Santander Data Prep.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import csv\n",
    "import datetime\n",
    "from operator import sub\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xgboost as xgb\n",
    "from sklearn import preprocessing, ensemble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ref - https://www.kaggle.com/c/santander-product-recommendation/forums/t/25579/when-less-is-more\n",
    "\n",
    "Xgb - https://www.kaggle.com/alexxanderlarko/santander-product-recommendation/xgb-pyt-2211ver2\n",
    "I have used this xgb as the base for my model but could not understand the logic fully... so implemented it on my own using below script. It gives me correct numbers but I am stuck at creating 46K records out of 36.7K customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ipm/anaconda/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2723: DtypeWarning: Columns (5,8,11,15) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "fecha_dato                object\n",
       "ncodpers                   int64\n",
       "ind_empleado              object\n",
       "pais_residencia           object\n",
       "sexo                      object\n",
       "age                       object\n",
       "fecha_alta                object\n",
       "ind_nuevo                float64\n",
       "antiguedad                object\n",
       "indrel                   float64\n",
       "ult_fec_cli_1t            object\n",
       "indrel_1mes               object\n",
       "tiprel_1mes               object\n",
       "indresi                   object\n",
       "indext                    object\n",
       "conyuemp                  object\n",
       "canal_entrada             object\n",
       "indfall                   object\n",
       "tipodom                  float64\n",
       "cod_prov                 float64\n",
       "nomprov                   object\n",
       "ind_actividad_cliente    float64\n",
       "renta                    float64\n",
       "segmento                  object\n",
       "ind_ahor_fin_ult1          int64\n",
       "ind_aval_fin_ult1          int64\n",
       "ind_cco_fin_ult1           int64\n",
       "ind_cder_fin_ult1          int64\n",
       "ind_cno_fin_ult1           int64\n",
       "ind_ctju_fin_ult1          int64\n",
       "ind_ctma_fin_ult1          int64\n",
       "ind_ctop_fin_ult1          int64\n",
       "ind_ctpp_fin_ult1          int64\n",
       "ind_deco_fin_ult1          int64\n",
       "ind_deme_fin_ult1          int64\n",
       "ind_dela_fin_ult1          int64\n",
       "ind_ecue_fin_ult1          int64\n",
       "ind_fond_fin_ult1          int64\n",
       "ind_hip_fin_ult1           int64\n",
       "ind_plan_fin_ult1          int64\n",
       "ind_pres_fin_ult1          int64\n",
       "ind_reca_fin_ult1          int64\n",
       "ind_tjcr_fin_ult1          int64\n",
       "ind_valo_fin_ult1          int64\n",
       "ind_viv_fin_ult1           int64\n",
       "ind_nomina_ult1          float64\n",
       "ind_nom_pens_ult1        float64\n",
       "ind_recibo_ult1            int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype_list = {'ind_cco_fin_ult1': 'int', 'ind_deme_fin_ult1': 'int', 'ind_aval_fin_ult1': 'int', 'ind_valo_fin_ult1': 'int', 'ind_reca_fin_ult1': 'int', 'ind_ctju_fin_ult1': 'int', 'ind_cder_fin_ult1': 'int', 'ind_plan_fin_ult1': 'int', 'ind_fond_fin_ult1': 'int', 'ind_hip_fin_ult1': 'int', 'ind_pres_fin_ult1': 'int', 'ind_nomina_ult1': 'int', 'ind_cno_fin_ult1': 'int', 'ncodpers': 'int64', 'ind_ctpp_fin_ult1': 'int', 'ind_ahor_fin_ult1': 'int', 'ind_dela_fin_ult1': 'int', 'ind_ecue_fin_ult1': 'int', 'ind_nom_pens_ult1': 'int', 'ind_recibo_ult1': 'int', 'ind_deco_fin_ult1': 'int', 'ind_tjcr_fin_ult1': 'int', 'ind_ctop_fin_ult1': 'int', 'ind_viv_fin_ult1': 'int', 'ind_ctma_fin_ult1': 'int'}\n",
    "\n",
    "train = pd.read_csv(\"../Capstone Project/Raw data/train_ver2.csv\", na_values=['NA', 'NaN'])\n",
    "train.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Convert date to datetime & Select May and June-15 data \n",
    "train[\"fecha_dato\"] = pd.to_datetime(train[\"fecha_dato\"])\n",
    "train_Jun15 = train.loc[(train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-06-28')]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_Jun15 = train_Jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>ind_ahor_fin_ult1</th>\n",
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       "      <th>...</th>\n",
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       "      <th>ind_pres_fin_ult1</th>\n",
       "      <th>ind_reca_fin_ult1</th>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
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       "  <tbody>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512430</th>\n",
       "      <td>2015-05-28</td>\n",
       "      <td>1061336</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2512431</th>\n",
       "      <td>2015-05-28</td>\n",
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       "<p>5 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        fecha_dato  ncodpers  ind_ahor_fin_ult1  ind_aval_fin_ult1  \\\n",
       "2512427 2015-05-28   1061260                  0                  0   \n",
       "2512428 2015-05-28   1061283                  0                  0   \n",
       "2512429 2015-05-28   1061284                  0                  0   \n",
       "2512430 2015-05-28   1061336                  0                  0   \n",
       "2512431 2015-05-28   1061286                  0                  0   \n",
       "\n",
       "         ind_cco_fin_ult1  ind_cder_fin_ult1  ind_cno_fin_ult1  \\\n",
       "2512427                 1                  0                 0   \n",
       "2512428                 1                  0                 0   \n",
       "2512429                 1                  0                 0   \n",
       "2512430                 1                  0                 0   \n",
       "2512431                 1                  0                 0   \n",
       "\n",
       "         ind_ctju_fin_ult1  ind_ctma_fin_ult1  ind_ctop_fin_ult1  \\\n",
       "2512427                  0                  0                  0   \n",
       "2512428                  0                  0                  0   \n",
       "2512429                  0                  0                  0   \n",
       "2512430                  0                  0                  0   \n",
       "2512431                  0                  0                  0   \n",
       "\n",
       "              ...         ind_hip_fin_ult1  ind_plan_fin_ult1  \\\n",
       "2512427       ...                        0                  0   \n",
       "2512428       ...                        0                  0   \n",
       "2512429       ...                        0                  0   \n",
       "2512430       ...                        0                  0   \n",
       "2512431       ...                        0                  0   \n",
       "\n",
       "         ind_pres_fin_ult1  ind_reca_fin_ult1  ind_tjcr_fin_ult1  \\\n",
       "2512427                  0                  0                  0   \n",
       "2512428                  0                  0                  0   \n",
       "2512429                  0                  0                  0   \n",
       "2512430                  0                  0                  0   \n",
       "2512431                  0                  0                  0   \n",
       "\n",
       "         ind_valo_fin_ult1  ind_viv_fin_ult1  ind_nomina_ult1  \\\n",
       "2512427                  0                 0              0.0   \n",
       "2512428                  0                 0              0.0   \n",
       "2512429                  0                 0              0.0   \n",
       "2512430                  0                 0              0.0   \n",
       "2512431                  0                 0              0.0   \n",
       "\n",
       "         ind_nom_pens_ult1  ind_recibo_ult1  \n",
       "2512427                0.0                0  \n",
       "2512428                0.0                0  \n",
       "2512429                0.0                0  \n",
       "2512430                0.0                0  \n",
       "2512431                0.0                0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Jun15.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Get month_ID from fetch_dato & drop fetcha dato\n",
    "train_Jun15[\"Month_ID\"] = train_Jun15[\"fecha_dato\"].dt.month\n",
    "train_Jun15.drop(\"fecha_dato\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1264067, 26)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_Jun15.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "24\n"
     ]
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Month_ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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      ],
      "text/plain": [
       "   ncodpers  Month_ID            Product  Product_flag\n",
       "0   1061260         5  ind_ahor_fin_ult1           0.0\n",
       "1   1061283         5  ind_ahor_fin_ult1           0.0\n",
       "2   1061284         5  ind_ahor_fin_ult1           0.0\n",
       "3   1061336         5  ind_ahor_fin_ult1           0.0\n",
       "4   1061286         5  ind_ahor_fin_ult1           0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = ['ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "print len(target)\n",
    "#Melt the data and create data which has only customer_code, Month_id, product and flag\n",
    "#Always try to use melt as it's far faster than pivot tables\n",
    "train2 = pd.melt(train_Jun15,id_vars=[\"ncodpers\", \"Month_ID\"], value_vars=target,var_name=\"Product\", value_name=\"Product_flag\" )\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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      ],
      "text/plain": [
       "        ncodpers  Month_ID            Product  Product_flag\n",
       "320424    291447         5  ind_ahor_fin_ult1           1.0\n",
       "321547    275486         5  ind_ahor_fin_ult1           1.0\n",
       "322273    270730         5  ind_ahor_fin_ult1           1.0\n",
       "325837    278015         5  ind_ahor_fin_ult1           1.0\n",
       "329593    315724         5  ind_ahor_fin_ult1           1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train2 = train2.loc[train2[\"Product_flag\"] == 1.0]\n",
    "train2.shape\n",
    "train2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_ID</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">15889</th>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15890</th>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID                      5    6\n",
       "ncodpers Product                    \n",
       "15889    ind_cco_fin_ult1   1.0  1.0\n",
       "         ind_ctpp_fin_ult1  1.0  1.0\n",
       "         ind_tjcr_fin_ult1  1.0  1.0\n",
       "         ind_valo_fin_ult1  1.0  1.0\n",
       "15890    ind_cno_fin_ult1   1.0  1.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the data in a format where we can clearly see which product was bought when\n",
    "train3 = train2.pivot_table(values=\"Product_flag\", index=[\"ncodpers\", \"Product\"], columns='Month_ID')\n",
    "train3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1165537, 2)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month_ID\n",
       "ncodpers    0\n",
       "Product     0\n",
       "Month_5     0\n",
       "Month_6     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Rename and chnage the index\n",
    "train3.reset_index(inplace=True)\n",
    "train3.rename_axis(\"ID\")\n",
    "train3.rename(columns={5:\"Month_5\", 6:\"Month_6\"}, inplace=True)\n",
    "\n",
    "#Missing value treatment\n",
    "train3.Month_5.replace(np.NaN, 0.0, inplace=True)\n",
    "train3.Month_6.replace(np.NaN, 0.0, inplace=True)\n",
    "train3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total products added in a month\n",
    "Jun15_additions = train3.ix[(train3[\"Month_5\"] == 0.0) & (train3[\"Month_6\"] == 1.0) ]\n",
    "Jun15_additions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36710, 23)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total unique customers who bought new product\n",
    "Jun15_additions[\"ncodpers\"].nunique()\n",
    "t = Jun15_additions.pivot(index=\"ncodpers\", values=\"Month_6\", columns=\"Product\")\n",
    "t[\"ncodpers\"] = t.index\n",
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0    29492\n",
      "2.0     5641\n",
      "3.0     1412\n",
      "4.0      156\n",
      "5.0        9\n",
      "Name: Total_new_prods, dtype: int64\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <th>ind_cder_fin_ult1</th>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <th>ind_ctju_fin_ult1</th>\n",
       "      <th>ind_ctma_fin_ult1</th>\n",
       "      <th>ind_ctop_fin_ult1</th>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <th>ind_deco_fin_ult1</th>\n",
       "      <th>ind_dela_fin_ult1</th>\n",
       "      <th>ind_deme_fin_ult1</th>\n",
       "      <th>...</th>\n",
       "      <th>ind_nomina_ult1</th>\n",
       "      <th>ind_plan_fin_ult1</th>\n",
       "      <th>ind_pres_fin_ult1</th>\n",
       "      <th>ind_reca_fin_ult1</th>\n",
       "      <th>ind_recibo_ult1</th>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <th>ind_viv_fin_ult1</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Total_new_prods</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36705</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1403803</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36706</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1403824</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36707</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1403827</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36708</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1403934</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36709</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1403935</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Product  ind_cco_fin_ult1  ind_cder_fin_ult1  ind_cno_fin_ult1  \\\n",
       "36705                 1.0                0.0               0.0   \n",
       "36706                 0.0                0.0               0.0   \n",
       "36707                 0.0                0.0               0.0   \n",
       "36708                 0.0                0.0               0.0   \n",
       "36709                 0.0                0.0               0.0   \n",
       "\n",
       "Product  ind_ctju_fin_ult1  ind_ctma_fin_ult1  ind_ctop_fin_ult1  \\\n",
       "36705                  0.0                0.0                0.0   \n",
       "36706                  1.0                0.0                0.0   \n",
       "36707                  0.0                1.0                0.0   \n",
       "36708                  1.0                0.0                0.0   \n",
       "36709                  1.0                0.0                0.0   \n",
       "\n",
       "Product  ind_ctpp_fin_ult1  ind_deco_fin_ult1  ind_dela_fin_ult1  \\\n",
       "36705                  0.0                0.0                0.0   \n",
       "36706                  0.0                0.0                0.0   \n",
       "36707                  0.0                0.0                0.0   \n",
       "36708                  0.0                0.0                0.0   \n",
       "36709                  0.0                0.0                0.0   \n",
       "\n",
       "Product  ind_deme_fin_ult1       ...         ind_nomina_ult1  \\\n",
       "36705                  0.0       ...                     0.0   \n",
       "36706                  0.0       ...                     0.0   \n",
       "36707                  0.0       ...                     0.0   \n",
       "36708                  0.0       ...                     0.0   \n",
       "36709                  0.0       ...                     0.0   \n",
       "\n",
       "Product  ind_plan_fin_ult1  ind_pres_fin_ult1  ind_reca_fin_ult1  \\\n",
       "36705                  0.0                0.0                0.0   \n",
       "36706                  0.0                0.0                0.0   \n",
       "36707                  0.0                0.0                0.0   \n",
       "36708                  0.0                0.0                0.0   \n",
       "36709                  0.0                0.0                0.0   \n",
       "\n",
       "Product  ind_recibo_ult1  ind_tjcr_fin_ult1  ind_valo_fin_ult1  \\\n",
       "36705                0.0                0.0                0.0   \n",
       "36706                0.0                0.0                0.0   \n",
       "36707                0.0                0.0                0.0   \n",
       "36708                0.0                0.0                0.0   \n",
       "36709                0.0                0.0                0.0   \n",
       "\n",
       "Product  ind_viv_fin_ult1  ncodpers  Total_new_prods  \n",
       "36705                 0.0   1403803              1.0  \n",
       "36706                 0.0   1403824              1.0  \n",
       "36707                 0.0   1403827              1.0  \n",
       "36708                 0.0   1403934              1.0  \n",
       "36709                 0.0   1403935              1.0  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.reset_index(drop=True,inplace=True)\n",
    "t.fillna(0,inplace=True)\n",
    "t[\"Total_new_prods\"] = t.sum(axis=1) - t[\"ncodpers\"]\n",
    "print t['Total_new_prods'].value_counts()\n",
    "t.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Good. We know that we had 36710 customers in May 2015 who bought 45679 products in Jun 2015\n",
    "# The next step is - to create train_X and train_y to train the model... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Here I would like to create my y variables where customer's have bought multiple products.\n",
    "# e.g. we have 5641 customers who have bought 2 products, so I would like to create 2 rows for these customers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Unique customers in May 2015 who bought new product in Jun 2015\n",
    "Jun15_ncodpers = Jun15_additions[\"ncodpers\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#We will have some customer's in Jun who bought a product in Jun but were not existing in May. These are totally new customers\n",
    "May15_x = train.loc[(train[\"ncodpers\"].isin(Jun15_ncodpers)) & ((train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-06-28'))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "May15_x=May15_x.drop_duplicates(\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "target = ['ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "May15_x.drop(target,axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36710, 24)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "May15_x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "new = t.merge(May15_x, on=\"ncodpers\",how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36710, 47)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Product</th>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <th>ind_cder_fin_ult1</th>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <th>ind_ctju_fin_ult1</th>\n",
       "      <th>ind_ctma_fin_ult1</th>\n",
       "      <th>ind_ctop_fin_ult1</th>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <th>ind_deco_fin_ult1</th>\n",
       "      <th>ind_dela_fin_ult1</th>\n",
       "      <th>ind_deme_fin_ult1</th>\n",
       "      <th>...</th>\n",
       "      <th>indext</th>\n",
       "      <th>conyuemp</th>\n",
       "      <th>canal_entrada</th>\n",
       "      <th>indfall</th>\n",
       "      <th>tipodom</th>\n",
       "      <th>cod_prov</th>\n",
       "      <th>nomprov</th>\n",
       "      <th>ind_actividad_cliente</th>\n",
       "      <th>renta</th>\n",
       "      <th>segmento</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>KAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>MADRID</td>\n",
       "      <td>1.0</td>\n",
       "      <td>430477.41</td>\n",
       "      <td>01 - TOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>KAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>MADRID</td>\n",
       "      <td>1.0</td>\n",
       "      <td>183021.75</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>KAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>MADRID</td>\n",
       "      <td>1.0</td>\n",
       "      <td>175450.05</td>\n",
       "      <td>01 - TOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>KAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>MADRID</td>\n",
       "      <td>1.0</td>\n",
       "      <td>81005.49</td>\n",
       "      <td>01 - TOP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>N</td>\n",
       "      <td>N</td>\n",
       "      <td>KAT</td>\n",
       "      <td>N</td>\n",
       "      <td>1.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>MADRID</td>\n",
       "      <td>1.0</td>\n",
       "      <td>42831.69</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Product  ind_cco_fin_ult1  ind_cder_fin_ult1  ind_cno_fin_ult1  \\\n",
       "0                     1.0                0.0               0.0   \n",
