Lesson9.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NUMPY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 高效的Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 118 µs per loop\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr = np.arange(1000)\n",
    "def get_sum(arr):\n",
    "    acc = 0\n",
    "    for item in arr:\n",
    "        acc += item\n",
    "    return acc\n",
    "%timeit get_sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 loops, best of 3: 89.9 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit sum(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 43.63 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "100000 loops, best of 3: 3.09 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit np.sum(arr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 创建Ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List Tuple\n",
    "a = np.array([1, 2, 3, 4])\n",
    "b = np.array((5, 6, 7, 8))\n",
    "c = np.array([[1, 2, 3, 4],[4, 5, 6, 7], [7, 8, 9, 10]], dtype=int)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 7,  8,  9, 10]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4,)\n",
      "(3, 4)\n"
     ]
    }
   ],
   "source": [
    "# shape属性\n",
    "print a.shape\n",
    "print c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.itemsize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 改变类型 numpy中的数据类型转换，不能直接改原数据的dtype!  只能用函数astype()。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = np.random.random(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.60821019  0.93451057  0.15028573  0.71730223]\n"
     ]
    }
   ],
   "source": [
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a.dtype = 'float32'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.75686619e-07   1.77705252e+00  -1.29937671e-05   1.85862756e+00\n",
      "   1.57849608e-21   1.52528572e+00  -7.68008346e+09   1.80432546e+00]\n"
     ]
    }
   ],
   "source": [
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4.75686619e-07,   1.77705252e+00,  -1.29937671e-05,\n",
       "         1.85862756e+00,   1.57849608e-21,   1.52528572e+00,\n",
       "        -7.68008346e+09,   1.80432546e+00], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = a.astype(np.float32)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([          0,           1,           0,           1,           0,\n",
       "                 1, -2147483648,           1], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = a.astype(np.int32)\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建特定形状的多维数组并进行填充"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 开始值、终值和步长来创建一维数组\n",
    "np.arange(0,1,0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.09090909,  0.18181818,  0.27272727,  0.36363636,\n",
       "        0.45454545,  0.54545455,  0.63636364,  0.72727273,  0.81818182,\n",
       "        0.90909091,  1.        ])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0, 1, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.        ,    1.27427499,    1.62377674,    2.06913808,\n",
       "          2.6366509 ,    3.35981829,    4.2813324 ,    5.45559478,\n",
       "          6.95192796,    8.8586679 ,   11.28837892,   14.38449888,\n",
       "         18.32980711,   23.35721469,   29.76351442,   37.92690191,\n",
       "         48.32930239,   61.58482111,   78.47599704,  100.        ])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logspace(0, 2, 20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.],\n",
       "       [ 0.,  0.]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " np.empty([2, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty([2, 2], dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  0.00000000e+000   0.00000000e+000   2.13553025e-314]\n",
      " [  2.16293988e-314   0.00000000e+000   0.00000000e+000]]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1., 2., 3.],[4.,5.,6.]])\n",
    "print np.empty_like(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0],\n",
       "       [0, 1]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(2, dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.],\n",
       "       [ 0.,  0.,  1.]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((5,), dtype=np.int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.],\n",
       "       [ 1.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.],\n",
       "       [ 1.,  1.]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 广播"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  6.])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1.0,2.0,3.0])\n",
    "b = np.array([2.0,2.0,2.0])\n",
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  4.,  6.])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 当不相等时，则会采用规则对其：\n",
    "a = np.array([1.0,2.0,3.0])\n",
    "b = 2\n",
    "a * b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4],\n",
       "       [5]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(0, 6).reshape(6, 1)\n",
    "a.shape\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(0, 5)\n",
    "b.shape\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "c = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 3 4]\n",
      " [1 2 3 4 5]\n",
      " [2 3 4 5 6]\n",
      " [3 4 5 6 7]\n",
      " [4 5 6 7 8]\n",
      " [5 6 7 8 9]]\n"
     ]
    }
   ],
   "source": [
    "print c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 变换，索引和切片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 2, 3, 4])\n",
    "d = a.reshape((2,2))\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(6).reshape((3, 2))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 0, 1]])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.array([[0,1],[2,3]])\n",
    "np.resize(a,(2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(a,(1,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [0, 1, 2, 3]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(a,(2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10) \n",
    "arr[5]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[5:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr_slice = arr[5:8]  \n",
