{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Reading MS Excel file data. \u0427\u0442\u0435\u043d\u0438\u0435 \u0434\u0430\u043d\u043d\u044b\u0445 \u0438\u0437 \u0444\u0430\u0439\u043b\u043e\u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0430 xls \u0438\u043b\u0438 xlsx."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0410\u0432\u0442\u043e\u0440: \u0428\u0430\u0431\u0430\u043d\u043e\u0432 \u041f\u0430\u0432\u0435\u043b"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "E-mail: meteomail@yandex.ru"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0418\u043d\u043e\u0433\u0434\u0430 \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b \u0442\u043e\u043b\u044c\u043a\u043e \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 xls \u0438\u043b\u0438 xlsx. \u041d\u0430\u0443\u0447\u0438\u043c\u0441\u044f \"\u0437\u0430\u043b\u0438\u0432\u0430\u0442\u044c\" \u0438\u0445 \u0432 python. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0418\u0441\u0445\u043e\u0434\u043d\u044b\u0435 \u0434\u0430\u043d\u043d\u044b\u0435 \u0441\u043a\u0430\u0447\u0438\u0432\u0430\u0435\u043c \u0441 \u0441\u0430\u0439\u0442\u0430 Earth Policy Institute (http://www.earth-policy.org/data_center/C23) \u0444\u0430\u0439\u043b Average Global Temperature, 1880-2013 \u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0435 XLS. \u041d\u0430 04/04/2014 \u0432\u0438\u0440\u0443\u0441\u044b \u0432 \u044d\u0442\u043e\u043c \u0444\u0430\u0439\u043b\u0435 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0435\u043d\u044b \u043d\u0435 \u0431\u044b\u043b\u0438. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0414\u043b\u044f \u0447\u0442\u0435\u043d\u0438\u044f \u0431\u0443\u0434\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043c\u043e\u0434\u0443\u043b\u044c xlrd (http://www.python-excel.org/) \u0434\u043b\u044f \u0447\u0442\u0435\u043d\u0438\u044f \u0444\u0430\u0439\u043b\u043e\u0432 \u0444\u043e\u0440\u043c\u0430\u0442\u0430 xls, xlsx (MS Excel 2007 \u0438 \u0432\u044b\u0448\u0435). \u041b\u043e\u0433\u0438\u043a\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u043d\u0438\u044f \u044d\u0442\u043e\u0433\u043e \u043c\u043e\u0434\u0443\u043b\u044f \u0441\u043e\u0441\u0442\u043e\u0438\u0442 \u0432 \u043f\u043e\u0441\u043b\u0435\u0434\u043e\u0432\u0430\u0442\u0435\u043b\u044c\u043d\u043e\u043c \u043f\u0435\u0440\u0435\u0445\u043e\u0434\u0435 \u0441 \u043e\u0434\u043d\u043e\u0433\u043e \u0443\u0440\u043e\u0432\u043d\u044f \u043d\u0430 \u0434\u0440\u0443\u0433\u043e\u0439, \u0431\u043e\u043b\u0435\u0435 \u043d\u0438\u0437\u043a\u0438\u0439: \u041b\u0438\u0441\u0442 -> \u0421\u0442\u0440\u043e\u043a\u0430/\u0421\u0442\u043e\u043b\u0431\u0435\u0446 -> \u042f\u0447\u0435\u0439\u043a\u0430."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import xlrd\n",
      "\n",
      "path = u'indicator8_2014_1.xlsx' # \u0438\u043c\u044f \u0438 \u043f\u0443\u0442\u044c \u043a \u0444\u0430\u0439\u043b\u0443\n",
      "wb = xlrd.open_workbook(path) # \u043e\u0442\u043a\u0440\u044b\u0432\u0430\u0435\u043c \u0444\u0430\u0439\u043b \u0438 \u043f\u0435\u0440\u0435\u0434\u0430\u0451\u043c \u0435\u0433\u043e \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u043a\u0442 \u0432 wb\n",
      "\n",
