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      "metadata": {
        "_cell_guid": "cf3c0bf8-5dcb-ca1e-0b86-ad17b8c45455"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "def get_product_agg(cols):\n",
        "    def_train = pd.read_csv('../input/train.csv', usecols=['Semana','Produco_ID']+cols, \n",
        "                            dtype={'Semana':'int32',\n",
        "                                   'Producto':'int32',\n",
        "                                   'Venta_hoy':'float32',\n",
        "                                   'Venta_uni_hoy':'int32',\n",
        "                                   'Dev_uni_proxima':'int32',\n",
        "                                   'Dev_proxima':'float32',\n",
        "                                   'Demanda_uni_equil':'int32'})\n",
        "        agg = df_train.groupby(['Semana','Producto_ID'], as_index=False).agg(['count','sum','min',\n",
        "                                                                              'median','mean'])\n",
        "        agg.columns =['_'.join(col).strip() for col in agg.columns.values]\n",
        "        del(df_train)\n",
        "        return agg\n",
        "    "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "593f901f-533e-8db3-6507-3071b361c605"
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