    "arr_slice[1] = 12345 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2,     3,     4,     5, 12345,     7,     8,     9])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr_slice[:] = 64  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 64, 64, 64,  8,  9])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr_copy = arr[5:8].copy()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "arr2d = np.array([[1,2,3],[4,5,6],[7,8,9]])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[2]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0][2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0,2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d = np.array([[[1,2,3],[4,5,6]],[[7,8,9],[10,11,12]]])  \n",
    "arr3d  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2, 3)"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[0]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[42, 42, 42],\n",
       "        [42, 42, 42]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_values = arr3d[0].copy()  \n",
    "arr3d[0]= 42  \n",
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,1:]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[1,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[2,:1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [4],\n",
       "       [7]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:,:1] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [4, 0, 0],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2,1:] = 0  \n",
    "arr2d  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "names = np.array(['Bob','Joe','Will','Bob','Will','Joe','Joe'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.18288168, -0.94369605, -0.11244431,  0.40186004],\n",
       "       [ 0.06163719,  1.64483343,  0.33081673,  0.80889786],\n",
       "       [-0.52448079,  0.36007662,  0.32996677, -0.20352982],\n",
       "       [ 0.57113387,  1.29816816,  0.29646005, -0.1379642 ],\n",
       "       [ 0.36725083,  0.48369201,  2.58987207,  0.66315957],\n",
       "       [ 1.60421507,  0.57683305,  1.03288016,  0.08122319],\n",
       "       [-0.12621481, -0.69359545,  0.67847661, -0.06644251]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.random  \n",
    "data = numpy.random.randn(7,4)  \n",
    "data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], \n",
       "      dtype='|S4')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True, False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names == 'Bob'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.18288168, -0.94369605, -0.11244431,  0.40186004],\n",
       "       [ 0.57113387,  1.29816816,  0.29646005, -0.1379642 ]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[names == 'Bob']  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ufunc运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.6981317 ,  1.3962634 ,  2.0943951 ,  2.7925268 ,\n",
       "        3.4906585 ,  4.1887902 ,  4.88692191,  5.58505361,  6.28318531])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.linspace(0, 2*np.pi, 10)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0.00000000e+00   6.42787610e-01   9.84807753e-01   8.66025404e-01\n",
      "   3.42020143e-01  -3.42020143e-01  -8.66025404e-01  -9.84807753e-01\n",
      "  -6.42787610e-01  -2.44929360e-16]\n"
     ]
    }
   ],
   "source": [
    "print y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "t = np.sin(x,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(t) == id(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "math.sin: 0.263345\n",
      "numpy.sin: 0.011054\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "x = [i * 0.001 for i in xrange(1000000)]\n",
    "start = time.clock()\n",
    "for i, t in enumerate(x):\n",
    "    x[i] = math.sin(t)\n",
    "print \"math.sin:\", time.clock() - start\n",
    "\n",
    "x = [i * 0.001 for i in xrange(1000000)]\n",
    "x = np.array(x)\n",
    "start = time.clock()\n",
    "np.sin(x,x)\n",
    "print \"numpy.sin:\", time.clock() - start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n",
      "[1 2 3 4]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(0,4)\n",
    "print a\n",
    "b = np.arange(1,5)\n",
    "print b\n",
    "\n",
    "np.add(a,b)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 封装ufuncs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def triangle_wave(x, c, c0, hc):\n",
    "    x = x - int(x) # 三角波\n",
    "    if x >= c: r = 0.0\n",
    "    elif x < c0: r = x / c0 * hc\n",
    "    else: r = (c-x) / (c-c0) * hc\n",
    "    return r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 2, 1000)\n",
    "y = np.array([triangle_wave(t, 0.6, 0.4, 1.0) for t in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "triangle_ufunc = np.frompyfunc( lambda x: triangle_wave(x, 0.6, 0.4, 1.0), 1, 1)\n",
    "y2 = triangle_ufunc(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.array([\n",
    "... [1,2,1],\n",
    "... [0,3,1],\n",
    "... [2,1,4],\n",
    "... [1,3,1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 1],\n",
       "       [0, 3, 1],\n",
       "       [2, 1, 4],\n",
       "       [1, 3, 1]])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 7, 5])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(data, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 1])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(data, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6666666666666667"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.average(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randint(0, 5, [4,3,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[3, 1, 2],\n",
       "         [0, 0, 2]],\n",
       "\n",
       "        [[2, 2, 4],\n",
       "         [1, 4, 1]],\n",
       "\n",
       "        [[2, 0, 0],\n",
       "         [3, 0, 2]]],\n",
       "\n",
       "\n",
       "       [[[2, 0, 0],\n",
       "         [0, 1, 3]],\n",
       "\n",
       "        [[4, 3, 4],\n",
       "         [0, 0, 1]],\n",
       "\n",
       "        [[4, 3, 0],\n",
       "         [2, 3, 2]]],\n",
       "\n",
       "\n",
       "       [[[1, 0, 2],\n",
       "         [3, 4, 0]],\n",
       "\n",
       "        [[4, 0, 4],\n",
       "         [0, 1, 2]],\n",
       "\n",
       "        [[0, 0, 2],\n",
       "         [4, 1, 4]]],\n",
       "\n",
       "\n",
       "       [[[2, 3, 1],\n",
       "         [2, 4, 3]],\n",
       "\n",
       "        [[1, 4, 1],\n",
       "         [1, 2, 3]],\n",
       "\n",
       "        [[2, 1, 2],\n",
       "         [4, 1, 1]]]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 8,  4,  5],\n",
       "        [ 5,  9,  8]],\n",
       "\n",
