datasets_limpios.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "installs_dtypes = {'application_id': 'category', 'ref_type': 'category', 'device_countrycode': 'category',\n",
    "                   'device_brand': 'category', 'device_model' : 'category', 'session_user_agent': 'category',\n",
    "                   'user_agent': 'category', 'kind': 'category', 'device_language': 'category'}\n",
    "\n",
    "installs = pd.read_csv('installs.csv', dtype = installs_dtypes)\n",
    "\n",
    "installs[\"created\"] =  pd.to_datetime(installs[\"created\"], errors = \"coerce\") #parseo de fechas\n",
    "\n",
    "installs.drop('attributed', axis = 1, inplace = True) #son todas false\n",
    "installs.drop('click_hash', axis = 1, inplace = True) #son todas NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{hash}                                  3\n",
       "44aeb6df-8db4-4b3f-ac67-0a802d479c86    1\n",
       "89bda305-bff1-4c22-ae23-1a237eac0002    1\n",
       "22380348598                             1\n",
       "Name: trans_id, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "installs['trans_id'].value_counts() #????? esta columna podria estar rota"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "clicks_dtypes = {'advertiser': np.int8, 'action_id': np.float16, 'source_id': np.int8, 'country_code': 'category',\n",
    "                 'latitude': np.float16, 'longitude': np.float16, 'carrier_id': np.float16, 'brand': np.float16,\n",
    "                 'touchX': np.float16, 'touchY': np.float16}\n",
    "\n",
    "clicks = pd.read_csv('clicks.csv', dtype = clicks_dtypes)\n",
    "\n",
    "clicks.drop('action_id', axis = 1, inplace = True) #todos NaN\n",
    "clicks.drop('wifi_connection', axis = 1, inplace = True) #todos False\n",
    "\n",
    "clicks[\"created\"] =  pd.to_datetime(clicks[\"created\"], errors = \"coerce\") #parseo de fechas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "events_dtypes = {'event_id': 'category', 'ref_type': 'category', 'ref_hash': 'category', 'application_id': 'category',\n",
    "                   'attributed': 'category', 'device_countrycode': 'category', 'device_os_version': 'category',\n",
    "                   'device_brand' : 'category', 'device_model' : 'category', 'device_city': 'category', 'session_user_agent': 'category', 'user_agent': 'category', 'carrier': 'category',\n",
    "                   'kind': 'category', 'device_os' : 'category', 'wifi': 'category', 'connection_type': 'category',\n",
    "                   'device_language': 'category'}\n",
    "\n",
    "events = pd.read_csv('events.csv', dtype = events_dtypes, low_memory=False)\n",
    "\n",
    "events[\"date\"] =  pd.to_datetime(events[\"date\"], errors = \"coerce\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "auctions_dtypes = {'auction_type_id': np.float16, 'country': 'category', 'platform': 'category',\n",
    "                   'ref_type_id': 'category', 'source_id': 'category', 'device_id': 'category'}\n",
    "\n",
    "auctions = pd.read_csv('auctions.csv', dtype = auctions_dtypes)\n",
    "\n",
    "auctions.drop('auction_type_id', axis = 1, inplace = True)  #todas Nan\n",
    "\n",
    "auctions[\"date\"] =  pd.to_datetime(auctions[\"date\"], errors = \"coerce\")"
   ]
  }
 ],
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