{
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  "name": "\u09aa\u09be\u09a8\u09cd\u09a1\u09be\u09b8 \u09aa\u09b0\u09bf\u099a\u09bf\u09a4\u09bf \u09e8"
 },
 "nbformat": 3,
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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0986\u09ae\u09be\u09b0 \u0986\u0997\u09c7\u09b0 [\u09aa\u09cb\u09b8\u09cd\u099f\u09c7](http://goo.gl/4QpoJu) \u0986\u09ae\u09bf \u09aa\u09be\u09a8\u09cd\u09a1\u09be\u09b8\u09c7\u09b0 \u09ac\u09c7\u09b8\u09bf\u0995 \u09aa\u09b0\u09bf\u099a\u09bf\u09a4\u09bf \u098f\u09ac\u0982 \u098f\u09b0 \u098f\u0995\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \u09a1\u09be\u099f\u09be \u09b8\u09cd\u099f\u09cd\u09b0\u09be\u0995\u099a\u09be\u09b0 \u09b8\u09bf\u09b0\u09bf\u099c \u09a8\u09bf\u09df\u09c7 \u0995\u09a5\u09be \u09ac\u09b2\u09c7\u099b\u09bf\u0964 \u098f\u0996\u09a8 \u0986\u09ae\u09bf \u09ae\u09be\u09a4\u09cd\u09b0\u09be \u09ac\u09be\u09dc\u09bf\u09df\u09c7 \u09a6\u09bf\u09ac \u0993 \u0986\u09ae\u09be\u09a6\u09c7\u09b0 \u098f\u0996\u09a8\u0995\u09be\u09b0 \u099f\u09aa\u09bf\u0995 \u09b9\u09ac\u09c7 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u0964\n",
      "\n",
      "\u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u09b9\u09b2 \u09aa\u09be\u09a8\u09cd\u09a1\u09be\u09b8\u09c7\u09b0 \u09a6\u09cd\u09ac\u09bf\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \u09a1\u09be\u099f\u09be \u09b8\u09cd\u099f\u09cd\u09b0\u09be\u0995\u099a\u09be\u09b0\u0964 \u09b0\u09cb \u0993 \u0995\u09b2\u09be\u09ae \u09a6\u09c1\u0987 \u09ac\u09b0\u09be\u09ac\u09b0 \u0986\u09aa\u09a8\u09bf \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8 \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8 \u0986\u09b0 \u09b8\u09b0\u09cd\u099f\u09bf\u0982, \u09b8\u09be\u09b0\u099a\u09bf\u0982, \u0997\u09cd\u09b0\u09c1\u09aa\u09bf\u0982 \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf \u0995\u09b0\u09be \u09af\u09be\u09ac\u09c7\u0964 \u09aa\u09cd\u09b2\u099f\u09bf\u0982 \u09a4\u09cb \u0986\u099b\u09c7\u0987\u0964 \u09ac\u09b2\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8, \u09aa\u09be\u0987\u09a5\u09a8\u09c7\u09b0 \u09ad\u09bf\u09a4\u09b0 \u098f\u0995\u09cd\u09b8\u09c7\u09b2 \u099f\u09be\u0987\u09aa \u0995\u09be\u099c \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09ac\u09c7\u09a8, \u09aa\u09cd\u09b0\u09cb\u0997\u09cd\u09b0\u09be\u09ae\u09c7\u099f\u09bf\u0995\u09be\u09b2\u09c0\u0964 (\u0985\u09a8\u09c7\u0995\u09c7 \u09ac\u09b2\u09c7 \u098f\u099f\u09bf R \u098f\u09b0 data.frame \u098f\u09b0 \u09ae\u09a4 \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0986\u09ae\u09bf R \u09aa\u09be\u09b0\u09bf \u09a8\u09be \u09a4\u09cb \u09a4\u09be \u09ac\u09b2\u09a4\u09c7 \u09aa\u09be\u09b0\u099b\u09bf \u09a8\u09be)\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# \u09a6\u09cd\u09ac\u09bf\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \u0985\u09cd\u09af\u09be\u09b0\u09c7 (\u0985\u09a5\u09ac\u09be \u09b2\u09bf\u09b8\u09cd\u099f\u09c7\u09b0 \u09ad\u09bf\u09a4\u09b0 \u09b2\u09bf\u09b8\u09cd\u099f) \u09a6\u09bf\u09df\u09c7 \u0986\u09ae\u09b0\u09be \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u0987\u09a8\u09bf\u09b8\u09bf\u09df\u09be\u09b2\u09be\u0987\u099c \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09bf\u0964 \n",
      "fake_2d_data = np.random.randn(25).reshape(5, 5)\n",
      "df = pd.DataFrame(fake_2d_data, \n",
      "                  index=[\"alpha\", \"beta\", \"gamma\", \"delta\", \"epsilon\"],\n",
      "                  columns=[\"A\", \"B\", \"C\", \"D\", \"E\"])\n",
      "df.plot(kind=\"barh\", stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb9c0b6c>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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yuYLmUe2qYtu2babH4dWjuspv2540aVL93881sa6Vi4NIS+gMQkTkIlY7pugMQkREfE4F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\n"
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0986\u09ae\u09b0\u09be \u0989\u09aa\u09b0\u09c7\u09b0 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u09c7\u09b0 \u0985\u0995\u09cd\u09b7 \u0998\u09c1\u09b0\u09be\u09a4\u09c7 \u09aa\u09be\u09b0\u09bf \u099f\u09cd\u09b0\u09be\u09a8\u09cd\u09b8\u09aa\u09cb\u099c\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_t = df.T\n",
      "df_t.plot(kind='barh', stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xb9f70ac>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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1USvgtT5IbyzUTrL3uWTOL3N2gNf6EE32/FqwUBMRGRx71EQ+4nuffMEeNRFR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\n"
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u09af\u0996\u09a8 \u0986\u0987\u09aa\u09be\u0987\u09a5\u09a8 \u09a8\u09cb\u099f\u09ac\u09c1\u0995\u09c7 \u0995\u09be\u099c \u0995\u09b0\u09be \u09b9\u09ac\u09c7 \u09a4\u0996\u09a8 \u09a8\u09bf\u099a\u09c7\u09b0 \u0995\u09cb\u09a1 \u09a6\u09bf\u09df\u09c7 \u09b8\u09c1\u09a8\u09cd\u09a6\u09b0\u09ad\u09be\u09ac\u09c7 \u098f\u0987\u099a\u099f\u09bf\u098f\u09ae\u098f\u09b2 \u099f\u09c7\u09ac\u09cd\u09b2 \u09aa\u09cd\u09b0\u09bf\u09a8\u09cd\u099f \u0995\u09b0\u09be \u09af\u09be\u09df\u0964 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "display(HTML(df.to_html()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>alpha</th>\n",
        "      <td> 0.960272</td>\n",
        "      <td>-0.826986</td>\n",
        "      <td>-0.466003</td>\n",
        "      <td>-2.164979</td>\n",
