2-Array_manipulation_routines.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array manipulation routines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.14.3'"
      ]
     },
     "execution_count": 3,
     "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": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        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.,\n",
       "        1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.ones([10,10,3])\n",
    "x.reshape(2,150)"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 2, 5, 3, 6])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= np.array([[1,2,3],[4,5,6]])\n",
    "y=np.column_stack(x)\n",
    "np.concatenate(y)"
   ]
  },
  {
   "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": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[1,1]"
   ]
  },
  {
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "shape() takes 1 positional argument but 3 were given",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-71-25e5a6385ee7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: shape() takes 1 positional argument but 3 were given"
     ]
    }
   ],
   "source": [
    "np.\n"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4L, 3L)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.shape([3,4])\n",
    "np.insert(x,1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3L, 1L, 4L)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3L, 4L)\n"
     ]
    }
   ],
   "source": []
  },
  {
   "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": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  7,  8,  9],\n",
       "       [ 4,  5,  6, 10, 11, 12]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= np.array([[ 1, 2, 3],[ 4, 5, 6]])\n",
    "y = np.array([[ 7, 8, 9],[10, 11, 12]])\n",
    "np.concatenate((x,y),axis=1)"
   ]
  },
  {
   "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": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((x,y),axis=0)"
   ]
  },
  {
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 4]\n",
      " [2 5]\n",
      " [3 6]]\n"
     ]
    }
   ],
   "source": [
    "x1 = np.column_stack(np.array([1,2,3]))\n",
    "y1 = np.column_stack(np.array([4,5,6]))\n",
    "x1 = np.column_stack(np.concatenate((x1,y1),axis=0))\n",
    "print(x1)"
   ]
  },
  {
   "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": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1],[2],[3]])\n",
    "y = np.array([[4],[5],[6]])\n",
    "x = np.stack(np.concatenate((x,y),axis=1))\n",
    "x.resize(3,3,2)"
   ]
  },
  {
   "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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([1, 2, 3, 4]), array([5, 6]), array([7, 8, 9])]\n"
     ]
    }
   ],
   "source": [
    "a = np.split(np.arange(1,10),[4,6])\n",
    "print(a)"
   ]
  },
  {
   "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": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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]]]), array([], shape=(2, 2, 0), dtype=int32)]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(16).reshape(2,2,4)\n",
    "np.dsplit(x,np.array([3,6]))"
   ]
  },
  {
   "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": 59,
   "metadata": {},
   "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(0,16).reshape(4,4)\n",
    "print(np.split(x,2,axis=1))"
   ]
  },
  {
   "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": 44,
   "metadata": {},
   "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",
    "print(np.split(x,2))"
   ]
  },
  {
   "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": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 0, 1, 2],\n",
       "       [0, 1, 2, 0, 1, 2]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([0,1,2])\n",
    "c = np.concatenate([x,x])\n",
    "np.stack([c,c])"
   ]
  },
  {
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 1, 1, 2, 2])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([0,1,2])\n",
    "np.concatenate(np.column_stack(np.vstack([x,x])))"
   ]
  },
  {
   "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": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 0, 2, 1])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([0,0,0,1,2,3,0,2,1,0])\n",
    "np.trim_zeros(x)"
   ]
  },
  {
   "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": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([1, 2, 3, 4, 5]), array([2, 3, 1, 1, 2], dtype=int64))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([2,2,1,5,4,5,1,2,3])\n",
    "np.unique(x,return_counts=True)"
   ]
  },
  {
   "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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 1]\n",
      " [4 3]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "print(np.flip(x ,axis=1))"
   ]
  },
  {
   "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": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3 4]\n",
      " [1 2]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "print(np.flip(x ,axis=0))"
   ]
  },
  {
   "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": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [1, 3]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "np.rot90(x,1)"
   ]
  },
  {
   "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": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[4 1 2 3]\n",
      " [8 5 6 7]]\n"
     ]
    }
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