NumPy_S5_sorting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy sorting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following NumPy array is to be sorted.\n",
    "\n",
    " a) According to the second row,\n",
    " \n",
    " b) According to the second column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-9dc4421c6108>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-1-9dc4421c6108>\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m    Input:\u001b[0m\n\u001b[1;37m          ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "\n",
    "Input:\n",
    "a = np.array([[34,43,73],[82,22,12],[53,94,66]])\n",
    "Output:\n",
    "a)\n",
    "array([[73, 43, 34],\n",
    "       [12, 22, 82],\n",
    "       [66, 94, 53]])\n",
    "b)\n",
    "array([[82, 22, 12],\n",
    "       [34, 43, 73],\n",
    "       [53, 94, 66]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [82, 22, 12],\n",
       "       [53, 94, 66]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[34,43,73],[82,22,12],[53,94,66]])\n",
    "\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [12, 22, 82],\n",
       "       [53, 66, 94]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a)  # sort along the last axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 22, 34, 43, 53, 66, 73, 82, 94])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis = None)  # sort the flattened array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [12, 22, 82],\n",
       "       [53, 66, 94]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 22, 12],\n",
       "       [53, 43, 66],\n",
       "       [82, 94, 73]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis = 0 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [53, 94, 66],\n",
       "       [82, 22, 12]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2=a[np.argsort(a[:, 0])]\n",
    "\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[73, 43, 34],\n",
       "       [12, 22, 82],\n",
       "       [66, 94, 53]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:, a[1,:].argsort()] # Sort by second row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [53, 94, 66],\n",
       "       [82, 22, 12]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a[:,0].argsort(), :] # Sort by first column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[82, 22, 12],\n",
       "       [34, 43, 73],\n",
       "       [53, 94, 66]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a[:,1].argsort(), :] # Sort by second column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 22, 12],\n",
       "       [53, 43, 66],\n",
       "       [82, 94, 73]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis=0)        # sort along the first axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(b'Galahad', 1.7, 38), (b'Arthur', 1.8, 41),\n",
       "       (b'Lancelot', 1.9, 38)],\n",
       "      dtype=[('name', 'S10'), ('height', '<f8'), ('age', '<i4')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtype = [('name', 'S10'), ('height', float), ('age', int)]\n",
    "\n",
    "values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),\n",
    "          ('Galahad', 1.7, 38)]\n",
    "\n",
    "a = np.array(values, dtype=dtype)       # create a structured array\n",
    "\n",
    "np.sort(a, order='height')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([(b'Galahad', 1.7, 38), (b'Lancelot', 1.9, 38),\n",
       "       (b'Arthur', 1.8, 41)],\n",
       "      dtype=[('name', 'S10'), ('height', '<f8'), ('age', '<i4')])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, order=['age', 'height'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['apple' 'banana' 'cherry']\n"
     ]
    }
   ],
   "source": [
    "arr = np.array(['banana', 'cherry', 'apple'])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([3, 2, 0, 1])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[False  True  True]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([True, False, True])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3 4]\n",
      " [0 1 5]]\n"
     ]
    }
   ],
   "source": [
    "arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
    "\n",
    "print(np.sort(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3 4]\n",
      " [9 8 5 6]]\n",
      "\n",
      "[3 5]\n"
     ]
    }
   ],
   "source": [
    "b=np.array([(1,2,3,4),(9,8,5,6)])\n",
    "\n",
    "print(b)\n",
    "print()\n",
    "\n",
    "print(b[0:,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}