td>\n </tr>\n <tr>\n <th>1474</th>\n <td>1375.0</td>\n <td>1408.0</td>\n <td>1598.0</td>\n <td>525.0</td>\n <td>305.0</td>\n <td>617.0</td>\n <td>604.0</td>\n <td>655.0</td>\n <td>822.0</td>\n <td>752.0</td>\n <td>855.0</td>\n <td>149.0</td>\n <td>19.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "new_blks = df_block_rank.loc[new_blks].sort_values(12).index\ndf_n_items_in_block.loc[new_blks]",
"execution_count": 63,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 63,
"data": {
"text/plain": " num_items\n2609 1\n587 2\n831 4\n1412 5\n1750 8\n487 8\n1969 4\n2208 5\n1153 6\n1005 4\n1124 9\n1580 8\n1474 7",
"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>num_items</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2609</th>\n <td>1</td>\n </tr>\n <tr>\n <th>587</th>\n <td>2</td>\n </tr>\n <tr>\n <th>831</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>5</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>8</td>\n </tr>\n <tr>\n <th>487</th>\n <td>8</td>\n </tr>\n <tr>\n <th>1969</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>5</td>\n </tr>\n <tr>\n <th>1153</th>\n <td>6</td>\n </tr>\n <tr>\n <th>1005</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1124</th>\n <td>9</td>\n </tr>\n <tr>\n <th>1580</th>\n <td>8</td>\n </tr>\n <tr>\n <th>1474</th>\n <td>7</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "np.cumsum(df_n_items_in_block.loc[[2609, 587, 831, 1412, 1750]])",
"execution_count": 64,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 64,
"data": {
"text/plain": " num_items\nblock_id \n2609 1\n587 3\n831 7\n1412 12\n1750 20",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2609</th>\n <td>1</td>\n </tr>\n <tr>\n <th>587</th>\n <td>3</td>\n </tr>\n <tr>\n <th>831</th>\n <td>7</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>12</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>20</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#Drop 1 item from current bet3_blks\ndf_n_items_in_block.loc[bet3_blks]",
"execution_count": 65,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 65,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 7\n439 1\n474 1\n1016 1\n1850 1\n2037 4\n2128 6\n2335 2\n2359 1\n2443 6",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>7</td>\n </tr>\n <tr>\n <th>439</th>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>6</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>6</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_block_rank.loc[[96, 439, 474, 1016, 1850, 2359]]",
"execution_count": 66,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 66,
"data": {
"text/plain": " 0 1 2 3 4 5 6 7 8 \\\n96 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n439 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n474 1.0 1.0 0.0 6.0 2.0 3.0 1.0 2.0 2.0 \n1016 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n1850 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 \n2359 2500.0 3.0 4.0 9.0 6.0 6.0 4.0 0.0 1.0 \n\n 9 10 11 12 \n96 0.0 0.0 1.0 0.0 \n439 2500.0 4.0 5.0 29.0 \n474 3.0 5.0 10.0 14.0 \n1016 9.0 11.0 11.0 22.0 \n1850 2500.0 1.0 0.0 1.0 \n2359 1.0 2.0 2.0 3.0 ",
"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>0</th>\n <th>1</th>\n <th>2</th>\n <th>3</th>\n <th>4</th>\n <th>5</th>\n <th>6</th>\n <th>7</th>\n <th>8</th>\n <th>9</th>\n <th>10</th>\n <th>11</th>\n <th>12</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>439</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>4.0</td>\n <td>5.0</td>\n <td>29.0</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>6.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n <td>5.0</td>\n <td>10.0</td>\n <td>14.0</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>9.0</td>\n <td>11.0</td>\n <td>11.0</td>\n <td>22.0</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>2500.0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>2500.0</td>\n <td>3.0</td>\n <td>4.0</td>\n <td>9.0</td>\n <td>6.0</td>\n <td>6.0</td>\n <td>4.0</td>\n <td>0.0</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>2.0</td>\n <td>3.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet3_blks=sorted(list(set.union(*[set([2609, 587, 831, 1412, 1750]), set(bet3_blks)])))",
"execution_count": 67,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "bet3_blks.remove(439)",
"execution_count": 68,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "np.cumsum(df_n_items_in_block.loc[bet3_blks])",
"execution_count": 69,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 69,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n358 8\n474 9\n587 11\n831 15\n1016 16\n1412 21\n1750 29\n1850 30\n2037 34\n2128 40\n2335 42\n2359 43\n2443 49\n2609 50",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>358</th>\n <td>8</td>\n </tr>\n <tr>\n <th>474</th>\n <td>9</td>\n </tr>\n <tr>\n <th>587</th>\n <td>11</td>\n </tr>\n <tr>\n <th>831</th>\n <td>15</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>16</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>21</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>29</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>30</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>34</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>40</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>42</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>43</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>49</td>\n </tr>\n <tr>\n <th>2609</th>\n <td>50</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "So how do these bet 3 blocks for week 13 perform? Load the complete dataset for sales to see."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_stock_and_sales = pd.read_csv(path+'sales_stock.csv')",
