{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8933a00f-4460-8ba4-5d84-eb30eee3329c"
      },
      "outputs": [],
      "source": [
        "# This Python 3 environment comes with many helpful analytics libraries installed\n",
        "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
        "# For example, here's several helpful packages to load in \n",
        "\n",
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "\n",
        "# Input data files are available in the \"../input/\" directory.\n",
        "# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n",
        "\n",
        "from subprocess import check_output\n",
        "print(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n",
        "\n",
        "# Any results you write to the current directory are saved as output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "880dcd54-4415-ab3c-d2d1-3f1fd5055afa"
      },
      "outputs": [],
      "source": [
        "train_num = pd.read_csv(\"../input/train_numeric.csv\", chunksize=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "0386aa44-8597-f8e8-4444-e7c5d50975cd"
      },
      "outputs": [],
      "source": [
        "from collections import Counter"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "b9076bde-e056-59e7-6d2c-452ec6a96062"
      },
      "outputs": [],
      "source": [
        "classes = Counter()\n",
        "for chunk in train_num:\n",
        "    for idx, row in chunk.iterrows():\n",
        "        classes[row.Response] += 1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "d479488b-9ade-485f-031a-ffbbb2d51c13"
      },
      "outputs": [],
      "source": [
        "classes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "eb3e4d0b-521f-9734-2c37-e8c35ae30737"
      },
      "outputs": [],
      "source": [
        "train_date = pd.read_csv(\"../input/train_date.csv\", nrows=1000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "790f10f6-c0fa-829a-27aa-d9302a79c000"
      },
      "outputs": [],
      "source": [
        "train_date.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "a955cf13-8444-5394-1bbb-45d6a154a261"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.5.2"
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}