        "      <td>-0.515156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>beta</th>\n",
        "      <td>-0.552988</td>\n",
        "      <td>-0.377938</td>\n",
        "      <td>-0.221681</td>\n",
        "      <td>-0.198346</td>\n",
        "      <td> 0.023387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gamma</th>\n",
        "      <td> 1.961069</td>\n",
        "      <td> 1.710462</td>\n",
        "      <td>-0.438632</td>\n",
        "      <td> 1.606601</td>\n",
        "      <td>-1.558174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>delta</th>\n",
        "      <td>-0.674267</td>\n",
        "      <td> 0.091664</td>\n",
        "      <td> 0.262994</td>\n",
        "      <td>-1.355863</td>\n",
        "      <td>-1.075137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>epsilon</th>\n",
        "      <td>-1.406780</td>\n",
        "      <td> 0.781694</td>\n",
        "      <td>-0.257940</td>\n",
        "      <td>-0.012960</td>\n",
        "      <td> 0.142525</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xbbf486c>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "display(HTML(df_t.to_html()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>alpha</th>\n",
        "      <th>beta</th>\n",
        "      <th>gamma</th>\n",
        "      <th>delta</th>\n",
        "      <th>epsilon</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td> 0.960272</td>\n",
        "      <td>-0.552988</td>\n",
        "      <td> 1.961069</td>\n",
        "      <td>-0.674267</td>\n",
        "      <td>-1.406780</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>-0.826986</td>\n",
        "      <td>-0.377938</td>\n",
        "      <td> 1.710462</td>\n",
        "      <td> 0.091664</td>\n",
        "      <td> 0.781694</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>-0.466003</td>\n",
        "      <td>-0.221681</td>\n",
        "      <td>-0.438632</td>\n",
        "      <td> 0.262994</td>\n",
        "      <td>-0.257940</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>D</th>\n",
        "      <td>-2.164979</td>\n",
        "      <td>-0.198346</td>\n",
        "      <td> 1.606601</td>\n",
        "      <td>-1.355863</td>\n",
        "      <td>-0.012960</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>E</th>\n",
        "      <td>-0.515156</td>\n",
        "      <td> 0.023387</td>\n",
        "      <td>-1.558174</td>\n",
        "      <td>-1.075137</td>\n",
        "      <td> 0.142525</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xbcf798c>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "JSON, CSV, HTML \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf \u09ab\u09b0\u09ae\u09cd\u09af\u09be\u099f\u0993 \u09b0\u09df\u09c7\u099b\u09c7\u0964 \u09a8\u09bf\u099a\u09c7\u09b0 \u0995\u09cb\u09a1 \u09a5\u09c7\u0995\u09c7 \u09a6\u09c7\u0996\u09be \u09af\u09be\u09ac\u09c7\u0964 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print filter(lambda i: i.startswith(\"to_\"), dir(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['to_clipboard', 'to_csv', 'to_dense', 'to_dict', 'to_excel', 'to_gbq', 'to_hdf', 'to_html', 'to_json', 'to_latex', 'to_msgpack', 'to_panel', 'to_period', 'to_pickle', 'to_records', 'to_sparse', 'to_sql', 'to_stata', 'to_string', 'to_timestamp', 'to_wide']\n"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0986\u09ae\u09b0\u09be \u0986\u09ae\u09be\u09a6\u09c7\u09b0 \u09a1\u09be\u099f\u09be \u09b8\u09ae\u09cd\u09ac\u09a8\u09cd\u09a7\u09bf\u09a4 \u09aa\u09b0\u09bf\u09b8\u0982\u0996\u09cd\u09af\u09be\u09a8 \u09aa\u09c7\u09a4\u09c7 \u09aa\u09be\u09b0\u09bf \u09a1\u09bf\u09b8\u09cd\u0995\u09cd\u09b0\u09be\u0987\u09ac \u09ab\u09be\u0982\u09b6\u09a8\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.describe()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>A</th>\n",
        "      <th>B</th>\n",
        "      <th>C</th>\n",
        "      <th>D</th>\n",
        "      <th>E</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>count</th>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "      <td> 5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mean</th>\n",
        "      <td> 0.057461</td>\n",
        "      <td> 0.275779</td>\n",
        "      <td>-0.224253</td>\n",
        "      <td>-0.425109</td>\n",
        "      <td>-0.596511</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>std</th>\n",
        "      <td> 1.368472</td>\n",
        "      <td> 0.998950</td>\n",
        "      <td> 0.292811</td>\n",
        "      <td> 1.435495</td>\n",
        "      <td> 0.723005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>min</th>\n",
        "      <td>-1.406780</td>\n",
        "      <td>-0.826986</td>\n",
        "      <td>-0.466003</td>\n",
        "      <td>-2.164979</td>\n",
        "      <td>-1.558174</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
        "      <td>-0.674267</td>\n",
        "      <td>-0.377938</td>\n",
        "      <td>-0.438632</td>\n",
        "      <td>-1.355863</td>\n",
        "      <td>-1.075137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
        "      <td>-0.552988</td>\n",
        "      <td> 0.091664</td>\n",
        "      <td>-0.257940</td>\n",
        "      <td>-0.198346</td>\n",
        "      <td>-0.515156</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
        "      <td> 0.960272</td>\n",