"execution_count": 70,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# add week\ndf_stock_and_sales['week']=(((df_stock_and_sales['date_number']-4)/7)+1).round(0).astype(int)\n# add blocks\ndf_stock_and_sales = df_stock_and_sales.merge(df_blocks, how='left', on=['product_id'])\n# add price\ndf_stock_and_sales = df_stock_and_sales.merge(df_products, how='left', on='product_id')\n# add revenue\ndf_stock_and_sales['revenue']=df_stock_and_sales['sales']*df_stock_and_sales['price']\ndf_sales_week_by_product=df_stock_and_sales.groupby(['week','product_id','family_id',\n 'subfamily_id', 'price','block_id']).agg({'sales':'sum', 'revenue':'sum'}).reset_index()\ndf_sales_week_by_block = df_sales_week_by_product.groupby(['week','block_id']).agg({'revenue':'sum'}).reset_index()\ndf_week_rev = df_sales_week_by_block.merge(df_n_items_in_block, on='block_id', how='left')\ndf_week_rev['revenue_per_item']=df_week_rev.revenue/df_week_rev.num_items",
"execution_count": 71,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==13]\nfor blk in bet3_blks:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nBet3_revenue = total",
"execution_count": 72,
"outputs": [
{
"output_type": "stream",
"text": "[170291.95]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print (Bet1_revenue+Bet2_revenue+Bet3_revenue)\n\n# 7th place",
"execution_count": 73,
"outputs": [
{
"output_type": "stream",
"text": "[621296.13]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "621296.13/695860.61",
"execution_count": 74,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 74,
"data": {
"text/plain": "0.8928456663181438"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "This result is just 89% of the winning solution, so is far from optimal, and only 76% of the possible (see below)."
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#2130 rows\ndf_ranked_blocks_by_week = pd.DataFrame() \nfor w in list(range(14)):\n df_ranked_blocks_by_week[w]=list(df_week_rev[df_week_rev['week']==w].sort_values('revenue_per_item', ascending=0).iloc[:,1])[:2130]",
"execution_count": 75,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "#df of rank for each block for each week\ndf_block_rank=pd.DataFrame(columns=list(range(14)))\nfor block in df_n_items_in_block.index:\n a = np.where(df_ranked_blocks_by_week==block)\n for i in range(len(a[1])): df_block_rank.loc[block,a[1][i]]=a[0][i]\n# give rank 2500 if dropped out of rankings\ndf_block_rank = df_block_rank.fillna(2500.)",
"execution_count": 76,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wk_10=list(df_block_rank.sort_values(10).index[:16])\nnp.cumsum(df_n_items_in_block.loc[wk_10])",
"execution_count": 77,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 77,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n1850 2\n2359 3\n2128 9\n439 10\n474 11\n2335 13\n2037 17\n2443 23\n393 25\n2306 28\n1016 29\n8 30\n1546 36\n2321 41\n1403 51",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>3</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>9</td>\n </tr>\n <tr>\n <th>439</th>\n <td>10</td>\n </tr>\n <tr>\n <th>474</th>\n <td>11</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>13</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>17</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>23</td>\n </tr>\n <tr>\n <th>393</th>\n <td>25</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>28</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>29</td>\n </tr>\n <tr>\n <th>8</th>\n <td>30</td>\n </tr>\n <tr>\n <th>1546</th>\n <td>36</td>\n </tr>\n <tr>\n <th>2321</th>\n <td>41</td>\n </tr>\n <tr>\n <th>1403</th>\n <td>51</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_n_items_in_block.loc[wk_10]",
"execution_count": 78,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 78,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n1850 1\n2359 1\n2128 6\n439 1\n474 1\n2335 2\n2037 4\n2443 6\n393 2\n2306 3\n1016 1\n8 1\n1546 6\n2321 5\n1403 10",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>6</td>\n </tr>\n <tr>\n <th>439</th>\n <td>1</td>\n </tr>\n <tr>\n <th>474</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2443</th>\n <td>6</td>\n </tr>\n <tr>\n <th>393</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2306</th>\n <td>3</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>1</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1546</th>\n <td>6</td>\n </tr>\n <tr>\n <th>2321</th>\n <td>5</td>\n </tr>\n <tr>\n <th>1403</th>\n <td>10</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wk_10.remove(8)",
