cleaning_dataset.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "snacks_df = pd.read_csv('./datasets/snacks.csv', dtype = { 'client_id': str })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>country_region</th>\n",
       "      <th>office_department_city</th>\n",
       "      <th>description</th>\n",
       "      <th>area_name</th>\n",
       "      <th>warehouse_id</th>\n",
       "      <th>warehouse_city</th>\n",
       "      <th>category_id</th>\n",
       "      <th>category_name</th>\n",
       "      <th>brand_id</th>\n",
       "      <th>price_type</th>\n",
       "      <th>...</th>\n",
       "      <th>client_name</th>\n",
       "      <th>point_of_sale_name</th>\n",
       "      <th>distributor</th>\n",
       "      <th>date</th>\n",
       "      <th>units</th>\n",
       "      <th>devolution_units</th>\n",
       "      <th>currency</th>\n",
       "      <th>sale_amount</th>\n",
       "      <th>sale_discount</th>\n",
       "      <th>sale_devolution</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Centro</td>\n",
       "      <td>Ibague</td>\n",
       "      <td>IBAGUE 1</td>\n",
       "      <td>SALADO</td>\n",
       "      <td>85228.0</td>\n",
       "      <td>IBAGUE</td>\n",
       "      <td>CAT1</td>\n",
       "      <td>Producto de Extrusion</td>\n",
       "      <td>M01</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>39600</td>\n",
       "      <td>0</td>\n",
       "      <td>COP</td>\n",
       "      <td>493,529.00</td>\n",
       "      <td>-93,769.56</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Sur</td>\n",
       "      <td>Cali</td>\n",
       "      <td>TULUA</td>\n",
       "      <td>EL PRINCIPE</td>\n",
       "      <td>85205.0</td>\n",
       "      <td>TULUA</td>\n",
       "      <td>CAT1</td>\n",
       "      <td>Producto de Extrusion</td>\n",
       "      <td>M01</td>\n",
       "      <td>OFERTADO</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>COP</td>\n",
       "      <td>181,530.00</td>\n",
       "      <td>-32,675.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Sur</td>\n",
       "      <td>Nariño</td>\n",
       "      <td>POPAYAN</td>\n",
       "      <td>MOTO CONQUISTA POPAYAN</td>\n",
       "      <td>85220.0</td>\n",
       "      <td>POPAYAN</td>\n",
       "      <td>CAT1</td>\n",
       "      <td>Producto de Extrusion</td>\n",
       "      <td>M01</td>\n",
       "      <td>OFERTADO</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>COP</td>\n",
       "      <td>20,170.00</td>\n",
       "      <td>-4,437.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Sur</td>\n",
       "      <td>Nariño</td>\n",
       "      <td>POPAYAN</td>\n",
       "      <td>MOTO CONQUISTA POPAYAN</td>\n",
       "      <td>85220.0</td>\n",
       "      <td>POPAYAN</td>\n",
       "      <td>CAT1</td>\n",
       "      <td>Producto de Extrusion</td>\n",
       "      <td>M01</td>\n",
       "      <td>REGULAR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>73200</td>\n",
       "      <td>0</td>\n",
       "      <td>COP</td>\n",
       "      <td>91,227.00</td>\n",
       "      <td>-20,069.98</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Norte</td>\n",
       "      <td>Bucaramanga</td>\n",
       "      <td>FLORIDA</td>\n",
       "      <td>REPOSO</td>\n",
       "      <td>85230.0</td>\n",
       "      <td>BUCARAMANGA</td>\n",
       "      <td>CAT1</td>\n",
       "      <td>Producto de Extrusion</td>\n",
       "      <td>M01</td>\n",
       "      <td>OFERTADO</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>3924</td>\n",
       "      <td>0</td>\n",
       "      <td>COP</td>\n",
       "      <td>2,198,533.00</td>\n",
       "      <td>-395,736.97</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region office_department_city description               area_name  \\\n",
       "0         Centro                 Ibague    IBAGUE 1                  SALADO   \n",
       "1            Sur                   Cali       TULUA             EL PRINCIPE   \n",
       "2            Sur                 Nariño     POPAYAN  MOTO CONQUISTA POPAYAN   \n",
       "3            Sur                 Nariño     POPAYAN  MOTO CONQUISTA POPAYAN   \n",
       "4          Norte            Bucaramanga     FLORIDA                  REPOSO   \n",
       "\n",
       "   warehouse_id warehouse_city category_id          category_name brand_id  \\\n",
       "0       85228.0         IBAGUE        CAT1  Producto de Extrusion      M01   \n",
       "1       85205.0          TULUA        CAT1  Producto de Extrusion      M01   \n",
       "2       85220.0        POPAYAN        CAT1  Producto de Extrusion      M01   \n",
       "3       85220.0        POPAYAN        CAT1  Producto de Extrusion      M01   \n",
       "4       85230.0    BUCARAMANGA        CAT1  Producto de Extrusion      M01   \n",
       "\n",
       "  price_type  ...  client_name  point_of_sale_name  distributor       date  \\\n",
       "0    REGULAR  ...          NaN                 NaN      Tiendas  1/04/2014   \n",
       "1   OFERTADO  ...          NaN                 NaN      Tiendas  1/04/2014   \n",
       "2   OFERTADO  ...          NaN                 NaN      Tiendas  1/04/2014   \n",
       "3    REGULAR  ...          NaN                 NaN      Tiendas  1/04/2014   \n",