      "''' \u041c\u0435\u0442\u043e\u0434\u044b sheet_names \u043f\u043e\u043a\u0430\u0437\u044b\u0432\u0430\u0435\u0442 \u0441\u043f\u0438\u0441\u043e\u043a \u0438\u043c\u0451\u043d \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0445 \u0434\u043b\u044f \u0440\u0430\u0431\u043e\u0442\u044b \u043b\u0438\u0441\u0442\u043e\u0432 \u0444\u0430\u0439\u043b\u0430'''\n",
      "\n",
      "sheets = wb.sheet_names()\n",
      "print 'MS Excel sheets:', sheets\n",
      "print type(sheets), len(sheets)\n",
      "\n",
      "''' \u041f\u0435\u0440\u0435\u0434\u0430\u0451\u043c \u043f\u0435\u0440\u0432\u044b\u0439 (\u0438 \u0435\u0434\u0438\u0441\u0442\u0432\u0435\u043d\u043d\u044b\u0439 \u0432 \u0434\u0430\u043d\u043d\u043e\u043c \u0441\u043b\u0443\u0447\u0430\u0435) \u043b\u0438\u0441\u0442 \u043a\u0430\u043a \u043e\u0431\u044a\u0435\u043a\u0442 sh '''\n",
      "sh = wb.sheet_by_name(sheets[0])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "MS Excel sheets: [u'Temp']\n",
        "<type 'list'> 1\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0422\u0430\u043a\u0438\u043c \u043e\u0431\u0440\u0430\u0437\u043e\u043c, \u043c\u044b \u0443\u0437\u043d\u0430\u043b\u0438, \u0447\u0442\u043e \u0432 \u0444\u0430\u0439\u043b\u0435 \u0432\u0441\u0435\u0433\u043e 1 \u043d\u043e\u0440\u043c\u0430\u043b\u044c\u043d\u044b\u0439 \u043b\u0438\u0441\u0442 \u0441 \u0434\u0430\u043d\u043d\u044b\u043c\u0438 \u0438 \u043f\u0435\u0440\u0435\u0448\u043b\u0438 \u043d\u0430 \u044d\u0442\u043e\u0442 \u043b\u0438\u0441\u0442 \u0434\u043b\u044f \u0434\u0430\u043b\u044c\u043d\u0435\u0439\u0448\u0435\u0439 \u0440\u0430\u0431\u043e\u0442\u044b. \u041f\u0435\u0440\u0435\u0439\u0434\u0451\u043c \u043a \u0441\u0442\u043e\u043b\u0431\u0446\u0430\u043c \u0438 \u0441\u0442\u043e\u043a\u0430\u043c: \u0443\u0437\u043d\u0430\u0435\u043c \u0441\u043a\u043e\u043b\u044c\u043a\u043e \u0438\u0445 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u043e."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Sheet name:',sh.name # \u0432\u044b\u0432\u0435\u0434\u0435\u043c \u0438\u043c\u044f \u0442\u0435\u043a\u0443\u0449\u0435\u0433\u043e \u043b\u0438\u0441\u0442\u0430\n",
      "print 'Number of rows:',sh.nrows, 'Number of cells in choosed column:',len(sh.col(0)) # \u0447\u0438\u0441\u043b\u043e \u0441\u0442\u0440\u043e\u043a \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0441\u043f\u043e\u0441\u043e\u0431\u0430\u043c\u0438\n",
      "print 'Number of columns:', sh.ncols, 'Number of cells in choosed row:', len(sh.row(0)) # \u0447\u0438\u0441\u043b\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u043e\u0432 \u0440\u0430\u0437\u043d\u044b\u043c\u0438 \u0441\u043f\u043e\u0441\u043e\u0431\u0430\u043c\u0438"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Sheet name: Temp\n",
        "Number of rows: 141 Number of cells in choosed column: 141\n",
        "Number of columns: 2 Number of cells in choosed row: 2\n"
       ]
      }
     ],
     "prompt_number": 46
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "''' \u041f\u0435\u0440\u0435\u0434\u0430\u0451\u043c \u043a\u043e\u043b\u043e\u043d\u043a\u0438 \u0432 \u0432\u0438\u0434\u0435 \u0441\u043f\u0438\u0441\u043a\u043e\u0432 year \u0438 temp '''\n",
      "\n",
      "year = sh.col_values(0) # \u0412\u0430\u0436\u043d\u043e \u043f\u0435\u0440\u0435\u0434\u0430\u0442\u044c \u0438\u043c\u0435\u043d\u043d\u043e values. \u0418\u043d\u0430\u0447\u0435* \u0431\u0443\u0434\u0435\u0442 \u043f\u0435\u0440\u0435\u0434\u0430\u043d \u0441\u043f\u0438\u0441\u043e\u043a \u0438\u0437 \u043e\u0441\u043e\u0431\u043e\u0433\u043e \u043a\u043b\u0430\u0441\u0441\u0430 \u044f\u0447\u0435\u0435\u043a Cells\n",