        "      <td> 0.781694</td>\n",
        "      <td>-0.221681</td>\n",
        "      <td>-0.012960</td>\n",
        "      <td> 0.023387</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\n",
        "      <td> 1.961069</td>\n",
        "      <td> 1.710462</td>\n",
        "      <td> 0.262994</td>\n",
        "      <td> 1.606601</td>\n",
        "      <td> 0.142525</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 32,
       "text": [
        "              A         B         C         D         E\n",
        "count  5.000000  5.000000  5.000000  5.000000  5.000000\n",
        "mean   0.057461  0.275779 -0.224253 -0.425109 -0.596511\n",
        "std    1.368472  0.998950  0.292811  1.435495  0.723005\n",
        "min   -1.406780 -0.826986 -0.466003 -2.164979 -1.558174\n",
        "25%   -0.674267 -0.377938 -0.438632 -1.355863 -1.075137\n",
        "50%   -0.552988  0.091664 -0.257940 -0.198346 -0.515156\n",
        "75%    0.960272  0.781694 -0.221681 -0.012960  0.023387\n",
        "max    1.961069  1.710462  0.262994  1.606601  0.142525"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u098f\u0995\u099f\u09c1 \u09a8\u09a8-\u09b0\u200d\u09cd\u09af\u09be\u09a8\u09cd\u09a1\u09ae \u09a1\u09be\u099f\u09be \u09a8\u09bf\u09df\u09c7 \u0995\u09be\u099c \u0995\u09b0\u09be \u09af\u09be\u0995 \u09af\u09be\u09a4\u09c7 \u0986\u09ae\u09be\u09a6\u09c7\u09b0 \u0995\u09be\u099c\u0997\u09c1\u09b2\u09cb \u09ad\u09c7\u09b0\u09bf\u09ab\u09be\u0987\u09a1 \u09b9\u09df\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.arange(16).reshape(4, 4),\n",
      "                  index=list(\"ABCD\"),\n",
      "                  columns=list(\"WXYZ\"))\n",
      "\n",
      "display(HTML(df.to_html()))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>W</th>\n",
        "      <th>X</th>\n",
        "      <th>Y</th>\n",
        "      <th>Z</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>A</th>\n",
        "      <td>  0</td>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>B</th>\n",
        "      <td>  4</td>\n",
        "      <td>  5</td>\n",
        "      <td>  6</td>\n",
        "      <td>  7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
        "      <td>  8</td>\n",
        "      <td>  9</td>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>D</th>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "      <td> 14</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>"
       ],
       "output_type": "display_data",
       "text": [
        "<IPython.core.display.HTML at 0xbe11d6c>"
       ]
      }
     ],
     "prompt_number": 33
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u098f\u09ac\u09be\u09b0 \u09aa\u09be\u09b2\u09be \u0995\u09bf\u099b\u09c1 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8\u09c7\u09b0\u0964 \u09ae\u09a8\u09c7 \u09b0\u09be\u0996\u09ac\u09c7\u09a8 \u09b0\u09cb \u09b9\u09b2 \u09e7, \u0986\u09b0 \u0995\u09b2\u09be\u09ae \u09b9\u09b2 \u09e6\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total_by_columns = df.sum(axis=0)\n",
      "total_by_rows = df.sum(axis=1)\n",
      "\n",
      "cumsum_by_columns = df.cumsum(axis=0)\n",
      "cumsum_by_rows = df.cumsum(axis=1)\n",
      "\n",
      "percentage_change_by_columns = df.pct_change(axis=0)\n",
      "percentage_change_by_row = df.pct_change(axis=1)\n",
      "\n",
      "# See also: mean, median, mad, var, skew, curt etc. Use ipython's ? and ?? for help."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 54
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u09a1\u09be\u099f\u09be \u0995\u09be\u09b2\u09c7\u0995\u09b6\u09a8\u09c7\u09b0 \u09b8\u09ae\u09df\u09c7 \u09aa\u09cd\u09b0\u09be\u09df\u09c7\u0987 \u0986\u09aa\u09a8\u09bf \u09ae\u09bf\u09b8\u09bf\u0982 \u09a1\u09be\u099f\u09be \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u09af\u09be \u0995\u09bf\u09a8\u09be \u0986\u09aa\u09a8\u09be\u09b0 \u0995\u09be\u09a0\u09be\u09ae\u09cb\u09a4\u09c7 \u0997\u09cd\u09af\u09be\u09aa \u09b9\u09bf\u09b8\u09be\u09ac\u09c7 \u09a5\u09be\u0995\u09ac\u09c7\u0964 \u098f\u0997\u09c1\u09b2\u09cb \u09b9\u09cd\u09af\u09be\u09a8\u09cd\u09a1\u09b2 \u0995\u09b0\u09be\u09b0 \u099f\u09c1\u09b2 \u09a6\u09bf\u09df\u09c7\u099b\u09c7 \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09aa\u09be\u09a8\u09cd\u09a1\u09be\u09b8\u0964 \u09af\u09be\u09b0 \u09ac\u09c7\u09b6\u09bf\u09b0\u09ad\u09be\u0997\u0987 \"na\" (\u0985\u09b0\u09cd\u09a5\u09be\u09ce not available) \u09a6\u09bf\u09df\u09c7 \u09b6\u09c7\u09b7 \u09b9\u09df\u0964 \u0989\u09a6\u09be\u09b9\u09b0\u09a3\u09b8\u09cd\u09ac\u09b0\u09c2\u09aa \u09a6\u09c7\u0996\u09bf-"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print filter(lambda i: i.endswith(\"na\") or \"null\" in i, dir(df))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['dropna', 'fillna', 'isnull', 'notnull']\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame([[1, None, 2, 4, None], [2, 3, 3, 5]]).dropna()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "Empty DataFrame\n",
        "Columns: [0, 1, 2, 3, 4]\n",
        "Index: []"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame([[1, None, 2, 4, None], [2, 3, 3, 5]]).fillna(\"\u09b6\u09c2\u09a8\u09cd\u09af\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td> \u09b6\u09c2\u09a8\u09cd\u09af</td>\n",
        "      <td> 2</td>\n",
        "      <td> 4</td>\n",