       "4   OFERTADO  ...          NaN                 NaN      Tiendas  1/04/2014   \n",
       "\n",
       "   units devolution_units currency   sale_amount sale_discount sale_devolution  \n",
       "0  39600                0      COP    493,529.00    -93,769.56               0  \n",
       "1    324                0      COP    181,530.00    -32,675.40               0  \n",
       "2     36                0      COP     20,170.00     -4,437.40               0  \n",
       "3  73200                0      COP     91,227.00    -20,069.98               0  \n",
       "4   3924                0      COP  2,198,533.00   -395,736.97               0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snacks_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' Centro', ' Sur', ' Norte', 'Ventas al Exterior', 'Norte',\n",
       "       'Centro', nan, 'Sur', 'Ventas Miscelaneas'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snacks_df['country_region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Centro', 'Sur', 'Norte', 'Ventas al Exterior', nan,\n",
       "       'Ventas Miscelaneas'], dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snacks_df['country_region'] = snacks_df['country_region'].str.strip()\n",
    "snacks_df['country_region'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f82e1354128>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "group = snacks_df.groupby('country_region')['units'].sum().reset_index(name='units').sort_values('units', ascending=False)\n",
    "\n",
    "plt.figure(figsize=(16, 10))\n",
    "sns.barplot(x='country_region', y='units', data=group)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1213737, 26)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = snacks_df[snacks_df['country_region'] == 'Sur']\n",
    "clean_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "101433"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df['category_name'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_rename = {\n",
    "    'pruduction_line': 'production_line',\n",
    "    'office_department_city': 'office_name',\n",
    "    'warehouse_city': 'warehouse_name'\n",
    "}\n",
    "clean_df = clean_df.rename(columns=to_rename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = clean_df[clean_df['distributor'].notna()]\n",
    "clean_df['distributor'].isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df['incentive'] = clean_df['price_type'].apply(lambda x: True if x == 'OFERTADO' else False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1213721, 17)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df = clean_df.drop([\n",
    "    'production_line',\n",
    "    'warehouse_id',\n",
    "    'area_name',\n",
    "    'weight',\n",
    "    'currency',\n",
    "    'category_id',\n",
    "    'plant',\n",
    "    'client_id',\n",
    "    'price_type',\n",
    "    'country_region'\n",
    "], axis=1)\n",
    "clean_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "float_columns = ['sale_amount', 'sale_discount', 'sale_devolution']\n",
    "for column in float_columns:\n",
    "    clean_df[column] = clean_df[column].str.replace(',', '').astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "brand_names = {\n",
    "    'M01': 'Cheetos',\n",
    "    'M02': 'Popcorn',\n",
    "    'M03': 'Cheetos',\n",
    "    'M08': 'Criollas',\n",
    "    'M07': 'Ruffles',\n",
    "    'M16': 'Todo en uno',\n",
    "    'Otras': 'Dulces',\n",
    "    'M27': 'Surtidas',\n",
    "    'M25': 'Costillas',\n",
    "    'M24': 'Choclos',\n",
    "    'M11': 'Ruffles econo',\n",
    "    'M20': 'Tortillitas',\n",
    "    'M14': 'Criollas econo',\n",
    "    'M18': 'Caleñas',\n",
    "    'M17': 'Surtidas',\n",
    "    'M23': 'Nachos',\n",
    "    'M28': 'Fritas econo',\n",
    "    'M13': 'Platanos econo',\n",
    "    'M26': 'Costillas econo',\n",
    "    'M09': 'Papas econo'\n",
    "}\n",
    "\n",
    "def name_brand(x):\n",
    "    if x in brand_names.keys():\n",
    "        return brand_names[x]\n",
    "    else:\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df['brand_id'] = clean_df['brand_id'].apply(name_brand)\n",
    "clean_df = clean_df.rename(columns={'brand_id': 'brand'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df['category_name'] = clean_df['category_name'].fillna('Dulces')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_warehouse_name(name):\n",
    "    if name is np.nan:\n",
    "        return name\n",
    "    algo = clean_df[clean_df['description'].str.contains(name) | False].groupby('warehouse_name')['date'].count().reset_index().sort_values('date')\n",
    "    if len(algo) == 0:\n",
    "        return np.nan\n",
    "    return algo.iloc[-1]['warehouse_name']\n",
    "\n",
    "descriptions = clean_df[clean_df['warehouse_name'].isna()]['description'].unique()\n",
    "desc_warehouse_dict = {}\n",
    "\n",
    "for each in descriptions:\n",
    "    desc_warehouse_dict[each] = get_warehouse_name(each)\n",
    "\n",
    "desc_warehouse_dict['VENTA DIRECTA MEDELLIN'] = 'SUR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df.loc[clean_df['warehouse_name'].isna(), 'warehouse_name'] = clean_df[clean_df['warehouse_name'].isna()]['description'].map(desc_warehouse_dict)\n",
