11_Set_routines_Solutions.ipynb

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   "source": [
    "# Set routines"
   ]
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   "source": [
    "import numpy as np"
   ]
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       "'1.11.2'"
      ]
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     "execution_count": 5,
     "metadata": {},
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   "source": [
    "np.__version__"
   ]
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   "source": [
    "author = 'kyubyong. longinglove@nate.com'"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "## Making proper sets"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "Q1. Get unique elements and reconstruction indices from x. And reconstruct x."
   ]
  },
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   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "unique elements = [1 2 3 4 6]\n",
      "reconstruction indices = [0 1 4 3 1 2 1]\n",
      "reconstructed = [1 2 6 4 2 3 2]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2, 6, 4, 2, 3, 2])\n",
    "out, indices = np.unique(x, return_inverse=True)\n",
    "print \"unique elements =\", out\n",
    "print \"reconstruction indices =\", indices\n",
    "print \"reconstructed =\", out[indices]\n"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean operations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Create a boolean array of the same shape as x. If each element of x is present in y, the result will be True, otherwise False."
   ]
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   "cell_type": "code",
   "execution_count": 19,
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   "outputs": [
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     "output_type": "stream",
     "text": [
      "[ True  True False False  True]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 5, 0])\n",
    "y = np.array([0, 1])\n",
    "print np.in1d(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Find the unique intersection of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 5, 0])\n",
    "y = np.array([0, 1, 4])\n",
    "print np.intersect1d(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Find the unique elements of x that are not present in y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 5]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 5, 0])\n",
    "y = np.array([0, 1, 4])\n",
    "print np.setdiff1d(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Find the xor elements of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 4 5]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 5, 0])\n",
    "y = np.array([0, 1, 4])\n",
    "out1 = np.setxor1d(x, y)\n",
    "out2 = np.sort(np.concatenate((np.setdiff1d(x, y), np.setdiff1d(y, x))))\n",
    "assert np.allclose(out1, out2)\n",
    "print out1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6. Find the union of x and y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 4 5]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2, 5, 0])\n",
    "y = np.array([0, 1, 4])\n",
    "out1 = np.union1d(x, y)\n",
    "out2 = np.sort(np.unique(np.concatenate((x, y))))\n",
    "assert np.allclose(out1, out2)\n",
    "print np.union1d(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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