       "       [[11,  9, 13],\n",
       "        [ 2,  7,  7]],\n",
       "\n",
       "       [[ 8,  4,  4],\n",
       "        [13,  5,  9]]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.sum(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### axis sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.random.randint(0, 5, [3,2,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 3, 2],\n",
       "        [1, 2, 0]],\n",
       "\n",
       "       [[1, 2, 1],\n",
       "        [4, 1, 0]],\n",
       "\n",
       "       [[3, 1, 0],\n",
       "        [1, 1, 2]]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3],\n",
       "        [0, 1, 2]],\n",
       "\n",
       "       [[1, 1, 2],\n",
       "        [0, 1, 4]],\n",
       "\n",
       "       [[0, 1, 3],\n",
       "        [1, 1, 2]]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(data)  ## 默认对最大的axis进行排序，这里即是axis=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 1, 0],\n",
       "        [1, 1, 0]],\n",
       "\n",
       "       [[1, 2, 1],\n",
       "        [1, 1, 0]],\n",
       "\n",
       "       [[3, 3, 2],\n",
       "        [4, 2, 2]]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(data, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 0],\n",
       "        [1, 3, 2]],\n",
       "\n",
       "       [[1, 1, 0],\n",
       "        [4, 2, 1]],\n",
       "\n",
       "       [[1, 1, 0],\n",
       "        [3, 1, 2]]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(data, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1, 2, 3],\n",
       "        [0, 1, 2]],\n",
       "\n",
       "       [[1, 1, 2],\n",
       "        [0, 1, 4]],\n",
       "\n",
       "       [[0, 1, 3],\n",
       "        [1, 1, 2]]])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(data, axis=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 4])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(data, axis=None) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### prod(即product，乘积)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 其他函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from tempfile import TemporaryFile\n",
    "\n",
    "origin_array = np.array([1, 2, 3, 4])\n",
    "np.save('/tmp/array', origin_array)\n",
    "\n",
    "array_from_file = np.load('/tmp/array.npy')\n",
    "print array_from_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  2.  3.  4.]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "origin_array = np.array([1, 2, 3, 4])\n",
    "np.savetxt('array.txt', origin_array)\n",
    "\n",
    "array_from_file = np.loadtxt('array.txt')\n",
    "print array_from_file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  2.  3.  4.]\n"
     ]
    }
   ],
   "source": [
    "array = np.fromstring('1 2 3 4', dtype=float, sep=' ')\n",
    "print array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  4.94065646e-324   9.88131292e-324   1.48219694e-323   1.97626258e-323]\n"
     ]
    }
   ],
   "source": [
    "# 悲剧了\n",
    "array = np.array([1, 2, 3, 4], dtype=int)\n",
    "print np.fromstring(array.tostring())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "print np.fromstring(array.tostring(), dtype=int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### from ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "s = pd.Series(np.random.randn(5), index=['a', 'b', 'c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.336826\n",
       "b    0.952793\n",
       "c    0.333174\n",
       "d    1.586247\n",
       "e    0.717200\n",
       "dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'a', u'b', u'c', u'd', u'e'], dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.556203\n",
       "1   -0.288984\n",
       "2   -0.291610\n",
       "3    0.960958\n",
       "4   -0.375165\n",
       "dtype: float64"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(np.random.randn(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### for dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {'a' : 0., 'b' : 1., 'c' : 2.}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.0\n",
       "b    1.0\n",
       "c    2.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    1.0\n",
       "c    2.0\n",
       "d    NaN\n",
       "a    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d, index=['b', 'c', 'd', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 常量构造"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    5.0\n",
       "b    5.0\n",
       "c    5.0\n",
       "d    5.0\n",
       "e    5.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(5., index=['a', 'b', 'c', 'd', 'e'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3368258988445077"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a   -0.336826\n",
       "b    0.952793\n",
       "c    0.333174\n",
       "dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b    0.952793\n",
       "d    1.586247\n",
       "dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[s > s.median()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "e    0.717200\n",
       "d    1.586247\n",
       "b    0.952793\n",
       "dtype: float64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[[4, 3, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.714033\n",
       "b    2.592941\n",
       "c    1.395390\n",
       "d    4.885381\n",
       "e    2.048690\n",
       "dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### Index Label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.3368258988445077"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s['a']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s['e'] = 12."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    -0.336826\n",
       "b     0.952793\n",
       "c     0.333174\n",
       "d     1.586247\n",
       "e    12.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e' in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'t' in s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 向量化操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    -0.673652\n",
       "b     1.905586\n",
       "c     0.666348\n",
       "d     3.172495\n",
       "e    24.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s + s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    -0.673652\n",
       "b     1.905586\n",
       "c     0.666348\n",
       "d     3.172495\n",
       "e    24.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a         0.714033\n",
