02 - Polars.ipynb

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   "source": [
    "import polars as pl\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pyarrow\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "# Download Dataset or run in a kaggle notebook\n",
    "# https://www.kaggle.com/datasets/mkechinov/ecommerce-behavior-data-from-multi-category-store\n",
    "df = pl.read_csv(\"2019-Nov.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdc01497",
   "metadata": {},
   "source": [
    "In the previous video I covered `select`, `filter`, a few basic operations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b4a85e",
   "metadata": {},
   "source": [
    "# How to update columns and intro to window functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b287eb3",
   "metadata": {},
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       "shape: (5, 9)\n",
       "┌──────────┬─────────────┬────────────┬───────────┬─────┬────────┬────────┬───────────┬────────────┐\n",
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       "│ 00:00:00 ┆             ┆            ┆ 5         ┆     ┆        ┆        ┆           ┆ 60e-ca7417 │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ 41...      │\n",
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       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ d9...      │\n",
       "│ 2019-11- ┆ view        ┆ 3601530    ┆ 205301356 ┆ ... ┆ lg     ┆ 712.87 ┆ 518085591 ┆ 3bfb58cd-7 │\n",
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       "└──────────┴─────────────┴────────────┴───────────┴─────┴────────┴────────┴───────────┴────────────┘"
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    "df.head()"
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  {
   "cell_type": "markdown",
   "id": "01c0b75b",
   "metadata": {},
   "source": [
    "### 1. Update values on one or multiple columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aaac90c5",
   "metadata": {},
   "outputs": [
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       "shape: (5, 9)\n",
       "┌──────────┬─────────────┬────────────┬───────────┬─────┬────────┬────────┬───────────┬────────────┐\n",
       "│ event_ti ┆ event_type  ┆ product_id ┆ category_ ┆ ... ┆ brand  ┆ price  ┆ user_id   ┆ user_sessi │\n",
       "│ me       ┆ ---         ┆ ---        ┆ id        ┆     ┆ ---    ┆ ---    ┆ ---       ┆ on         │\n",
       "│ ---      ┆ str         ┆ i64        ┆ ---       ┆     ┆ str    ┆ f64    ┆ i64       ┆ ---        │\n",
       "│ str      ┆             ┆            ┆ i64       ┆     ┆        ┆        ┆           ┆ str        │\n",
       "╞══════════╪═════════════╪════════════╪═══════════╪═════╪════════╪════════╪═══════════╪════════════╡\n",
       "│ 2019-11- ┆ view        ┆ 1003461    ┆ 205301355 ┆ ... ┆ xiaomi ┆ 489.07 ┆ 520088904 ┆ 4d3b30da-a │\n",
       "│ 01       ┆             ┆            ┆ 563188265 ┆     ┆        ┆        ┆           ┆ 5e4-49df-b │\n",
       "│ 00:00:00 ┆             ┆            ┆ 5         ┆     ┆        ┆        ┆           ┆ 1a8-ba5943 │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ f1...      │\n",
       "│ 2019-11- ┆ view        ┆ 5000088    ┆ 205301356 ┆ ... ┆ janome ┆ 293.65 ┆ 530496790 ┆ 8e5f4f83-3 │\n",
       "│ 01       ┆             ┆            ┆ 610086603 ┆     ┆        ┆        ┆           ┆ 66c-4f70-8 │\n",
       "│ 00:00:00 ┆             ┆            ┆ 5         ┆     ┆        ┆        ┆           ┆ 60e-ca7417 │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ 41...      │\n",
       "│ 2019-11- ┆ view        ┆ 17302664   ┆ 205301355 ┆ ... ┆ creed  ┆ 28.31  ┆ 561587266 ┆ 755422e7-9 │\n",
       "│ 01       ┆             ┆            ┆ 385349765 ┆     ┆        ┆        ┆           ┆ 040-477b-9 │\n",
       "│ 00:00:01 ┆             ┆            ┆ 5         ┆     ┆        ┆        ┆           ┆ bd2-6a6e8f │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ d9...      │\n",
       "│ 2019-11- ┆ view        ┆ 3601530    ┆ 205301356 ┆ ... ┆ lg     ┆ 712.87 ┆ 518085591 ┆ 3bfb58cd-7 │\n",
       "│ 01       ┆             ┆            ┆ 381077592 ┆     ┆        ┆        ┆           ┆ 892-48cc-8 │\n",
       "│ 00:00:01 ┆             ┆            ┆ 3         ┆     ┆        ┆        ┆           ┆ 020-2f17e6 │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ de...      │\n",
       "│ 2019-11- ┆ view        ┆ 1004775    ┆ 205301355 ┆ ... ┆ xiaomi ┆ 183.27 ┆ 558856683 ┆ 313628f1-6 │\n",
       "│ 01       ┆             ┆            ┆ 563188265 ┆     ┆        ┆        ┆           ┆ 8b8-460d-8 │\n",
       "│ 00:00:01 ┆             ┆            ┆ 5         ┆     ┆        ┆        ┆           ┆ 4f6-cec7a8 │\n",
       "│ UTC      ┆             ┆            ┆           ┆     ┆        ┆        ┆           ┆ 79...      │\n",
