{
  "metadata": {
    "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": "3.6.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0,
  "cells": [
    {
      "metadata": {
        "_cell_guid": "cdd2e406-691d-fe9b-b86d-b019a1e38fc6",
        "_active": false,
        "collapsed": false
      },
      "source": null,
      "execution_count": null,
      "cell_type": "markdown",
      "outputs": [],
      "execution_state": "idle"
    },
    {
      "metadata": {
        "_cell_guid": "a8334902-89de-ef2b-bea4-fdfa183e9d6f",
        "_active": false,
        "collapsed": false
      },
      "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\nimport numpy as np # linear algebra\nimport 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\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.",
      "execution_count": 2,
      "cell_type": "code",
      "outputs": [],
      "execution_state": "idle"
    },
    {
      "metadata": {
        "_cell_guid": "9201711c-1a4a-c042-bf7c-e2c11025f521",
        "_active": false,
        "collapsed": false
      },
      "source": "from matplotlib import pyplot as plt\n\ntraining_data = pd.read_csv('../input/train.csv')\ny_data = training_data.pop('label').values\nX_data = training_data.values\n\nplt.imshow(X_data[0].reshape((28, 28)), cmap='gray_r')",
      "execution_count": 3,
      "cell_type": "code",
      "outputs": [],
      "execution_state": "idle"
    },
    {
      "metadata": {
        "_cell_guid": "1cb8b61f-d3f3-f87c-9a80-ad75aba04236",
        "_active": true,
        "collapsed": false
      },
      "source": null,
      "execution_count": 4,
      "cell_type": "code",
      "outputs": [],
      "execution_state": "idle"
    },
    {
      "metadata": {
        "_cell_guid": "ab6c634a-7fbb-820f-00ba-93d7744208a6",
        "_active": false,
        "collapsed": false
      },
      "source": null,
      "execution_count": 4,
      "cell_type": "code",
      "outputs": [],
      "execution_state": "idle"
    }
  ]
}