        "      <td> \u09b6\u09c2\u09a8\u09cd\u09af</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td>     3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> \u09b6\u09c2\u09a8\u09cd\u09af</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "   0      1  2  3      4\n",
        "0  1  \u09b6\u09c2\u09a8\u09cd\u09af  2  4  \u09b6\u09c2\u09a8\u09cd\u09af\n",
        "1  2      3  3  5  \u09b6\u09c2\u09a8\u09cd\u09af"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0986\u09aa\u09a8\u09bf \u09af\u09c7\u0987 \u0985\u0995\u09cd\u09b7\u0987 \u099a\u09bf\u09a8\u09cd\u09a4\u09be \u0995\u09b0\u09c7\u09a8\u09a8\u09be \u0995\u09c7\u09a8, \u0986\u09aa\u09a8\u09bf \u098f\u0995 \u09a7\u09be\u09aa \u09a7\u09b0\u09b2\u09c7 \u098f\u0995\u099f\u09bf \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u09aa\u09be\u09ac\u09c7\u09a8\u0964 \u09af\u09a6\u09bf \u0985\u0995\u09cd\u09b7 \u09b9\u09df \u09e6 \u09a4\u09be\u09b9\u09b2\u09c7 \u0995\u09b2\u09be\u09ae, \u0985\u09a8\u09cd\u09af\u09a5\u09be\u09df \u09b0\u09cb\u0964 \u098f\u0996\u09a8, \u0986\u09aa\u09a8\u09bf sum, cumsum, mean, median, var \u0987\u09a4\u09cd\u09af\u09be\u09a6\u09bf \u09aa\u09be\u099a\u09cd\u099b\u09c7\u09a8 \u09b8\u0982\u09b6\u09cd\u09b2\u09bf\u09b7\u09cd\u099f \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8\u09c7\u09b0 \u099c\u09a8\u09cd\u09af\u0964 \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0986\u09aa\u09a8\u09bf \u09af\u09a6\u09bf \u09a8\u09bf\u099c\u09c7\u09b0 \u0995\u09bf\u099b\u09c1 \u099a\u09be\u09a8 \u09a4\u09be\u09b9\u09b2\u09c7? \u09a4\u09be\u09b9\u09b2\u09c7 \u0986\u09aa\u09a8\u09be\u09b0 \u098f\u09ae\u09a8 \u09ab\u09be\u0982\u09b6\u09a8 \u09b2\u09bf\u0996\u09a4\u09c7 \u09b9\u09ac\u09c7 \u09af\u09be\u09b0 \u09aa\u09cd\u09b0\u09a5\u09ae \u09aa\u09cd\u09af\u09be\u09b0\u09be\u09ae\u09bf\u099f\u09be\u09b0 \u0985\u09cd\u09af\u09be\u09b0\u09c7, \u098f\u09ac\u0982 \u0986\u09aa\u09a8\u09bf \u0986\u09aa\u09a8\u09be\u09b0 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09be\u09ae\u09c7\u09b0 \u09b8\u09be\u09a5\u09c7 \u09a4\u09be\u0995\u09c7 \u0986\u09aa\u09cd\u09b2\u09be\u0987 \u0995\u09b0\u09ac\u09c7\u09a8\u0964 \u09af\u09c7\u09ae\u09a8 \u09a8\u09bf\u099a\u09c7\u09b0 \u0995\u09cb\u09a1\u099f\u09bf\u0995\u09c7\u0987 \u09a7\u09b0\u09c1\u09a8-"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.arange(10).reshape(5, 2))\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 4</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 6</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 8</td>\n",
        "      <td> 9</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "   0  1\n",
        "0  0  1\n",
        "1  2  3\n",
        "2  4  5\n",
        "3  6  7\n",
        "4  8  9"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def multiply_with(df, n):\n",
      "    return df * n\n",
      "\n",
      "df.apply(multiply_with, n=10)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  0</td>\n",
        "      <td> 10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 20</td>\n",
        "      <td> 30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 40</td>\n",
        "      <td> 50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 60</td>\n",
        "      <td> 70</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 80</td>\n",
        "      <td> 90</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 82,
       "text": [
        "    0   1\n",
        "0   0  10\n",
        "1  20  30\n",
        "2  40  50\n",
        "3  60  70\n",
        "4  80  90"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame(np.random.randn(3, 5))\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>0</th>\n",
        "      <th>1</th>\n",
        "      <th>2</th>\n",
        "      <th>3</th>\n",
        "      <th>4</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 0.080426</td>\n",
        "      <td> 2.012099</td>\n",
        "      <td> 0.919150</td>\n",
        "      <td>-0.379672</td>\n",
        "      <td>-1.204189</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 0.231752</td>\n",
        "      <td> 0.451970</td>\n",
        "      <td>-1.732385</td>\n",
        "      <td> 0.702375</td>\n",
        "      <td>-0.860986</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>-0.551936</td>\n",
        "      <td> 0.911473</td>\n",
        "      <td>-1.215514</td>\n",
        "      <td>-0.694774</td>\n",
        "      <td> 1.312840</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "          0         1         2         3         4\n",
        "0  0.080426  2.012099  0.919150 -0.379672 -1.204189\n",
        "1  0.231752  0.451970 -1.732385  0.702375 -0.860986\n",
        "2 -0.551936  0.911473 -1.215514 -0.694774  1.312840"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u098f\u0987\u09ac\u09be\u09b0 \u0987\u09a8\u09cd\u09a1\u09c7\u0995\u09cd\u09b8\u09bf\u0982\u0964 \u09ac\u09bf\u09b6\u09c7\u09b7 \u098f\u0995 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u09a4\u09c8\u09b0\u09bf \u0995\u09b0\u09bf \u09af\u09c7\u09a8 \u0987\u09a8\u09cd\u09a1\u09c7\u0995\u09cd\u09b8\u09bf\u0982 \u09ac\u09c1\u099d\u09a4\u09c7 \u09b8\u09c1\u09ac\u09bf\u09a7\u09be \u09b9\u09df\u0964"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.DataFrame([[\"00\", \"01\", \"02\", \"03\"],\n",
      "                   [\"10\", \"11\", \"12\", \"13\"],\n",
      "                   [\"20\", \"21\", \"22\", \"23\"],\n",