      "temp = sh.col_values(1) \n",
      "\n",
      "# * \u0418\u041d\u0410\u0427\u0415\n",
      "cell = sh.col(0)[0]\n",
      "print 'Type of cell', type(cell)\n",
      "print 'XL_CELL_TEXT\t1 (Unicode string):', cell.ctype\n",
      "print 'Cell value:', cell.value\n",
      "\n",
      "cell = sh.col(0)[5]\n",
      "print 'XL_CELL_NUMBER 2 (float):', cell.ctype\n",
      "print 'Cell value:', cell.value\n",
      "print '----------------------------------------'\n",
      "print year\n",
      "print temp"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Type of cell <class 'xlrd.sheet.Cell'>\n",
        "XL_CELL_TEXT\t1 (Unicode string): 1\n",
        "Cell value: Average Global Temperature, 1880-2013\n",
        "XL_CELL_NUMBER 2 (float): 2\n",
        "Cell value: 1880.0\n",
        "----------------------------------------\n",
        "[u'Average Global Temperature, 1880-2013', '', u'Year', '', '', 1880.0, 1881.0, 1882.0, 1883.0, 1884.0, 1885.0, 1886.0, 1887.0, 1888.0, 1889.0, 1890.0, 1891.0, 1892.0, 1893.0, 1894.0, 1895.0, 1896.0, 1897.0, 1898.0, 1899.0, 1900.0, 1901.0, 1902.0, 1903.0, 1904.0, 1905.0, 1906.0, 1907.0, 1908.0, 1909.0, 1910.0, 1911.0, 1912.0, 1913.0, 1914.0, 1915.0, 1916.0, 1917.0, 1918.0, 1919.0, 1920.0, 1921.0, 1922.0, 1923.0, 1924.0, 1925.0, 1926.0, 1927.0, 1928.0, 1929.0, 1930.0, 1931.0, 1932.0, 1933.0, 1934.0, 1935.0, 1936.0, 1937.0, 1938.0, 1939.0, 1940.0, 1941.0, 1942.0, 1943.0, 1944.0, 1945.0, 1946.0, 1947.0, 1948.0, 1949.0, 1950.0, 1951.0, 1952.0, 1953.0, 1954.0, 1955.0, 1956.0, 1957.0, 1958.0, 1959.0, 1960.0, 1961.0, 1962.0, 1963.0, 1964.0, 1965.0, 1966.0, 1967.0, 1968.0, 1969.0, 1970.0, 1971.0, 1972.0, 1973.0, 1974.0, 1975.0, 1976.0, 1977.0, 1978.0, 1979.0, 1980.0, 1981.0, 1982.0, 1983.0, 1984.0, 1985.0, 1986.0, 1987.0, 1988.0, 1989.0, 1990.0, 1991.0, 1992.0, 1993.0, 1994.0, 1995.0, 1996.0, 1997.0, 1998.0, 1999.0, 2000.0, 2001.0, 2002.0, 2003.0, 2004.0, 2005.0, 2006.0, 2007.0, 2008.0, 2009.0, 2010.0, 2011.0, 2012.0, 2013.0, '', u'Source: Compiled by Earth Policy Institute from National Aeronautics and Space Administration, Goddard Institute for Space Studies, \\u201cGlobal Land-Ocean Temperature Index in 0.01 Degrees Celsius,\\u201d at data.giss.nasa.gov/gistemp/tabledata_v3/GLB.Ts+dSST.txt, updated 21 January 2014.']\n",
        "['', '', u'Temperature', u'Degrees Fahrenheit', '', 56.822, 56.984, 56.912000000000006, 56.876000000000005, 56.732, 56.75, 56.768, 56.642, 56.858000000000004, 57.038000000000004, 56.624, 56.732, 56.642, 56.57, 56.624, 56.75, 56.876000000000005, 56.876000000000005, 56.642, 56.84, 56.93, 56.822, 56.660000000000004, 56.552, 56.408, 56.660000000000004, 56.732, 56.444, 56.426, 56.354000000000006, 56.372, 56.408, 56.462, 56.498000000000005, 56.786, 56.912000000000006, 56.552, 56.408, 56.642, 56.678000000000004, 56.714000000000006, 56.84, 56.678000000000004, 56.75, 56.768, 56.822, 57.038000000000004, 56.876000000000005, 56.912000000000006, 56.642, 56.984, 57.074000000000005, 57.002, 56.75, 57.038000000000004, 56.93, 57.02, 57.254000000000005, 57.308, 57.218, 57.326, 57.344, 57.290000000000006, 57.308, 57.452000000000005, 57.2, 57.056000000000004, 57.128, 