       "1                     1.0                0.0               0.0   \n",
       "2                     0.0                0.0               0.0   \n",
       "3                     0.0                0.0               0.0   \n",
       "4                     0.0                0.0               0.0   \n",
       "\n",
       "Product  ind_ctju_fin_ult1  ind_ctma_fin_ult1  ind_ctop_fin_ult1  \\\n",
       "0                      0.0                0.0                0.0   \n",
       "1                      0.0                0.0                0.0   \n",
       "2                      0.0                0.0                0.0   \n",
       "3                      0.0                0.0                0.0   \n",
       "4                      0.0                0.0                0.0   \n",
       "\n",
       "Product  ind_ctpp_fin_ult1  ind_deco_fin_ult1  ind_dela_fin_ult1  \\\n",
       "0                      0.0                0.0                0.0   \n",
       "1                      0.0                0.0                0.0   \n",
       "2                      0.0                0.0                0.0   \n",
       "3                      0.0                0.0                0.0   \n",
       "4                      0.0                0.0                0.0   \n",
       "\n",
       "Product  ind_deme_fin_ult1        ...          indext  conyuemp  \\\n",
       "0                      0.0        ...               N         N   \n",
       "1                      0.0        ...               N         N   \n",
       "2                      0.0        ...               N         N   \n",
       "3                      0.0        ...               N         N   \n",
       "4                      0.0        ...               N         N   \n",
       "\n",
       "Product  canal_entrada  indfall  tipodom  cod_prov  nomprov  \\\n",
       "0                  KAT        N      1.0      28.0   MADRID   \n",
       "1                  KAT        N      1.0      28.0   MADRID   \n",
       "2                  KAT        N      1.0      28.0   MADRID   \n",
       "3                  KAT        N      1.0      28.0   MADRID   \n",
       "4                  KAT        N      1.0      28.0   MADRID   \n",
       "\n",
       "Product  ind_actividad_cliente      renta           segmento  \n",
       "0                          1.0  430477.41           01 - TOP  \n",
       "1                          1.0  183021.75  02 - PARTICULARES  \n",
       "2                          1.0  175450.05           01 - TOP  \n",
       "3                          1.0   81005.49           01 - TOP  \n",
       "4                          1.0   42831.69  02 - PARTICULARES  \n",
       "\n",
       "[5 rows x 47 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train1 = train.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train1[\"Month_ID\"] = train1[\"fecha_dato\"].map(lambda x:x.strftime('%Y-%m'))\n",
    "train1.drop(\"fecha_dato\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(13647309, 26)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Month_ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Product_flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1375586</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>ind_ahor_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1050611</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>ind_ahor_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1050612</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>ind_ahor_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1050613</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>ind_ahor_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1050614</td>\n",
       "      <td>2015-01</td>\n",
       "      <td>ind_ahor_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ncodpers Month_ID            Product  Product_flag\n",
       "0   1375586  2015-01  ind_ahor_fin_ult1           0.0\n",
       "1   1050611  2015-01  ind_ahor_fin_ult1           0.0\n",
       "2   1050612  2015-01  ind_ahor_fin_ult1           0.0\n",
       "3   1050613  2015-01  ind_ahor_fin_ult1           0.0\n",
       "4   1050614  2015-01  ind_ahor_fin_ult1           0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target = ['ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "print len(target)\n",
    "#Melt the data and create data which has only customer_code, Month_id, product and flag\n",
    "#Always try to use melt as it's far faster than pivot tables\n",
    "train_2 = pd.melt(train1,id_vars=[\"ncodpers\", \"Month_ID\"], value_vars=target,var_name=\"Product\", value_name=\"Product_flag\" )\n",
    "train_2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_3 = train_2.loc[train_2[\"Product_flag\"] == 1.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_ID</th>\n",
       "      <th>2015-01</th>\n",
       "      <th>2015-02</th>\n",
       "      <th>2015-03</th>\n",
       "      <th>2015-04</th>\n",
       "      <th>2015-05</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-07</th>\n",
       "      <th>2015-08</th>\n",
       "      <th>2015-09</th>\n",
       "      <th>2015-10</th>\n",
       "      <th>2015-11</th>\n",
       "      <th>2015-12</th>\n",
       "      <th>2016-01</th>\n",
       "      <th>2016-02</th>\n",
       "      <th>2016-03</th>\n",
       "      <th>2016-04</th>\n",
       "      <th>2016-05</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</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",
       "      <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 rowspan=\"4\" valign=\"top\">15889</th>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15890</th>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID                    2015-01  2015-02  2015-03  2015-04  2015-05  \\\n",
       "ncodpers Product                                                          \n",
       "15889    ind_cco_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_ctpp_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_tjcr_fin_ult1      1.0      NaN      NaN      NaN      1.0   \n",
       "         ind_valo_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "15890    ind_cno_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID                    2015-06  2015-07  2015-08  2015-09  2015-10  \\\n",
       "ncodpers Product                                                          \n",
       "15889    ind_cco_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_ctpp_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_tjcr_fin_ult1      1.0      1.0      NaN      NaN      NaN   \n",
       "         ind_valo_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "15890    ind_cno_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID                    2015-11  2015-12  2016-01  2016-02  2016-03  \\\n",
       "ncodpers Product                                                          \n",
       "15889    ind_cco_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_ctpp_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "         ind_tjcr_fin_ult1      NaN      1.0      1.0      NaN      1.0   \n",
       "         ind_valo_fin_ult1      1.0      1.0      1.0      1.0      1.0   \n",
       "15890    ind_cno_fin_ult1       1.0      1.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID                    2016-04  2016-05  \n",
       "ncodpers Product                              \n",
       "15889    ind_cco_fin_ult1       1.0      1.0  \n",
       "         ind_ctpp_fin_ult1      1.0      1.0  \n",
       "         ind_tjcr_fin_ult1      NaN      1.0  \n",
       "         ind_valo_fin_ult1      1.0      1.0  \n",
       "15890    ind_cno_fin_ult1       1.0      1.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_3 = train_3.pivot_table(values=\"Product_flag\", index=[\"ncodpers\", \"Product\"], columns='Month_ID')\n",
    "train_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month_ID\n",
       "ncodpers    0\n",
       "Product     0\n",
       "2015-01     0\n",
       "2015-02     0\n",
       "2015-03     0\n",
       "2015-04     0\n",
       "2015-05     0\n",
       "2015-06     0\n",
       "2015-07     0\n",
       "2015-08     0\n",
       "2015-09     0\n",
       "2015-10     0\n",
       "2015-11     0\n",
       "2015-12     0\n",
       "2016-01     0\n",
       "2016-02     0\n",
       "2016-03     0\n",
       "2016-04     0\n",
       "2016-05     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rename and change the index\n",
    "train_3.reset_index(inplace=True)\n",
    "train_3.rename_axis(\"ID\")\n",
    "\n",
    "#Missing value treatment\n",
    "train_3[\"2015-01\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-02\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-03\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-04\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-05\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-06\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-07\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-08\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-09\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-10\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-11\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2015-12\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2016-01\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2016-02\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2016-03\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2016-04\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3[\"2016-05\"].replace(np.NaN, 0.0, inplace=True)\n",
    "train_3.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>2015-01</th>\n",
       "      <th>2015-02</th>\n",
       "      <th>2015-03</th>\n",
       "      <th>2015-04</th>\n",
       "      <th>2015-05</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-07</th>\n",
       "      <th>2015-08</th>\n",
       "      <th>2015-09</th>\n",
       "      <th>2015-10</th>\n",
       "      <th>2015-11</th>\n",
       "      <th>2015-12</th>\n",
       "      <th>2016-01</th>\n",
       "      <th>2016-02</th>\n",
       "      <th>2016-03</th>\n",
       "      <th>2016-04</th>\n",
       "      <th>2016-05</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_ctpp_fin_ult1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_valo_fin_ult1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15890</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product  2015-01  2015-02  2015-03  2015-04  \\\n",
       "0            15889   ind_cco_fin_ult1      1.0      1.0      1.0      1.0   \n",
       "1            15889  ind_ctpp_fin_ult1      1.0      1.0      1.0      1.0   \n",
       "2            15889  ind_tjcr_fin_ult1      1.0      0.0      0.0      0.0   \n",
       "3            15889  ind_valo_fin_ult1      1.0      1.0      1.0      1.0   \n",
       "4            15890   ind_cno_fin_ult1      1.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID  2015-05  2015-06  2015-07  2015-08  2015-09  2015-10  2015-11  \\\n",
       "0             1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1             1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "2             1.0      1.0      1.0      0.0      0.0      0.0      0.0   \n",
       "3             1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "4             1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID  2015-12  2016-01  2016-02  2016-03  2016-04  2016-05  \n",
       "0             1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1             1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "2             1.0      1.0      0.0      1.0      0.0      1.0  \n",
       "3             1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "4             1.0      1.0      1.0      1.0      1.0      1.0  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>2015-01</th>\n",
       "      <th>2015-02</th>\n",
       "      <th>2015-03</th>\n",
       "      <th>2015-04</th>\n",
       "      <th>2015-05</th>\n",
       "      <th>2015-06</th>\n",
       "      <th>2015-07</th>\n",
       "      <th>2015-08</th>\n",
       "      <th>2015-09</th>\n",
       "      <th>2015-10</th>\n",
       "      <th>2015-11</th>\n",
       "      <th>2015-12</th>\n",
       "      <th>2016-01</th>\n",
       "      <th>2016-02</th>\n",
       "      <th>2016-03</th>\n",
       "      <th>2016-04</th>\n",
       "      <th>2016-05</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>15924</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15925</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>15928</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>15965</td>\n",
       "      <td>ind_ctop_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>354</th>\n",
       "      <td>15965</td>\n",
       "      <td>ind_ctpp_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>16026</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>16027</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>672</th>\n",
       "      <td>16055</td>\n",
       "      <td>ind_valo_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>817</th>\n",
       "      <td>16149</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>841</th>\n",
       "      <td>16162</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>962</th>\n",
       "      <td>16217</td>\n",
       "      <td>ind_ctop_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>16217</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1024</th>\n",
       "      <td>16241</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>1049</th>\n",
       "      <td>16254</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1104</th>\n",
       "      <td>16287</td>\n",
       "      <td>ind_valo_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>16498</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1339</th>\n",
       "      <td>16521</td>\n",
       "      <td>ind_fond_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>16568</td>\n",
       "      <td>ind_ecue_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>16575</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1478</th>\n",
       "      <td>16592</td>\n",
       "      <td>ind_ecue_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1513</th>\n",
       "      <td>16613</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1529</th>\n",
       "      <td>16622</td>\n",
       "      <td>ind_ctop_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1561</th>\n",
       "      <td>16634</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1573</th>\n",
       "      <td>16641</td>\n",
       "      <td>ind_ctop_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>16641</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1644</th>\n",
       "      <td>16681</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</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>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1690</th>\n",
       "      <td>16717</td>\n",
       "      <td>ind_ctpp_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1728</th>\n",
       "      <td>16728</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1810</th>\n",
       "      <td>16787</td>\n",
       "      <td>ind_plan_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975</th>\n",
       "      <td>16895</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328070</th>\n",
       "      <td>1384815</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328084</th>\n",
       "      <td>1384834</td>\n",
       "      <td>ind_ctma_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328086</th>\n",
       "      <td>1384835</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328087</th>\n",
       "      <td>1384836</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328096</th>\n",
       "      <td>1384853</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328099</th>\n",
       "      <td>1384854</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328103</th>\n",
       "      <td>1384856</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328120</th>\n",
       "      <td>1384868</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328195</th>\n",
       "      <td>1384914</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328205</th>\n",
       "      <td>1384918</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328206</th>\n",
       "      <td>1384919</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328218</th>\n",
       "      <td>1384922</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328226</th>\n",
       "      <td>1384930</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328234</th>\n",
       "      <td>1384934</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328237</th>\n",
       "      <td>1384937</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328244</th>\n",
       "      <td>1384945</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328259</th>\n",
       "      <td>1384950</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328263</th>\n",
       "      <td>1384955</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328285</th>\n",
       "      <td>1384968</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328291</th>\n",
       "      <td>1384971</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328299</th>\n",
       "      <td>1384980</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328301</th>\n",
       "      <td>1384982</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328308</th>\n",
       "      <td>1384986</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328309</th>\n",
       "      <td>1384988</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328318</th>\n",
       "      <td>1384995</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328348</th>\n",
       "      <td>1385006</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328350</th>\n",
       "      <td>1385008</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328382</th>\n",
       "      <td>1385024</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328393</th>\n",
       "      <td>1385033</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1328399</th>\n",
       "      <td>1385035</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36218 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product  2015-01  2015-02  2015-03  2015-04  \\\n",
       "184          15924  ind_tjcr_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "187          15925    ind_recibo_ult1      0.0      1.0      0.0      0.0   \n",
       "204          15928  ind_tjcr_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "353          15965  ind_ctop_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "354          15965  ind_ctpp_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "575          16026  ind_tjcr_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "577          16027   ind_cno_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "672          16055  ind_valo_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "817          16149  ind_tjcr_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "841          16162   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "962          16217  ind_ctop_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "964          16217  ind_nom_pens_ult1      0.0      1.0      0.0      1.0   \n",
       "1024         16241    ind_recibo_ult1      0.0      1.0      0.0      0.0   \n",
       "1049         16254   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1104         16287  ind_valo_fin_ult1      0.0      1.0      0.0      1.0   \n",
       "1255         16498    ind_recibo_ult1      0.0      1.0      1.0      1.0   \n",
       "1339         16521  ind_fond_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1429         16568  ind_ecue_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1441         16575   ind_cno_fin_ult1      0.0      1.0      0.0      1.0   \n",
       "1478         16592  ind_ecue_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1513         16613    ind_recibo_ult1      0.0      1.0      1.0      1.0   \n",
       "1529         16622  ind_ctop_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1561         16634  ind_tjcr_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1573         16641  ind_ctop_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1581         16641  ind_tjcr_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1644         16681    ind_recibo_ult1      0.0      1.0      1.0      1.0   \n",
       "1690         16717  ind_ctpp_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1728         16728  ind_tjcr_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1810         16787  ind_plan_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1975         16895  ind_tjcr_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "...            ...                ...      ...      ...      ...      ...   \n",
       "1328070    1384815   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328084    1384834  ind_ctma_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328086    1384835  ind_ctju_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328087    1384836  ind_ctju_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328096    1384853   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328099    1384854   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328103    1384856   ind_cco_fin_ult1      0.0      1.0      0.0      1.0   \n",