    "clean_df = clean_df.drop(['description'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "to_rename = {\n",
    "    'office_name': 'office',\n",
    "    'warehouse_name': 'warehouse',\n",
    "    'category_name': 'category',\n",
    "    'flavor_name': 'flavor',\n",
    "    'client_name': 'client',\n",
    "    'point_of_sale_name': 'point_of_sale'\n",
    "}\n",
    "clean_df = clean_df.rename(columns=to_rename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Normalize categorical columns\n",
    "# office, warehouse, category, brand, flavor, client, point_of_sale, distributor\n",
    "columns = ['office', 'warehouse', 'category', 'brand', 'flavor', 'client', 'point_of_sale', 'distributor']\n",
    "for column in columns:\n",
    "    clean_df[column] = clean_df[column].map(lambda name: name.title() if isinstance(name, str) else name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = ['sale_discount', 'sale_devolution']\n",
    "\n",
    "for column in columns:\n",
    "    if (clean_df[column] <= 0).all():\n",
    "        clean_df[column] *= -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>office</th>\n",
       "      <th>warehouse</th>\n",
       "      <th>category</th>\n",
       "      <th>brand</th>\n",
       "      <th>product_id</th>\n",
       "      <th>flavor</th>\n",
       "      <th>client</th>\n",
       "      <th>point_of_sale</th>\n",
       "      <th>distributor</th>\n",
       "      <th>date</th>\n",
       "      <th>units</th>\n",
       "      <th>devolution_units</th>\n",
       "      <th>sale_amount</th>\n",
       "      <th>sale_discount</th>\n",
       "      <th>sale_devolution</th>\n",
       "      <th>incentive</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Cali</td>\n",
       "      <td>Tulua</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53407164</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>324</td>\n",
       "      <td>0</td>\n",
       "      <td>181530.0</td>\n",
       "      <td>32675.40</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Nariño</td>\n",
       "      <td>Popayan</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53407164</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>36</td>\n",
       "      <td>0</td>\n",
       "      <td>20170.0</td>\n",
       "      <td>4437.40</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Nariño</td>\n",
       "      <td>Popayan</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53102174</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>73200</td>\n",
       "      <td>0</td>\n",
       "      <td>91227.0</td>\n",
       "      <td>20069.98</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>Cali</td>\n",
       "      <td>Sur</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53408215</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tiendas</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>1080</td>\n",
       "      <td>0</td>\n",
       "      <td>605100.0</td>\n",
       "      <td>108918.43</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Cali</td>\n",
       "      <td>Norte</td>\n",
       "      <td>Producto De Extrusion</td>\n",
       "      <td>Cheetos</td>\n",
       "      <td>53408024</td>\n",
       "      <td>Tradicional</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Superficies Menores</td>\n",
       "      <td>1/04/2014</td>\n",
       "      <td>100</td>\n",
       "      <td>40</td>\n",
       "      <td>73280.0</td>\n",
       "      <td>10992.00</td>\n",
       "      <td>29312.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    office warehouse               category    brand  product_id       flavor  \\\n",
       "1     Cali     Tulua  Producto De Extrusion  Cheetos    53407164  Tradicional   \n",
       "2   Nariño   Popayan  Producto De Extrusion  Cheetos    53407164  Tradicional   \n",
       "3   Nariño   Popayan  Producto De Extrusion  Cheetos    53102174  Tradicional   \n",
       "8     Cali       Sur  Producto De Extrusion  Cheetos    53408215  Tradicional   \n",
       "10    Cali     Norte  Producto De Extrusion  Cheetos    53408024  Tradicional   \n",
       "\n",
       "   client point_of_sale          distributor       date  units  \\\n",
       "1     NaN           NaN              Tiendas  1/04/2014    324   \n",
       "2     NaN           NaN              Tiendas  1/04/2014     36   \n",
       "3     NaN           NaN              Tiendas  1/04/2014  73200   \n",
       "8     NaN           NaN              Tiendas  1/04/2014   1080   \n",
       "10    NaN           NaN  Superficies Menores  1/04/2014    100   \n",
       "\n",
       "    devolution_units  sale_amount  sale_discount  sale_devolution  incentive  \n",
       "1                  0     181530.0       32675.40             -0.0       True  \n",
       "2                  0      20170.0        4437.40             -0.0       True  \n",
       "3                  0      91227.0       20069.98             -0.0      False  \n",
       "8                  0     605100.0      108918.43             -0.0       True  \n",
       "10                40      73280.0       10992.00          29312.0       True  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "clean_df.to_csv('./datasets/clean_snacks.csv', encoding='utf-8', index=False)"
   ]
  }
 ],
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