      "                   [\"40\", \"41\", \"42\", \"43\"],],\n",
      "                  index=[\"r0\", \"r1\", \"r2\", \"r3\"],\n",
      "                  columns=[\"c0\", \"c1\", \"c2\", \"c3\"])\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>c0</th>\n",
        "      <th>c1</th>\n",
        "      <th>c2</th>\n",
        "      <th>c3</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>r0</th>\n",
        "      <td> 00</td>\n",
        "      <td> 01</td>\n",
        "      <td> 02</td>\n",
        "      <td> 03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>r1</th>\n",
        "      <td> 10</td>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "      <td> 13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>r2</th>\n",
        "      <td> 20</td>\n",
        "      <td> 21</td>\n",
        "      <td> 22</td>\n",
        "      <td> 23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>r3</th>\n",
        "      <td> 40</td>\n",
        "      <td> 41</td>\n",
        "      <td> 42</td>\n",
        "      <td> 43</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "    c0  c1  c2  c3\n",
        "r0  00  01  02  03\n",
        "r1  10  11  12  13\n",
        "r2  20  21  22  23\n",
        "r3  40  41  42  43"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u0986\u09b0 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u098f\u0995\u0987 \u09b0\u0995\u09ae \u0987\u09a8\u09cd\u09a1\u09c7\u0995\u09cd\u09b8\u09bf\u0982 \u09ac\u09cd\u09af\u09ac\u09b9\u09be\u09b0 \u0995\u09b0\u09c7 \u098f\u0995 \u09ac\u09bf\u09b6\u09c7\u09b7 \u09ab\u09be\u0982\u09b6\u09a8 ix \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[2, :] # \u09b0\u09cb \u09e8 \u098f \u09b8\u09cd\u09a5\u09bf\u09b0\u0964"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 108,
       "text": [
        "c0    20\n",
        "c1    21\n",
        "c2    22\n",
        "c3    23\n",
        "Name: r2, dtype: object"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[:, 3] # \u0995\u09b2\u09be\u09ae \u09e9 \u098f \u09b8\u09cd\u09a5\u09bf\u09b0\u0964"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 109,
       "text": [
        "r0    03\n",
        "r1    13\n",
        "r2    23\n",
        "r3    43\n",
        "Name: c3, dtype: object"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[1:2, 1:3] # \u09e7 \u09a5\u09c7\u0995\u09c7 \u09e8 \u098f\u09b0 \u0986\u0997 \u09aa\u09b0\u09cd\u09af\u09a8\u09cd\u09a4 \u09b0\u09cb, \u09e7 \u09a5\u09c7\u0995\u09c7 \u09e9 \u098f\u09b0 \u0986\u0997 \u09aa\u09b0\u09cd\u09af\u09a8\u09cd\u09a4 \u0995\u09b2\u09be\u09ae\u0964"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>c1</th>\n",
        "      <th>c2</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>r1</th>\n",
        "      <td> 11</td>\n",
        "      <td> 12</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "    c1  c2\n",
        "r1  11  12"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0995\u09bf\u09a8\u09cd\u09a4\u09c1 ix \u0995\u09c7\u09a8? \u09b8\u09b0\u09be\u09b8\u09b0\u09bf [] \u0995\u09c7\u09a8 \u0987\u0989\u099c \u0995\u09b0\u09b2\u09be\u09ae \u09a8\u09be? \u0995\u09be\u09b0\u09a3, [] \u09a6\u09bf\u09df\u09c7 \u0995\u09b2\u09be\u09ae \u09ac\u09c7\u09b0 \u0995\u09b0\u09ac\u0964 \u0986\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u0995\u09b2\u09be\u09ae \u09b9\u09b2 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u09c7\u09b0 \u0985\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u09ac\u09bf\u0989\u099f\u0964 \u0986\u09b0 \u09a4\u09be \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09a6\u09bf\u09ac\u09c7 \u098f\u0995\u099f\u09bf \u09b8\u09bf\u09b0\u09bf\u099c \u09af\u09be\u09b0 \u0985\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u09ac\u09bf\u0989\u099f \u09b9\u09ac\u09c7 \u09b0\u09cb\u0964 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[\"c1\"] # df.c1 \u09a6\u09bf\u09b2\u09c7\u0993 \u099a\u09b2\u09a4\u0964 \u099c\u09be\u09ad\u09be\u09b8\u09cd\u0995\u09cd\u09b0\u09bf\u09aa\u09cd\u099f\u09c7\u09b0 \u09ae\u09a4\u0964 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "r0    01\n",
        "r1    11\n",
        "r2    21\n",
        "r3    41\n",
        "Name: c1, dtype: object"
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "type(df.c1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "pandas.core.series.Series"
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.c1.r1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 121,
       "text": [
        "'11'"
       ]
      }
     ],
     "prompt_number": 121
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u098f\u09a4\u0995\u09cd\u09b7\u09a3 \u09a6\u09c7\u0996\u09b2\u09be\u09ae \u09af\u09c7 \u09e8-\u09a1\u09bf \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u09a6\u09bf\u09df\u09c7\u0987 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u09a4\u09c8\u09b0\u09bf \u0995\u09b0\u09c7\u099b\u09bf\u0964 \u0995\u09bf\u09a8\u09cd\u09a4\u09c1 \u0986\u09b8\u09b2\u09c7 \u0986\u09b0\u09c7\u0995\u09ad\u09be\u09ac\u09c7 \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u09a4\u09c8\u09b0\u09bf \u0995\u09b0\u09be \u09af\u09be\u09df\u0964 \u09a1\u09bf\u0995\u09b6\u09a8\u09be\u09b0\u09bf \u09a6\u09bf\u09df\u09c7\u0964 \u098f\u099f\u09bf zip \u09a8\u09bf\u09df\u09ae \u09ab\u09b2\u09cb \u0995\u09b0\u09c7 \u09a8\u09be\u09ae\u09aa\u09be\u0987\u098f\u09b0 \u09ae\u09a4\u0964 "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = {\n",