"execution_count": 79,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==10]\nfor blk in wk_10:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nwk_10_revenue = total",
"execution_count": 80,
"outputs": [
{
"output_type": "stream",
"text": "[306218.37]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wk_12=list(df_block_rank.sort_values(12).index[:14])\nnp.cumsum(df_n_items_in_block.loc[wk_12])",
"execution_count": 81,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 81,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n1850 2\n2609 3\n2359 4\n587 6\n831 10\n2335 12\n2037 16\n1412 21\n1750 29\n2128 35\n487 43\n1969 47\n2208 52",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2609</th>\n <td>3</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>4</td>\n </tr>\n <tr>\n <th>587</th>\n <td>6</td>\n </tr>\n <tr>\n <th>831</th>\n <td>10</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>12</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>16</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>21</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>29</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>35</td>\n </tr>\n <tr>\n <th>487</th>\n <td>43</td>\n </tr>\n <tr>\n <th>1969</th>\n <td>47</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>52</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "df_n_items_in_block.loc[wk_12]",
"execution_count": 82,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 82,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n1850 1\n2609 1\n2359 1\n587 2\n831 4\n2335 2\n2037 4\n1412 5\n1750 8\n2128 6\n487 8\n1969 4\n2208 5",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2609</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>1</td>\n </tr>\n <tr>\n <th>587</th>\n <td>2</td>\n </tr>\n <tr>\n <th>831</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>4</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>5</td>\n </tr>\n <tr>\n <th>1750</th>\n <td>8</td>\n </tr>\n <tr>\n <th>2128</th>\n <td>6</td>\n </tr>\n <tr>\n <th>487</th>\n <td>8</td>\n </tr>\n <tr>\n <th>1969</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wk_12.remove(2335)",
"execution_count": 83,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==12]\nfor blk in wk_12:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nwk_12_revenue = total",
"execution_count": 84,
"outputs": [
{
"output_type": "stream",
"text": "[278998.38]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "wk_13=list(df_block_rank.sort_values(13).index[:17])\nnp.cumsum(df_n_items_in_block.loc[wk_13])",
"execution_count": 85,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 85,
"data": {
"text/plain": " num_items\nblock_id \n96 1\n2335 3\n2359 4\n2208 9\n603 10\n2609 11\n1850 12\n416 15\n1580 23\n2037 27\n1412 32\n943 35\n2254 39\n1996 42\n1546 48\n1016 49\n2713 50",
"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>num_items</th>\n </tr>\n <tr>\n <th>block_id</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>96</th>\n <td>1</td>\n </tr>\n <tr>\n <th>2335</th>\n <td>3</td>\n </tr>\n <tr>\n <th>2359</th>\n <td>4</td>\n </tr>\n <tr>\n <th>2208</th>\n <td>9</td>\n </tr>\n <tr>\n <th>603</th>\n <td>10</td>\n </tr>\n <tr>\n <th>2609</th>\n <td>11</td>\n </tr>\n <tr>\n <th>1850</th>\n <td>12</td>\n </tr>\n <tr>\n <th>416</th>\n <td>15</td>\n </tr>\n <tr>\n <th>1580</th>\n <td>23</td>\n </tr>\n <tr>\n <th>2037</th>\n <td>27</td>\n </tr>\n <tr>\n <th>1412</th>\n <td>32</td>\n </tr>\n <tr>\n <th>943</th>\n <td>35</td>\n </tr>\n <tr>\n <th>2254</th>\n <td>39</td>\n </tr>\n <tr>\n <th>1996</th>\n <td>42</td>\n </tr>\n <tr>\n <th>1546</th>\n <td>48</td>\n </tr>\n <tr>\n <th>1016</th>\n <td>49</td>\n </tr>\n <tr>\n <th>2713</th>\n <td>50</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "total = 0\ndf_tmp = df_week_rev[df_week_rev.week==13]\nfor blk in wk_13:\n total+=df_tmp[df_tmp.block_id==blk].revenue.values\nprint(total)\nwk_13_revenue = total",
"execution_count": 86,
"outputs": [
{
"output_type": "stream",
"text": "[216285.14]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "print (wk_10_revenue+wk_12_revenue+wk_13_revenue)",
"execution_count": 87,
"outputs": [
{
"output_type": "stream",
"text": "[801501.89]\n",
"name": "stdout"
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "621296.13/801501.89",
"execution_count": 88,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 88,
"data": {
"text/plain": "0.7751648969910726"
},
"metadata": {}
}
]
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "",
"execution_count": null,
"outputs": []
}
],
"metadata": {
"gist": {
"id": "c1cdf98c4f8840f18d507c8fe2b3e992",
"data": {
"description": "Zara Data Challenge - top 10 with no ML",
"public": true
}
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.7",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
},
"_draft": {
"nbviewer_url": "https://gist.github.com/c1cdf98c4f8840f18d507c8fe2b3e992"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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