       "└──────────┴─────────────┴────────────┴───────────┴─────┴────────┴────────┴───────────┴────────────┘"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top = df.head()\n",
    "df_top"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b82c88df",
   "metadata": {},
   "outputs": [
    {
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       "shape: (5, 10)\n",
       "┌──────────┬──────────┬──────────┬───────────┬─────┬────────┬───────────┬────────────┬─────────────┐\n",
       "│ event_ti ┆ event_ty ┆ product_ ┆ category_ ┆ ... ┆ price  ┆ user_id   ┆ user_sessi ┆ brand2      │\n",
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       "│ UTC      ┆          ┆          ┆           ┆     ┆        ┆           ┆ 41...      ┆             │\n",
       "│ 2019-11- ┆ view     ┆ 17302664 ┆ 205301355 ┆ ... ┆ 28.31  ┆ 561587266 ┆ 755422e7-9 ┆ brand-creed │\n",
       "│ 01       ┆          ┆          ┆ 385349765 ┆     ┆        ┆           ┆ 040-477b-9 ┆             │\n",
       "│ 00:00:01 ┆          ┆          ┆ 5         ┆     ┆        ┆           ┆ bd2-6a6e8f ┆             │\n",
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       "│ UTC      ┆          ┆          ┆           ┆     ┆        ┆           ┆ 79...      ┆             │\n",
       "└──────────┴──────────┴──────────┴───────────┴─────┴────────┴───────────┴────────────┴─────────────┘"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update a string column\n",
    "df_top.with_columns(\n",
    "    (\"brand-\" + pl.col(\"brand\")).alias(\"brand2\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ba295523",
   "metadata": {},
   "outputs": [
    {
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       "shape: (5, 11)\n",
       "┌──────────┬──────────┬──────────┬───────────┬─────┬────────────┬────────────┬───────────┬─────────┐\n",
       "│ event_ti ┆ event_ty ┆ product_ ┆ category_ ┆ ... ┆ user_id    ┆ user_sessi ┆ brand2    ┆ price2  │\n",
       "│ me       ┆ pe       ┆ id       ┆ id        ┆     ┆ ---        ┆ on         ┆ ---       ┆ ---     │\n",
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       "│ str      ┆ str      ┆ i64      ┆ i64       ┆     ┆            ┆ str        ┆           ┆         │\n",
       "╞══════════╪══════════╪══════════╪═══════════╪═════╪════════════╪════════════╪═══════════╪═════════╡\n",
       "│ 2019-11- ┆ view     ┆ 1003461  ┆ 205301355 ┆ ... ┆ 520088904  ┆ 4d3b30da-a ┆ brand-xia ┆ 48907.0 │\n",
       "│ 01       ┆          ┆          ┆ 563188265 ┆     ┆            ┆ 5e4-49df-b ┆ omi       ┆         │\n",
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       "│ 00:00:01 ┆          ┆          ┆ 3         ┆     ┆            ┆ 020-2f17e6 ┆           ┆         │\n",
       "│ UTC      ┆          ┆          ┆           ┆     ┆            ┆ de...      ┆           ┆         │\n",
       "│ 2019-11- ┆ view     ┆ 1004775  ┆ 205301355 ┆ ... ┆ 558856683  ┆ 313628f1-6 ┆ brand-xia ┆ 18327.0 │\n",
       "│ 01       ┆          ┆          ┆ 563188265 ┆     ┆            ┆ 8b8-460d-8 ┆ omi       ┆         │\n",
       "│ 00:00:01 ┆          ┆          ┆ 5         ┆     ┆            ┆ 4f6-cec7a8 ┆           ┆         │\n",
       "│ UTC      ┆          ┆          ┆           ┆     ┆            ┆ 79...      ┆           ┆         │\n",
       "└──────────┴──────────┴──────────┴───────────┴─────┴────────────┴────────────┴───────────┴─────────┘"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update a numeric column\n",
    "df_top.with_columns([\n",
    "    (\"brand-\" + pl.col(\"brand\")).alias(\"brand2\"),\n",
    "    (pl.col(\"price\") * 100).alias(\"price2\")\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bbf7138c",
   "metadata": {},
   "outputs": [
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       "shape: (5, 10)\n",
       "┌──────────┬──────────┬──────────┬───────────┬─────┬────────┬───────────┬──────────────┬───────────┐\n",
       "│ event_ti ┆ event_ty ┆ product_ ┆ category_ ┆ ... ┆ price  ┆ user_id   ┆ user_session ┆ price_avg │\n",
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      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a column with a constant (based on the mean) \n",
    "df_top.select([\n",
    "    pl.all(),\n",
    "    pl.col(\"price\").mean().alias(\"price_avg\")    \n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e3fdcb4",
   "metadata": {},
   "source": [
    "### 2. Update values based on window function\n",
    "\n",
    "- In pandas this is done with `transform`. In R's dplyr with `mutate`. \n",
    "- The idea is we update values of a column BY a group and the new column has the same size of the original dataframe.\n",
    "- The over() call defines the group we are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a33aba95",
