Sort and Search.ipynb

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   "cell_type": "code",
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   "source": [
    "import numpy as np"
   ]
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   "cell_type": "code",
   "execution_count": 3,
   "id": "411e3534",
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   "outputs": [
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       "array([[10, 40],\n",
       "       [30, 20]])"
      ]
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     "execution_count": 3,
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   "source": [
    "a=np.array([[10,40], [30,20]])\n",
    "a"
   ]
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   "cell_type": "code",
   "execution_count": 4,
   "id": "d752e04b",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20],\n",
       "       [30, 40]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a,axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "60c8be75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [20, 30]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eebe29fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a, axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b4b5abe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([[40,30,10],[10,30,20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78f44b6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 30, 40],\n",
       "       [10, 20, 30]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sort(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0bdf3ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original array:\n",
      "[(b'James', 5, 48.5 ) (b'Nail', 6, 52.5 ) (b'Paul', 5, 42.1 )\n",
      " (b'Pit', 5, 40.11)]\n",
      "Sort by height\n",
      "[(b'Pit', 5, 40.11) (b'Paul', 5, 42.1 ) (b'James', 5, 48.5 )\n",
      " (b'Nail', 6, 52.5 )]\n"
     ]
    }
   ],
   "source": [
    "# Importing the NumPy library\n",
    "import numpy as np\n",
    "\n",
    "# Defining the data types for the structured array\n",
    "data_type = [('name', 'S15'), ('class', int), ('height', float)]\n",
    "\n",
    "# Defining the details of students as a list of tuples\n",
    "students_details = [('James', 5, 48.5), ('Nail', 6, 52.5), ('Paul', 5, 42.10), ('Pit', 5, 40.11)]\n",
    "\n",
    "# Creating a structured array 'students' using the defined data type and provided details\n",
    "students = np.array(students_details, dtype=data_type)\n",
    "\n",
    "# Displaying the original structured array\n",
    "print(\"Original array:\")\n",
    "print(students)\n",
    "\n",
    "# Sorting the structured array by 'height' field\n",
    "print(\"Sort by height\")\n",
    "print(np.sort(students, order='height'))   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d3d0cb71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original array:\n",
      "[(b'James', 5, 48.5 ) (b'Nail', 6, 52.5 ) (b'Paul', 5, 42.1 )\n",
      " (b'Pit', 5, 40.11)]\n",
      "Sort by class, then height if class are equal:\n",
      "[(b'Pit', 5, 40.11) (b'Paul', 5, 42.1 ) (b'James', 5, 48.5 )\n",
      " (b'Nail', 6, 52.5 )]\n"
     ]
    }
   ],
   "source": [
    "# Importing the NumPy library\n",
    "import numpy as np\n",
    "\n",
    "# Defining the data types for the structured array\n",
    "data_type = [('name', 'S15'), ('class', int), ('height', float)]\n",
    "\n",
    "# Defining the details of students as a list of tuples\n",
    "students_details = [('James', 5, 48.5), ('Nail', 6, 52.5), ('Paul', 5, 42.10), ('Pit', 5, 40.11)]\n",
    "\n",
    "# Creating a structured array 'students' using the defined data type and provided details\n",
    "students = np.array(students_details, dtype=data_type)\n",
    "\n",
    "# Displaying the original structured array\n",
    "print(\"Original array:\")\n",
    "print(students)\n",
    "\n",
    "# Sorting the structured array by 'class' and 'height' fields\n",
    "print(\"Sort by class, then height if class are equal:\")\n",
    "print(np.sort(students, order=['class', 'height'])) \n"
   ]
  }
 ],
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