       "1328120    1384868   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328195    1384914   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328205    1384918   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328206    1384919   ind_cco_fin_ult1      0.0      1.0      1.0      0.0   \n",
       "1328218    1384922   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328226    1384930   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328234    1384934   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328237    1384937   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328244    1384945   ind_cco_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1328259    1384950   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328263    1384955   ind_cno_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1328285    1384968   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328291    1384971   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328299    1384980   ind_cco_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1328301    1384982   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328308    1384986   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328309    1384988   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328318    1384995   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328348    1385006   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328350    1385008   ind_cco_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1328382    1385024   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "1328393    1385033   ind_cco_fin_ult1      0.0      1.0      0.0      0.0   \n",
       "1328399    1385035   ind_cco_fin_ult1      0.0      1.0      1.0      1.0   \n",
       "\n",
       "Month_ID  2015-05  2015-06  2015-07  2015-08  2015-09  2015-10  2015-11  \\\n",
       "184           1.0      0.0      1.0      0.0      0.0      0.0      0.0   \n",
       "187           0.0      1.0      0.0      0.0      0.0      0.0      1.0   \n",
       "204           0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "353           0.0      0.0      1.0      1.0      0.0      0.0      0.0   \n",
       "354           1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "575           1.0      0.0      0.0      1.0      0.0      0.0      0.0   \n",
       "577           1.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
       "672           1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "817           1.0      1.0      0.0      1.0      1.0      1.0      1.0   \n",
       "841           1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "962           1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "964           0.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1024          0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1049          1.0      1.0      1.0      0.0      0.0      0.0      0.0   \n",
       "1104          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1255          0.0      1.0      0.0      1.0      1.0      0.0      0.0   \n",
       "1339          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1429          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1441          0.0      0.0      0.0      0.0      0.0      1.0      1.0   \n",
       "1478          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1513          0.0      1.0      1.0      0.0      1.0      1.0      1.0   \n",
       "1529          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1561          0.0      0.0      0.0      0.0      0.0      0.0      1.0   \n",
       "1573          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1581          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1644          0.0      0.0      1.0      0.0      0.0      1.0      1.0   \n",
       "1690          0.0      0.0      0.0      1.0      1.0      1.0      1.0   \n",
       "1728          1.0      1.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1810          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1975          1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "...           ...      ...      ...      ...      ...      ...      ...   \n",
       "1328070       1.0      1.0      1.0      1.0      0.0      0.0      0.0   \n",
       "1328084       1.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328086       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328087       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328096       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328099       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328103       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328120       0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328195       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328205       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328206       0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328218       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328226       0.0      1.0      1.0      0.0      0.0      0.0      0.0   \n",
       "1328234       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328237       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328244       0.0      0.0      0.0      0.0      0.0      1.0      1.0   \n",
       "1328259       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328263       0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328285       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328291       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328299       0.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328301       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328308       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328309       1.0      1.0      1.0      1.0      0.0      0.0      0.0   \n",
       "1328318       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328348       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328350       0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328382       1.0      1.0      1.0      1.0      1.0      1.0      1.0   \n",
       "1328393       0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "1328399       1.0      1.0      1.0      1.0      1.0      0.0      1.0   \n",
       "\n",
       "Month_ID  2015-12  2016-01  2016-02  2016-03  2016-04  2016-05  \n",
       "184           0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "187           0.0      1.0      0.0      0.0      1.0      0.0  \n",
       "204           0.0      0.0      1.0      0.0      0.0      0.0  \n",
       "353           0.0      0.0      0.0      1.0      1.0      1.0  \n",
       "354           1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "575           0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "577           0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "672           0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "817           1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "841           1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "962           1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "964           1.0      0.0      1.0      1.0      1.0      1.0  \n",
       "1024          0.0      0.0      0.0      0.0      1.0      0.0  \n",
       "1049          0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1104          1.0      1.0      1.0      0.0      1.0      1.0  \n",
       "1255          0.0      1.0      0.0      0.0      0.0      0.0  \n",
       "1339          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1429          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1441          1.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1478          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1513          1.0      0.0      1.0      1.0      1.0      1.0  \n",
       "1529          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1561          0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1573          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1581          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1644          0.0      0.0      1.0      0.0      1.0      0.0  \n",
       "1690          1.0      1.0      1.0      1.0      1.0      0.0  \n",
       "1728          0.0      0.0      0.0      0.0      1.0      1.0  \n",
       "1810          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1975          1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "...           ...      ...      ...      ...      ...      ...  \n",
       "1328070       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328084       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328086       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328087       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328096       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328099       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328103       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328120       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328195       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328205       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328206       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328218       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328226       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328234       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328237       1.0      1.0      1.0      1.0      1.0      0.0  \n",
       "1328244       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328259       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328263       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328285       1.0      1.0      1.0      1.0      1.0      0.0  \n",
       "1328291       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328299       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328301       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328308       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328309       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328318       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328348       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328350       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328382       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "1328393       0.0      0.0      0.0      0.0      0.0      0.0  \n",
       "1328399       1.0      1.0      1.0      1.0      1.0      1.0  \n",
       "\n",
       "[36218 rows x 19 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_4 = train_3\n",
    "train_4.ix[(train_4[\"2015-01\"] == 0.0) & (train_4[\"2015-02\"] == 1.0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_4[\"Feb15_add\"] = (train_4[\"2015-01\"] == 0.0) & (train_4[\"2015-02\"] == 1.0)\n",
    "train_4[\"Mar15_add\"] = (train_4[\"2015-02\"] == 0.0) & (train_4[\"2015-03\"] == 1.0)\n",
    "train_4[\"Apr15_add\"] = (train_4[\"2015-03\"] == 0.0) & (train_4[\"2015-04\"] == 1.0)\n",
    "train_4[\"May15_add\"] = (train_4[\"2015-04\"] == 0.0) & (train_4[\"2015-05\"] == 1.0)\n",
    "train_4[\"Jun15_add\"] = (train_4[\"2015-05\"] == 0.0) & (train_4[\"2015-06\"] == 1.0)\n",
    "train_4[\"Jul15_add\"] = (train_4[\"2015-06\"] == 0.0) & (train_4[\"2015-07\"] == 1.0)\n",
    "train_4[\"Aug15_add\"] = (train_4[\"2015-07\"] == 0.0) & (train_4[\"2015-08\"] == 1.0)\n",
    "train_4[\"Sep15_add\"] = (train_4[\"2015-08\"] == 0.0) & (train_4[\"2015-09\"] == 1.0)\n",
    "train_4[\"Oct15_add\"] = (train_4[\"2015-09\"] == 0.0) & (train_4[\"2015-10\"] == 1.0)\n",
    "train_4[\"Nov15_add\"] = (train_4[\"2015-10\"] == 0.0) & (train_4[\"2015-11\"] == 1.0)\n",
    "train_4[\"Dec15_add\"] = (train_4[\"2015-11\"] == 0.0) & (train_4[\"2015-12\"] == 1.0)\n",
    "train_4[\"Jan16_add\"] = (train_4[\"2015-12\"] == 0.0) & (train_4[\"2016-01\"] == 1.0)\n",
    "train_4[\"Feb16_add\"] = (train_4[\"2016-01\"] == 0.0) & (train_4[\"2016-02\"] == 1.0)\n",
    "train_4[\"Mar16_add\"] = (train_4[\"2016-02\"] == 0.0) & (train_4[\"2016-03\"] == 1.0)\n",
    "train_4[\"Apr16_add\"] = (train_4[\"2016-03\"] == 0.0) & (train_4[\"2016-04\"] == 1.0)\n",
    "train_4[\"May16_add\"] = (train_4[\"2016-04\"] == 0.0) & (train_4[\"2016-05\"] == 1.0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_4.drop([\"2015-01\", \"2015-02\", \"2015-03\",\"2015-04\",\"2015-05\",\"2015-06\",\"2015-07\",\"2015-08\", \"2015-09\", \"2015-10\", \"2015-11\", \"2015-12\", \"2016-01\", \"2016-02\", \"2016-03\", \"2016-04\", \"2016-05\"], axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>Feb15_add</th>\n",
       "      <th>Mar15_add</th>\n",
       "      <th>Apr15_add</th>\n",
       "      <th>May15_add</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>Jul15_add</th>\n",
       "      <th>Aug15_add</th>\n",
       "      <th>Sep15_add</th>\n",
       "      <th>Oct15_add</th>\n",
       "      <th>Nov15_add</th>\n",
       "      <th>Dec15_add</th>\n",
       "      <th>Jan16_add</th>\n",
       "      <th>Feb16_add</th>\n",
       "      <th>Mar16_add</th>\n",
       "      <th>Apr16_add</th>\n",
       "      <th>May16_add</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_ctpp_fin_ult1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15889</td>\n",
       "      <td>ind_valo_fin_ult1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>15890</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product Feb15_add Mar15_add Apr15_add May15_add  \\\n",
       "0            15889   ind_cco_fin_ult1     False     False     False     False   \n",
       "1            15889  ind_ctpp_fin_ult1     False     False     False     False   \n",
       "2            15889  ind_tjcr_fin_ult1     False     False     False      True   \n",
       "3            15889  ind_valo_fin_ult1     False     False     False     False   \n",
       "4            15890   ind_cno_fin_ult1     False     False     False     False   \n",
       "\n",
       "Month_ID Jun15_add Jul15_add Aug15_add Sep15_add Oct15_add Nov15_add  \\\n",
       "0            False     False     False     False     False     False   \n",
       "1            False     False     False     False     False     False   \n",
       "2            False     False     False     False     False     False   \n",
       "3            False     False     False     False     False     False   \n",
       "4            False     False     False     False     False     False   \n",
       "\n",
       "Month_ID Dec15_add Jan16_add Feb16_add Mar16_add Apr16_add May16_add  \n",
       "0            False     False     False     False     False     False  \n",
       "1            False     False     False     False     False     False  \n",
       "2             True     False     False      True     False      True  \n",
       "3            False     False     False     False     False     False  \n",
       "4            False     False     False     False     False     False  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feb15_added_products: 36218\n",
      "Mar15_added_products: 36466\n",
      "Apr15_added_products: 34421\n",
      "May15_added_products: 30417\n",
      "Jun15_added_products: 45679\n",
      "Jul15_added_products: 47316\n",
      "Aug15_added_products: 40886\n",
      "Sep15_added_products: 52200\n",
      "Oct15_added_products: 56476\n",
      "Nov15_added_products: 44231\n",
      "Dec15_added_products: 46164\n",
      "Jan16_added_products: 35393\n",
      "Feb16_added_products: 52203\n",
      "Mar16_added_products: 38312\n",
      "Apr16_added_products: 35229\n",
      "May16_added_products: 37897\n"
     ]
    }
   ],
   "source": [
    "print \"Feb15_added_products:\", train_4[\"Feb15_add\"].sum()\n",
    "print \"Mar15_added_products:\", train_4[\"Mar15_add\"].sum()\n",
    "print \"Apr15_added_products:\", train_4[\"Apr15_add\"].sum()\n",
    "print \"May15_added_products:\", train_4[\"May15_add\"].sum()\n",
    "print \"Jun15_added_products:\", train_4[\"Jun15_add\"].sum()\n",
    "print \"Jul15_added_products:\", train_4[\"Jul15_add\"].sum()\n",
    "print \"Aug15_added_products:\", train_4[\"Aug15_add\"].sum()\n",
    "print \"Sep15_added_products:\", train_4[\"Sep15_add\"].sum()\n",
    "print \"Oct15_added_products:\", train_4[\"Oct15_add\"].sum()\n",
    "print \"Nov15_added_products:\", train_4[\"Nov15_add\"].sum()\n",
    "print \"Dec15_added_products:\", train_4[\"Dec15_add\"].sum()\n",
    "print \"Jan16_added_products:\", train_4[\"Jan16_add\"].sum()\n",
    "print \"Feb16_added_products:\", train_4[\"Feb16_add\"].sum()\n",
    "print \"Mar16_added_products:\", train_4[\"Mar16_add\"].sum()\n",
    "print \"Apr16_added_products:\", train_4[\"Apr16_add\"].sum()\n",
    "print \"May16_added_products:\", train_4[\"May16_add\"].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feb15_added_products by Unique Customers: 28088\n",
      "Mar15_added_products by Unique Customers: 29124\n",
      "Apr15_added_products by Unique Customers: 28987\n",
      "May15_added_products by Unique Customers: 24879\n",
      "Jun15_added_products by Unique Customers: 36710\n",
      "Jul15_added_products by Unique Customers: 39324\n",
      "Aug15_added_products by Unique Customers: 35385\n",
      "Sep15_added_products by Unique Customers: 44580\n",
      "Oct15_added_products by Unique Customers: 48507\n",
      "Nov15_added_products by Unique Customers: 36678\n",
      "Dec15_added_products by Unique Customers: 37108\n",
      "Jan16_added_products by Unique Customers: 30331\n",
      "Feb16_added_products by Unique Customers: 38902\n",
      "Mar16_added_products by Unique Customers: 30544\n",
      "Apr16_added_products by Unique Customers: 28707\n",
      "May16_added_products by Unique Customers: 29717\n"
     ]
    }
   ],
   "source": [
    "print \"Feb15_added_products by Unique Customers:\", train_4.groupby(\"Feb15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Mar15_added_products by Unique Customers:\", train_4.groupby(\"Mar15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Apr15_added_products by Unique Customers:\", train_4.groupby(\"Apr15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"May15_added_products by Unique Customers:\", train_4.groupby(\"May15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Jun15_added_products by Unique Customers:\", train_4.groupby(\"Jun15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Jul15_added_products by Unique Customers:\", train_4.groupby(\"Jul15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Aug15_added_products by Unique Customers:\", train_4.groupby(\"Aug15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Sep15_added_products by Unique Customers:\", train_4.groupby(\"Sep15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Oct15_added_products by Unique Customers:\", train_4.groupby(\"Oct15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Nov15_added_products by Unique Customers:\", train_4.groupby(\"Nov15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Dec15_added_products by Unique Customers:\", train_4.groupby(\"Dec15_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Jan16_added_products by Unique Customers:\", train_4.groupby(\"Jan16_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Feb16_added_products by Unique Customers:\", train_4.groupby(\"Feb16_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Mar16_added_products by Unique Customers:\", train_4.groupby(\"Mar16_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"Apr16_added_products by Unique Customers:\", train_4.groupby(\"Apr16_add\")[\"ncodpers\"].nunique()[True]\n",
    "print \"May16_added_products by Unique Customers:\", train_4.groupby(\"May16_add\")[\"ncodpers\"].nunique()[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ind_tjcr_fin_ult1', 'ind_recibo_ult1', 'ind_fond_fin_ult1',\n",
       "       'ind_ecue_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctop_fin_ult1',\n",
       "       'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_dela_fin_ult1',\n",
       "       'ind_cco_fin_ult1', 'ind_deme_fin_ult1', 'ind_reca_fin_ult1',\n",
       "       'ind_ctpp_fin_ult1', 'ind_valo_fin_ult1', 'ind_pres_fin_ult1',\n",
       "       'ind_deco_fin_ult1', 'ind_hip_fin_ult1', 'ind_plan_fin_ult1',\n",
       "       'ind_ctma_fin_ult1', 'ind_cder_fin_ult1', 'ind_viv_fin_ult1',\n",
       "       'ind_ctju_fin_ult1'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_4.groupby(\"May15_add\")[\"Product\"].unique()[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 18)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun15_temp = train_4.loc[train_4[\"Jun15_add\"] == True]\n",
    "Jun15_temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1487379, 18)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_4.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feb15_added_unique_products: 24\n",
      "Mar15_added_unique_products: 23\n",
      "Apr15_added_unique_products: 23\n",
      "May15_added_unique_products: 22\n",
      "Jun15_added_unique_products: 22\n",
      "Jul15_added_unique_products: 22\n",