57.02, 57.002, 56.858000000000004, 57.092000000000006, 57.236000000000004, 57.362, 57.002, 56.984, 56.876000000000005, 57.272000000000006, 57.272000000000006, 57.236000000000004, 57.128, 57.290000000000006, 57.272000000000006, 57.326, 56.84, 57.02, 57.128, 57.182, 57.11, 57.308, 57.272000000000006, 57.074000000000005, 57.236000000000004, 57.488, 57.074000000000005, 57.182, 56.984, 57.470000000000006, 57.308, 57.416000000000004, 57.614000000000004, 57.704, 57.362, 57.686, 57.416000000000004, 57.344, 57.470000000000006, 57.722, 57.830000000000005, 57.632000000000005, 57.902, 57.884, 57.542, 57.578, 57.722, 57.974000000000004, 57.794000000000004, 58.028000000000006, 58.316, 57.938, 57.938, 58.154, 58.316, 58.298, 58.136, 58.388000000000005, 58.28, 58.334, 58.082, 58.28, 58.406000000000006, 58.190000000000005, 58.244, 58.298, '', '']\n"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u041c\u044b \u043f\u0440\u0438\u0441\u0432\u043e\u0438\u043b\u0438 \u0441\u043f\u0438\u0441\u043a\u0430\u043c \u043f\u0435\u0440\u0432\u044b\u0439 \u0438 \u0432\u0442\u043e\u0440\u043e\u0439 \u0441\u0442\u043e\u043b\u0431\u0446\u044b \u0444\u0430\u0439\u043b\u0430. \u041a\u0430\u043a \u0432\u0438\u0434\u043d\u043e, \u043c\u044b \u043f\u0440\u043e\u0447\u0438\u0442\u0430\u043b\u0438 \u043b\u0438\u0448\u043d\u0438\u0435 \u044f\u0447\u0435\u0439\u043a\u0438 - \"\u0448\u0430\u043f\u043a\u0443\" \u0438 \"\u043a\u043e\u043c\u043c\u0435\u043d\u0442\u0430\u0440\u0438\u0438\". \u0421\u0447\u0438\u0442\u0430\u0435\u043c \u0442\u043e\u043b\u044c\u043a\u043e \u0447\u0438\u0441\u043b\u043e\u0432\u0443\u044e \u0438\u043d\u0444\u043e\u0440\u043c\u0430\u0446\u0438\u044e \u044f\u0432\u043d\u043e \u0443\u043a\u0430\u0437\u0430\u0432 \u0434\u0438\u0430\u043f\u0430\u0437\u043e\u043d \u043d\u0435\u043e\u0431\u0445\u043e\u0434\u0438\u043c\u044b\u0445 \u043d\u0430\u043c \u0441\u0442\u0440\u043e\u043a. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "row1 = 6\n",
      "row2 = 139\n",
      "year = sh.col_values(0,row1,row2)\n",
      "temp = sh.col_values(1,row1,row2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u041d\u0430\u0440\u0438\u0441\u0443\u0435\u043c \u0440\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "fig = plt.figure()\n",
      "ax = fig.add_subplot(111)\n",
      "ax.plot(year,temp,'k-')\n",
      "ax.set_xlabel('Source: NASA GIS')\n",
      "ax.set_ylabel('Degrees Fahrenheit')\n",
      "ax.set_title('Average Global Temperature, 1880-2013')\n",
      "ax.grid(axis='y', color='k', linestyle='-', linewidth=0.5)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEZCAYAAABWwhjiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlEQIIYTojJIIIYQQnVESIYQQorP/A1Obb76hiZTOAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x57da250>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u041f\u0440\u0430\u0432\u0434\u0430, \u043f\u043e\u0445\u043e\u0436\u0435 \u043d\u0430 \u0440\u0438\u0441\u0443\u043d\u043e\u043a \u0441\u043e \u0432\u0442\u043e\u0440\u043e\u0433\u043e \u043b\u0438\u0441\u0442\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u043c\u044b \u043d\u0435 \u043e\u0431\u043d\u0430\u0440\u0443\u0436\u0438\u043b\u0438 \u043f\u0440\u0438 \u043f\u043e\u0438\u0441\u043a\u0435 \u0434\u043e\u0441\u0442\u0443\u043f\u043d\u044b\u0445 \u043b\u0438\u0441\u0442\u043e\u0432? =)"
     ]
    }
   ],
   "metadata": {}
  }
 ]
}