{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Семинар 2. Практика. Введение в NumPy, SciPy, Pandas, Scikit-Learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Векторы и матрицы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "v = np.array([1, 2, 3])\n",
    "m = np.array([[1, 2, 1],\n",
    "              [2, 4, 2],\n",
    "              [4, 2, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1],\n",
       "       [2, 4, 2],\n",
       "       [4, 2, 4]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  3],\n",
       "       [ 2,  8,  6],\n",
       "       [ 4,  4, 12]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# умножение звездочкой -- это не то, о чем вы подумали:\n",
    "m * v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 16, 20])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# надо использовать функцию dot:\n",
    "m.dot(v)\n",
    "np.dot(m, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Индексация"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [4, 2]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0:2, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[0, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True], dtype=bool)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v > 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4, 2],\n",
       "       [4, 2, 4]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[v > 1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4, 2])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Манипуляции с матрицами"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1],\n",
       "       [2, 4, 2],\n",
       "       [4, 2, 4]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 4],\n",
       "       [2, 4, 2],\n",
       "       [1, 2, 4]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1, 2, 4, 2, 4, 2, 4]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reshape((1, 9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "m = np.array([[1, 2, 3, 4],\n",
    "              [5, 6, 7, 8],\n",
    "              [3, 2, 1, 5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5, 6],\n",
       "       [7, 8, 3, 2, 1, 5]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reshape((2, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5, 6],\n",
       "       [7, 8, 3, 2, 1, 5]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m.reshape((2, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "total size of new array must be unchanged",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-26-f65ae9455c14>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: total size of new array must be unchanged"
     ]
    }
   ],
   "source": [
    "m.reshape((5, -1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.reshape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Полезные функции"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 13, 16, 19])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10, 20, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 18, 16, 14, 12])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(20, 10, -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 10.        ,  11.11111111,  12.22222222,  13.33333333,\n",
       "        14.44444444,  15.55555556,  16.66666667,  17.77777778,\n",
       "        18.88888889,  20.        ])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(10, 20, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1]\n",
      " [2]\n",
      " [3]]\n",
      "[[1 2 3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1], [2], [3]])\n",
    "y = np.array([\n",
    "        [1, 2, 3]\n",
    "    ])\n",
    "print x\n",
    "print y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3 4]\n",
      " [3 4 5]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "print x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [2 4 6]\n",
      " [3 6 9]]\n"
     ]
    }
   ],
   "source": [
    "print x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0, 3.0, 8.0, 15.0, 24.0, 35.0, 48.0, 63.0, 80.0, 99.0]\n"
     ]
    }
   ],
   "source": [