   "metadata": {},
   "outputs": [
    {
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      ]
     },
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   ],
   "source": [
    "df.with_columns([\n",
    "    pl.col(\"price\").mean().over(\"category_code\").alias(\"price_by_categorycode\")\n",
    "]).head(10)"
   ]
  },
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       "-0.621059\n",
       "</td>\n",
       "</tr>\n",
       "</tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "shape: (5, 4)\n",
       "┌───────────────────────────┬────────┬───────────────────┬───────────────────────┐\n",
       "│ category_code             ┆ price  ┆ price_by_category ┆ price_div_cat_average │\n",
       "│ ---                       ┆ ---    ┆ ---               ┆ ---                   │\n",
       "│ str                       ┆ f64    ┆ f64               ┆ f64                   │\n",
       "╞═══════════════════════════╪════════╪═══════════════════╪═══════════════════════╡\n",
       "│ electronics.smartphone    ┆ 489.07 ┆ 483.637803        ┆ 0.011232              │\n",
       "│ appliances.sewing_machine ┆ 293.65 ┆ 244.516497        ┆ 0.200941              │\n",
       "│ null                      ┆ 28.31  ┆ 176.64639         ┆ -0.839736             │\n",
       "│ appliances.kitchen.washer ┆ 712.87 ┆ 340.601164        ┆ 1.092976              │\n",
       "│ electronics.smartphone    ┆ 183.27 ┆ 483.637803        ┆ -0.621059             │\n",
       "└───────────────────────────┴────────┴───────────────────┴───────────────────────┘"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now I can compute the difference between the price and the category average.\n",
    "df_cat_window = df.select([\n",
    "    pl.col(\"category_code\"),\n",
    "    pl.col(\"price\"),\n",
    "    pl.col(\"price\").mean().over(\"category_code\").alias(\"price_by_category\"),\n",
    "    (pl.col(\"price\") / pl.col(\"price\").mean().over(\"category_code\") - 1).alias(\"price_div_cat_average\")\n",
    "])\n",
    "df_cat_window.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "69b30dfd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cat_window_sample = df_cat_window.sample(1000000).to_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "dd3f3552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_cat_window_sample.price_div_cat_average.hist(bins=30, range=[0, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d627d24c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "category_code             object\n",
       "price                    float64\n",
       "price_by_category        float64\n",
       "price_div_cat_average    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_window_sample.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f30f1123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_code</th>\n",
       "      <th>price</th>\n",
       "      <th>price_by_category</th>\n",
       "      <th>price_div_cat_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>electronics.smartphone</td>\n",
       "      <td>291.84</td>\n",
       "      <td>483.637803</td>\n",
       "      <td>-0.396573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>furniture.living_room.cabinet</td>\n",
       "      <td>437.33</td>\n",
       "      <td>261.172347</td>\n",
       "      <td>0.674488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>electronics.smartphone</td>\n",
       "      <td>128.65</td>\n",
       "      <td>483.637803</td>\n",
       "      <td>-0.733995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>33.33</td>\n",
       "      <td>176.646390</td>\n",
       "      <td>-0.811318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>115.81</td>\n",
       "      <td>176.646390</td>\n",
       "      <td>-0.344396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   category_code   price  price_by_category  \\\n",
       "0         electronics.smartphone  291.84         483.637803   \n",
       "1  furniture.living_room.cabinet  437.33         261.172347   \n",
       "2         electronics.smartphone  128.65         483.637803   \n",
       "3                           None   33.33         176.646390   \n",
       "4                           None  115.81         176.646390   \n",
       "\n",
       "   price_div_cat_average  \n",
       "0              -0.396573  \n",
       "1               0.674488  \n",
       "2              -0.733995  \n",
       "3              -0.811318  \n",
       "4              -0.344396  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_window_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ccfb7537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0    -1.000000\n",
       "0.1    -0.799964\n",
       "0.2    -0.705295\n",
       "0.3    -0.596723\n",