      "Aug15_added_unique_products: 22\n",
      "Sep15_added_unique_products: 22\n",
      "Oct15_added_unique_products: 22\n",
      "Nov15_added_unique_products: 22\n",
      "Dec15_added_unique_products: 21\n",
      "Jan16_added_unique_products: 21\n",
      "Feb16_added_unique_products: 21\n",
      "Mar16_added_unique_products: 20\n",
      "Apr16_added_unique_products: 20\n",
      "May16_added_unique_products: 21\n"
     ]
    }
   ],
   "source": [
    "print \"Feb15_added_unique_products:\", train_4.groupby(\"Feb15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Mar15_added_unique_products:\", train_4.groupby(\"Mar15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Apr15_added_unique_products:\", train_4.groupby(\"Apr15_add\")[\"Product\"].nunique()[True]\n",
    "print \"May15_added_unique_products:\", train_4.groupby(\"May15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Jun15_added_unique_products:\", train_4.groupby(\"Jun15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Jul15_added_unique_products:\", train_4.groupby(\"Jul15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Aug15_added_unique_products:\", train_4.groupby(\"Aug15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Sep15_added_unique_products:\", train_4.groupby(\"Sep15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Oct15_added_unique_products:\", train_4.groupby(\"Oct15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Nov15_added_unique_products:\", train_4.groupby(\"Nov15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Dec15_added_unique_products:\", train_4.groupby(\"Dec15_add\")[\"Product\"].nunique()[True]\n",
    "print \"Jan16_added_unique_products:\", train_4.groupby(\"Jan16_add\")[\"Product\"].nunique()[True]\n",
    "print \"Feb16_added_unique_products:\", train_4.groupby(\"Feb16_add\")[\"Product\"].nunique()[True]\n",
    "print \"Mar16_added_unique_products:\", train_4.groupby(\"Mar16_add\")[\"Product\"].nunique()[True]\n",
    "print \"Apr16_added_unique_products:\", train_4.groupby(\"Apr16_add\")[\"Product\"].nunique()[True]\n",
    "print \"May16_added_unique_products:\", train_4.groupby(\"May16_add\")[\"Product\"].nunique()[True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feb15_added_unique_products: ['ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Mar15_added_unique_products: ['ind_aval_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Apr15_added_unique_products: ['ind_aval_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "May15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Jun15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Jul15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Aug15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Sep15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Oct15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Nov15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_deme_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Dec15_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Jan16_added_unique_products: ['ind_aval_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Feb16_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Mar16_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "Apr16_added_unique_products: ['ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n",
      "\n",
      "May16_added_unique_products: ['ind_ahor_fin_ult1', 'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1', 'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1', 'ind_nom_pens_ult1', 'ind_nomina_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1', 'ind_recibo_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1']\n"
     ]
    }
   ],
   "source": [
    "print \"Feb15_added_unique_products:\", sorted(train_4.groupby(\"Feb15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nMar15_added_unique_products:\", sorted(train_4.groupby(\"Mar15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nApr15_added_unique_products:\", sorted(train_4.groupby(\"Apr15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nMay15_added_unique_products:\", sorted(train_4.groupby(\"May15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nJun15_added_unique_products:\", sorted(train_4.groupby(\"Jun15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nJul15_added_unique_products:\", sorted(train_4.groupby(\"Jul15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nAug15_added_unique_products:\", sorted(train_4.groupby(\"Aug15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nSep15_added_unique_products:\", sorted(train_4.groupby(\"Sep15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nOct15_added_unique_products:\", sorted(train_4.groupby(\"Oct15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nNov15_added_unique_products:\", sorted(train_4.groupby(\"Nov15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nDec15_added_unique_products:\", sorted(train_4.groupby(\"Dec15_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nJan16_added_unique_products:\", sorted(train_4.groupby(\"Jan16_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nFeb16_added_unique_products:\", sorted(train_4.groupby(\"Feb16_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nMar16_added_unique_products:\", sorted(train_4.groupby(\"Mar16_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nApr16_added_unique_products:\", sorted(train_4.groupby(\"Apr16_add\")[\"Product\"].unique()[True])\n",
    "print \"\\nMay16_added_unique_products:\", sorted(train_4.groupby(\"May16_add\")[\"Product\"].unique()[True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prod_summary = pd.concat([train_4.groupby(\"Product\")[\"May16_add\"].sum(), train_4.groupby(\"Product\")[\"Apr16_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Mar16_add\"].sum(), train_4.groupby(\"Product\")[\"Feb16_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Jan16_add\"].sum(), train_4.groupby(\"Product\")[\"Dec15_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Nov15_add\"].sum(), train_4.groupby(\"Product\")[\"Oct15_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Sep15_add\"].sum(), train_4.groupby(\"Product\")[\"Aug15_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Jul15_add\"].sum(), train_4.groupby(\"Product\")[\"Jun15_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"May15_add\"].sum(), train_4.groupby(\"Product\")[\"Apr15_add\"].sum(),\n",
    "          train_4.groupby(\"Product\")[\"Mar15_add\"].sum(), train_4.groupby(\"Product\")[\"Feb15_add\"].sum()], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>May16_add</th>\n",
       "      <th>Apr16_add</th>\n",
       "      <th>Mar16_add</th>\n",
       "      <th>Feb16_add</th>\n",
       "      <th>Jan16_add</th>\n",
       "      <th>Dec15_add</th>\n",
       "      <th>Nov15_add</th>\n",
       "      <th>Oct15_add</th>\n",
       "      <th>Sep15_add</th>\n",
       "      <th>Aug15_add</th>\n",
       "      <th>Jul15_add</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>May15_add</th>\n",
       "      <th>Apr15_add</th>\n",
       "      <th>Mar15_add</th>\n",
       "      <th>Feb15_add</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</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",
       "      <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>ind_ahor_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_aval_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <td>5511.0</td>\n",
       "      <td>5761.0</td>\n",
       "      <td>7136.0</td>\n",
       "      <td>6546.0</td>\n",
       "      <td>8210.0</td>\n",
       "      <td>12096.0</td>\n",
       "      <td>13034.0</td>\n",
       "      <td>21727.0</td>\n",
       "      <td>20255.0</td>\n",
       "      <td>16559.0</td>\n",
       "      <td>15585.0</td>\n",
       "      <td>9457.0</td>\n",
       "      <td>5355.0</td>\n",
       "      <td>5335.0</td>\n",
       "      <td>5694.0</td>\n",
       "      <td>5831.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cder_fin_ult1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <td>2369.0</td>\n",
       "      <td>2424.0</td>\n",
       "      <td>2459.0</td>\n",
       "      <td>2260.0</td>\n",
       "      <td>2213.0</td>\n",
       "      <td>4033.0</td>\n",
       "      <td>3039.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>1811.0</td>\n",
       "      <td>2448.0</td>\n",
       "      <td>2888.0</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2068.0</td>\n",
       "      <td>1852.0</td>\n",
       "      <td>1964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctju_fin_ult1</th>\n",
       "      <td>77.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctma_fin_ult1</th>\n",
       "      <td>713.0</td>\n",
       "      <td>854.0</td>\n",
       "      <td>913.0</td>\n",
       "      <td>1061.0</td>\n",
       "      <td>868.0</td>\n",
       "      <td>806.0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>582.0</td>\n",
       "      <td>347.0</td>\n",
       "      <td>356.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>324.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>409.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctop_fin_ult1</th>\n",
       "      <td>226.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>217.0</td>\n",
       "      <td>259.0</td>\n",
       "      <td>283.0</td>\n",
       "      <td>209.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>231.0</td>\n",
       "      <td>265.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>282.0</td>\n",
       "      <td>292.0</td>\n",
       "      <td>261.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <td>131.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>141.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>153.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>154.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>174.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_deco_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>472.0</td>\n",
       "      <td>581.0</td>\n",
       "      <td>508.0</td>\n",
       "      <td>526.0</td>\n",
       "      <td>503.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>485.0</td>\n",
       "      <td>763.0</td>\n",
       "      <td>808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_dela_fin_ult1</th>\n",
       "      <td>46.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>715.0</td>\n",
       "      <td>471.0</td>\n",
       "      <td>1277.0</td>\n",
       "      <td>971.0</td>\n",
       "      <td>1472.0</td>\n",
       "      <td>1411.0</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>1282.0</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>880.0</td>\n",
       "      <td>906.0</td>\n",
       "      <td>786.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_deme_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ecue_fin_ult1</th>\n",
       "      <td>2723.0</td>\n",
       "      <td>2266.0</td>\n",
       "      <td>1918.0</td>\n",
       "      <td>2358.0</td>\n",
       "      <td>2284.0</td>\n",
       "      <td>1826.0</td>\n",
       "      <td>1597.0</td>\n",
       "      <td>1216.0</td>\n",
       "      <td>1081.0</td>\n",
       "      <td>930.0</td>\n",
       "      <td>1106.0</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1486.0</td>\n",
       "      <td>1619.0</td>\n",
       "      <td>1492.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_fond_fin_ult1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>147.0</td>\n",
       "      <td>206.0</td>\n",
       "      <td>171.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>133.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>207.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>578.0</td>\n",
       "      <td>456.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_hip_fin_ult1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_nom_pens_ult1</th>\n",
       "      <td>5541.0</td>\n",
       "      <td>4474.0</td>\n",
       "      <td>5073.0</td>\n",
       "      <td>12731.0</td>\n",
       "      <td>2942.0</td>\n",
       "      <td>5211.0</td>\n",
       "      <td>4475.0</td>\n",
       "      <td>4845.0</td>\n",
       "      <td>4799.0</td>\n",
       "      <td>3005.0</td>\n",
       "      <td>5026.0</td>\n",
       "      <td>8229.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>5712.0</td>\n",
       "      <td>4566.0</td>\n",
       "      <td>5729.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_nomina_ult1</th>\n",
       "      <td>5516.0</td>\n",
       "      <td>3839.0</td>\n",
       "      <td>5009.0</td>\n",
       "      <td>9541.0</td>\n",
       "      <td>2487.0</td>\n",
       "      <td>5133.0</td>\n",
       "      <td>4460.0</td>\n",
       "      <td>4872.0</td>\n",
       "      <td>4727.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>4569.0</td>\n",
       "      <td>5161.0</td>\n",
       "      <td>3430.0</td>\n",
       "      <td>2742.0</td>\n",
       "      <td>4570.0</td>\n",
       "      <td>5723.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_plan_fin_ult1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>31.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_pres_fin_ult1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_reca_fin_ult1</th>\n",
       "      <td>283.0</td>\n",
       "      <td>812.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>565.0</td>\n",
       "      <td>455.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>425.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>266.0</td>\n",
       "      <td>598.0</td>\n",
       "      <td>2942.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>221.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_recibo_ult1</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>9857.0</td>\n",
       "      <td>10199.0</td>\n",
       "      <td>11613.0</td>\n",
       "      <td>10153.0</td>\n",
       "      <td>9962.0</td>\n",
       "      <td>10182.0</td>\n",
       "      <td>12195.0</td>\n",
       "      <td>11303.0</td>\n",
       "      <td>7186.0</td>\n",
       "      <td>9320.0</td>\n",
       "      <td>9131.0</td>\n",
       "      <td>7183.0</td>\n",
       "      <td>8471.0</td>\n",
       "      <td>9273.0</td>\n",
       "      <td>8394.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <td>4252.0</td>\n",
       "      <td>4107.0</td>\n",
       "      <td>4407.0</td>\n",
       "      <td>3883.0</td>\n",
       "      <td>3794.0</td>\n",
       "      <td>4298.0</td>\n",
       "      <td>4317.0</td>\n",
       "      <td>4462.0</td>\n",
       "      <td>4466.0</td>\n",
       "      <td>4383.0</td>\n",
       "      <td>5021.0</td>\n",
       "      <td>4755.0</td>\n",
       "      <td>4191.0</td>\n",
       "      <td>4724.0</td>\n",
       "      <td>5176.0</td>\n",
       "      <td>3545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <td>183.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>325.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>261.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>914.0</td>\n",
       "      <td>331.0</td>\n",
       "      <td>447.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>270.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_viv_fin_ult1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   May16_add  Apr16_add  Mar16_add  Feb16_add  Jan16_add  \\\n",
       "Product                                                                    \n",
       "ind_ahor_fin_ult1        1.0        0.0        0.0        0.0        0.0   \n",
       "ind_aval_fin_ult1        0.0        0.0        0.0        0.0        1.0   \n",
       "ind_cco_fin_ult1      5511.0     5761.0     7136.0     6546.0     8210.0   \n",
       "ind_cder_fin_ult1        5.0        5.0        5.0       10.0       12.0   \n",
       "ind_cno_fin_ult1      2369.0     2424.0     2459.0     2260.0     2213.0   \n",
       "ind_ctju_fin_ult1       77.0       77.0       88.0       93.0       89.0   \n",
       "ind_ctma_fin_ult1      713.0      854.0      913.0     1061.0      868.0   \n",
       "ind_ctop_fin_ult1      226.0      240.0      225.0      217.0      259.0   \n",
       "ind_ctpp_fin_ult1      131.0      161.0      131.0      141.0      149.0   \n",
       "ind_deco_fin_ult1        0.0        0.0        0.0        1.0        0.0   \n",
       "ind_dela_fin_ult1       46.0      101.0      245.0      715.0      471.0   \n",
       "ind_deme_fin_ult1        0.0        0.0        0.0        0.0        0.0   \n",
       "ind_ecue_fin_ult1     2723.0     2266.0     1918.0     2358.0     2284.0   \n",
       "ind_fond_fin_ult1       61.0       85.0       92.0       82.0      147.0   \n",
       "ind_hip_fin_ult1         3.0        5.0        2.0        6.0        2.0   \n",
       "ind_nom_pens_ult1     5541.0     4474.0     5073.0    12731.0     2942.0   \n",
       "ind_nomina_ult1       5516.0     3839.0     5009.0     9541.0     2487.0   \n",
       "ind_plan_fin_ult1       22.0       22.0       31.0       47.0      104.0   \n",
       "ind_pres_fin_ult1        7.0        4.0        8.0        6.0        7.0   \n",
       "ind_reca_fin_ult1      283.0      812.0      201.0      565.0      455.0   \n",
       "ind_recibo_ult1      10220.0     9857.0    10199.0    11613.0    10153.0   \n",
       "ind_tjcr_fin_ult1     4252.0     4107.0     4407.0     3883.0     3794.0   \n",
       "ind_valo_fin_ult1      183.0      132.0      166.0      325.0      744.0   \n",
       "ind_viv_fin_ult1         7.0        3.0        4.0        2.0        2.0   \n",
       "\n",
       "                   Dec15_add  Nov15_add  Oct15_add  Sep15_add  Aug15_add  \\\n",
       "Product                                                                    \n",
       "ind_ahor_fin_ult1        0.0        0.0        0.0        0.0        0.0   \n",
       "ind_aval_fin_ult1        0.0        0.0        0.0        0.0        0.0   \n",
       "ind_cco_fin_ult1     12096.0    13034.0    21727.0    20255.0    16559.0   \n",
       "ind_cder_fin_ult1        9.0        6.0        3.0        7.0       11.0   \n",
       "ind_cno_fin_ult1      4033.0     3039.0     2450.0     1811.0     2448.0   \n",
       "ind_ctju_fin_ult1      170.0       74.0       74.0       52.0       65.0   \n",
       "ind_ctma_fin_ult1      806.0      823.0      589.0      582.0      347.0   \n",
       "ind_ctop_fin_ult1      283.0      209.0      224.0      191.0      231.0   \n",
       "ind_ctpp_fin_ult1      155.0      140.0      153.0      122.0      145.0   \n",
       "ind_deco_fin_ult1        8.0      115.0      472.0      581.0      508.0   \n",
       "ind_dela_fin_ult1     1277.0      971.0     1472.0     1411.0     1073.0   \n",
       "ind_deme_fin_ult1        0.0        1.0       22.0       30.0       33.0   \n",
       "ind_ecue_fin_ult1     1826.0     1597.0     1216.0     1081.0      930.0   \n",
       "ind_fond_fin_ult1      206.0      171.0      148.0      133.0      224.0   \n",
       "ind_hip_fin_ult1         3.0        3.0        7.0        8.0        1.0   \n",
       "ind_nom_pens_ult1     5211.0     4475.0     4845.0     4799.0     3005.0   \n",
       "ind_nomina_ult1       5133.0     4460.0     4872.0     4727.0     2987.0   \n",
       "ind_plan_fin_ult1      168.0       40.0       20.0       23.0       22.0   \n",
       "ind_pres_fin_ult1       11.0        9.0        9.0       13.0       13.0   \n",
       "ind_reca_fin_ult1      240.0      425.0      600.0      272.0      266.0   \n",
       "ind_recibo_ult1       9962.0    10182.0    12195.0    11303.0     7186.0   \n",
       "ind_tjcr_fin_ult1     4298.0     4317.0     4462.0     4466.0     4383.0   \n",
       "ind_valo_fin_ult1      261.0      135.0      914.0      331.0      447.0   \n",
       "ind_viv_fin_ult1         8.0        5.0        2.0        2.0        2.0   \n",
       "\n",
       "                   Jul15_add  Jun15_add  May15_add  Apr15_add  Mar15_add  \\\n",
       "Product                                                                    \n",
       "ind_ahor_fin_ult1        0.0        0.0        0.0        0.0        0.0   \n",
       "ind_aval_fin_ult1        0.0        0.0        0.0        1.0        1.0   \n",
       "ind_cco_fin_ult1     15585.0     9457.0     5355.0     5335.0     5694.0   \n",
       "ind_cder_fin_ult1       11.0        9.0        7.0       12.0       18.0   \n",
       "ind_cno_fin_ult1      2888.0     1934.0     1987.0     2068.0     1852.0   \n",
       "ind_ctju_fin_ult1       79.0       55.0       84.0       69.0       74.0   \n",
       "ind_ctma_fin_ult1      356.0      349.0      308.0      324.0      408.0   \n",
       "ind_ctop_fin_ult1      265.0      222.0      298.0      282.0      292.0   \n",
       "ind_ctpp_fin_ult1      165.0      154.0      174.0      173.0      167.0   \n",
       "ind_deco_fin_ult1      526.0      503.0      435.0      485.0      763.0   \n",
       "ind_dela_fin_ult1     1282.0     1085.0     1005.0      880.0      906.0   \n",
       "ind_deme_fin_ult1       24.0       33.0       25.0       26.0       23.0   \n",
       "ind_ecue_fin_ult1     1106.0     1219.0     1480.0     1486.0     1619.0   \n",
       "ind_fond_fin_ult1      207.0      246.0      438.0      501.0      578.0   \n",
       "ind_hip_fin_ult1         6.0        4.0       11.0        4.0        8.0   \n",
       "ind_nom_pens_ult1     5026.0     8229.0     3409.0     5712.0     4566.0   \n",