    "func = lambda x: (x ** 2 - 1)\n",
    "x_vals = np.linspace(1, 10, 10)\n",
    "y_vals = [func(x) for x in x_vals]\n",
    "print y_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.   3.   8.  15.  24.  35.  48.  63.  80.  99.]\n"
     ]
    }
   ],
   "source": [
    "y_vals = x_vals ** 2 - 1\n",
    "print y_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1066d4990>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Z3cA+7t6znMd4dby2SC5Yswauuy6sdh0zJsyuESmPmeHuVToIsiZWxj4EXGRmc4FWqc9F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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10493bc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x_vals, y_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.00000000e-05,   1.29154967e-04,   1.66810054e-03,\n",
       "         2.15443469e-02,   2.78255940e-01,   3.59381366e+00,\n",
       "         4.64158883e+01,   5.99484250e+02,   7.74263683e+03,\n",
       "         1.00000000e+05])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-5, 5, 10, base=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  3.12500000e-02,   6.75037337e-02,   1.45816130e-01,\n",
       "         3.14980262e-01,   6.80395000e-01,   1.46973449e+00,\n",
       "         3.17480210e+00,   6.85795186e+00,   1.48139954e+01,\n",
       "         3.20000000e+01])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(-5, 5, 10, base=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.arange(1, 5), np.arange(1, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4],\n",
       "       [1, 2, 3, 4]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 1, 1, 1],\n",
       "       [2, 2, 2, 2],\n",
       "       [3, 3, 3, 3],\n",
       "       [4, 4, 4, 4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(np.linspace(0, 10, 100), np.linspace(1, 10, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = np.sin(xx) + np.cos(yy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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PNlGL3203SmuSrbwt5DZyOaW0w0U0iSm4M2colKEWcgRZVy2hquAkVb4woTVC6wQWAtII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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106776e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(z, cmap='rainbow')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Случайные числа"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.53412939,  0.8196329 ],\n",
       "       [ 0.58269721,  0.74509401],\n",
       "       [ 0.24873413,  0.11797795]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random((3, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.71048382, -1.53426875, -1.1253789 , -1.13324683, -0.89916963,\n",
       "         0.48033396, -1.51948937, -0.16379221, -0.47640953, -0.38935345]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(size=(1, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 10.08583642,   9.88409547,   9.99406243,  10.12650767,\n",
       "         10.07928963,   9.98324056,  10.05022304,  10.08290551,\n",
       "         10.16132753,  10.27825509]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.normal(loc=10, scale=0.1, size=(1, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?np.random.chisquare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   1.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    3.,    0.,    2.,    1.,    2.,    0.,    3.,    1.,\n",
       "           2.,    6.,    5.,    4.,   11.,    8.,    9.,   11.,   18.,\n",
       "          28.,   30.,   28.,   38.,   39.,   43.,   59.,   52.,   71.,\n",
       "          75.,   95.,   97.,  119.,  130.,  140.,  149.,  164.,  193.,\n",
       "         216.,  229.,  230.,  250.,  271.,  273.,  254.,  284.,  304.,\n",
       "         299.,  310.,  324.,  306.,  349.,  307.,  299.,  258.,  273.,\n",
       "         278.,  266.,  234.,  258.,  224.,  209.,  199.,  166.,  185.,\n",
       "         170.,  158.,  137.,  126.,  100.,   88.,   73.,   59.,   78.,\n",
       "          52.,   43.,   33.,   37.,   28.,   24.,   20.,   17.,   13.,\n",