       "b         2.592941\n",
       "c         1.395390\n",
       "d         4.885381\n",
       "e    162754.791419\n",
       "dtype: float64"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a         NaN\n",
       "b    1.905586\n",
       "c    0.666348\n",
       "d    3.172495\n",
       "e         NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s[1:] + s[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),\n",
    "    'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  1.0\n",
       "b  2.0  2.0\n",
       "c  3.0  3.0\n",
       "d  NaN  4.0"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "d  NaN  4.0\n",
       "b  2.0  2.0\n",
       "a  1.0  1.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['d', 'b', 'a'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   two three\n",
       "d  4.0   NaN\n",
       "b  2.0   NaN\n",
       "a  1.0   NaN"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['d', 'b', 'a'], columns=['two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "### 字典构造\n",
    "d = {'one' : [1., 2., 3., 4.],\n",
    "    'two' : [4., 3., 2., 1.]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "0  1.0  4.0\n",
       "1  2.0  3.0\n",
       "2  3.0  2.0\n",
       "3  4.0  1.0"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two\n",
       "a  1.0  4.0\n",
       "b  2.0  3.0\n",
       "c  3.0  2.0\n",
       "d  4.0  1.0"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(d, index=['a', 'b', 'c', 'd'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## 结构化数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = np.zeros((2,), dtype=[('A', 'i4'),('B', 'f4'),('C', 'a10')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(0,  0., ''), (0,  0., '')], \n",
       "      dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[:] = [(1,2.,'Hello'), (2,3.,\"World\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(1,  2., 'Hello'), (2,  3., 'World')], \n",
       "      dtype=[('A', '<i4'), ('B', '<f4'), ('C', 'S10')])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>World</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   A    B      C\n",
       "0  1  2.0  Hello\n",
       "1  2  3.0  World"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Hello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>World</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        A    B      C\n",
       "first   1  2.0  Hello\n",
       "second  2  3.0  World"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, index=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hello</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>World</td>\n",
       "      <td>2</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       C  A    B\n",
       "0  Hello  1  2.0\n",
       "1  World  2  3.0"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data, columns=['C', 'A', 'B'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data2 = [{'a': 1, 'b': 2}, {'a': 5, 'b': 10, 'c': 20}]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b     c\n",
       "0  1   2   NaN\n",
       "1  5  10  20.0"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>second</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        a   b     c\n",
       "first   1   2   NaN\n",
       "second  5  10  20.0"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data2, index=['first', 'second'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a   b\n",
       "0  1   2\n",
       "1  5  10"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data2, columns=['a', 'b'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#### records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two  three\n",
       "A    1    2      3\n",
       "B    4    5      6"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_items([('A', [1, 2, 3]), ('B', [4, 5, 6])],\n",
    "orient='index', columns=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    1.0\n",
       "b    2.0\n",
       "c    3.0\n",
       "d    NaN\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['three'] = df['one'] * df['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['flag'] = df['one'] > 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  two  three   flag\n",
       "a  1.0  1.0    1.0  False\n",
       "b  2.0  2.0    4.0  False\n",
       "c  3.0  3.0    9.0   True\n",
       "d  NaN  4.0    NaN  False"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del df['two']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "three = df.pop('three')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>flag</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag\n",
       "a  1.0  False\n",
       "b  2.0  False\n",
       "c  3.0   True\n",
       "d  NaN  False"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['foo'] = 'bar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['one_trunc'] = df['one'][:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>flag</th>\n",
       "      <th>foo</th>\n",
       "      <th>one_trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>bar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one   flag  foo  one_trunc\n",
       "a  1.0  False  bar        1.0\n",
       "b  2.0  False  bar        2.0\n",
       "c  3.0   True  bar        NaN\n",
       "d  NaN  False  bar        NaN"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.insert(1, 'bar', df['one'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>bar</th>\n",
       "      <th>flag</th>\n",
       "      <th>foo</th>\n",
       "      <th>one_trunc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>True</td>\n",
       "      <td>bar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>bar</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   one  bar   flag  foo  one_trunc\n",
       "a  1.0  1.0  False  bar        1.0\n",
       "b  2.0  2.0  False  bar        2.0\n",
       "c  3.0  3.0   True  bar        NaN\n",
       "d  NaN  NaN  False  bar        NaN"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 索引和选择"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one              2\n",
       "bar              2\n",
       "flag         False\n",
       "foo            bar\n",
       "one_trunc        2\n",
       "Name: b, dtype: object"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc['b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "one             3\n",