       "ind_nomina_ult1       4569.0     5161.0     3430.0     2742.0     4570.0   \n",
       "ind_plan_fin_ult1       28.0       21.0       24.0       24.0       26.0   \n",
       "ind_pres_fin_ult1        5.0        8.0       13.0       22.0       16.0   \n",
       "ind_reca_fin_ult1      598.0     2942.0      343.0      877.0      220.0   \n",
       "ind_recibo_ult1       9320.0     9131.0     7183.0     8471.0     9273.0   \n",
       "ind_tjcr_fin_ult1     5021.0     4755.0     4191.0     4724.0     5176.0   \n",
       "ind_valo_fin_ult1      240.0      159.0      213.0      198.0      212.0   \n",
       "ind_viv_fin_ult1         9.0        3.0        4.0        5.0        4.0   \n",
       "\n",
       "                   Feb15_add  \n",
       "Product                       \n",
       "ind_ahor_fin_ult1        1.0  \n",
       "ind_aval_fin_ult1        1.0  \n",
       "ind_cco_fin_ult1      5831.0  \n",
       "ind_cder_fin_ult1       12.0  \n",
       "ind_cno_fin_ult1      1964.0  \n",
       "ind_ctju_fin_ult1       54.0  \n",
       "ind_ctma_fin_ult1      409.0  \n",
       "ind_ctop_fin_ult1      261.0  \n",
       "ind_ctpp_fin_ult1      174.0  \n",
       "ind_deco_fin_ult1      808.0  \n",
       "ind_dela_fin_ult1      786.0  \n",
       "ind_deme_fin_ult1       33.0  \n",
       "ind_ecue_fin_ult1     1492.0  \n",
       "ind_fond_fin_ult1      456.0  \n",
       "ind_hip_fin_ult1         5.0  \n",
       "ind_nom_pens_ult1     5729.0  \n",
       "ind_nomina_ult1       5723.0  \n",
       "ind_plan_fin_ult1       31.0  \n",
       "ind_pres_fin_ult1       10.0  \n",
       "ind_reca_fin_ult1      221.0  \n",
       "ind_recibo_ult1       8394.0  \n",
       "ind_tjcr_fin_ult1     3545.0  \n",
       "ind_valo_fin_ult1      270.0  \n",
       "ind_viv_fin_ult1         8.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prod_summary[\"May16_May15\"] = prod_summary[\"May16_add\"]/(prod_summary[\"May15_add\"])\n",
    "prod_summary[\"Jun15_May15\"] = prod_summary[\"Jun15_add\"]/(prod_summary[\"May15_add\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>May16_add</th>\n",
       "      <th>May15_add</th>\n",
       "      <th>May16_May15</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>Jun15_May15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</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>ind_ahor_fin_ult1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>inf</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_aval_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <td>5511.0</td>\n",
       "      <td>5355.0</td>\n",
       "      <td>1.029132</td>\n",
       "      <td>9457.0</td>\n",
       "      <td>1.766013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cder_fin_ult1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <td>2369.0</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>1.192250</td>\n",
       "      <td>1934.0</td>\n",
       "      <td>0.973327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctju_fin_ult1</th>\n",
       "      <td>77.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.654762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctma_fin_ult1</th>\n",
       "      <td>713.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>2.314935</td>\n",
       "      <td>349.0</td>\n",
       "      <td>1.133117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctop_fin_ult1</th>\n",
       "      <td>226.0</td>\n",
       "      <td>298.0</td>\n",
       "      <td>0.758389</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.744966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ctpp_fin_ult1</th>\n",
       "      <td>131.0</td>\n",
       "      <td>174.0</td>\n",
       "      <td>0.752874</td>\n",
       "      <td>154.0</td>\n",
       "      <td>0.885057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_deco_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>503.0</td>\n",
       "      <td>1.156322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_dela_fin_ult1</th>\n",
       "      <td>46.0</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>0.045771</td>\n",
       "      <td>1085.0</td>\n",
       "      <td>1.079602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_deme_fin_ult1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1.320000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_ecue_fin_ult1</th>\n",
       "      <td>2723.0</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>1.839865</td>\n",
       "      <td>1219.0</td>\n",
       "      <td>0.823649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_fond_fin_ult1</th>\n",
       "      <td>61.0</td>\n",
       "      <td>438.0</td>\n",
       "      <td>0.139269</td>\n",
       "      <td>246.0</td>\n",
       "      <td>0.561644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_hip_fin_ult1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.363636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_nom_pens_ult1</th>\n",
       "      <td>5541.0</td>\n",
       "      <td>3409.0</td>\n",
       "      <td>1.625403</td>\n",
       "      <td>8229.0</td>\n",
       "      <td>2.413904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_nomina_ult1</th>\n",
       "      <td>5516.0</td>\n",
       "      <td>3430.0</td>\n",
       "      <td>1.608163</td>\n",
       "      <td>5161.0</td>\n",
       "      <td>1.504665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_plan_fin_ult1</th>\n",
       "      <td>22.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.875000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_pres_fin_ult1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.538462</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.615385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_reca_fin_ult1</th>\n",
       "      <td>283.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>0.825073</td>\n",
       "      <td>2942.0</td>\n",
       "      <td>8.577259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_recibo_ult1</th>\n",
       "      <td>10220.0</td>\n",
       "      <td>7183.0</td>\n",
       "      <td>1.422804</td>\n",
       "      <td>9131.0</td>\n",
       "      <td>1.271196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <td>4252.0</td>\n",
       "      <td>4191.0</td>\n",
       "      <td>1.014555</td>\n",
       "      <td>4755.0</td>\n",
       "      <td>1.134574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <td>183.0</td>\n",
       "      <td>213.0</td>\n",
       "      <td>0.859155</td>\n",
       "      <td>159.0</td>\n",
       "      <td>0.746479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ind_viv_fin_ult1</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.750000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   May16_add  May15_add  May16_May15  Jun15_add  Jun15_May15\n",
       "Product                                                                     \n",
       "ind_ahor_fin_ult1        1.0        0.0          inf        0.0          NaN\n",
       "ind_aval_fin_ult1        0.0        0.0          NaN        0.0          NaN\n",
       "ind_cco_fin_ult1      5511.0     5355.0     1.029132     9457.0     1.766013\n",
       "ind_cder_fin_ult1        5.0        7.0     0.714286        9.0     1.285714\n",
       "ind_cno_fin_ult1      2369.0     1987.0     1.192250     1934.0     0.973327\n",
       "ind_ctju_fin_ult1       77.0       84.0     0.916667       55.0     0.654762\n",
       "ind_ctma_fin_ult1      713.0      308.0     2.314935      349.0     1.133117\n",
       "ind_ctop_fin_ult1      226.0      298.0     0.758389      222.0     0.744966\n",
       "ind_ctpp_fin_ult1      131.0      174.0     0.752874      154.0     0.885057\n",
       "ind_deco_fin_ult1        0.0      435.0     0.000000      503.0     1.156322\n",
       "ind_dela_fin_ult1       46.0     1005.0     0.045771     1085.0     1.079602\n",
       "ind_deme_fin_ult1        0.0       25.0     0.000000       33.0     1.320000\n",
       "ind_ecue_fin_ult1     2723.0     1480.0     1.839865     1219.0     0.823649\n",
       "ind_fond_fin_ult1       61.0      438.0     0.139269      246.0     0.561644\n",
       "ind_hip_fin_ult1         3.0       11.0     0.272727        4.0     0.363636\n",
       "ind_nom_pens_ult1     5541.0     3409.0     1.625403     8229.0     2.413904\n",
       "ind_nomina_ult1       5516.0     3430.0     1.608163     5161.0     1.504665\n",
       "ind_plan_fin_ult1       22.0       24.0     0.916667       21.0     0.875000\n",
       "ind_pres_fin_ult1        7.0       13.0     0.538462        8.0     0.615385\n",
       "ind_reca_fin_ult1      283.0      343.0     0.825073     2942.0     8.577259\n",
       "ind_recibo_ult1      10220.0     7183.0     1.422804     9131.0     1.271196\n",
       "ind_tjcr_fin_ult1     4252.0     4191.0     1.014555     4755.0     1.134574\n",
       "ind_valo_fin_ult1      183.0      213.0     0.859155      159.0     0.746479\n",
       "ind_viv_fin_ult1         7.0        4.0     1.750000        3.0     0.750000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prod_summary[[\"May16_add\", \"May15_add\",\"May16_May15\", \"Jun15_add\",\"Jun15_May15\" ]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ind_dela_fin_ult1 - This product seems to be less popular in 2016. In 2015 1K+ customer's have bought this product whereas in 2016 May, only 46 customers have bought it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "prod_summary.loc[\"ind_dela_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pr62o2sTJzTazAjjlphiX5znA+NT6IOBJVd0KeAoYDCAifYFjgT7AwcB1Ist+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d0f74d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "prod_summary.loc[\"ind_nom_pens_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MRN+pE2y0EXz+eXHe37li8ETvUqeYiR58dKwrP57oXeqUItF7Pb0rJ57oXep4oneuIU/0\nLnU80TvXkCd6lzqe6J1ryBO9S5UVK+CLL6BHj+IdwxO9Kzee6F2qLFhgibhdET/ZnuhdufFE71Kl\n2NU24InelR9P9C5VSpHo+/b1RO/Kiyd6lyqlLNH7NAiuXHiid6lSikT/pS9Bhw7wn/8U9zjOFYon\nepcqpUj04PX0rrx4onep4oneuXV5onep4oneuXU1m+hF5C4RWSAi07P2dReRZ0RktohMEJGuWY9d\nJCJzRGSWiAzP2j9ERKaLyHsiclPhfxXnPNE7l0tLSvT3AAc22jcSeE5VtwcmAhcBiMgg4ChgIDAC\nuFVEJHnNbcApqjoAGCAijd/TuTZZswY++QR69y7+sTzRu3LSbKJX1UnA4ka7DwXuTbbvBQ5Ltg8B\nHlbVWlX9AJgDDBWRvkAXVZ2cPG9s1mucK4hFi6B7d9hgg+IfyxO9KyetraPvraoLAFR1PpApQ20O\nfJz1vHnJvs2BuVn75yb7nCuYUlXbgCd6V14K1RjrQ0dccKVM9D461pWTDq183QIR6aOqC5JqmYXJ\n/nnAFlnP65/sy7c/r9GjR6/drqqqoqqqqpWhukpR6hK9LyfoQquurqa6urrZ54m2YBy3iGwFPKmq\nOyX3rwM+U9XrROSnQHdVHZk0xj4A7IFVzTwLbKeqKiKvAucCk4GngF+r6vg8x9OWxOVctiuvhOXL\n4eqri3+sL76wRt9ly2BtdwPnAhMRVHWdT2RLulc+CLyC9ZT5SEROAq4FDhCR2cD+yX1UdSbwKDAT\neBo4MytjnwXcBbwHzMmX5J1rrVKW6Dt3tgT/xRelOZ5zbdFs1Y2qHpvnoWF5nn8NcE2O/W8AO61X\ndM6th5oa2G+/0h0v0yDbpUvpjulca/jIWJcapSzRg/e8ceXDE71LDU/0zuXmid6lgqr1gunbt3TH\n9ETvyoUnepcKS5bARhvZXPGl4onelYvW9qN3LiqlrrYBS/Rvv13aYzqXbdUqmDIFXngBJk/O/zwv\n0btUCJHofXSsK7UVKyypX345DBsGm24KZ58NCxfC8cfnf52X6F0qhCrR++hYV0zLl8Pf/27J/YUX\n4I03YMcdYd994fzzYZ99oFu35t/HE71LhVCJ3kv0rpCWLoWXX4YXX7TEPm0a7LwzfPObcPHF8PWv\nt27chid6lwo1NbDZZqU9pid611ZLlsCkSfUl9pkzYbfdLLFffjnstVdhOhh4onepUFNjJ0gpdeli\ni50sWwYbb1zaY7vyd9pp8OCDsMcelth/+UsYOhQ6diz8sTzRu1QIUXUjUl+q32ab0h7blbfp0+GJ\nJ6yNpxSFBO9141IhRKIHr75xrXP11XDhhaX7JuglepcK8+d7onflYfZsmDgR7rijdMf0Er0re8uX\n28CRrl1Lf2xP9G59XXMNnHtuaWc99RK9K3s1NTZ4KcQCIJ7o3fr417/gL3+Bf/yjtMf1Er0re6Hq\n58FHx7r1c911cPrpLRvkVEheondlL2Si79MHnn8+zLFdeZk7Fx59FN57r/TH9hK9K3uhE72X6F1L\n/OIXcPLJ0LNn6Y/tJXpX9jzRu9gtWAD33QfvvBPm+F6id2XPE72L3Y03wrHHhvuceonelb2Qib5r\nV+va+d//QqdOYWJwcfv0U+sz/9Zb4WLwEr0re6EGS4F16ezd20v1Lr9f/xoOPxy23DJcDF6id2Uv\n048+lEz1zVZbhYvBxenzz+G3v4VXXw0bh5foXVlbvRoWL4ZevcLF4PX0Lp9bb4URI2DbbcPGkfoS\n/apVsOGGoaNwxbJggSX59u3DxeCJ3uWybBncdFMc4yxSXaJ/7DEbgXb99VBbGzoaVwwhG2Izijk6\ntrbWFp/48Y+tUc+Vj9tvtyX/Bg0KHUmKE/306XDGGfDAAzB+vC3BFaoPqyueGBJ9MdeO/dOfbHGT\n5cth++3h2mtt28VtxQpbSOSSS0JHYlKZ6D/9FA47zFq7v/tdeO45G5FWVWXzQHvpPj1iSfTFKNGr\nwg03wEUXwW232VqiU6bAgAFw113hP8e1tfD44zB5ctg4YnT33TBkCOyyS+hITJsSvYh8ICLTRGSq\niLye7OsuIs+IyGwRmSAiXbOef5GIzBGRWSIyvK3B51JbC9//PhxxBBxzjO1r184mEpoyBaqrbemu\n6dOLcXRXamlO9K+8YoWWQw6x+9tvb9WRjz0GY8faotFPPGEXhFJasACuuspW1brqKovv449LG0PM\nVq2yyctiKc1D20v0dUCVqu6qqkOTfSOB51R1e2AicBGAiAwCjgIGAiOAW0UKP7HsT39qDXPXXLPu\nY1/+MkyYAGeeCfvvb4vvrl5d6AhcKaU50d9wA1xwwboNzXvuaQWW66+3ZPKNb1hpv5hUbRHrY46B\nHXaADz+00vzrr1uMRx5pCc7B/ffbt6499wwdSRZVbfUN+BewaaN97wJ9ku2+wLvJ9kjgp1nP+yuw\nR5731dYYO1b1K19R/fTT5p/78ceqI0ao7ryz6ptvtupwLgKHHKI6blzYGD79VLVr18K+55w5qj17\nqn7xRdPPq61VHTNGdYstVA89VHXmzMLGsXSp6u9+pzp4sOqAAao33aS6eHHD59TV2bHPOaewxy5H\nq1erbrutanV1mOMnuXOdnNrWEr0Cz4rIZBE5NdnXR1UXJNl6PtA72b85kP0Fb16yryCmTLE1GP/8\nZ+jRo/nn9+8PTz1lpZEDD4RLL/USSTmKoUTfvbs1kK5YUbj3vOkm+NGPml9TtH17OOEEm/p2n32s\nl8cPfwjz5rXt+LNmwTnn2GjO8ePt28W778J55607l7oIjBkDTz8NDz/ctuOWu0cftV5Y++4bOpKG\n2pro91bVIcC3gbNE5BtY8s9W9BrEBQtsiPHvfw9f/WrLXydiJ8lbb8G0abDbbnbBcOUj9KhYqJ8G\nYeHCwrzfZ5/Bgw/C2We3/DUdO8JPfmIJf9NNYfBguPhiWLKk5e+xejX88Y/wrW/BfvtZQp82DcaN\ng2HDml7Bq1s3azs45xyYObPlx0yTujprs/j5z8OsdtaUNg2YUtWa5OciEfkzMBRYICJ9VHWBiPQF\nMh//ecAWWS/vn+zLafTo0Wu3q6qqqKqqyvm8Vaus4fXEEy3Zt8Zmm9k3gYcegu98x3rojBplJ4+L\nV12dXeRDJ3qor6cvxHwmv/sdHHpo676pdO9uXTDPOgtGj7a64pEj7f5GG+V+TU2N9fm+/XZrYD3r\nLDuX1neg4S67WCPkEUdY3X3nzusffzn785/tG9gBB5TumNXV1VRXVzf/xFz1OS25AV8COifbGwMv\nA8OB60jq4oGfAtcm24OAqcCGwNbAPwDJ894trpM64wyrp12zptXVWg3U1KgefrjqwIGqf/97Yd7T\nFcfChao9eoSOwnz726pPPtn291mxQrVfP9Xp09v+Xqqqb79t58eXv2xtWLW1tr+uzuqRjzxStVs3\n1dNPV502rTDHPPlk1aOPtmNUiro61V13VX388bBxkKeOvi2JfmvgrSR5zwBGJvt7AM8Bs4FngG5Z\nr7koSfCzgOFNvHeLfqnbb1fdYQfVzz9v+x8oW12d6iOPqPbpo/qTn6guX17Y93eFMW2a6o47ho7C\nnHSS6p13tv197rlHdfjwtr9PYy++qLrXXtaoetVV9ncbOFD1lltUlywp7LGWL1fdZRd770rx1FP2\nty1UgbO1Cp7oi3lrSaKfNEm1Vy/V2bNb+ydp3sKFqkcdZb0NJk0q3nFc64wfrzpsWOgozMiRqlde\n2bb3qKtT3Wkn1QkTChNTrvcfN0711FNVJ04sbon7H/+w87MSvhXX1anuuacVDkPLl+jLcmTsvHlw\n1FHW0j9gQPGO06sXPPKI9ck/8kjrobNsWfGO59ZPDD1uMgrRl/7ZZ+1nsep4RWzE+B13WGNrMRsM\nv/IVO873vw+ffFK848Tg+edtBtXvfS90JPmVXaJfscKmNTj7bPj2t0tzzMMPhxkzYNEiGDjQhjeH\nHn7u0pfob7jBugjH1mOjtQ49FI4+Go47zubrSasrrrAeTiFnUG1OWSV6VZvKYKutrCdBKW26qY14\ne+QR+yYxeLCNDNQSDz939dKU6GfMsFtm2o60uOoqWLnSkmEaTZpko4Rj/7+VVaK/5RaYOhXuuSdc\nqWevveCFF+AXv7D+svvsAy+9FCaWShdyCcHG2prob7zRvqXm6wJZrjp0sEFUd9xhA6/S5qqrrNC5\nwQahI2la2ST6iRNt5slMX9WQRKy//dSpcNpp8IMfwMEHw9tvh42r0qSlRF9TY98OTz+9sDHFom9f\nG6Ny4onw0UehoymcKVPsnD/hhNCRNK8sEv0HH8Cxx9rc8ltvHTqaeu3bw/HHw+zZNkna/vvbP/3D\nD0NHVhliGBWb0aMHLF3aumk0fvMb+3y3ZOqOcrXvvrZ4ypFHWlVOGlx1Ffzf/5XHt7DoE/2yZdZT\nYORIS6Qx2mgjOP98G36+5ZY2D/WFF6a/t0FIqnGV6Nu1s15a6zsNwrJlNiL1/POLE1dMfvITG4X+\n4x+HjqTtZsywBb9PPbX558Yg6kSvCqecYvNun3de6Gia17WrNTq98471DtphB7vql2OXzFtusXmD\n/vjHOBucly61KrQuXUJHUq81SwqOGWPTDIdePLoURKx9bfx4m8unnF19tRXmOnUKHUnLRJ3or78e\n/vlPm/ujnLqc9e1rq7+/+qpd+bfbzn6Hcpn7/qWX4MorbW7/q66yebVjWOA4W0yl+Yz1XVJwzRr4\n1a/SUcJtqW7drPBw3nnlO/nZ7Nnwt7+VV5tKtIn+r3+1pQDHjSufq2Zj225rPQ6efNI+3DvuaNOY\n1tWFjiy/+fOtq9iYMdbIPGWKVSuceiqMGGEzfcYg1kS/PiX6J56wbrtf/3rxYorRzjtbIe5737Nv\nZuXm2mttls6Yvk02J9pEf8IJlhT79w8dSdvttpuNevztb212v6FDrUQQm9paG+ByyimW1MHqno85\nxuYn/853bP9xx8H774eNNQ2J/oYbrDRfTt9WC+Wkk2DvvW3u/BirBvP54AO7QJ9zTuhI1k+0if6K\nK+yDkCYHHGALKf/v/1q3zOHD4+pudskl1rB86aXrPrbhhtbP+733bO3SoUPh3HMLNwf7+ir3RP/a\nazaVR2un1k6DW26xapDf/CZ0JC137bVWZdN48ZXYRZvoTzstdATF0a6dzf8xc6Y1wg0bVpz1RtfX\nuHFWzfTAA00P5e7SxS4EM2daSXTgQLjsstJ/BY9psFTG+iT6G26weuoObVoRorx16mRVmldcYe1Z\nscqse/Dyy1bLUI49pCr4YxbWhhvayNq6OivZV1fbohEhzJljF9a//AV69mzZa3r3hptvtg/9pZda\ng/Mll9j7rO+CFa1RU7N+q4mVQksT/b/+ZVV3d91V/Jhit802cOedNknhG29YF9VSqa21/1dNDfz7\n3/Yz123hQthkEytYjB5d2hgLRTTCCjIR0RjjKgZV66b12mvwzDOlX5Vn+XLrVXPGGXZrrWnT4KKL\n7Kv4FVdYXX+7In5fHDbMegWVcjWf5syYYb/3O+80/bzzz7eL4fXXlyaucjByJLz5pnXCaMvkYCtW\n2FKMn30Gn35qt0zCbpzMP/vMCjb9+tXfNtus4f1+/awXXSkKL4UgIqjqOq0+nugjUFdnvVo+/th6\n6JRqCUNVa/RWhbFjC9MoWF1tCXjVKpve+cADi9PYuOOOVtW0006Ff+/WWrgQBg1qeqDckiVWip0+\nPR0dDQqlttYu3t/8plUFrlpVn6yzfza3b/Vq68nUo0f9z3yJvFev9FWdeaKP3Jo1VhqsrYU//KE0\nH8Df/94awl59tbDzB6lanf/FF9uJde211njbnJUrc5+8ubZfecVKZTFNG7BmjV2kly/PP8nV9ddb\nkr///tLGVg7mz4evfc0uhqtW2f82c2ucvPNtb7xxZfZiyvBEXwZWrrQ5vPv0sRGExaz6mDzZ5vN/\n+eXiLd5SW2u/x2WXWfXQnns2nbxzlcbybW+2mY08jk2/flbXvNlm6z62apWV5p98EnbdtfSxlYPl\ny+2C2blzZSfs1vJEXyaWLbPqjiFDrLGzGB/2Tz+1vv033lia7n3Ll9vI4HnzciftNJXGdtnFFqYZ\nMmTdxx41NHDFAAAYZUlEQVR4wBpgJ04sfVyuMniiLyNLlthSbwcfDJdfXtj3XrPGBj7ttJPNqe8K\n68ADrbE1M+AsQ9UurldcYX9/54ohX6JPWVNEOnTrBhMmWD/7rl0LOxfKlVfCf/9rDaWu8PJ1sayu\ntr974wuAc6XgiT5SvXvDc8/VJ/tCTIc6frxNiTtlSvp6G8QiX6LPrAdbzHYX5/Lx0z1iW2xhc+RU\nVdmAjaOOav17ffihdaX8wx/iG1GaJn36WH/tbLNm2cX1scfCxOScJ/rIbbedDSI54ADrifDtb6//\ne6xcCUccYavh7Ltv4WN09fr0sSUms/3qVzYYrVTjI5xrzBN9GRg82NbKPeQQmxtkfZP1+efbylcX\nXlic+Fy9xlU3CxdaSX727HAxOec1hmVir71sVZ4jjrB+2i01dqx157vnnvLvulgOGif6W2+1dVLL\ncX4Ulx7evbLMjBtn1QDPP28zRzZl+nRbZ/f55+ObACyt5s+3b2ALF1ovm622ghdeiHNwl0sf716Z\nEt/9LvznPzbj5UsvWSLJ5fPPbQWfm27yJF9KvXrB4sU2Kvi++2zqB0/yLrSSV92IyEEi8q6IvCci\nPy318dPghBOsYXXYMJvvpTFVOPFEuxgcd1zJw6to7dvbKN+FC23kcSWtB+viVdJELyLtgN8ABwI7\nAseISFmVd6qrq0OHANhSZplk/tlnti8T2y9/aV38brwxWHg5xfK3y6WQsWXmKtp4Y5uNsRAq5W9X\naDHHBqWLr9Ql+qHAHFX9UFVXAw8Dh5Y4hjaJ6YNzySU25H7ECFvhqbq6mupqG5zzhz/YsoAxielv\n11ihE/111xV2PdhK+dsVWsyxQXoT/ebAx1n35yb7XCuI2Hw1O+0Ehx1mdcPHHms9bbbcMnR0latP\nHxvNfOSRoSNxznj3yjInYvPK9+xpXfnOOMOqc1w4u+8OP/tZ/jnpnSu1knavFJE9gdGqelByfySg\nqnpdo+d530rnnGuF4NMUi0h7YDawP1ADvA4co6qzShaEc85VmJL2o1fVNSJyNvAMVm10lyd555wr\nrihHxjrnnCscb4x1zrmU80TvnHMp53PdNENEejT1uKp+VqpYXNuJSJPLoavqn0oVSy4iMgPIW5+q\nqoNLGE7Z8fM1t+CJPvYTD3gDO/EE2BJYnGx3Az4Ctg4VmIgcpKrjk+2uwI3A7sDbwAWqmmNRu5LF\ndgtNJ6xzSxhOtoOTn72BrwMTk/v7Aa8AoT9v/y/5eVby877kZxSzFpXBhSjK81VEltL0322TYh4/\neKIn8hNPVbcGEJE7gHGq+nRyfwRwWMjYgKuB8cn2DViX1YOBw4HfEza+KcnPvYFBwCPJ/SOBmUEi\nAlT1JAAReQYYpKo1yf1+wJhQcWWo6ocAInKAqu6a9dBIEXkTGBkmsrWivhDFer6qapckjiuw8/Q+\n7AJ0HFD8xT1VNYob1uWyX9b9fsCE0HFlxTOjJftKHNObWdtvNXrsrVLG0kSMrwIdsu5vALwaQVyz\nGt1v13hf4PjeAvbOuv/1WP6nSTxTc+x7s9RxNBFfdOdrEsO0luwr9C2GEn3GFpqUrhILsK9esfi3\niPwMuD+5fxzw7yaeXwq9ReRCrGTQVRqu2BJLQ3t3YBMgUzfaOdkX2t9EZALwUHL/+8BzAeNp7BTg\n7qRKTrAqiJPDhtSAiMjeqvpycufrxPOZgzjPV4BlInIcNqGjAscAy4p90JgSfewn3jHAKGBccv/F\nZF9IdwBdku0xQE9gkYj0xUqEMbgWmCoiz2MJa19gdNCIAFU9O2kf+kay63ZVHdfUa0pJVd8Adk4S\nPar6eeCQGov9QhTj+QpwLHBzclPg5WRfUUU1YKrRifdiTCeea73kwrNHcvc1VZ0fMp6YJd/Q8lLV\nqFYZiPhC5LJElehjJCJP0nRr+SElDKfFROQkVb0n4PGHNPW4qr5Zqliyhe790BwRGZVsbo/1oHoi\nuX8w8Lqq/k+QwBKxX4hiPV9D90ILXnUT+4kH/DL5eTjQl/o6v2OwdoRYXQYES/RYLyCAjsDXgGnY\nV/zBWI+cvUIEpaF7PzRDVS8DEJEXgSGqujS5Pxp4KmBoGZmqwpwXoiARNRTr+Rq0F1o0Jfp8J56q\nXho0sISITFHVrzW3r8QxTc/3EDBAVYOvMSUifwJGqeqM5P5Xsamqjwgc1zRV3bm5faGIyGxgsKqu\nTO5vBExX1e3DRmaSC9F3si5EXYCnVHXfsJGZGM/XJIZXgX1UtTa5vwHwkqruWczjBi/RZzmk0Ul2\nm4hMA6JI9MDGIrKNqr4PICJbAxsHjqkPtv7u4kb7BRuDEIPtM0keQFXfFpGBIQNKBOn9sB7GAq+L\nSKad6jDg3oDxNNYHWJV1f1WyLxYxnq8QqBdaTIk+9hPvAqBaRN7HEumXgdPChsRfgM6quk4PGxGp\nLn04OU0XkTtp2M0t3zeRUgrS+6GlVPUqERkP7JPsOklVp4aMqZHYL0Qxnq8QqBdaTFU3W2En3d7U\nn3jnq+oH4aJqKPn6vENy993M12qXn4h0BM7APtBg3dxuU9UV4aIqHyLSG2vnAEBVPwoYTgMishv1\nF6IXI7sQRXu+huiFFk2iLwdJ/fIgGp54Y8NFZETkBuBuVX0ndCzlIrkAnQLsSMP/ZxR9wUXkEKxB\nezNgITZ48F1V3TFoYI1EfiGK9XztDmxHw7heLOYxo6m6KYMTbxRQhX1wngZGAJOwr7ChzQJuF5EO\nWE+bh2Lp1ywi2wHXsO4Jt02woMx9wLtYG8flWJVSTKudXQHsCTynqruKyH5A0K6V2fJdiLDzN7hY\nz1cRORU4D+iPDWrcE/g78K1iHjemIcv3Yd2hDgRewP4QS4NG1NAR2Fq389UmxtoZ6Bo2JKOqd6rq\n3sDxwFZYvfiDSXII7R7gNqAWm6huLPX19SFtq6o/B5ap6r3Ad6j/Oh2D1ar6KdBORNqp6vNYN9VY\nZC5E76lNJDYMm9coFrGer+dh3VI/VNX9gF2BJcU+aEyJPvYT77+qWgfUisgmWClmi8AxrZUsvL5D\ncvsE67d+oYg8HDQw6KSqf8OqCT9U1dHY/za01cnPJclX/K7YDKqxWCIinbE2jQdE5Gbi6pwQ+4Uo\n1vN1RaZ9SkQ2UtV3sTEJRRVN1Q3rnnjzievEmyIi3bD5Zd4AvsC+cgUnIr/Cpo+dCFytqpmBK9cl\n/bFDWiki7YA5ycLw87AuZaHdntSV/gwb9NMZ+HnYkBo4FPgv1nvkOOxCdHnQiBpqfCFaSFwXoljP\n17lJXH8GnhWRxcCHxT5oNI2xSd3VH4GdsAm6OgM/V9Xfh4wrl6SH0CaqOj1r346hGkNF5CTgUVVd\n50QTka4h6+tFZHes7rsb9nV/E+AXqhrT1/x1iMgJyTfLKInI31U1yOji5PgbYxeidtRfiB5ISvlR\nie18zYrhm9jfbbyqrkr2dVfVxuNi2n6sWBJ9c8rgxHtTVZuc36XIxy95S34hiMgtqnpO6DgaC/3/\nbI6ITNWGC5NEJfSFqDmx/n+LFVdMdfTNOS90AM2QYAe2b0MvAhOwOW4mEMFUwC20d+gA8gj2/2yh\n2EtoHZt/SlCx/n+LElc5JfpY/zEZIU+8IC35KRd7Io1d7H+/WOMrSlzllOhj/cfEIEhLfsrFXrCI\nPT4XkZh63TQn9g/2quafUjRBWvILJNb/68uhA2jGD0IH0IxY/68ZIc/XphTl71ZOjbG/UdWzI4ij\nMzAAeF9Vo6seydWSHzMROVFVxwQ8/j7AUOBtVX0mVBxZ8Zysqncn2/2xicJ2w+YsP1FV3wsZX2NJ\nH/XtsPNhcdb+r6rq2+EiWxtHlOeriGyLDeKapaozs/b3UNXP8r+ydaKruhGRfUTkQhEZnr0/VJIX\nkVuztvfBTrgbgBki8u0QMWXF06PxDZiBDfUO2lddRM4WkZ7J9rYi8qKILBGR10Rkp8zzSp3kReT1\nrO0fAr/BFtMYJSIjSxlLHtmf8xuxBSp6AL/ARhgHJSL3Z/1fDwTeBq4D3hKRIzPPC5XkYz1fReT5\nrL/bD6ifluEREVnb66wYST7zxkFv2PJome0fYvM/jMK+Oo+MIL43s7afx1b9AdgGmBI4tjrgI+D9\n5PavrNv7gWN7J2v7KeC7yXYV8HLAuKZmbU8GeiXbGwMzQv7NkjiyP2/T8sUeML4ZWduvAFsl2z0b\nxxvB3y+a8xX7xpjZngxsmmx/CVtQpqjHj6GOfoOs7R8BB6jqIhH5JTZ3xrVhwsqpqyZrnarq+8mI\nz5B+jc0f8zLwEDBJk09PBLI/W701WehdVavFViMKpV0y5qAd0F5VFyVxLROR2oBxZfQXkV9jdbU9\nRWQDVc2MGt+gideVSjsR2URV/0N9QQNV/SSZVC8mMZ2vq0Vkc1Wdh43SzQxuXAm0L/bBY/jHxH7i\n7SC2ZJ8AW2VGriUfmg1DBqaq54uIYKXkHwC3iMgz2Hzv/woZG/CYiIzBhu2PE5HzgXHYLH0hp7Lt\nig2JF0BFpJ+q1iR1uTE0IP5v1vYUrApusdgc5k/kfklJXQY8LyK/xQoYfxCRJ7ACx/igkZlYz9cL\ngGdE5I/AO8BEEZmAzedf9LWdgzfGisgHWMlAsC6Ue2edeJNUdZfA8X250a5/q+rqpL5tX1X9U4i4\nGkt63RyNTTNwsareETgkROREbNGRrwAbAR9jPYOu00imUc4QkS8BfSK4QEYvaUj8IdbI2QGYC/xZ\nVScEDYy4z1cR6YqtYpb9d3tcrTt0cY8dOtHn4yde85L5Rg4Fvg/0Av6EzXkTzeIP5UREOqvqF6Hj\nyEdELlXVmCY2c2UidB1zXqq6HFgUOo6miMhfA4ewEPg/bFa+G7AG2a+JyOEicnjQyJogIgeEjiGP\nmc0/JahTQwfQFBG5NHQMTYngfM1JRG4v+jFiLdEDiMhHqrpl4BjyTTAkwF9UtV8p42kQgNWB5/sH\nqkayOldjIf+vInJhvoeAS1S1RynjWScIkf/kewib2z+GdrWc/HzNL+n6nPMhrLdS/2IeP/iHppkT\nL4Z5yydjK17laqjrVuJYGlDVE1vyvBAzfyYNdDkfAjYtZSyNXI31Sc/V0B/DN9wlwO6quqDxAyLy\ncYB4GsfQ5IWolLHkEev5uggbrZ4dlyb3i77uRvBET/wn3izgNFWd0/iBGE68FjoPG2FZSt/A1jht\nXOct2EjUUN7EGg7faPxAMgtoaGOBLwPrJHrgwRLHkkvUFyLiPV/fB/bP1X5WirhiSPSxn3ijyX/B\niW4e9TxCdBt8FViuqi+sE0zYVa9OAvItjhF8KTxV/VkTj/20lLHkEfuFaDRxnq83Ad3J3bX4+mIf\nPHgdvYhsD3yqqp/keKxPrpJDCCLSXlXXhI6jNWJdZMGtq4k6ZgAyA4Bc+Un68u+pqq+U/NihE31j\nySRJqqpLQ8eSTUQ+wgaEPAJMjGgEarMk0GpEYguWP6c2R35URGQANjjpy2R9s1XVbwULCpsTpYmH\nNXR82ZKeXftgdc2TMqOfYyAiVwPXazKRWTIo88dNfWMqUVxhzsVY8pXY2qJ3YxNMCVYXeHKuKp0Q\nkn79/w8blDQE+AvwsKpOChpYC0jAmT9F5G/A4REOkJoG/A4bJbv2m1osn7fYJZOHbYtNvQE2luOf\nqnpWuKjq5UqoMXyzTaZ2+Tvwp1IWFmNK9NOBs1T1peT+PsCtqjo4bGTrSkoHNwPHqWrR56loIo49\nsGlO/yMinYCR2EVoJnB1DMlVRB7HVrx6lvr5PVDVc4MFBYjIG6q6W8gYmiIix+far6pjSx1LLiLy\nLjAwk6ySaol3VHVg2MhMkk92V9WVyf1O2KRmOwaOayk2gd4abHF1wb6pbVLM48bQGJuxJpPkAVR1\nUiRz3awlNtf794GDsHlIjgobEXdjc1qDXXiWY1PG7o/NnxHDoKk/JbfYPCkiZ2Lz76zM7NRiTRO7\n/nbP2u6I/U/fxBpDY/APYEvqF7jZItkXiweAv4lIZh6Zkyh9z7N1qGqQCf1iKtHfhPXDfQir8/s+\nsAK4H8I3QiVz8kwFHgWeUNVlTb+i+ERkVqYE1fhrqYi8FXqeoJiJSK6pNVRVtyl5MC2QzGX0sKoe\nFDiOJ7Hzsyt2MXo9ub8HNuV4VbjoGhKRg4Bhyd1nY5iLB9Zp23hJVf9c7GPGVKLPlExHNdq/K/YH\nCd0INTiZmjUmb4vISap6DzBNRL6mqlOShsbVzb24FERkO+AaYBBWMgUgdEJV1a1DHr8VlgExxPzL\n0AGsh1lArao+JyJfEpEuoTt55GjbOF1EDih220Y0JfrYiS3rdguwd7LrJeA8VZ0bMKauWJXNN4BP\nsPr5j5Pbuao6LVRsGSIyCbt4/wo4GPsK3U5Vg86LUgZ14JmSM9h85YOwCeti6EsPrJ0pcrskkXYC\nOoROpBliq4f9COihql9JChy/U9X9A8cVpG0jmhK9iPTBRslupqojRGQQsJeq3hU4tIx7sAEhmeXS\n/ifZF2yCrqSx9cSkS+rWJFOfxjL2INFJVf8mIqKqHwKjReQNIPQEWLHXgf+S+kRfC3yotmhFFLIT\nKTYNdX+sF1PQRJrlLGwE9msAqjpHRIo+1UALBGnbiCbRA2OwxHlJcv89rM96LIm+V1JFkjFGbDGN\n4JIqpQal94im3F2ZlFrmiMjZwDwimMNIVRuMkszUgQcKJzuOpdTPgZJNRWQl8E9s8rW/lTy4hmJN\npBkrVXWViP0ZxVa/iqH6ogswS+rXLt4dmJyZG0pVDynGQWNK9D1V9VERuQhAVWtFJKaRqJ+KyP9Q\nX7d2DPmH0sdgJlZyCO08bF3Mc7FFUb4FnBA0otyiqANvqldGMgDtq1iPkq+WLKjcYk2kGS+IyMVA\np2Ra7DOBJwPHBA2/yQpW7Xo067ZNFlRMiX6ZiGxK8mERkT2B4P3As5yM1dH/CovxFay+OZgymPkT\nVZ2cbH5B4L9Xtnx14OEial4yBcc0EbkldCzEm0gzRgKnADOA04CngTuDRgSo6gsisiu20tSRwL+w\ntoN15oQqpGgaY5M5Pm7BSipvYysmHaGq04MGFjERWUH+mT8vUNVg07I2MU0xULyvqC2VjImItg48\ndkl13CnAcKxgMQG4M6apQUSkF4Am61AHjmUAVgtwDNZx4hHgJ6raeOnD4hw/9P8lmfrgY1Wdn3z9\nOw34Hlb1cGnoASzS9Ko5qqpXlCyYRkTkFeCcPDN/fqyqWwQIK3P8RVjvn4ewetwGdc7FLsE0EVfe\nOnBs4FQsdeBREpEtNeKlKsXqkkYBZ1M/i+Ua4BYNuAyjiNRhPfVOUdV/JPveL1U34xjme/89sCrZ\n/jrWGPtbYDFQ9CW2WmBZjhtYaSZ0V7eTqG+9byz0lLt9gYuxb2g3Y72TPlHVF0IlebA6cFXdJPmZ\nfdskifm0JF6X29rBPSLyx5CB5HEB1gV6d1XtobZi2B7A3iJyQcC4DgdqgOdF5A4R2Z9STh+uqkFv\n2DJame3fAqOz7r8VOr5GsXYBfobVq10H9A4dUxLXTqFjaCa+jYATsVV2zg4dTwviPS10DLHegKm5\ntmO5YaPXe+bY3yuGeLF5bo7F2jOWAbcBw4t93BhK9O2TKhuwPrgTsx6LorFYRHqIyJXAdCymIar6\nU1VdGDi0jFtF5HUROTMZRBUFEdkoGe59P9Yd79fY3DJRU9Xfh44hYppnOxYbaI61LdTq6TcIEE/j\nOJap6oOqejA29mAqJagZiCGRPoS14H+CzeaWmb1yWyLodSMiv8C+dt2OlZxj6JvegKp+Ixn5dzLw\nRtJH9x5VfTZUTCIyFqu2eRq4TFXfDhWLK6idxdaNFazHTWZakJLMwtgCq1r5WMmpaqZ6uuhV1MEb\nY2FtV8p+wDOaTBaWtFJ31vCTmdVhjXS1NCzBxPLBXivpZ30YVnLOnIwXq2rJZ49M/m6Z9oyo/24u\nPZKxN7kmHBSgo6oGL9WHEEWid20jIoOxhtnvYPO+36Wqb4rIZsDftURduJxzcfJEnwIi8gI2GOQx\nVf1vo8d+oKr3hYnMORcDT/TOOZdyMTTGujaKdc5351wcYuhe6druHqw/bi2wHzbV7v1BI3LORcOr\nblJAkoWuRWSGqu6UvS90bM658LzqJh2inPPdORcHL9GnQDIx3CygGzbne1fgelV9NWhgzrkoeKJ3\nzrmU86qbMhb7nO/OuTh4oi9ve9HEnO/OOQdedVPWkrltDsBWrRkMPAU8pKrvBA3MORcV70dfxlR1\njaqOV9UTgD2BfwDVSc8b55wDvOqm7InIRthkZscAW1Emc74750rHq27KWKM53x/2Od+dc7l4oi9j\nPue7c64lPNE751zKeWOsc86lnCd655xLOU/0zjmXcp7onXMu5TzRO+dcyv1/j3dONQ1iFlMAAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x25259a250>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_reca_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1Qdh114YdJ0WfPvDGG4W50ZWiEUhx6KGrXHtHHFG4mW05FIhlYrvtrJXJ4sXF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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ebbcb10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_cco_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "##All products with very small(<10) customer's buying in a month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6uLOHNLrnyCPt4r14cbbHVQ2zCkopeZtzM8abAbbc0u6qFiyo/RhTp1qBsWnT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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ca9a050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_viv_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1q8fYRCZLimyUcM89sNZatrI0Jly0M2SffWDpUvjHP7J/79B+NpR7pA35pf4V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\nHMcpEf8fjsFl0tjrQkcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116c24810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_hip_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7MmLpHTl/Phx8MHz8sZWO7dkzrDxOZRRW2oUntxkzLIFpueWyHbdahavAcBF5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\njuPUEK60Hcdxaoj/B9AYbmkntuRgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f7298d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_pres_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  0.,   2.,   4.,   6.,   8.,  10.,  12.,  14.,  16.,  18.]),\n",
       " <a list of 10 Text xticklabel objects>)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVSsXXmhy1vdLTC838kg77bA/D/fruhQ77aVLbTCQZuX1UgRz2l/+MkyZAv/9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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ec6a390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prod_summary.loc[\"ind_cder_fin_ult1\"].sort_index(ascending=False).plot()\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 4)"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun15_add = train_4[[\"ncodpers\", \"Product\", \"Jun15_add\"]]\n",
    "Jun15_add.drop_duplicates()\n",
    "Jun15_add = Jun15_add.loc[Jun15_add[\"Jun15_add\"] == True]\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "le.fit(Jun15_add[\"Product\"])\n",
    "Jun15_add[\"Product_Enc\"] = le.transform(Jun15_add[\"Product\"])\n",
    "Jun15_add.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>Product_Enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15892</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers           Product Jun15_add  Product_Enc\n",
       "13           15892  ind_cco_fin_ult1      True            0"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun15_add.loc[Jun15_add[\"ncodpers\"] == 15892]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>Jun15_add</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15892</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>15896</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15897</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>15906</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15925</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product  Jun15_add\n",
       "13           15892   ind_cco_fin_ult1          1\n",
       "42           15896   ind_cco_fin_ult1          1\n",
       "51           15897  ind_nom_pens_ult1          1\n",
       "84           15906  ind_nom_pens_ult1          1\n",
       "187          15925    ind_recibo_ult1          1"
      ]
     },
     "execution_count": 276,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base = train_4.loc[train_4[\"Jun15_add\"] == True][[\"ncodpers\", \"Product\"]]\n",
    "base[\"Jun15_add\"] = 1\n",
    "base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "total_new_prod = base.groupby(\"ncodpers\")[\"Jun15_add\"].sum()\n",
    "total_new_prod[\"ncodpers\"] = total_new_prod.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "base.merge(total_new_prod.to_frame(), left_index=True, right_index=True)\n",
    "base.rename(columns={'Jun15_add_y': 'New_prod_added'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>Jun15_add</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15892</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>15896</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15897</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>15906</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15925</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>15927</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>15964</td>\n",
       "      <td>ind_reca_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>15982</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>420</th>\n",
       "      <td>15982</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>572</th>\n",
       "      <td>16026</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>16026</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>638</th>\n",
       "      <td>16043</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>676</th>\n",
       "      <td>16056</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>16056</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>16056</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>727</th>\n",
       "      <td>16098</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>735</th>\n",
       "      <td>16100</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>16133</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>16137</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>16145</td>\n",
       "      <td>ind_reca_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>834</th>\n",
       "      <td>16155</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>16187</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>885</th>\n",
       "      <td>16187</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>899</th>\n",
       "      <td>16193</td>\n",
       "      <td>ind_cno_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>16201</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>926</th>\n",
       "      <td>16201</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>938</th>\n",
       "      <td>16202</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>16217</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>16217</td>\n",
       "      <td>ind_tjcr_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1006</th>\n",
       "      <td>16236</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353782</th>\n",
       "      <td>1403574</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353783</th>\n",
       "      <td>1403575</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353788</th>\n",
       "      <td>1403576</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353791</th>\n",
       "      <td>1403577</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353811</th>\n",
       "      <td>1403587</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353818</th>\n",
       "      <td>1403593</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353820</th>\n",
       "      <td>1403594</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353825</th>\n",
       "      <td>1403599</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353831</th>\n",
       "      <td>1403608</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353839</th>\n",
       "      <td>1403611</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353842</th>\n",
       "      <td>1403612</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353858</th>\n",
       "      <td>1403620</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353873</th>\n",
       "      <td>1403633</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353882</th>\n",
       "      <td>1403639</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353898</th>\n",
       "      <td>1403649</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353906</th>\n",
       "      <td>1403659</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353913</th>\n",
       "      <td>1403664</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353915</th>\n",
       "      <td>1403666</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353927</th>\n",
       "      <td>1403674</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353931</th>\n",
       "      <td>1403679</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353957</th>\n",
       "      <td>1403699</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353964</th>\n",
       "      <td>1403708</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1353966</th>\n",
       "      <td>1403711</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354005</th>\n",
       "      <td>1403731</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354056</th>\n",
       "      <td>1403767</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354103</th>\n",
       "      <td>1403803</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354125</th>\n",
       "      <td>1403824</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354130</th>\n",
       "      <td>1403827</td>\n",
       "      <td>ind_ctma_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354259</th>\n",
       "      <td>1403934</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1354260</th>\n",
       "      <td>1403935</td>\n",
       "      <td>ind_ctju_fin_ult1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45679 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product  Jun15_add\n",
       "13           15892   ind_cco_fin_ult1          1\n",
       "42           15896   ind_cco_fin_ult1          1\n",
       "51           15897  ind_nom_pens_ult1          1\n",
       "84           15906  ind_nom_pens_ult1          1\n",
       "187          15925    ind_recibo_ult1          1\n",
       "194          15927   ind_cco_fin_ult1          1\n",
       "348          15964  ind_reca_fin_ult1          1\n",
       "419          15982  ind_nom_pens_ult1          1\n",
       "420          15982    ind_nomina_ult1          1\n",
       "572          16026  ind_nom_pens_ult1          1\n",
       "573          16026    ind_nomina_ult1          1\n",
       "638          16043  ind_tjcr_fin_ult1          1\n",
       "676          16056  ind_nom_pens_ult1          1\n",
       "677          16056    ind_nomina_ult1          1\n",
       "678          16056    ind_recibo_ult1          1\n",
       "727          16098  ind_tjcr_fin_ult1          1\n",
       "735          16100   ind_cco_fin_ult1          1\n",
       "781          16133   ind_cco_fin_ult1          1\n",
       "792          16137    ind_recibo_ult1          1\n",
       "804          16145  ind_reca_fin_ult1          1\n",
       "834          16155    ind_recibo_ult1          1\n",
       "884          16187  ind_nom_pens_ult1          1\n",
       "885          16187    ind_nomina_ult1          1\n",
       "899          16193   ind_cno_fin_ult1          1\n",
       "925          16201  ind_nom_pens_ult1          1\n",
       "926          16201    ind_nomina_ult1          1\n",
       "938          16202  ind_tjcr_fin_ult1          1\n",
       "964          16217  ind_nom_pens_ult1          1\n",
       "965          16217  ind_tjcr_fin_ult1          1\n",
       "1006         16236  ind_nom_pens_ult1          1\n",
       "...            ...                ...        ...\n",
       "1353782    1403574   ind_cco_fin_ult1          1\n",
       "1353783    1403575   ind_cco_fin_ult1          1\n",
       "1353788    1403576   ind_cco_fin_ult1          1\n",
       "1353791    1403577   ind_cco_fin_ult1          1\n",
       "1353811    1403587   ind_cco_fin_ult1          1\n",
       "1353818    1403593   ind_cco_fin_ult1          1\n",
       "1353820    1403594   ind_cco_fin_ult1          1\n",
       "1353825    1403599   ind_cco_fin_ult1          1\n",
       "1353831    1403608   ind_cco_fin_ult1          1\n",
       "1353839    1403611   ind_cco_fin_ult1          1\n",
       "1353842    1403612   ind_cco_fin_ult1          1\n",
       "1353858    1403620   ind_cco_fin_ult1          1\n",
       "1353873    1403633   ind_cco_fin_ult1          1\n",
       "1353882    1403639   ind_cco_fin_ult1          1\n",
       "1353898    1403649   ind_cco_fin_ult1          1\n",
       "1353906    1403659   ind_cco_fin_ult1          1\n",
       "1353913    1403664   ind_cco_fin_ult1          1\n",
       "1353915    1403666   ind_cco_fin_ult1          1\n",
       "1353927    1403674   ind_cco_fin_ult1          1\n",
       "1353931    1403679   ind_cco_fin_ult1          1\n",
       "1353957    1403699   ind_cco_fin_ult1          1\n",
       "1353964    1403708   ind_cco_fin_ult1          1\n",
       "1353966    1403711   ind_cco_fin_ult1          1\n",
       "1354005    1403731   ind_cco_fin_ult1          1\n",
       "1354056    1403767  ind_ctju_fin_ult1          1\n",
       "1354103    1403803   ind_cco_fin_ult1          1\n",
       "1354125    1403824  ind_ctju_fin_ult1          1\n",
       "1354130    1403827  ind_ctma_fin_ult1          1\n",
       "1354259    1403934  ind_ctju_fin_ult1          1\n",
       "1354260    1403935  ind_ctju_fin_ult1          1\n",
       "\n",
       "[45679 rows x 3 columns]"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#train_Jan_jun15.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lag 1 for all products and segment and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_May_jun15 = train.loc[(train[\"fecha_dato\"] == '2015-06-28')|(train[\"fecha_dato\"] == '2015-05-28') ]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'age' ,'segmento' ,'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_May_jun15 = train_May_jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base = pd.merge(train_May_jun15, Jun15_add, how=\"left\", on=\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha_dato</th>\n",
       "      <th>age</th>\n",
       "      <th>segmento</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>ind_ahor_fin_ult1</th>\n",
       "      <th>ind_aval_fin_ult1</th>\n",
       "      <th>ind_cco_fin_ult1</th>\n",
       "      <th>ind_cder_fin_ult1</th>\n",
       "      <th>ind_cno_fin_ult1</th>\n",
       "      <th>ind_ctju_fin_ult1</th>\n",
       "      <th>...</th>\n",
       "      <th>ind_reca_fin_ult1</th>\n",
       "      <th>ind_tjcr_fin_ult1</th>\n",
       "      <th>ind_valo_fin_ult1</th>\n",
       "      <th>ind_viv_fin_ult1</th>\n",
       "      <th>ind_nomina_ult1</th>\n",
       "      <th>ind_nom_pens_ult1</th>\n",
       "      <th>ind_recibo_ult1</th>\n",
       "      <th>Product</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>Product_Enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>426858</th>\n",
       "      <td>2015-05-28</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>15982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426859</th>\n",
       "      <td>2015-05-28</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>15982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067700</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>15982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067701</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>15982</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>ind_nomina_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        fecha_dato  age           segmento  ncodpers  ind_ahor_fin_ult1  \\\n",
       "426858  2015-05-28   57  02 - PARTICULARES     15982                  0   \n",
       "426859  2015-05-28   57  02 - PARTICULARES     15982                  0   \n",
       "1067700 2015-06-28   57  02 - PARTICULARES     15982                  0   \n",
       "1067701 2015-06-28   57  02 - PARTICULARES     15982                  0   \n",
       "\n",
       "         ind_aval_fin_ult1  ind_cco_fin_ult1  ind_cder_fin_ult1  \\\n",
       "426858                   0                 1                  0   \n",
       "426859                   0                 1                  0   \n",
       "1067700                  0                 1                  0   \n",
       "1067701                  0                 1                  0   \n",
       "\n",
       "         ind_cno_fin_ult1  ind_ctju_fin_ult1     ...       ind_reca_fin_ult1  \\\n",
       "426858                  1                  0     ...                       0   \n",
       "426859                  1                  0     ...                       0   \n",
       "1067700                 1                  0     ...                       0   \n",
       "1067701                 1                  0     ...                       0   \n",
       "\n",
       "         ind_tjcr_fin_ult1  ind_valo_fin_ult1  ind_viv_fin_ult1  \\\n",
       "426858                   1                  0                 0   \n",
       "426859                   1                  0                 0   \n",
       "1067700                  1                  0                 0   \n",
       "1067701                  1                  0                 0   \n",
       "\n",
       "         ind_nomina_ult1  ind_nom_pens_ult1  ind_recibo_ult1  \\\n",
       "426858               0.0                0.0                1   \n",
       "426859               0.0                0.0                1   \n",
       "1067700              1.0                1.0                1   \n",
       "1067701              1.0                1.0                1   \n",
       "\n",
       "                   Product  Jun15_add  Product_Enc  \n",
       "426858   ind_nom_pens_ult1       True         13.0  \n",
       "426859     ind_nomina_ult1       True         14.0  \n",
       "1067700  ind_nom_pens_ult1       True         13.0  \n",
       "1067701    ind_nomina_ult1       True         14.0  \n",
       "\n",
       "[4 rows x 31 columns]"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags_base.loc[lags_base[\"ncodpers\"] == 15982]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.sort_values([\"ncodpers\",\"fecha_dato\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lag_cols = ['age' ,'segmento','ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "for col in lag_cols:\n",