       "          10.,    7.,    9.,    6.,    5.,    3.,    2.,    5.,    3.,    2.]),\n",
       " array([-4.45572752, -4.37821438, -4.30070124, -4.22318811, -4.14567497,\n",
       "        -4.06816184, -3.9906487 , -3.91313556, -3.83562243, -3.75810929,\n",
       "        -3.68059616, -3.60308302, -3.52556989, -3.44805675, -3.37054361,\n",
       "        -3.29303048, -3.21551734, -3.13800421, -3.06049107, -2.98297793,\n",
       "        -2.9054648 , -2.82795166, -2.75043853, -2.67292539, -2.59541226,\n",
       "        -2.51789912, -2.44038598, -2.36287285, -2.28535971, -2.20784658,\n",
       "        -2.13033344, -2.0528203 , -1.97530717, -1.89779403, -1.8202809 ,\n",
       "        -1.74276776, -1.66525463, -1.58774149, -1.51022835, -1.43271522,\n",
       "        -1.35520208, -1.27768895, -1.20017581, -1.12266267, -1.04514954,\n",
       "        -0.9676364 , -0.89012327, -0.81261013, -0.735097  , -0.65758386,\n",
       "        -0.58007072, -0.50255759, -0.42504445, -0.34753132, -0.27001818,\n",
       "        -0.19250505, -0.11499191, -0.03747877,  0.04003436,  0.1175475 ,\n",
       "         0.19506063,  0.27257377,  0.35008691,  0.42760004,  0.50511318,\n",
       "         0.58262631,  0.66013945,  0.73765258,  0.81516572,  0.89267886,\n",
       "         0.97019199,  1.04770513,  1.12521826,  1.2027314 ,  1.28024454,\n",
       "         1.35775767,  1.43527081,  1.51278394,  1.59029708,  1.66781021,\n",
       "         1.74532335,  1.82283649,  1.90034962,  1.97786276,  2.05537589,\n",
       "         2.13288903,  2.21040217,  2.2879153 ,  2.36542844,  2.44294157,\n",
       "         2.52045471,  2.59796784,  2.67548098,  2.75299412,  2.83050725,\n",
       "         2.90802039,  2.98553352,  3.06304666,  3.14055979,  3.21807293,\n",
       "         3.29558607]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UH03ynSS/3izbWVVnmvYZYOcm96EFksRBMl3QyvHhMTI7m5398q6q+sskfx84kuTE8ItV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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f0a56d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.normal(size=(10000,)), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([    2.,     0.,     0.,     0.,    21.,     0.,     0.,     0.,\n",
       "            0.,    95.,     0.,     0.,     0.,   175.,     0.,     0.,\n",
       "            0.,     0.,   389.,     0.,     0.,     0.,   639.,     0.,\n",
       "            0.,     0.,     0.,   869.,     0.,     0.,     0.,  1141.,\n",
       "            0.,     0.,     0.,     0.,  1267.,     0.,     0.,     0.,\n",
       "         1228.,     0.,     0.,     0.,     0.,  1167.,     0.,     0.,\n",
       "            0.,     0.,   937.,     0.,     0.,     0.,   725.,     0.,\n",
       "            0.,     0.,     0.,   536.,     0.,     0.,     0.,   330.,\n",
       "            0.,     0.,     0.,     0.,   224.,     0.,     0.,     0.,\n",
       "          124.,     0.,     0.,     0.,     0.,    66.,     0.,     0.,\n",
       "            0.,    33.,     0.,     0.,     0.,     0.,    18.,     0.,\n",
       "            0.,     0.,    10.,     0.,     0.,     0.,     0.,     2.,\n",
       "            0.,     0.,     0.,     2.]),\n",
       " array([  1.  ,   1.22,   1.44,   1.66,   1.88,   2.1 ,   2.32,   2.54,\n",
       "          2.76,   2.98,   3.2 ,   3.42,   3.64,   3.86,   4.08,   4.3 ,\n",
       "          4.52,   4.74,   4.96,   5.18,   5.4 ,   5.62,   5.84,   6.06,\n",
       "          6.28,   6.5 ,   6.72,   6.94,   7.16,   7.38,   7.6 ,   7.82,\n",
       "          8.04,   8.26,   8.48,   8.7 ,   8.92,   9.14,   9.36,   9.58,\n",
       "          9.8 ,  10.02,  10.24,  10.46,  10.68,  10.9 ,  11.12,  11.34,\n",
       "         11.56,  11.78,  12.  ,  12.22,  12.44,  12.66,  12.88,  13.1 ,\n",
       "         13.32,  13.54,  13.76,  13.98,  14.2 ,  14.42,  14.64,  14.86,\n",
       "         15.08,  15.3 ,  15.52,  15.74,  15.96,  16.18,  16.4 ,  16.62,\n",
       "         16.84,  17.06,  17.28,  17.5 ,  17.72,  17.94,  18.16,  18.38,\n",
       "         18.6 ,  18.82,  19.04,  19.26,  19.48,  19.7 ,  19.92,  20.14,\n",