       "bar             3\n",
       "flag         True\n",
       "foo           bar\n",
       "one_trunc     NaN\n",
       "Name: c, dtype: object"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(5, 3), index=['a', 'c', 'e', 'f', 'h'],\n",
    "   columns=['one', 'two', 'three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three\n",
       "a  0.389612  1.024433 -0.813130\n",
       "c  0.352398  0.808131 -0.305175\n",
       "e -1.017042 -0.857841 -0.580242\n",
       "f  0.179291 -1.993233  0.664604\n",
       "h  0.456743 -1.174220  0.304638"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['four'] = 'bar'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['five'] = df['one'] > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = df.reindex(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
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       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
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       "  <tbody>\n",
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       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "b       NaN       NaN       NaN  NaN    NaN\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "d       NaN       NaN       NaN  NaN    NaN\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "g       NaN       NaN       NaN  NaN    NaN\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.389612\n",
       "b         NaN\n",
       "c    0.352398\n",
       "d         NaN\n",
       "e   -1.017042\n",
       "f    0.179291\n",
       "g         NaN\n",
       "h    0.456743\n",
       "Name: one, dtype: float64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['one']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    False\n",
       "b     True\n",
       "c    False\n",
       "d     True\n",
       "e    False\n",
       "f    False\n",
       "g     True\n",
       "h    False\n",
       "Name: one, dtype: bool"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isnull(df2['one'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a     True\n",
       "b    False\n",
       "c     True\n",
       "d    False\n",
       "e     True\n",
       "f     True\n",
       "g    False\n",
       "h     True\n",
       "Name: four, dtype: bool"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['four'].notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <td>False</td>\n",
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       "      <th>d</th>\n",
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       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     one    two  three   four   five\n",
       "a  False  False  False  False  False\n",
       "b   True   True   True   True   True\n",
       "c  False  False  False  False  False\n",
       "d   True   True   True   True   True\n",
       "e  False  False  False  False  False\n",
       "f  False  False  False  False  False\n",
       "g   True   True   True   True   True\n",
       "h  False  False  False  False  False"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "s = pd.Series([1, 2, 3])\n",
    "s.loc[0] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    NaN\n",
       "1    2.0\n",
       "2    3.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>b</th>\n",
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       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "b       NaN       NaN       NaN  NaN    NaN\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "d       NaN       NaN       NaN  NaN    NaN\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "g       NaN       NaN       NaN  NaN    NaN\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "b  0.000000  0.000000  0.000000    0      0\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "d  0.000000  0.000000  0.000000    0      0\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "g  0.000000  0.000000  0.000000    0      0\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df2.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a        bar\n",
       "b    missing\n",
       "c        bar\n",
       "d    missing\n",
       "e        bar\n",
       "f        bar\n",
       "g    missing\n",
       "h        bar\n",
       "Name: four, dtype: object"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['four'].fillna('missing')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='pad')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>one</th>\n",
       "      <th>two</th>\n",
       "      <th>three</th>\n",
       "      <th>four</th>\n",
       "      <th>five</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.389612</td>\n",
       "      <td>1.024433</td>\n",
       "      <td>-0.813130</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.352398</td>\n",
       "      <td>0.808131</td>\n",
       "      <td>-0.305175</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>-1.017042</td>\n",
       "      <td>-0.857841</td>\n",
       "      <td>-0.580242</td>\n",
       "      <td>bar</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>f</th>\n",
       "      <td>0.179291</td>\n",
       "      <td>-1.993233</td>\n",
       "      <td>0.664604</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>-1.174220</td>\n",
       "      <td>0.304638</td>\n",
       "      <td>bar</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        one       two     three four   five\n",
       "a  0.389612  1.024433 -0.813130  bar   True\n",
       "c  0.352398  0.808131 -0.305175  bar   True\n",
       "e -1.017042 -0.857841 -0.580242  bar  False\n",
       "f  0.179291 -1.993233  0.664604  bar   True\n",
       "h  0.456743 -1.174220  0.304638  bar   True"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(method='pad', limit=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1=pd.DataFrame({'key':['a','b','b'],'data1':range(3)})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    " df2=pd.DataFrame({'key':['a','b','c'],'data2':range(3)}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
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       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key\n",
       "0      0   a\n",
       "1      1   b\n",
       "2      2   b"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key\n",
       "0      0   a\n",
       "1      1   b\n",