      "    \"names\": [\"Uruguay\", \"Brazil\", \"Argentina\", \"Germany\", \"Italy\", \"Spain\", \"England\"],\n",
      "    \"participated\": [12, 20, 16, 18, 18, 14, 14,],\n",
      "    \"continent\": [\"SA\", \"SA\", \"SA\", \"E\", \"E\", \"E\", \"E\"],\n",
      "    \"wins\": [1, 5, 2, 4, 4, 1, 1],\n",
      "    \"runnerup\": [2, 7, 5, 8, 6, 1, 1]\n",
      "}\n",
      "\n",
      "df = pd.DataFrame(data, \n",
      "        index=[\"Uruguay\", \"Brazil\", \"Argentina\", \"Germany\", \"Italy\", \"Spain\", \"England\"],)\n",
      "df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>continent</th>\n",
        "      <th>names</th>\n",
        "      <th>participated</th>\n",
        "      <th>runnerup</th>\n",
        "      <th>wins</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Uruguay</th>\n",
        "      <td> SA</td>\n",
        "      <td>   Uruguay</td>\n",
        "      <td> 12</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Brazil</th>\n",
        "      <td> SA</td>\n",
        "      <td>    Brazil</td>\n",
        "      <td> 20</td>\n",
        "      <td> 7</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Argentina</th>\n",
        "      <td> SA</td>\n",
        "      <td> Argentina</td>\n",
        "      <td> 16</td>\n",
        "      <td> 5</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Germany</th>\n",
        "      <td>  E</td>\n",
        "      <td>   Germany</td>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Italy</th>\n",
        "      <td>  E</td>\n",
        "      <td>     Italy</td>\n",
        "      <td> 18</td>\n",
        "      <td> 6</td>\n",
        "      <td> 4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spain</th>\n",
        "      <td>  E</td>\n",
        "      <td>     Spain</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>England</th>\n",
        "      <td>  E</td>\n",
        "      <td>   England</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 160,
       "text": [
        "          continent      names  participated  runnerup  wins\n",
        "Uruguay          SA    Uruguay            12         2     1\n",
        "Brazil           SA     Brazil            20         7     5\n",
        "Argentina        SA  Argentina            16         5     2\n",
        "Germany           E    Germany            18         8     4\n",
        "Italy             E      Italy            18         6     4\n",
        "Spain             E      Spain            14         1     1\n",
        "England           E    England            14         1     1"
       ]
      }
     ],
     "prompt_number": 160
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.plot(kind=\"barh\", grid=True, stacked=True, rot=30)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 161,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0xdfc81cc>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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34JHKBZxzVP24F/tEGlFl6zvsSEXr0wlr5uyvx+l0Ok6fPk3evHmthcSRBeWx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\nKSkJDw8Pbt26Re7cualWrRo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/pW7duhw/fpzp06dbC4rsxB1Hy+2yWs4Okl9tkl9b7B78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\nxfbg8JCxFw0AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 161
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.continent.value_counts().plot(kind=\"pie\", label=\"Continents\").axis(\"equal\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 159,
       "text": [
        "(-1.0000000210975502,\n",
        " 1.0000000424121425,\n",
        " -1.0253279992249265,\n",
        " 1.0083560303418613)"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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fzxOPPUH8Z/Fnvi1biFrAsdrBr8f/Wsr6AsiEHWEVFRW06dCGvVl7ob3qNEJE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\nAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 159
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u0985\u09a8\u09c7\u0995 \u0995\u09bf\u099b\u09c1\u0987 \u09a6\u09c7\u0996\u09b2\u09be\u09ae, \u098f\u0996\u09a8 \u099a\u09b2\u09c1\u09a8 \u0995\u09bf\u099b\u09c1 \u0985\u09cd\u09af\u09be\u09a1\u09ad\u09be\u09a8\u09cd\u09b8\u09a1 \u0995\u09df\u09c7\u09b0\u09bf \u0995\u09b0\u09bf\u0964 \n",
      "\n",
      "\u09a7\u09b0\u09c1\u09a8, \u0986\u09aa\u09a8\u09bf \u099a\u09be\u099a\u09cd\u099b\u09c7\u09a8 \u09af\u09c7 \u09b8\u09ae\u09be\u09a8 \u09b8\u0982\u0996\u09cd\u09af\u0995\u09ac\u09be\u09b0 \u099a\u09cd\u09af\u09be\u09ae\u09cd\u09aa\u09bf\u09df\u09a8 \u09b9\u0993\u09df\u09be \u09a6\u09c7\u09b6\u0997\u09c1\u09b2\u09bf \u098f\u0995\u09a4\u09cd\u09b0\u09bf\u09a4 \u09b9\u09cb\u0995\u0964 \u098f\u09b0 \u099c\u09a8\u09cd\u09af \u0986\u09aa\u09a8\u09be\u0995\u09c7 \u09ac\u09c1\u099d\u09a4\u09c7 \u09b9\u09ac\u09c7 \u09aa\u09bf\u09ad\u099f\u0987\u0982 \u0993 \u09b8\u09cd\u099f\u09df\u09be\u0995\u09bf\u0982\u0964 \u09aa\u09bf\u09ad\u099f \u09b9\u09b2 \u0995\u09cb\u09a8 \u098f\u0995\u099f\u09bf \u0985\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u09ac\u09bf\u0989\u099f\u0995\u09c7 \u0987\u0989\u09a8\u09bf\u0995 \u09b0\u09c7\u0996\u09c7 \u09b8\u09c7\u0987 \u09ad\u09cd\u09af\u09be\u09b2\u09c1\u09b0 \u09b8\u09be\u09a5\u09c7\u0995\u09be\u09b0 \u0985\u09a8\u09cd\u09af\u09be\u09a8\u09cd\u09af \u0985\u09cd\u09af\u09be\u099f\u09cd\u09b0\u09bf\u09ac\u09bf\u0989\u099f\u0995\u09c7 \u09b8\u09be\u099c\u09be\u09a8\u0964 \u0985\u09a8\u09c7\u0995\u099f\u09be \u09b8\u09c7\u099f \u098f\u09b0 \u0995\u09cd\u09b0\u09b8 \u09aa\u09cd\u09b0\u09cb\u09a1\u09be\u0995\u09cd\u099f\u09c7\u09b0 \u09ae\u09a4, \u098f\u0995\u099f\u09c1 \u09b0\u09bf\u09df\u09be\u09b0\u09c7\u099e\u09cd\u099c \u0986\u09b0\u0995\u09bf\u0964 \u09ae\u09a8\u09c7 \u0995\u09b0\u09c1\u09a8 \u0986\u09aa\u09a8\u09bf \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8\u0995\u09c7 \u09a7\u09b0\u09c7 \u09aa\u09c1\u09b0\u09be \u099f\u09be\u09ac\u09cd\u09b2\u0995\u09c7 \u0998\u09c1\u09b0\u09be\u09b2\u09c7\u09a8\u0964 stack \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u099f\u09c7\u09ac\u09cd\u09b2\u09c7 \u0986\u0981\u0995\u09be\u09b0\u09c7 \u09b8\u09be\u099c\u09be\u09a8 \u09b9\u09df \u0986\u09b0 unstack \u098f\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u09b2\u09bf\u09b8\u09cd\u099f \u0986\u0981\u0995\u09be\u09b0\u09c7\u0964 \u09ae\u09a8\u09c7 \u09b0\u09be\u0996\u09ac\u09c7\u09a8, \u0995\u09b2\u09be\u09ae \u0987\u0989\u09a8\u09bf\u0995 \u09a5\u09be\u0995\u09ac\u09c7, \u0986\u09b0 \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8 \u0985\u09a8\u09c1\u09af\u09be\u09df\u09c0 \u098f\u0995\u09a4\u09cd\u09b0\u09bf\u09a4 \u09b9\u09ac\u09c7\u0964 \u0985\u09b0\u09cd\u09a5\u09be\u09ce, \u09af\u09a6\u09bf \u0995\u09b2\u09be\u09ae \u09b9\u09df names \u0986\u09b0 wins \u09b9\u09df \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8, \u09a4\u09be\u09b9\u09b2\u09c7, \u098f\u09ae\u09a8 \u09b9\u09ac\u09c7, \"\u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u0995\u09b2\u09be\u09ae \u09ad\u09cd\u09af\u09be\u09b2\u09c1 \u09ac\u09a8\u09be\u09ae \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8\u09c7\u09b0 \u09aa\u09cd\u09b0\u09a4\u09bf\u099f\u09bf \u09ad\u09cd\u09af\u09be\u09b2\u09c1, \u09a5\u09be\u0995\u09b2\u09c7 \u09b8\u09c7\u0987 \u09ad\u09cd\u09af\u09be\u09b2\u09c1, \u09a8\u09be \u09a5\u09be\u0995\u09b2\u09c7 NaN."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivoted = df.pivot(index=\"wins\", columns=\"names\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 213
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivoted.unstack()\n",
      "# \u09ac\u09bf\u09b6\u09be\u09b2 \u098f\u0995 \u0995\u09cd\u09b0\u09b8 \u09aa\u09cd\u09b0\u09cb\u09a1\u09be\u0995\u09cd\u099f\u0964 NaN \u09ad\u09cd\u09af\u09be\u09b2\u09c1 \u09b9\u09b2 \u0996\u09be\u09b2\u09bf \u09a1\u09be\u099f\u09be\u09b0 \u099c\u09a8\u09cd\u09af\u09c7\u0964 "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 210,
       "text": [
        "              names      wins\n",
        "participated  Argentina  1      NaN\n",
        "                         2       16\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Brazil     1      NaN\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5       20\n",
        "              England    1       14\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Germany    1      NaN\n",
        "                         2      NaN\n",
        "                         4       18\n",
        "                         5      NaN\n",
        "              Italy      1      NaN\n",
        "                         2      NaN\n",
        "                         4       18\n",
        "                         5      NaN\n",
        "              Spain      1       14\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Uruguay    1       12\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "runnerup      Argentina  1      NaN\n",
        "                         2        5\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Brazil     1      NaN\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5        7\n",
        "              England    1        1\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Germany    1      NaN\n",
        "                         2      NaN\n",
        "                         4        8\n",
        "                         5      NaN\n",
        "              Italy      1      NaN\n",
        "                         2      NaN\n",
        "                         4        6\n",
        "                         5      NaN\n",
        "              Spain      1        1\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "              Uruguay    1        2\n",
        "                         2      NaN\n",
        "                         4      NaN\n",
        "                         5      NaN\n",
        "Length: 56, dtype: float64"
       ]
      }
     ],
     "prompt_number": 210
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pivoted.stack()\n",
      "# (wins, names) \u0995\u09c7 \u09a7\u09b0\u09c7 \u09a1\u09be\u099f\u09be\u09b0 \u099a\u09c7\u09b9\u09be\u09b0\u09be, unstack \u09b9\u09df\u09c7\u099b\u09c7 \u0995\u09b2\u09df\u09be\u09aa\u09cd\u09b8\u09c7\u09a1\u0964 stack \u09b8\u09ae\u09b8\u09cd\u09a4 N/A \u0995\u09c7 \u099a\u09c1\u09aa\u09b8\u09c7 \u09a6\u09bf\u09df\u09c7\u099b\u09c7\u0964"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th>continent</th>\n",
        "      <th>participated</th>\n",
        "      <th>runnerup</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>wins</th>\n",
        "      <th>names</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"3\" valign=\"top\">1</th>\n",
        "      <th>England</th>\n",
        "      <td>  E</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Spain</th>\n",