       "0.4    -0.479408\n",
       "0.5    -0.335519\n",
       "0.6    -0.125449\n",
       "0.7     0.105435\n",
       "0.8     0.479715\n",
       "0.9     1.055960\n",
       "1.0    30.149060\n",
       "Name: price_div_cat_average, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_window_sample.price_div_cat_average.quantile(np.arange(0, 1.1, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ec344fbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>category_code</th>\n",
       "      <th>price</th>\n",
       "      <th>price_by_category</th>\n",
       "      <th>price_div_cat_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>325574</th>\n",
       "      <td>appliances.environment.air_heater</td>\n",
       "      <td>1876.56</td>\n",
       "      <td>60.244515</td>\n",
       "      <td>30.149060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>574639</th>\n",
       "      <td>computers.peripherals.mouse</td>\n",
       "      <td>893.54</td>\n",
       "      <td>34.095449</td>\n",
       "      <td>25.207016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221090</th>\n",
       "      <td>accessories.bag</td>\n",
       "      <td>1717.16</td>\n",
       "      <td>66.893017</td>\n",
       "      <td>24.670243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364557</th>\n",
       "      <td>accessories.bag</td>\n",
       "      <td>1717.16</td>\n",
       "      <td>66.893017</td>\n",
       "      <td>24.670243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17460</th>\n",
       "      <td>accessories.bag</td>\n",
       "      <td>1603.64</td>\n",
       "      <td>66.893017</td>\n",
       "      <td>22.973205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195865</th>\n",
       "      <td>None</td>\n",
       "      <td>1948.86</td>\n",
       "      <td>176.646390</td>\n",
       "      <td>10.032549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>821909</th>\n",
       "      <td>None</td>\n",
       "      <td>1948.73</td>\n",
       "      <td>176.646390</td>\n",
       "      <td>10.031813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549041</th>\n",
       "      <td>computers.peripherals.printer</td>\n",
       "      <td>2470.85</td>\n",
       "      <td>224.317950</td>\n",
       "      <td>10.014946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141680</th>\n",
       "      <td>computers.peripherals.printer</td>\n",
       "      <td>2470.85</td>\n",
       "      <td>224.317950</td>\n",
       "      <td>10.014946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>585630</th>\n",
       "      <td>computers.peripherals.printer</td>\n",
       "      <td>2470.85</td>\n",
       "      <td>224.317950</td>\n",
       "      <td>10.014946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1667 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            category_code    price  price_by_category  \\\n",
       "325574  appliances.environment.air_heater  1876.56          60.244515   \n",
       "574639        computers.peripherals.mouse   893.54          34.095449   \n",
       "221090                    accessories.bag  1717.16          66.893017   \n",
       "364557                    accessories.bag  1717.16          66.893017   \n",
       "17460                     accessories.bag  1603.64          66.893017   \n",
       "...                                   ...      ...                ...   \n",
       "195865                               None  1948.86         176.646390   \n",
       "821909                               None  1948.73         176.646390   \n",
       "549041      computers.peripherals.printer  2470.85         224.317950   \n",
       "141680      computers.peripherals.printer  2470.85         224.317950   \n",
       "585630      computers.peripherals.printer  2470.85         224.317950   \n",
       "\n",
       "        price_div_cat_average  \n",
       "325574              30.149060  \n",
       "574639              25.207016  \n",
       "221090              24.670243  \n",
       "364557              24.670243  \n",
       "17460               22.973205  \n",
       "...                       ...  \n",
       "195865              10.032549  \n",
       "821909              10.031813  \n",
       "549041              10.014946  \n",
       "141680              10.014946  \n",
       "585630              10.014946  \n",
       "\n",
       "[1667 rows x 4 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cat_window_sample[df_cat_window_sample.price_div_cat_average > 10].sort_values(\"price_div_cat_average\", ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b67a0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eeafdd16",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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