       "2      2   c"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>key</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data1 key  data2\n",
       "0      0   a      0\n",
       "1      1   b      1\n",
       "2      2   b      1"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.merge(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data2</th>\n",
       "      <th>key</th>\n",
       "      <th>data1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>a</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>b</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>c</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   data2 key  data1\n",
       "0      0   a    0.0\n",
       "1      1   b    1.0\n",
       "2      1   b    2.0\n",
       "3      2   c    NaN"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df2,df1,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  lval_x  lval_y\n",
       "0  foo  one     1.0     4.0\n",
       "1  foo  one     1.0     5.0\n",
       "2  foo  two     2.0     NaN\n",
       "3  bar  one     3.0     6.0\n",
       "4  bar  two     NaN     7.0"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left=pd.DataFrame({'key1':['foo','foo','bar'],  \n",
    "'key2':['one','two','one'],  \n",
    "'lval':[1,2,3]})  \n",
    "  \n",
    "right=pd.DataFrame({'key1':['foo','foo','bar','bar'],  \n",
    "'key2':['one','one','one','two'],  \n",
    "'lval':[4,5,6,7]})  \n",
    "  \n",
    "pd.merge(left,right,on=['key1','key2'],how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3=pd.DataFrame({'key3':['foo','foo','bar','bar'], #将上面的right的key 改了名字  如果两个对象的列名不同，可以分别指定\n",
    "'key4':['one','one','one','two'],  \n",
    "'lval':[4,5,6,7]})  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>lval_x</th>\n",
       "      <th>key3</th>\n",
       "      <th>key4</th>\n",
       "      <th>lval_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>1</td>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>foo</td>\n",
       "      <td>two</td>\n",
       "      <td>2</td>\n",
       "      <td>foo</td>\n",
       "      <td>one</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>bar</td>\n",
       "      <td>one</td>\n",
       "      <td>3</td>\n",
       "      <td>bar</td>\n",
       "      <td>two</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  key1 key2  lval_x key3 key4  lval_y\n",
       "0  foo  one       1  foo  one       4\n",
       "1  foo  one       1  foo  one       5\n",
       "2  foo  two       2  foo  one       4\n",
       "3  foo  two       2  foo  one       5\n",
       "4  bar  one       3  bar  one       6\n",
       "5  bar  one       3  bar  two       7"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,df3,left_on='key1',right_on='key3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1=pd.DataFrame(np.random.randn(3,4),columns=['a','b','c','d'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2=pd.DataFrame(np.random.randn(2,3),columns=['b','d','a'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.968729</td>\n",
       "      <td>-0.323539</td>\n",
       "      <td>-0.519171</td>\n",
       "      <td>1.090456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.081356</td>\n",
       "      <td>-0.440603</td>\n",
       "      <td>0.003373</td>\n",
       "      <td>-1.567866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.155166</td>\n",
       "      <td>0.252753</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>-0.010611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.770177</td>\n",
       "      <td>-1.357404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.576567</td>\n",
       "      <td>1.171635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.968729 -0.323539 -0.519171  1.090456\n",
       "1  0.081356 -0.440603  0.003373 -1.567866\n",
       "2  1.155166  0.252753  0.339318 -0.010611\n",
       "0  0.770177 -1.357404       NaN -0.067142\n",
       "1 -0.576567  1.171635       NaN -0.068974"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.968729</td>\n",
       "      <td>-0.323539</td>\n",
       "      <td>-0.519171</td>\n",
       "      <td>1.090456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.081356</td>\n",
       "      <td>-0.440603</td>\n",
       "      <td>0.003373</td>\n",
       "      <td>-1.567866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.155166</td>\n",
       "      <td>0.252753</td>\n",
       "      <td>0.339318</td>\n",
       "      <td>-0.010611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.770177</td>\n",
       "      <td>-1.357404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.067142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.576567</td>\n",
       "      <td>1.171635</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.068974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d\n",
       "0 -0.968729 -0.323539 -0.519171  1.090456\n",
       "1  0.081356 -0.440603  0.003373 -1.567866\n",
       "2  1.155166  0.252753  0.339318 -0.010611\n",
       "3  0.770177 -1.357404       NaN -0.067142\n",
       "4 -0.576567  1.171635       NaN -0.068974"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1,df2],ignore_index=True)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group By"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'key1':['a','a','b','b','a'],\n",
    "                   'key2':['one','two','one','two','one'],\n",
    "                   'data1':np.random.randn(5),\n",
    "                   'data2':np.random.randn(5)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-1.132667</td>\n",
       "      <td>0.432118</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.344343</td>\n",
       "      <td>-1.410204</td>\n",
       "      <td>a</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-2.579932</td>\n",
       "      <td>-0.464842</td>\n",
       "      <td>b</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.770310</td>\n",
       "      <td>0.332682</td>\n",
       "      <td>b</td>\n",
       "      <td>two</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.044376</td>\n",
       "      <td>0.530382</td>\n",
       "      <td>a</td>\n",
       "      <td>one</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data1     data2 key1 key2\n",
       "0 -1.132667  0.432118    a  one\n",
       "1  1.344343 -1.410204    a  two\n",
       "2 -2.579932 -0.464842    b  one\n",
