{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "cd8e5a43-3551-1e4a-f099-c8b3abc8dc42"
      },
      "outputs": [],
      "source": ""
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "4509ec9a-d6d8-9c6c-94eb-819f2e88ac4b"
      },
      "outputs": [],
      "source": [
        "test=pd.read_csv('../input/test.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "1126788b-7a68-1738-181f-5c4a3c0a52c3"
      },
      "outputs": [],
      "source": [
        "train=pd.read_csv('../input/train.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "72d5da67-0cc3-e453-d0d5-08742c24816c"
      },
      "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": "942dfc9c-9789-d59a-4488-3a45b1245f7d"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "2c58a7b2-5ef1-1665-db86-382683658819"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "cddd6818-ac59-ead3-25fa-03b464f40ad3"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pylab as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "ccf3fe0b-a9fe-ebb5-b621-99c76fb7188d"
      },
      "outputs": [],
      "source": [
        "from __future__ import print_function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "bd5d90f2-932e-c8d4-8412-9388ebcf6ce6"
      },
      "outputs": [],
      "source": [
        "from scipy import  stats"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "f8852010-cf4a-37a3-d548-ac8b338368b1"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.graphics.api import qqplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "2c00fd18-ffe8-191a-5bea-c8a963c4f64b"
      },
      "outputs": [],
      "source": [
        "dta=[10930,10318,10595,10972,7706,6756,9092,10551,9722,10913,11151,8186,6422, \n",
        "6337,11649,11652,10310,12043,7937,6476,9662,9570,9981,9331,9449,6773,6304,9355, \n",
        "10477,10148,10395,11261,8713,7299,10424,10795,11069,11602,11427,9095,7707,10767, \n",
        "12136,12812,12006,12528,10329,7818,11719,11683,12603,11495,13670,11337,10232, \n",
        "13261,13230,15535,16837,19598,14823,11622,19391,18177,19994,14723,15694,13248, \n",
        "9543,12872,13101,15053,12619,13749,10228,9725,14729,12518,14564,15085,14722, \n",
        "11999,9390,13481,14795,15845,15271,14686,11054,10395]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "1c2849dd-b41a-5f24-9a76-0d2a486732e3"
      },
      "outputs": [],
      "source": [
        "dta=pd.Series(dta)\n",
        "dta.index = pd.Index(sm.tsa.datetools.dates_from_range('2001','2090'))\n",
        "dta.plot(figsize=(12,8))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "cda96f1b-0aa7-1885-1a6c-6988f02ba638"
      },
      "outputs": [],
      "source": [
        "fig = plt.figure(figsize=(12,8))\n",
        "ax1= fig.add_subplot(111)\n",
        "diff1 = dta.diff(1)\n",
        "diff1.plot(ax=ax1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "_cell_guid": "8591c132-e698-a4da-7606-f57cf2c0de23"
      },
      "outputs": [],
      "source": ""
    }
  ],
  "metadata": {
    "_change_revision": 0,
    "_is_fork": false,
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
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      },
      "file_extension": ".py",
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      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
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}