       "         20.36,  20.58,  20.8 ,  21.02,  21.24,  21.46,  21.68,  21.9 ,\n",
       "         22.12,  22.34,  22.56,  22.78,  23.  ]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEACAYAAAC08h1NAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n0ZpD7zNxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f948610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.poisson(lam=10.0, size=(10000,)), bins=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 944.,  871.,  840.,  756.,  652.,  588.,  519.,  469.,  436.,\n",
       "         358.,  345.,  346.,  280.,  272.,  203.,  211.,  203.,  172.,\n",
       "         159.,  143.,  137.,   98.,   98.,   91.,   91.,   62.,   60.,\n",
       "          58.,   53.,   63.,   36.,   28.,   33.,   41.,   38.,   28.,\n",
       "          20.,   22.,   18.,   20.,   12.,   10.,   16.,    7.,    9.,\n",
       "           6.,    8.,    4.,    4.,    8.,    4.,    4.,    6.,    1.,\n",
       "           3.,    2.,    4.,    1.,    3.,    2.,    3.,    4.,    4.,\n",
       "           1.,    0.,    1.,    2.,    1.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    2.,    0.,    0.,    1.,    0.,    2.,    1.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "           0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    1.,    1.]),\n",
       " array([  2.85624509e-04,   2.03722320e-01,   4.07159015e-01,\n",
       "          6.10595711e-01,   8.14032406e-01,   1.01746910e+00,\n",
       "          1.22090580e+00,   1.42434249e+00,   1.62777919e+00,\n",
       "          1.83121588e+00,   2.03465258e+00,   2.23808927e+00,\n",
       "          2.44152597e+00,   2.64496267e+00,   2.84839936e+00,\n",
       "          3.05183606e+00,   3.25527275e+00,   3.45870945e+00,\n",
       "          3.66214614e+00,   3.86558284e+00,   4.06901953e+00,\n",
       "          4.27245623e+00,   4.47589293e+00,   4.67932962e+00,\n",
       "          4.88276632e+00,   5.08620301e+00,   5.28963971e+00,\n",
       "          5.49307640e+00,   5.69651310e+00,   5.89994979e+00,\n",
       "          6.10338649e+00,   6.30682318e+00,   6.51025988e+00,\n",
       "          6.71369658e+00,   6.91713327e+00,   7.12056997e+00,\n",
       "          7.32400666e+00,   7.52744336e+00,   7.73088005e+00,\n",
       "          7.93431675e+00,   8.13775344e+00,   8.34119014e+00,\n",
       "          8.54462683e+00,   8.74806353e+00,   8.95150023e+00,\n",
       "          9.15493692e+00,   9.35837362e+00,   9.56181031e+00,\n",
       "          9.76524701e+00,   9.96868370e+00,   1.01721204e+01,\n",
       "          1.03755571e+01,   1.05789938e+01,   1.07824305e+01,\n",
       "          1.09858672e+01,   1.11893039e+01,   1.13927406e+01,\n",
       "          1.15961773e+01,   1.17996140e+01,   1.20030507e+01,\n",
       "          1.22064874e+01,   1.24099240e+01,   1.26133607e+01,\n",
       "          1.28167974e+01,   1.30202341e+01,   1.32236708e+01,\n",
       "          1.34271075e+01,   1.36305442e+01,   1.38339809e+01,\n",
       "          1.40374176e+01,   1.42408543e+01,   1.44442910e+01,\n",
       "          1.46477277e+01,   1.48511644e+01,   1.50546011e+01,\n",
       "          1.52580378e+01,   1.54614745e+01,   1.56649112e+01,\n",
       "          1.58683479e+01,   1.60717846e+01,   1.62752213e+01,\n",
       "          1.64786580e+01,   1.66820947e+01,   1.68855313e+01,\n",
       "          1.70889680e+01,   1.72924047e+01,   1.74958414e+01,\n",
       "          1.76992781e+01,   1.79027148e+01,   1.81061515e+01,\n",
       "          1.83095882e+01,   1.85130249e+01,   1.87164616e+01,\n",
       "          1.89198983e+01,   1.91233350e+01,   1.93267717e+01,\n",
       "          1.95302084e+01,   1.97336451e+01,   1.99370818e+01,\n",
       "          2.01405185e+01,   2.03439552e+01]),\n",
       " <a list of 100 Patch objects>)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x110319fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.chisquare(df=2, size=(10000,)), bins=100)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}