       "3  0.770310  0.332682    b  two\n",
       "4  0.044376  0.530382    a  one"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "grouped = df.groupby(df['key1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10aff4190>"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.085351</td>\n",
       "      <td>-0.149235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-0.904811</td>\n",
       "      <td>-0.066080</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     0.085351 -0.149235\n",
       "b    -0.904811 -0.066080"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key1\n",
       "a    0.085351\n",
       "b   -0.904811\n",
       "Name: data1, dtype: float64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['data1'].groupby(df['key1']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key2\n",
       "one    0.165886\n",
       "two   -0.538761\n",
       "Name: data2, dtype: float64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(df['key2'])['data2'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data1</th>\n",
       "      <th>data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>2.477010</td>\n",
       "      <td>1.940585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>3.350242</td>\n",
       "      <td>0.797524</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1     data2\n",
       "key1                    \n",
       "a     2.477010  1.940585\n",
       "b     3.350242  0.797524"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1')['data1','data2'].agg(lambda arr:arr.max()-arr.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">data1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">data2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-1.132667</td>\n",
       "      <td>1.344343</td>\n",
       "      <td>-1.410204</td>\n",
       "      <td>0.530382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>-2.579932</td>\n",
       "      <td>0.770310</td>\n",
       "      <td>-0.464842</td>\n",
       "      <td>0.332682</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         data1               data2          \n",
       "           min       max       min       max\n",
       "key1                                        \n",
       "a    -1.132667  1.344343 -1.410204  0.530382\n",
       "b    -2.579932  0.770310 -0.464842  0.332682"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('key1')['data1','data2'].agg(['min','max'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ser = pd.Series(np.arange(1, 10.1, .25)**2 + np.random.randn(37))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bad = np.array([4, 13, 14, 15, 16, 17, 18, 20, 29])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ser[bad] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "methods = ['linear', 'quadratic', 'cubic']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame({m: ser.interpolate(method=m) for m in methods})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10c754c50>"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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WQ7Eo6PoR1OrB8ZQTDF3yKL8dXkvaqVpcb3+A/z3SlHLFQqxOrVSe0qJX/mP/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\nP6dFr5RSfk6LXiml/Nz/Aauy0upUmXeMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c754490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c821190>]"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G1olBq7Gf/6wWdNuCTXbOgazddbpaKNc9PJChcaG8//s+XTXYolk7Y7r1xI9c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\nsBgkSQOLQZI0sBgkSQOLQZI0sBgkSYNPFpT8HZ35U9IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c821a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n",
    "C,S = np.cos(X), np.sin(X)\n",
    "\n",
    "plt.plot(X,C)\n",
    "plt.plot(X,S)\n",
    "\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,6), dpi=80)\n",
    "\n",
    "# 创建一个新的 1 * 1 的子图，接下来的图样绘制在其中的第 1 块（也是唯一的一块）\n",
    "plt.subplot(1,1,1)\n",
    "\n",
    "X = np.linspace(-np.pi, np.pi, 256,endpoint=True)\n",
    "C,S = np.cos(X), np.sin(X)\n",
    "\n",
    "# 绘制余弦曲线，使用蓝色的、连续的、宽度为 1 （像素）的线条\n",
    "plt.plot(X, C, color=\"blue\", linewidth=1.0, linestyle=\"-\")\n",
    "\n",
    "# 绘制正弦曲线，使用绿色的、连续的、宽度为 1 （像素）的线条\n",
    "plt.plot(X, S, color=\"green\", linewidth=1.0, linestyle=\"-\")\n",
    "\n",
    "# 设置横轴的上下限\n",
    "plt.xlim(-4.0,4.0)\n",
    "\n",
    "# 设置横轴记号\n",
    "plt.xticks(np.linspace(-4,4,9,endpoint=True))\n",
    "\n",
    "# 设置纵轴的上下限\n",
    "plt.ylim(-1.0,1.0)\n",
    "\n",
    "# 设置纵轴记号\n",
    "plt.yticks(np.linspace(-1,1,5,endpoint=True))\n",
    "\n",
    "# 以分辨率 72 来保存图片\n",
    "# savefig(\"exercice_2.png\",dpi=72)\n",
    "\n",
    "# 在屏幕上显示\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c9d37d0>]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3knNuk3NuSqZb6UyPt3POzXPOLXDOPeWcKx7PGEUk/zp2hPXrbdy2bXg6WmSX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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ca5e2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6), dpi=80)\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10c8f0610>]"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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lS4LH+gTy2mswb56N+/e3rqNOGbz3XtHKgl6HvNogYoGCOTlWQ8yj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10c8b0350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlim(X.min()*1.1, X.max()*1.1)\n",
    "plt.ylim(C.min()*1.1, C.max()*1.1)\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10cef3e90>]"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+2qw9DiMhAbjvPh5Pngz89ZdZeyIV+wu7d9PYn37i0qsRhLe3Ls1+/MDjrSsl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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cedb450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks( [-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n",
    "plt.yticks([-1, 0, +1])\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d077910>]"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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aaNbMrj0eo25dDq8C6L2vC2/Vi5AL3hD37INolyyJ+CBa28iozyAZPRpIS+Na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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cb41fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],\n",
    "       [r'$-\\pi$', r'$-\\pi/2$', r'$0$', r'$+\\pi/2$', r'$+\\pi$'])\n",
    "\n",
    "plt.yticks([-1, 0, +1],\n",