    "    lags_base[col +\"_lag1\"] = lags_base.groupby(\"ncodpers\")[col].shift(-1).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.ix[lags_base[\"Product\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87424, 57)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags_base.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base.drop([\n",
    "       'segmento', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1',\n",
    "       'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',\n",
    "       'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1',\n",
    "       'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_deme_fin_ult1',\n",
    "       'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1',\n",
    "       'ind_hip_fin_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1',\n",
    "       'ind_reca_fin_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1',\n",
    "       'ind_viv_fin_ult1', 'ind_nomina_ult1', 'ind_nom_pens_ult1',\n",
    "       'ind_recibo_ult1', 'Product', 'Jun15_add', 'Product_Enc'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base1 = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-06-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 29)"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags_base1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lag 2 for all products and segment and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_Apr_jun15 = train.loc[(train[\"fecha_dato\"] == '2015-06-28')|(train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-04-28') ]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'age' ,'segmento' ,'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_Apr_jun15 = train_Apr_jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = pd.merge(train_Apr_jun15,Jun15_add,how=\"left\", on=\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.sort_values([\"ncodpers\",\"fecha_dato\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lag_cols = ['age' ,'segmento','ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "for col in lag_cols:\n",
    "    lags_base[col +\"_lag2\"] = lags_base.groupby(\"ncodpers\")[col].shift(-2).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.ix[lags_base[\"Product\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base.drop(['age',\n",
    "       'segmento', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1',\n",
    "       'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',\n",
    "       'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1',\n",
    "       'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_deme_fin_ult1',\n",
    "       'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1',\n",
    "       'ind_hip_fin_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1',\n",
    "       'ind_reca_fin_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1',\n",
    "       'ind_viv_fin_ult1', 'ind_nomina_ult1', 'ind_nom_pens_ult1',\n",
    "       'ind_recibo_ult1', 'Product', 'Jun15_add', 'Product_Enc'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base2 = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-06-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 28)"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags_base2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67485, 55)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.merge(lags_base1, lags_base2, on= [\"ncodpers\", \"fecha_dato\"])\n",
    "t.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lag 3 for all products and segment and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_Mar_jun15 = train.loc[(train[\"fecha_dato\"] == '2015-06-28')|(train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-04-28') | (train[\"fecha_dato\"] == '2015-03-28')]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'age' ,'segmento' ,'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_Mar_jun15 = train_Mar_jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = pd.merge(train_Mar_jun15,Jun15_add,how=\"left\", on=\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.sort_values([\"ncodpers\",\"fecha_dato\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lag_cols = ['age' ,'segmento','ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "for col in lag_cols:\n",
    "    lags_base[col +\"_lag3\"] = lags_base.groupby(\"ncodpers\")[col].shift(-3).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.ix[lags_base[\"Product\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base.drop(['age',\n",
    "       'segmento', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1',\n",
    "       'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',\n",
    "       'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1',\n",
    "       'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_deme_fin_ult1',\n",
    "       'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1',\n",
    "       'ind_hip_fin_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1',\n",
    "       'ind_reca_fin_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1',\n",
    "       'ind_viv_fin_ult1', 'ind_nomina_ult1', 'ind_nom_pens_ult1',\n",
    "       'ind_recibo_ult1', 'Product', 'Jun15_add', 'Product_Enc'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base3 = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-06-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(45679, 28)"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lags_base3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fecha_dato', 'age', 'ncodpers', 'age_lag1', 'segmento_lag1',\n",
       "       'ind_ahor_fin_ult1_lag1', 'ind_aval_fin_ult1_lag1',\n",
       "       'ind_cco_fin_ult1_lag1', 'ind_cder_fin_ult1_lag1',\n",
       "       'ind_cno_fin_ult1_lag1', 'ind_ctju_fin_ult1_lag1',\n",
       "       'ind_ctma_fin_ult1_lag1', 'ind_ctop_fin_ult1_lag1',\n",
       "       'ind_ctpp_fin_ult1_lag1', 'ind_deco_fin_ult1_lag1',\n",
       "       'ind_deme_fin_ult1_lag1', 'ind_dela_fin_ult1_lag1',\n",
       "       'ind_ecue_fin_ult1_lag1', 'ind_fond_fin_ult1_lag1',\n",
       "       'ind_hip_fin_ult1_lag1', 'ind_plan_fin_ult1_lag1',\n",
       "       'ind_pres_fin_ult1_lag1', 'ind_reca_fin_ult1_lag1',\n",
       "       'ind_tjcr_fin_ult1_lag1', 'ind_valo_fin_ult1_lag1',\n",
       "       'ind_viv_fin_ult1_lag1', 'ind_nomina_ult1_lag1',\n",
       "       'ind_nom_pens_ult1_lag1', 'ind_recibo_ult1_lag1', 'age_lag2',\n",
       "       'segmento_lag2', 'ind_ahor_fin_ult1_lag2', 'ind_aval_fin_ult1_lag2',\n",
       "       'ind_cco_fin_ult1_lag2', 'ind_cder_fin_ult1_lag2',\n",
       "       'ind_cno_fin_ult1_lag2', 'ind_ctju_fin_ult1_lag2',\n",
       "       'ind_ctma_fin_ult1_lag2', 'ind_ctop_fin_ult1_lag2',\n",
       "       'ind_ctpp_fin_ult1_lag2', 'ind_deco_fin_ult1_lag2',\n",
       "       'ind_deme_fin_ult1_lag2', 'ind_dela_fin_ult1_lag2',\n",
       "       'ind_ecue_fin_ult1_lag2', 'ind_fond_fin_ult1_lag2',\n",
       "       'ind_hip_fin_ult1_lag2', 'ind_plan_fin_ult1_lag2',\n",
       "       'ind_pres_fin_ult1_lag2', 'ind_reca_fin_ult1_lag2',\n",
       "       'ind_tjcr_fin_ult1_lag2', 'ind_valo_fin_ult1_lag2',\n",
       "       'ind_viv_fin_ult1_lag2', 'ind_nomina_ult1_lag2',\n",
       "       'ind_nom_pens_ult1_lag2', 'ind_recibo_ult1_lag2', 'age_lag3',\n",
       "       'segmento_lag3', 'ind_ahor_fin_ult1_lag3', 'ind_aval_fin_ult1_lag3',\n",
       "       'ind_cco_fin_ult1_lag3', 'ind_cder_fin_ult1_lag3',\n",
       "       'ind_cno_fin_ult1_lag3', 'ind_ctju_fin_ult1_lag3',\n",
       "       'ind_ctma_fin_ult1_lag3', 'ind_ctop_fin_ult1_lag3',\n",
       "       'ind_ctpp_fin_ult1_lag3', 'ind_deco_fin_ult1_lag3',\n",
       "       'ind_deme_fin_ult1_lag3', 'ind_dela_fin_ult1_lag3',\n",
       "       'ind_ecue_fin_ult1_lag3', 'ind_fond_fin_ult1_lag3',\n",
       "       'ind_hip_fin_ult1_lag3', 'ind_plan_fin_ult1_lag3',\n",
       "       'ind_pres_fin_ult1_lag3', 'ind_reca_fin_ult1_lag3',\n",
       "       'ind_tjcr_fin_ult1_lag3', 'ind_valo_fin_ult1_lag3',\n",
       "       'ind_viv_fin_ult1_lag3', 'ind_nomina_ult1_lag3',\n",
       "       'ind_nom_pens_ult1_lag3', 'ind_recibo_ult1_lag3'], dtype=object)"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.merge(t, lags_base3, on= [\"ncodpers\", \"fecha_dato\"])\n",
    "t.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lag 4 for all products and segment and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_Feb_jun15 = train.loc[(train[\"fecha_dato\"] == '2015-06-28')|(train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-04-28') | (train[\"fecha_dato\"] == '2015-03-28') | (train[\"fecha_dato\"] == '2015-02-28') ]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'age' ,'segmento' ,'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_Feb_jun15 = train_Feb_jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = pd.merge(train_Feb_jun15,Jun15_add,how=\"left\", on=\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.sort_values([\"ncodpers\",\"fecha_dato\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lag_cols = ['age' ,'segmento','ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "for col in lag_cols:\n",
    "    lags_base[col +\"_lag4\"] = lags_base.groupby(\"ncodpers\")[col].shift(-4).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.ix[lags_base[\"Product\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base.drop(['age',\n",
    "       'segmento', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1',\n",
    "       'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',\n",
    "       'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1',\n",
    "       'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_deme_fin_ult1',\n",
    "       'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1',\n",
    "       'ind_hip_fin_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1',\n",
    "       'ind_reca_fin_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1',\n",
    "       'ind_viv_fin_ult1', 'ind_nomina_ult1', 'ind_nom_pens_ult1',\n",
    "       'ind_recibo_ult1', 'Product', 'Jun15_add', 'Product_Enc'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base4 = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-06-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fecha_dato', 'age', 'ncodpers', 'age_lag1', 'segmento_lag1',\n",
       "       'ind_ahor_fin_ult1_lag1', 'ind_aval_fin_ult1_lag1',\n",
       "       'ind_cco_fin_ult1_lag1', 'ind_cder_fin_ult1_lag1',\n",
       "       'ind_cno_fin_ult1_lag1', 'ind_ctju_fin_ult1_lag1',\n",
       "       'ind_ctma_fin_ult1_lag1', 'ind_ctop_fin_ult1_lag1',\n",
       "       'ind_ctpp_fin_ult1_lag1', 'ind_deco_fin_ult1_lag1',\n",
       "       'ind_deme_fin_ult1_lag1', 'ind_dela_fin_ult1_lag1',\n",
       "       'ind_ecue_fin_ult1_lag1', 'ind_fond_fin_ult1_lag1',\n",
       "       'ind_hip_fin_ult1_lag1', 'ind_plan_fin_ult1_lag1',\n",
       "       'ind_pres_fin_ult1_lag1', 'ind_reca_fin_ult1_lag1',\n",
       "       'ind_tjcr_fin_ult1_lag1', 'ind_valo_fin_ult1_lag1',\n",
       "       'ind_viv_fin_ult1_lag1', 'ind_nomina_ult1_lag1',\n",
       "       'ind_nom_pens_ult1_lag1', 'ind_recibo_ult1_lag1', 'age_lag2',\n",
       "       'segmento_lag2', 'ind_ahor_fin_ult1_lag2', 'ind_aval_fin_ult1_lag2',\n",
       "       'ind_cco_fin_ult1_lag2', 'ind_cder_fin_ult1_lag2',\n",
       "       'ind_cno_fin_ult1_lag2', 'ind_ctju_fin_ult1_lag2',\n",
       "       'ind_ctma_fin_ult1_lag2', 'ind_ctop_fin_ult1_lag2',\n",
       "       'ind_ctpp_fin_ult1_lag2', 'ind_deco_fin_ult1_lag2',\n",
       "       'ind_deme_fin_ult1_lag2', 'ind_dela_fin_ult1_lag2',\n",
       "       'ind_ecue_fin_ult1_lag2', 'ind_fond_fin_ult1_lag2',\n",
       "       'ind_hip_fin_ult1_lag2', 'ind_plan_fin_ult1_lag2',\n",
       "       'ind_pres_fin_ult1_lag2', 'ind_reca_fin_ult1_lag2',\n",
       "       'ind_tjcr_fin_ult1_lag2', 'ind_valo_fin_ult1_lag2',\n",
       "       'ind_viv_fin_ult1_lag2', 'ind_nomina_ult1_lag2',\n",
       "       'ind_nom_pens_ult1_lag2', 'ind_recibo_ult1_lag2', 'age_lag3',\n",
       "       'segmento_lag3', 'ind_ahor_fin_ult1_lag3', 'ind_aval_fin_ult1_lag3',\n",
       "       'ind_cco_fin_ult1_lag3', 'ind_cder_fin_ult1_lag3',\n",
       "       'ind_cno_fin_ult1_lag3', 'ind_ctju_fin_ult1_lag3',\n",
       "       'ind_ctma_fin_ult1_lag3', 'ind_ctop_fin_ult1_lag3',\n",
       "       'ind_ctpp_fin_ult1_lag3', 'ind_deco_fin_ult1_lag3',\n",
       "       'ind_deme_fin_ult1_lag3', 'ind_dela_fin_ult1_lag3',\n",
       "       'ind_ecue_fin_ult1_lag3', 'ind_fond_fin_ult1_lag3',\n",
       "       'ind_hip_fin_ult1_lag3', 'ind_plan_fin_ult1_lag3',\n",
       "       'ind_pres_fin_ult1_lag3', 'ind_reca_fin_ult1_lag3',\n",
       "       'ind_tjcr_fin_ult1_lag3', 'ind_valo_fin_ult1_lag3',\n",
       "       'ind_viv_fin_ult1_lag3', 'ind_nomina_ult1_lag3',\n",
       "       'ind_nom_pens_ult1_lag3', 'ind_recibo_ult1_lag3', 'age_lag4',\n",
       "       'segmento_lag4', 'ind_ahor_fin_ult1_lag4', 'ind_aval_fin_ult1_lag4',\n",
       "       'ind_cco_fin_ult1_lag4', 'ind_cder_fin_ult1_lag4',\n",
       "       'ind_cno_fin_ult1_lag4', 'ind_ctju_fin_ult1_lag4',\n",
       "       'ind_ctma_fin_ult1_lag4', 'ind_ctop_fin_ult1_lag4',\n",
       "       'ind_ctpp_fin_ult1_lag4', 'ind_deco_fin_ult1_lag4',\n",
       "       'ind_deme_fin_ult1_lag4', 'ind_dela_fin_ult1_lag4',\n",
       "       'ind_ecue_fin_ult1_lag4', 'ind_fond_fin_ult1_lag4',\n",
       "       'ind_hip_fin_ult1_lag4', 'ind_plan_fin_ult1_lag4',\n",
       "       'ind_pres_fin_ult1_lag4', 'ind_reca_fin_ult1_lag4',\n",
       "       'ind_tjcr_fin_ult1_lag4', 'ind_valo_fin_ult1_lag4',\n",
       "       'ind_viv_fin_ult1_lag4', 'ind_nomina_ult1_lag4',\n",
       "       'ind_nom_pens_ult1_lag4', 'ind_recibo_ult1_lag4'], dtype=object)"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.merge(t, lags_base4, on= [\"ncodpers\", \"fecha_dato\"])\n",
    "t.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Lag 5 for all products and segment and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_Jan_jun15 = train.loc[(train[\"fecha_dato\"] == '2015-06-28')|(train[\"fecha_dato\"] == '2015-05-28') | (train[\"fecha_dato\"] == '2015-04-28') | (train[\"fecha_dato\"] == '2015-03-28') | (train[\"fecha_dato\"] == '2015-02-28') | (train[\"fecha_dato\"] == '2015-01-28')]\n",
    "\n",
    "#Keep only required columns\n",
    "cols_in = ['fecha_dato', 'age' ,'segmento' ,'ncodpers', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "train_Feb_jun15 = train_Jan_jun15.loc[:,cols_in]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = pd.merge(train_Jan_jun15, Jun15_add,how=\"left\", on=\"ncodpers\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.sort_values([\"ncodpers\",\"fecha_dato\"], ascending=[True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lag_cols = ['age' ,'segmento','ind_ahor_fin_ult1', 'ind_aval_fin_ult1', 'ind_cco_fin_ult1','ind_cder_fin_ult1', 'ind_cno_fin_ult1', 'ind_ctju_fin_ult1','ind_ctma_fin_ult1', 'ind_ctop_fin_ult1', 'ind_ctpp_fin_ult1','ind_deco_fin_ult1', 'ind_deme_fin_ult1', 'ind_dela_fin_ult1','ind_ecue_fin_ult1', 'ind_fond_fin_ult1', 'ind_hip_fin_ult1','ind_plan_fin_ult1', 'ind_pres_fin_ult1', 'ind_reca_fin_ult1','ind_tjcr_fin_ult1', 'ind_valo_fin_ult1', 'ind_viv_fin_ult1','ind_nomina_ult1',   'ind_nom_pens_ult1', 'ind_recibo_ult1']\n",
    "for col in lag_cols:\n",
    "    lags_base[col +\"_lag5\"] = lags_base.groupby(\"ncodpers\")[col].shift(-5).fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base = lags_base.ix[lags_base[\"Product\"].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base.drop([\n",
    "       'segmento', 'ind_ahor_fin_ult1', 'ind_aval_fin_ult1',\n",
    "       'ind_cco_fin_ult1', 'ind_cder_fin_ult1', 'ind_cno_fin_ult1',\n",
    "       'ind_ctju_fin_ult1', 'ind_ctma_fin_ult1', 'ind_ctop_fin_ult1',\n",
    "       'ind_ctpp_fin_ult1', 'ind_deco_fin_ult1', 'ind_deme_fin_ult1',\n",
    "       'ind_dela_fin_ult1', 'ind_ecue_fin_ult1', 'ind_fond_fin_ult1',\n",
    "       'ind_hip_fin_ult1', 'ind_plan_fin_ult1', 'ind_pres_fin_ult1',\n",
    "       'ind_reca_fin_ult1', 'ind_tjcr_fin_ult1', 'ind_valo_fin_ult1',\n",
    "       'ind_viv_fin_ult1', 'ind_nomina_ult1', 'ind_nom_pens_ult1',\n",
    "       'ind_recibo_ult1', 'Product', 'Jun15_add', 'Product_Enc'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "lags_base5 = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-06-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['fecha_dato', 'age_x', 'ncodpers', 'age_lag1', 'segmento_lag1',\n",
       "       'ind_ahor_fin_ult1_lag1', 'ind_aval_fin_ult1_lag1',\n",
       "       'ind_cco_fin_ult1_lag1', 'ind_cder_fin_ult1_lag1',\n",
       "       'ind_cno_fin_ult1_lag1', 'ind_ctju_fin_ult1_lag1',\n",
       "       'ind_ctma_fin_ult1_lag1', 'ind_ctop_fin_ult1_lag1',\n",
       "       'ind_ctpp_fin_ult1_lag1', 'ind_deco_fin_ult1_lag1',\n",
       "       'ind_deme_fin_ult1_lag1', 'ind_dela_fin_ult1_lag1',\n",
       "       'ind_ecue_fin_ult1_lag1', 'ind_fond_fin_ult1_lag1',\n",
       "       'ind_hip_fin_ult1_lag1', 'ind_plan_fin_ult1_lag1',\n",
       "       'ind_pres_fin_ult1_lag1', 'ind_reca_fin_ult1_lag1',\n",
       "       'ind_tjcr_fin_ult1_lag1', 'ind_valo_fin_ult1_lag1',\n",
       "       'ind_viv_fin_ult1_lag1', 'ind_nomina_ult1_lag1',\n",
       "       'ind_nom_pens_ult1_lag1', 'ind_recibo_ult1_lag1', 'age_lag2',\n",
       "       'segmento_lag2', 'ind_ahor_fin_ult1_lag2', 'ind_aval_fin_ult1_lag2',\n",
       "       'ind_cco_fin_ult1_lag2', 'ind_cder_fin_ult1_lag2',\n",
       "       'ind_cno_fin_ult1_lag2', 'ind_ctju_fin_ult1_lag2',\n",
       "       'ind_ctma_fin_ult1_lag2', 'ind_ctop_fin_ult1_lag2',\n",
       "       'ind_ctpp_fin_ult1_lag2', 'ind_deco_fin_ult1_lag2',\n",
       "       'ind_deme_fin_ult1_lag2', 'ind_dela_fin_ult1_lag2',\n",
       "       'ind_ecue_fin_ult1_lag2', 'ind_fond_fin_ult1_lag2',\n",
       "       'ind_hip_fin_ult1_lag2', 'ind_plan_fin_ult1_lag2',\n",
       "       'ind_pres_fin_ult1_lag2', 'ind_reca_fin_ult1_lag2',\n",
       "       'ind_tjcr_fin_ult1_lag2', 'ind_valo_fin_ult1_lag2',\n",
       "       'ind_viv_fin_ult1_lag2', 'ind_nomina_ult1_lag2',\n",
       "       'ind_nom_pens_ult1_lag2', 'ind_recibo_ult1_lag2', 'age_lag3',\n",
       "       'segmento_lag3', 'ind_ahor_fin_ult1_lag3', 'ind_aval_fin_ult1_lag3',\n",
       "       'ind_cco_fin_ult1_lag3', 'ind_cder_fin_ult1_lag3',\n",
       "       'ind_cno_fin_ult1_lag3', 'ind_ctju_fin_ult1_lag3',\n",
       "       'ind_ctma_fin_ult1_lag3', 'ind_ctop_fin_ult1_lag3',\n",
       "       'ind_ctpp_fin_ult1_lag3', 'ind_deco_fin_ult1_lag3',\n",
       "       'ind_deme_fin_ult1_lag3', 'ind_dela_fin_ult1_lag3',\n",
       "       'ind_ecue_fin_ult1_lag3', 'ind_fond_fin_ult1_lag3',\n",
       "       'ind_hip_fin_ult1_lag3', 'ind_plan_fin_ult1_lag3',\n",
       "       'ind_pres_fin_ult1_lag3', 'ind_reca_fin_ult1_lag3',\n",
       "       'ind_tjcr_fin_ult1_lag3', 'ind_valo_fin_ult1_lag3',\n",
       "       'ind_viv_fin_ult1_lag3', 'ind_nomina_ult1_lag3',\n",
       "       'ind_nom_pens_ult1_lag3', 'ind_recibo_ult1_lag3', 'age_lag4',\n",
       "       'segmento_lag4', 'ind_ahor_fin_ult1_lag4', 'ind_aval_fin_ult1_lag4',\n",
       "       'ind_cco_fin_ult1_lag4', 'ind_cder_fin_ult1_lag4',\n",
       "       'ind_cno_fin_ult1_lag4', 'ind_ctju_fin_ult1_lag4',\n",
       "       'ind_ctma_fin_ult1_lag4', 'ind_ctop_fin_ult1_lag4',\n",
       "       'ind_ctpp_fin_ult1_lag4', 'ind_deco_fin_ult1_lag4',\n",
       "       'ind_deme_fin_ult1_lag4', 'ind_dela_fin_ult1_lag4',\n",
       "       'ind_ecue_fin_ult1_lag4', 'ind_fond_fin_ult1_lag4',\n",
       "       'ind_hip_fin_ult1_lag4', 'ind_plan_fin_ult1_lag4',\n",
       "       'ind_pres_fin_ult1_lag4', 'ind_reca_fin_ult1_lag4',\n",
       "       'ind_tjcr_fin_ult1_lag4', 'ind_valo_fin_ult1_lag4',\n",
       "       'ind_viv_fin_ult1_lag4', 'ind_nomina_ult1_lag4',\n",
       "       'ind_nom_pens_ult1_lag4', 'ind_recibo_ult1_lag4', 'ind_empleado',\n",
       "       'pais_residencia', 'sexo', 'age_y', 'fecha_alta', 'ind_nuevo',\n",
       "       'antiguedad', 'indrel', 'ult_fec_cli_1t', 'indrel_1mes',\n",
       "       'tiprel_1mes', 'indresi', 'indext', 'conyuemp', 'canal_entrada',\n",
       "       'indfall', 'tipodom', 'cod_prov', 'nomprov',\n",
       "       'ind_actividad_cliente', 'renta', 'age_lag5', 'segmento_lag5',\n",
       "       'ind_ahor_fin_ult1_lag5', 'ind_aval_fin_ult1_lag5',\n",
       "       'ind_cco_fin_ult1_lag5', 'ind_cder_fin_ult1_lag5',\n",
       "       'ind_cno_fin_ult1_lag5', 'ind_ctju_fin_ult1_lag5',\n",
       "       'ind_ctma_fin_ult1_lag5', 'ind_ctop_fin_ult1_lag5',\n",
       "       'ind_ctpp_fin_ult1_lag5', 'ind_deco_fin_ult1_lag5',\n",
       "       'ind_deme_fin_ult1_lag5', 'ind_dela_fin_ult1_lag5',\n",
       "       'ind_ecue_fin_ult1_lag5', 'ind_fond_fin_ult1_lag5',\n",
       "       'ind_hip_fin_ult1_lag5', 'ind_plan_fin_ult1_lag5',\n",
       "       'ind_pres_fin_ult1_lag5', 'ind_reca_fin_ult1_lag5',\n",
       "       'ind_tjcr_fin_ult1_lag5', 'ind_valo_fin_ult1_lag5',\n",
       "       'ind_viv_fin_ult1_lag5', 'ind_nomina_ult1_lag5',\n",
       "       'ind_nom_pens_ult1_lag5', 'ind_recibo_ult1_lag5'], dtype=object)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = pd.merge(t, lags_base5, on= [\"ncodpers\", \"fecha_dato\"])\n",
