jupyter-notebooks/Arrays.ipynb

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     "text": [
      "The original array is:\n",
      "[[ 0  1  2  3  4  5  6  7]\n",
      " [ 8  9 10 11 12 13 14 15]\n",
      " [16 17 18 19 20 21 22 23]\n",
      " [24 25 26 27 28 29 30 31]\n",
      " [32 33 34 35 36 37 38 39]\n",
      " [40 41 42 43 44 45 46 47]\n",
      " [48 49 50 51 52 53 54 55]]\n",
      "\n",
      "\n",
      "The transposed array is:\n",
      "[[ 0  8 16 24 32 40 48]\n",
      " [ 1  9 17 25 33 41 49]\n",
      " [ 2 10 18 26 34 42 50]\n",
      " [ 3 11 19 27 35 43 51]\n",
      " [ 4 12 20 28 36 44 52]\n",
      " [ 5 13 21 29 37 45 53]\n",
      " [ 6 14 22 30 38 46 54]\n",
      " [ 7 15 23 31 39 47 55]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np \n",
    "a = np.arange(56).reshape(7,8) \n",
    "\n",
    "print('The original array is:')\n",
    "print(a)\n",
    "print('\\n') \n",
    "\n",
    "print('The transposed array is:')\n",
    "print(np.transpose(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D Arrays indexing\n",
    "# array[line, column]\n",
    "a[3,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2D Arrays slicing\n",
    "# array[start:stop:step]\n",
    "a[::3] # each 3 lines from the first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "       [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:7:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[24, 25, 26, 27, 28, 29, 30, 31],\n",
       "       [32, 33, 34, 35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "       [48, 49, 50, 51, 52, 53, 54, 55]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3::]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.   , 0.125, 0.25 , 0.375, 0.5  , 0.625, 0.75 , 0.875, 1.   ,\n",
       "       1.125, 1.25 ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clevs = np.arange(0,1.26,0.125)\n",
    "clevs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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