    "       [r'$-1$', r'$0$', r'$+1$'])\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d1ac8d0>]"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9lccbNggruUyaNAHGjOHxjh3A/v2yiiMUsor47TfgyBEeC+vYCHbtkh5Dx45V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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d114710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plt.gca()\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.xaxis.set_ticks_position('bottom')\n",
    "ax.spines['bottom'].set_position(('data',0))\n",
    "ax.yaxis.set_ticks_position('left')\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10d3928d0>"
      ]
     },
     "execution_count": 225,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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V2dtauOwyf+WJe7KyPGvh6KNj1sc5GoRaDX/9BaNG+SpOYhAM6crN9cZxRCQU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\nEsGbRxyzbZvnW2jYEK6+2l9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RKCNHepnRX35ps7w5Of7KFEmcYogw775r1Rtycqy2+6uv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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d0d8890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\", label=\"sine\")\n",
    "\n",
    "plt.legend(loc='upper left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x10d4f7210>]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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nq2MItUOClON/I0fCkSOhtyGCsA7JEjSOHXM0J2vVgvvuS//4qCcpyRFNLVVK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\nmfsG2iEdPSry6aciPXqo4/H9ZylYUP9ZUiUeRRa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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3b73d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "t = 2*np.pi/3\n",
    "plt.plot([t,t],[0,np.cos(t)], color ='blue', linewidth=2.5, linestyle=\"--\")\n",
    "plt.scatter([t,],[np.cos(t),], 50, color ='blue')\n",
    "\n",
    "plt.annotate(r'$\\sin(\\frac{2\\pi}{3})=\\frac{\\sqrt{3}}{2}$',\n",
    "         xy=(t, np.sin(t)), xycoords='data',\n",
    "         xytext=(+10, +30), textcoords='offset points', fontsize=16,\n",
    "         arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "\n",
    "plt.plot([t,t],[0,np.sin(t)], color ='red', linewidth=2.5, linestyle=\"--\")\n",
    "plt.scatter([t,],[np.sin(t),], 50, color ='red')\n",
    "\n",
    "plt.annotate(r'$\\cos(\\frac{2\\pi}{3})=-\\frac{1}{2}$',\n",
    "         xy=(t, np.cos(t)), xycoords='data',\n",
    "         xytext=(-90, -50), textcoords='offset points', fontsize=16,\n",
    "         arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=.2\"))\n",
    "\n",
    "plt.plot(X, C, color=\"blue\", linewidth=2.5, linestyle=\"-\", label=\"cosine\")\n",
    "plt.plot(X, S, color=\"red\",  linewidth=2.5, linestyle=\"-\", label=\"sine\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/heming03/python-env/lib/python2.7/site-packages/matplotlib-2.0.0-py2.7-macosx-10.6-intel.egg/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The axisbg attribute was deprecated in version 2.0. Use facecolor instead.\n",
      "  warnings.warn(message, mplDeprecation, stacklevel=1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._axes.Axes at 0x10d636190>"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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W1qqvr0/Lly/XnDlz1NjYqFgspng8rscff1ytra0qLCxUUVGRNm7cKEkqKirSQw89pOrq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d3cf7d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.axes([.1,.1,1,1])\n",
    "plt.axes([.2,.2,.3,.3],axisbg='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10d6fb8d0>"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gX1UN7ja6FNid5DWa0Pni4NlGkqTxy6mf2+M3MTFRU1NT4y5Dkt5SkjzeHo9907yyWJJ6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\nbyyKbqP0OZkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d702750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2,1,1,axisbg='y')\n",
    "plt.subplot(2,1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ylSbbqeuLlLxNSTcC48D3h6yZVFvSOVRXXfxKpnpJdWtLqZrXNcAW4MeSLhxR7S3A3ohY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\nzcwK8X+9Sk+MKqnHHAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d72d090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for idx, color in enumerate('rgbyck'):\n",
    "    plt.subplot(3,2,1+idx,axisbg=color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10dd3e690>"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RiXt4loswFo5l0U1CAhUy5JTxpMOu/Dj/sPgaM4Vxxv2tRXTYLdBTEYWrOizN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\n/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8bHvqkY11yjwT945CH+6PRZFlttC2UZw1S1wt+/714Oj97c13on3vh42yZ2KSUf+nuP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\npmlakdGJXdM0rcjoxK5pmlZk/h+gpYV0zRCqEAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cfc9150>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 1024\n",
    "X = np.random.normal(0,1,n)\n",
    "Y = np.random.normal(0,1,n)\n",
    "T = np.arctan2(Y,X)\n",
    "plt.scatter(X,Y,s=75,c=T,alpha=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-1.25, 1.25)"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ac889R1paGt27d+eFF17g/PPPB+D+++/nlVdeAapu83HrrbfGvP5SVxs4HEiijUu3FWEH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\nrlwL+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10d114b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n = 12\n",
    "X = np.arange(n)\n",
    "Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)\n",
    "\n",
    "plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')\n",
    "plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')\n",
    "\n",
    "for x,y in zip(X,Y1):\n",
    "    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')\n",
    "\n",
    "plt.ylim(-1.25,+1.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0WSL799RgYSkwZeEQ7n5qGPYu7ev7zk0r4dcPT/L7xkiqS+vwDHGk1yP30Ov+sVi7ScVB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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc7d2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x,y): return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)\n",
    "\n",
    "n = 256\n",
    "x = np.linspace(-3,3,n)\n",
    "y = np.linspace(-3,3,n)\n",
    "X,Y = np.meshgrid(x,y)\n",
    "\n",
    "plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap='jet')\n",
    "C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}