Array_manipulation_routines_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array manipulation routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.11.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Q1. Let x be a ndarray [10, 10, 3] with all elements set to one. Reshape x so that the size of the second dimension equals 150."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.  1.\n",
      "   1.  1.  1.  1.  1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.ones([10, 10, 3])\n",
    "out = np.reshape(x, [-1, 150])\n",
    "print out\n",
    "assert np.allclose(out, np.ones([10, 10, 3]).reshape([-1, 150]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q2. Let x be array [[1, 2, 3], [4, 5, 6]]. Convert it to [1 4 2 5 3 6]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 2 5 3 6]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "out1 = np.ravel(x, order='F')\n",
    "out2 = x.flatten(order=\"F\")\n",
    "assert np.allclose(out1, out2)\n",
    "print out1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q3. Let x be array [[1, 2, 3], [4, 5, 6]]. Get the 5th element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "out1 = x.flat[4]\n",
    "out2 = np.ravel(x)[4]\n",
    "assert np.allclose(out1, out2)\n",
    "print out1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q4. Let x be an arbitrary 3-D array of shape (3, 4, 5). Permute the dimensions of x such that the new shape will be (4,3,5).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4L, 3L, 5L)\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((3, 4, 5))\n",
    "out1 = np.swapaxes(x, 1, 0)\n",
    "out2 = x.transpose([1, 0, 2])\n",
    "assert out1.shape == out2.shape\n",
    "print out1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Let x be an arbitrary 2-D array of shape (3, 4). Permute the dimensions of x such that the new shape will be (4,3)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4L, 3L)\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((3, 4))\n",
    "out1 = np.swapaxes(x, 1, 0)\n",
    "out2 = x.transpose()\n",
    "out3 = x.T\n",
    "assert out1.shape == out2.shape == out3.shape\n",
    "print out1.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q5. Let x be an arbitrary 2-D array of shape (3, 4). Insert a nex axis such that the new shape will be (3, 1, 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3L, 1L, 4L)\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((3, 4))\n",
    "print np.expand_dims(x, axis=1).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q6. Let x be an arbitrary 3-D array of shape (3, 4, 1). Remove a single-dimensional entries such that the new shape will be (3, 4)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3L, 4L)\n"
     ]
    }
   ],
   "source": [
    "x = np.zeros((3, 4, 1))\n",
    "print np.squeeze(x).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q7. Lex x be an array <br/>\n",
    "[[ 1 2 3]<br/>\n",
    "[ 4 5 6].<br/><br/>\n",
    "and y be an array <br/>\n",
    "[[ 7 8 9]<br/>\n",
    "[10 11 12]].<br/>\n",
    "Concatenate x and y so that a new array looks like <br/>[[1, 2, 3, 7, 8, 9], <br/>[4, 5, 6, 10, 11, 12]].\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  7  8  9]\n",
      " [ 4  5  6 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "y = np.array([[7, 8, 9], [10, 11, 12]])\n",
    "out1 = np.concatenate((x, y), 1)\n",
    "out2 = np.hstack((x, y))\n",
    "assert np.allclose(out1, out2)\n",
    "print out2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q8. Lex x be an array <br/>\n",
    "[[ 1 2 3]<br/>\n",
    "[ 4 5 6].<br/><br/>\n",
    "and y be an array <br/>\n",
    "[[ 7 8 9]<br/>\n",
    "[10 11 12]].<br/>\n",
    "Concatenate x and y so that a new array looks like <br/>[[ 1  2  3]<br/>\n",
    " [ 4  5  6]<br/>\n",
    " [ 7  8  9]<br/>\n",
    " [10 11 12]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "y = np.array([[7, 8, 9], [10, 11, 12]])\n",
    "out1 = np.concatenate((x, y), 0)\n",
    "out2 = np.vstack((x, y))\n",
    "assert np.allclose(out1, out2)\n",
    "print out2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q8. Let x be an array [1 2 3] and y be [4 5 6]. Convert it to [[1, 4], [2, 5], [3, 6]]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array((1,2,3))\n",
    "y = np.array((4,5,6))\n",
    "out1 = np.column_stack((x, y))\n",
    "out2 = np.dstack((x, y))\n",
    "out3 = np.vstack((x, y)).T\n",
    "assert np.allclose(out1, out2)\n",
    "assert np.allclose(out2, out3)\n",
    "print out1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q9. Let x be an array [[1],[2],[3]] and y be [[4], [5], [6]]. Convert x to [[[1, 4]], [[2, 5]], [[3, 6]]]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[1 4]]\n",
      "\n",
      " [[2 5]]\n",
      "\n",
      " [[3 6]]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1],[2],[3]])\n",
    "y = np.array([[4],[5],[6]])\n",
    "out = np.dstack((x, y))\n",
    "print out\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q10. Let x be an array [1, 2, 3, ..., 9]. Split x into 3 arrays, each of which has 4, 2, and 3 elements in the original order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([1, 2, 3, 4]), array([5, 6]), array([7, 8, 9])]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 10)\n",
    "print np.split(x, [4, 6])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q11. Let x be an array<br/>\n",
    "[[[  0.,   1.,   2.,   3.],<br/>\n",
    "  [  4.,   5.,   6.,   7.]],<br/>\n",
    " \n",
    " [[  8.,   9.,  10.,  11.],<br/>\n",