    "t.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fecha_dato</th>\n",
       "      <th>age_x</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>age_lag1</th>\n",
       "      <th>segmento_lag1</th>\n",
       "      <th>ind_ahor_fin_ult1_lag1</th>\n",
       "      <th>ind_aval_fin_ult1_lag1</th>\n",
       "      <th>ind_cco_fin_ult1_lag1</th>\n",
       "      <th>ind_cder_fin_ult1_lag1</th>\n",
       "      <th>ind_cno_fin_ult1_lag1</th>\n",
       "      <th>...</th>\n",
       "      <th>ind_hip_fin_ult1_lag5</th>\n",
       "      <th>ind_plan_fin_ult1_lag5</th>\n",
       "      <th>ind_pres_fin_ult1_lag5</th>\n",
       "      <th>ind_reca_fin_ult1_lag5</th>\n",
       "      <th>ind_tjcr_fin_ult1_lag5</th>\n",
       "      <th>ind_valo_fin_ult1_lag5</th>\n",
       "      <th>ind_viv_fin_ult1_lag5</th>\n",
       "      <th>ind_nomina_ult1_lag5</th>\n",
       "      <th>ind_nom_pens_ult1_lag5</th>\n",
       "      <th>ind_recibo_ult1_lag5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
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       "      <th>8</th>\n",
       "      <td>2015-06-28</td>\n",
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       "      <td>15982</td>\n",
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       "      <td>02 - PARTICULARES</td>\n",
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       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>2015-06-28</td>\n",
       "      <td>57</td>\n",
       "      <td>15982</td>\n",
       "      <td>57</td>\n",
       "      <td>02 - PARTICULARES</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   fecha_dato age_x  ncodpers age_lag1      segmento_lag1  \\\n",
       "7  2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "8  2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "9  2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "10 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "11 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "12 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "13 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "14 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "15 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "16 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "17 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "18 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "19 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "20 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "21 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "22 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "23 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "24 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "25 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "26 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "27 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "28 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "29 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "30 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "31 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "32 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "33 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "34 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "35 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "36 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "37 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "38 2015-06-28    57     15982       57  02 - PARTICULARES   \n",
       "\n",
       "    ind_ahor_fin_ult1_lag1  ind_aval_fin_ult1_lag1  ind_cco_fin_ult1_lag1  \\\n",
       "7                      0.0                     0.0                    1.0   \n",
       "8                      0.0                     0.0                    1.0   \n",
       "9                      0.0                     0.0                    1.0   \n",
       "10                     0.0                     0.0                    1.0   \n",
       "11                     0.0                     0.0                    1.0   \n",
       "12                     0.0                     0.0                    1.0   \n",
       "13                     0.0                     0.0                    1.0   \n",
       "14                     0.0                     0.0                    1.0   \n",
       "15                     0.0                     0.0                    1.0   \n",
       "16                     0.0                     0.0                    1.0   \n",
       "17                     0.0                     0.0                    1.0   \n",
       "18                     0.0                     0.0                    1.0   \n",
       "19                     0.0                     0.0                    1.0   \n",
       "20                     0.0                     0.0                    1.0   \n",
       "21                     0.0                     0.0                    1.0   \n",
       "22                     0.0                     0.0                    1.0   \n",
       "23                     0.0                     0.0                    1.0   \n",
       "24                     0.0                     0.0                    1.0   \n",
       "25                     0.0                     0.0                    1.0   \n",
       "26                     0.0                     0.0                    1.0   \n",
       "27                     0.0                     0.0                    1.0   \n",
       "28                     0.0                     0.0                    1.0   \n",
       "29                     0.0                     0.0                    1.0   \n",
       "30                     0.0                     0.0                    1.0   \n",
       "31                     0.0                     0.0                    1.0   \n",
       "32                     0.0                     0.0                    1.0   \n",
       "33                     0.0                     0.0                    1.0   \n",
       "34                     0.0                     0.0                    1.0   \n",
       "35                     0.0                     0.0                    1.0   \n",
       "36                     0.0                     0.0                    1.0   \n",
       "37                     0.0                     0.0                    1.0   \n",
       "38                     0.0                     0.0                    1.0   \n",
       "\n",
       "    ind_cder_fin_ult1_lag1  ind_cno_fin_ult1_lag1          ...           \\\n",
       "7                      0.0                    1.0          ...            \n",
       "8                      0.0                    1.0          ...            \n",
       "9                      0.0                    1.0          ...            \n",
       "10                     0.0                    1.0          ...            \n",
       "11                     0.0                    1.0          ...            \n",
       "12                     0.0                    1.0          ...            \n",
       "13                     0.0                    1.0          ...            \n",
       "14                     0.0                    1.0          ...            \n",
       "15                     0.0                    1.0          ...            \n",
       "16                     0.0                    1.0          ...            \n",
       "17                     0.0                    1.0          ...            \n",
       "18                     0.0                    1.0          ...            \n",
       "19                     0.0                    1.0          ...            \n",
       "20                     0.0                    1.0          ...            \n",
       "21                     0.0                    1.0          ...            \n",
       "22                     0.0                    1.0          ...            \n",
       "23                     0.0                    1.0          ...            \n",
       "24                     0.0                    1.0          ...            \n",
       "25                     0.0                    1.0          ...            \n",
       "26                     0.0                    1.0          ...            \n",
       "27                     0.0                    1.0          ...            \n",
       "28                     0.0                    1.0          ...            \n",
       "29                     0.0                    1.0          ...            \n",
       "30                     0.0                    1.0          ...            \n",
       "31                     0.0                    1.0          ...            \n",
       "32                     0.0                    1.0          ...            \n",
       "33                     0.0                    1.0          ...            \n",
       "34                     0.0                    1.0          ...            \n",
       "35                     0.0                    1.0          ...            \n",
       "36                     0.0                    1.0          ...            \n",
       "37                     0.0                    1.0          ...            \n",
       "38                     0.0                    1.0          ...            \n",
       "\n",
       "    ind_hip_fin_ult1_lag5  ind_plan_fin_ult1_lag5  ind_pres_fin_ult1_lag5  \\\n",
       "7                     0.0                     0.0                     0.0   \n",
       "8                     0.0                     0.0                     0.0   \n",
       "9                     0.0                     0.0                     0.0   \n",
       "10                    0.0                     0.0                     0.0   \n",
       "11                    0.0                     0.0                     0.0   \n",
       "12                    0.0                     0.0                     0.0   \n",
       "13                    0.0                     0.0                     0.0   \n",
       "14                    0.0                     0.0                     0.0   \n",
       "15                    0.0                     0.0                     0.0   \n",
       "16                    0.0                     0.0                     0.0   \n",
       "17                    0.0                     0.0                     0.0   \n",
       "18                    0.0                     0.0                     0.0   \n",
       "19                    0.0                     0.0                     0.0   \n",
       "20                    0.0                     0.0                     0.0   \n",
       "21                    0.0                     0.0                     0.0   \n",
       "22                    0.0                     0.0                     0.0   \n",
       "23                    0.0                     0.0                     0.0   \n",
       "24                    0.0                     0.0                     0.0   \n",
       "25                    0.0                     0.0                     0.0   \n",
       "26                    0.0                     0.0                     0.0   \n",
       "27                    0.0                     0.0                     0.0   \n",
       "28                    0.0                     0.0                     0.0   \n",
       "29                    0.0                     0.0                     0.0   \n",
       "30                    0.0                     0.0                     0.0   \n",
       "31                    0.0                     0.0                     0.0   \n",
       "32                    0.0                     0.0                     0.0   \n",
       "33                    0.0                     0.0                     0.0   \n",
       "34                    0.0                     0.0                     0.0   \n",
       "35                    0.0                     0.0                     0.0   \n",
       "36                    0.0                     0.0                     0.0   \n",
       "37                    0.0                     0.0                     0.0   \n",
       "38                    0.0                     0.0                     0.0   \n",
       "\n",
       "    ind_reca_fin_ult1_lag5  ind_tjcr_fin_ult1_lag5  ind_valo_fin_ult1_lag5  \\\n",
       "7                      0.0                     1.0                     0.0   \n",
       "8                      0.0                     1.0                     0.0   \n",
       "9                      0.0                     1.0                     0.0   \n",
       "10                     0.0                     1.0                     0.0   \n",
       "11                     0.0                     1.0                     0.0   \n",
       "12                     0.0                     1.0                     0.0   \n",
       "13                     0.0                     1.0                     0.0   \n",
       "14                     0.0                     1.0                     0.0   \n",
       "15                     0.0                     1.0                     0.0   \n",
       "16                     0.0                     1.0                     0.0   \n",
       "17                     0.0                     1.0                     0.0   \n",
       "18                     0.0                     1.0                     0.0   \n",
       "19                     0.0                     1.0                     0.0   \n",
       "20                     0.0                     1.0                     0.0   \n",
       "21                     0.0                     1.0                     0.0   \n",
       "22                     0.0                     1.0                     0.0   \n",
       "23                     0.0                     1.0                     0.0   \n",
       "24                     0.0                     1.0                     0.0   \n",
       "25                     0.0                     1.0                     0.0   \n",
       "26                     0.0                     1.0                     0.0   \n",
       "27                     0.0                     1.0                     0.0   \n",
       "28                     0.0                     1.0                     0.0   \n",
       "29                     0.0                     1.0                     0.0   \n",
       "30                     0.0                     1.0                     0.0   \n",
       "31                     0.0                     1.0                     0.0   \n",
       "32                     0.0                     1.0                     0.0   \n",
       "33                     0.0                     1.0                     0.0   \n",
       "34                     0.0                     1.0                     0.0   \n",
       "35                     0.0                     1.0                     0.0   \n",
       "36                     0.0                     1.0                     0.0   \n",
       "37                     0.0                     1.0                     0.0   \n",
       "38                     0.0                     1.0                     0.0   \n",
       "\n",
       "    ind_viv_fin_ult1_lag5  ind_nomina_ult1_lag5  ind_nom_pens_ult1_lag5  \\\n",
       "7                     0.0                   1.0                     1.0   \n",
       "8                     0.0                   1.0                     1.0   \n",
       "9                     0.0                   1.0                     1.0   \n",
       "10                    0.0                   1.0                     1.0   \n",
       "11                    0.0                   1.0                     1.0   \n",
       "12                    0.0                   1.0                     1.0   \n",
       "13                    0.0                   1.0                     1.0   \n",
       "14                    0.0                   1.0                     1.0   \n",
       "15                    0.0                   1.0                     1.0   \n",
       "16                    0.0                   1.0                     1.0   \n",
       "17                    0.0                   1.0                     1.0   \n",
       "18                    0.0                   1.0                     1.0   \n",
       "19                    0.0                   1.0                     1.0   \n",
       "20                    0.0                   1.0                     1.0   \n",
       "21                    0.0                   1.0                     1.0   \n",
       "22                    0.0                   1.0                     1.0   \n",
       "23                    0.0                   1.0                     1.0   \n",
       "24                    0.0                   1.0                     1.0   \n",
       "25                    0.0                   1.0                     1.0   \n",
       "26                    0.0                   1.0                     1.0   \n",
       "27                    0.0                   1.0                     1.0   \n",
       "28                    0.0                   1.0                     1.0   \n",
       "29                    0.0                   1.0                     1.0   \n",
       "30                    0.0                   1.0                     1.0   \n",
       "31                    0.0                   1.0                     1.0   \n",
       "32                    0.0                   1.0                     1.0   \n",
       "33                    0.0                   1.0                     1.0   \n",
       "34                    0.0                   1.0                     1.0   \n",
       "35                    0.0                   1.0                     1.0   \n",
       "36                    0.0                   1.0                     1.0   \n",
       "37                    0.0                   1.0                     1.0   \n",
       "38                    0.0                   1.0                     1.0   \n",
       "\n",
       "    ind_recibo_ult1_lag5  \n",
       "7                    1.0  \n",
       "8                    1.0  \n",
       "9                    1.0  \n",
       "10                   1.0  \n",
       "11                   1.0  \n",
       "12                   1.0  \n",
       "13                   1.0  \n",
       "14                   1.0  \n",
       "15                   1.0  \n",
       "16                   1.0  \n",
       "17                   1.0  \n",
       "18                   1.0  \n",
       "19                   1.0  \n",
       "20                   1.0  \n",
       "21                   1.0  \n",
       "22                   1.0  \n",
       "23                   1.0  \n",
       "24                   1.0  \n",
       "25                   1.0  \n",
       "26                   1.0  \n",
       "27                   1.0  \n",
       "28                   1.0  \n",
       "29                   1.0  \n",
       "30                   1.0  \n",
       "31                   1.0  \n",
       "32                   1.0  \n",
       "33                   1.0  \n",
       "34                   1.0  \n",
       "35                   1.0  \n",
       "36                   1.0  \n",
       "37                   1.0  \n",
       "38                   1.0  \n",
       "\n",
       "[32 rows x 154 columns]"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t.loc[t[\"ncodpers\"] == 15982]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month_ID</th>\n",
       "      <th>ncodpers</th>\n",
       "      <th>Product</th>\n",
       "      <th>Jun15_add</th>\n",
       "      <th>Product_Enc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>15892</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>15896</td>\n",
       "      <td>ind_cco_fin_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>15897</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>15906</td>\n",
       "      <td>ind_nom_pens_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>15925</td>\n",
       "      <td>ind_recibo_ult1</td>\n",
       "      <td>True</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Month_ID  ncodpers            Product Jun15_add  Product_Enc\n",
       "13           15892   ind_cco_fin_ult1      True            0\n",
       "42           15896   ind_cco_fin_ult1      True            0\n",
       "51           15897  ind_nom_pens_ult1      True           13\n",
       "84           15906  ind_nom_pens_ult1      True           13\n",
       "187          15925    ind_recibo_ult1      True           18"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Jun15_add.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(740989, 154)\n"
     ]
    }
   ],
   "source": [
    "t.merge(Jun15_add, t, on=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lag1_df = pd.concat([lags_base.ix[:,3], lags_base.ix[:,31:]], axis=1)\n",
    "lag1_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lags_base[\"ind_cco_fin_ult1_lag1\"] = lags_base.groupby(\"ncodpers\")[\"ind_cco_fin_ult1\"].shift(1)\n",
    "lags_base.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "base = lags_base.loc[lags_base[\"fecha_dato\"] == '2015-05-28']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "base.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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