        "      <td>  E</td>\n",
        "      <td> 14</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Uruguay</th>\n",
        "      <td> SA</td>\n",
        "      <td> 12</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <th>Argentina</th>\n",
        "      <td> SA</td>\n",
        "      <td> 16</td>\n",
        "      <td> 5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
        "      <th>Germany</th>\n",
        "      <td>  E</td>\n",
        "      <td> 18</td>\n",
        "      <td> 8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Italy</th>\n",
        "      <td>  E</td>\n",
        "      <td> 18</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <th>Brazil</th>\n",
        "      <td> SA</td>\n",
        "      <td> 20</td>\n",
        "      <td> 7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "output_type": "pyout",
       "prompt_number": 214,
       "text": [
        "               continent  participated  runnerup\n",
        "wins names                                      \n",
        "1    England           E            14         1\n",
        "     Spain             E            14         1\n",
        "     Uruguay          SA            12         2\n",
        "2    Argentina        SA            16         5\n",
        "4    Germany           E            18         8\n",
        "     Italy             E            18         6\n",
        "5    Brazil           SA            20         7"
       ]
      }
     ],
     "prompt_number": 214
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "\u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u09c7\u09b0 \u09b6\u09c7\u09b7 \u098f\u0996\u09be\u09a8\u09c7\u0987 \u09a8\u09be, \u0986\u09b0\u0993 \u0986\u09b2\u09cb\u099a\u09a8\u09be \u09b9\u09ac\u09c7 \u09aa\u09b0\u09ac\u09b0\u09cd\u09a4\u09c0 \u09aa\u09cb\u09b8\u09cd\u099f\u0997\u09c1\u09b2\u09a4\u09c7\u0964 \u09b6\u09c7\u09b7 \u0995\u09b0\u09be\u09b0 \u0986\u0997\u09c7 \u09a6\u09c7\u0996\u09c7 \u09a8\u09c7\u0987 \u0995\u09bf \u0995\u09bf \u09a1\u09bf\u09b8\u0995\u09be\u09b8 \u09b9\u09b2 \u098f\u0996\u09be\u09a8\u09c7\n",
      "\n",
      "* \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u0995\u09bf\u0964\n",
      "* \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae \u09ac\u09bf\u09ad\u09bf\u09a8\u09cd\u09a8\u09ad\u09be\u09ac\u09c7 \u09b8\u09c3\u09b7\u09cd\u099f \u09b9\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u0964 \u09a6\u09cd\u09ac\u09bf\u09ae\u09be\u09a4\u09cd\u09b0\u09bf\u0995 \u0985\u09cd\u09af\u09be\u09b0\u09c7 \u0985\u09a5\u09ac\u09be \u09a1\u09bf\u0995\u09b6\u09a8\u09be\u09b0\u09bf\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964\n",
      "* \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u09c7\u09b0 \u0989\u09aa\u09b0 \u0986\u09aa\u09a8\u09bf \u09ac\u09bf\u09ad\u09bf\u09a8\u09cd\u09a8 \u09ab\u09be\u0982\u09b6\u09a8 \u0995\u09b2 \u098f\u09ac\u0982 \u0985\u09aa\u09be\u09b0\u09c7\u09b6\u09be\u09a8 \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964\n",
      "* ix \u09ab\u09be\u0982\u09b6\u09a8\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7 \u0987\u09a8\u09cd\u09a1\u09c7\u0995\u09cd\u09b8\u09bf\u0982 \u0995\u09b0\u09be \u09af\u09be\u09df \u09a0\u09bf\u0995 \u09a8\u09be\u09ae\u09aa\u09be\u0987 \u0985\u09cd\u09af\u09be\u09b0\u09c7\u09b0 \u09ae\u09a4\u0987\u0964 \u0995\u09b2\u09be\u09ae \u0993 \u0987\u09a8\u09a1\u09c7\u0995\u09cd\u09b8 \u09a6\u09bf\u09df\u09c7 \u0986\u09aa\u09a8\u09bf [] \u098f\u09b0 \u0995\u09be\u099c \u0995\u09b0\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964\n",
      "* \u09aa\u09cd\u09b2\u099f\u09bf\u0982 \u0995\u09b0\u09be \u09af\u09be\u09df \u09a1\u09be\u099f\u09be\u09ab\u09cd\u09b0\u09c7\u09ae\u09c7\u09b0 \u09ae\u09be\u09a7\u09cd\u09af\u09ae\u09c7\u0964 \u0986\u09b0 axis \u09a6\u09bf\u09df\u09c7 \u0986\u09aa\u09a8\u09bf \u09dc\u09cb/\u0995\u09b2\u09be\u09ae\u0995\u09c7 \u09ae\u09c1\u0996\u09cd\u09af \u09a7\u09b0\u09c7 \u09a8\u09bf\u09a4\u09c7 \u09aa\u09be\u09b0\u09c7\u09a8\u0964 \n",
      "* \u09b8\u09ac \u09b6\u09c7\u09b7\u09c7 \u0986\u09ae\u09b0\u09be \u0995\u09bf\u099b\u09c1\u099f\u09be \u09a6\u09c7\u0996\u09c7\u099b\u09bf \u09aa\u09bf\u09ad\u099f \u099f\u09c7\u09ac\u09cd\u09b2\u0964 \u098f\u09b0 \u0989\u09aa\u09b0 \u0986\u09b0\u0993 \u0995\u09be\u099c \u09b9\u09ac\u09c7\u0964 \u0986\u09ae\u09bf \u0995\u09cb\u09a8 \u09ad\u09be\u09b2 \u09a1\u09be\u099f\u09be (\u09af\u09be \u0986\u09ae\u09be\u09b0 \u0985\u09ab\u09bf\u09b8 \u098f\u09b0 \u09a8\u09be\u0964 \u0986\u09b2\u09b8\u09cd\u09af!!!) \u09aa\u09be\u0987 \u09a8\u09be\u0987, \u09a4\u09be \u09aa\u09c7\u09b2\u09c7 \u0986\u09b0\u0993 \u09ad\u09be\u09b2\u09ad\u09be\u09ac\u09c7 \u09ac\u09b2\u09be \u09af\u09c7\u09a4\u0964\n",
      "\n",
      "\u0986\u0997\u09be\u09ae\u09c0\u09a4\u09c7 \u0986\u09ae\u09bf \u09a1\u09be\u099f\u09be \u0995\u09be\u09b2\u09c7\u0995\u09b6\u09a8 \u09a8\u09bf\u09df\u09c7 \u0995\u09a5\u09be \u09ac\u09b2\u09ac\u09a4 \u0995\u09bf\u099b\u09c1, \u0986\u09b0\u0993 \u0995\u09bf\u099b\u09c1 \u0995\u09a5\u09be \u09ac\u09b2\u09ac \u09aa\u09bf\u09ad\u099f \u09a8\u09bf\u09df\u09c7 \u09b9\u09df\u09a4\u0964 \u0986\u09b0 \u0995\u09a5\u09be \u09ac\u09b2\u09ac \u0997\u09cd\u09b0\u0989\u09aa\u09bf\u0982 \u09a8\u09bf\u09df\u09c7\u0964 \u09a4\u09cb \u09a6\u09c7\u0996\u09be \u09b9\u09ac\u09c7 \u0986\u0997\u09be\u09ae\u09c0\u0995\u09be\u09b2\u0964 "
     ]
    }
   ],
   "metadata": {}
  }
 ]
}