    "  [ 12.,  13.,  14.,  15.]]].<br/>\n",
    "Split it into two such that the first array looks like<br/>\n",
    "[[[  0.,   1.,   2.],<br/>\n",
    "  [  4.,   5.,   6.]],<br/>\n",
    " \n",
    " [[  8.,   9.,  10.],<br/>\n",
    "  [ 12.,  13.,  14.]]].<br/>\n",
    "  \n",
    "and the second one look like:<br/>\n",
    "  \n",
    "[[[  3.],<br/>\n",
    "  [  7.]],<br/>\n",
    " \n",
    " [[  11.],<br/>\n",
    "  [ 15.]]].<br/>  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[[ 0,  1,  2],\n",
      "        [ 4,  5,  6]],\n",
      "\n",
      "       [[ 8,  9, 10],\n",
      "        [12, 13, 14]]]), array([[[ 3],\n",
      "        [ 7]],\n",
      "\n",
      "       [[11],\n",
      "        [15]]])]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(16).reshape(2, 2, 4)\n",
    "out1 = np.split(x, [3],axis=2)\n",
    "out2 = np.dsplit(x, [3])\n",
    "assert np.allclose(out1[0], out2[0])\n",
    "assert np.allclose(out1[1], out2[1])\n",
    "print out1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q12. Let x be an array <br />\n",
    "[[  0.,   1.,   2.,   3.],<br>\n",
    " [  4.,   5.,   6.,   7.],<br>\n",
    " [  8.,   9.,  10.,  11.],<br>\n",
    " [ 12.,  13.,  14.,  15.]].<br>\n",
    "Split it into two arrays along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 0,  1],\n",
      "       [ 4,  5],\n",
      "       [ 8,  9],\n",
      "       [12, 13]]), array([[ 2,  3],\n",
      "       [ 6,  7],\n",
      "       [10, 11],\n",
      "       [14, 15]])]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(16).reshape((4, 4))\n",
    "out1 = np.hsplit(x, 2)\n",
    "out2 = np.split(x, 2, 1)\n",
    "assert np.allclose(out1[0], out2[0])\n",
    "assert np.allclose(out1[1], out2[1])\n",
    "print out1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q13. Let x be an array <br />\n",
    "[[  0.,   1.,   2.,   3.],<br>\n",
    " [  4.,   5.,   6.,   7.],<br>\n",
    " [  8.,   9.,  10.,  11.],<br>\n",
    " [ 12.,  13.,  14.,  15.]].<br>\n",
    "Split it into two arrays along the first axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[0, 1, 2, 3],\n",
      "       [4, 5, 6, 7]]), array([[ 8,  9, 10, 11],\n",
      "       [12, 13, 14, 15]])]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(16).reshape((4, 4))\n",
    "out1 = np.vsplit(x, 2)\n",
    "out2 = np.split(x, 2, 0)\n",
    "assert np.allclose(out1[0], out2[0])\n",
    "assert np.allclose(out1[1], out2[1])\n",
    "print out1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q14. Let x be an array [0, 1, 2]. Convert it to <br/>\n",
    "[[0, 1, 2, 0, 1, 2],<br/>\n",
    " [0, 1, 2, 0, 1, 2]]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2 0 1 2]\n",
      " [0 1 2 0 1 2]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2])\n",
    "out1 = np.tile(x, [2, 2])\n",
    "out2 = np.resize(x, [2, 6])\n",
    "assert np.allclose(out1, out2)\n",
    "print out1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q15. Let x be an array [0, 1, 2]. Convert it to <br/>\n",
    "[0, 0, 1, 1, 2, 2]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 1 2 2]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([0, 1, 2])\n",
    "print np.repeat(x, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q16. Let x be an array [0, 0, 0, 1, 2, 3, 0, 2, 1, 0].<br/>\n",
    "remove the leading the trailing zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 0 2 1]\n"
     ]
    }
   ],
   "source": [
    "x = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))\n",
    "out = np.trim_zeros(x)\n",
    "print out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q17. Let x be an array [2, 2, 1, 5, 4, 5, 1, 2, 3]. Get two arrays of unique elements and their counts.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5] [2 3 1 1 2]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([2, 2, 1, 5, 4, 5, 1, 2, 3])\n",
    "u, indices = np.unique(x, return_counts=True)\n",
    "print u, indices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q18. Lex x be an array <br/>\n",
    "[[ 1 2]<br/>\n",
    " [ 3 4].<br/>\n",
    "Flip x along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 1]\n",
      " [4 3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2], [3,4]])\n",
    "out1 = np.fliplr(x)\n",
    "out2 = x[:, ::-1]\n",
    "assert np.allclose(out1, out2)\n",
    "print out1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q19. Lex x be an array <br/>\n",
    "[[ 1 2]<br/>\n",
    " [ 3 4].<br/>\n",
    "Flip x along the first axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 4]\n",
      " [1 2]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2], [3,4]])\n",
    "out1 = np.flipud(x)\n",
    "out2 = x[::-1, :]\n",
    "assert np.allclose(out1, out2)\n",
    "print out1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q20. Lex x be an array <br/>\n",
    "[[ 1 2]<br/>\n",
    " [ 3 4].<br/>\n",
    "Rotate x 90 degrees counter-clockwise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 4]\n",
      " [1 3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2], [3,4]])\n",
    "out = np.rot90(x)\n",
    "print out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Q21 Lex x be an array <br/>\n",
    "[[ 1 2 3 4]<br/>\n",
    " [ 5 6 7 8].<br/>\n",
    "Shift elements one step to right along the second axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 1 2 3]\n",
      " [8 5 6 7]]\n"
     ]
    }
   ],
   "source": [
    "x = np.arange(1, 9).reshape([2, 4])\n",
    "print np.roll(x, 1, axis=1)"
   ]
  },
  {
   "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}