scenario_numpy_slicing-1d.ipynb

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    "## numpy - _slicing and reshaping 1-d_\n",
    "\n",
    "- matrix operations x,y -> (x, y)\n",
    "- understand shapes\n",
    "- understand T\n",
    "- slicing v/h/dstack\n",
    "- reshaping\n",
    "- 1d is (x,) shape.. just an array not a matrix\n",
    "- always think the first number as row!"
   ],
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   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
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   "cell_type": "code",
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     "name": "stdout",
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     "text": [
      "x [ 0  5 10 15 20 25 30 35 40 45] shape (10,)\n",
      "y [50 55 60 65 70 75 80 85 90 95] shape (10,)\n"
     ]
    }
   ],
   "source": [
    "# generate x, y\n",
    "# then combine as dataset\n",
    "x = np.arange(start=0, stop=50, step=5)\n",
    "y = np.arange(start=50, stop=100, step=5)\n",
    "\n",
    "\n",
    "print(\"x\", x, \"shape\", x.shape)\n",
    "print(\"y\", y, \"shape\", y.shape)"
   ],
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   "cell_type": "code",
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     "text": [
      "concatenate (20,)\n",
      "hstack (20,)\n",
      "vstack (2, 10)\n",
      "vstack.Transpose (10, 2)\n",
      "dstack (1, 10, 2)\n",
      "dstack (10, 2)\n"
     ]
    }
   ],
   "source": [
    "# (10,) -> (10,2)\n",
    "\n",
    "# relation b/w  h/v/dstack and their Transposes\n",
    "print(\"concatenate\", np.concatenate((x,y)).shape)   #(20, )\n",
    "print(\"hstack\", np.hstack((x,y)).shape)             #(20,)\n",
    "\n",
    "print(\"vstack\", np.vstack((x,y)).shape)             #(2, 10)\n",
    "print(\"vstack.Transpose\", np.vstack((x,y)).T.shape) #(10, 2)\n",
    "\n",
    "print(\"dstack\", np.dstack((x,y)).shape)             #(1, 10, 2)\n",
    "print(\"dstack\", np.dstack((x,y))[0].shape)          #(10, 2)\n"
   ],
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     "text": [
      "[[ 0 50]\n",
      " [ 5 55]\n",
      " [10 60]\n",
      " [15 65]\n",
      " [20 70]\n",
      " [25 75]\n",
      " [30 80]\n",
      " [35 85]\n",
      " [40 90]\n",
      " [45 95]]\n"
     ]
    }
   ],
   "source": [
    "data = np.vstack((x, y)).T\n",
    "\n",
    "print(data)"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "row.0 [ 0 50]\n",
      "col.0 [ 0  5 10 15 20 25 30 35 40 45]\n"
     ]
    }
   ],
   "source": [
    "# take row and column\n",
    "# that ops makes (x,) shape.. even you slice the column, it ain't a column !!\n",
    "# due to dimension\n",
    "#original => (10,2), 2-d\n",
    "\n",
    "print(\"row.0\", data[0,:])         # take 1st row  (2,)\n",
    "print(\"col.0\", data[:,0])         # take 1st column  (10,0)"
   ],
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   "cell_type": "code",
   "execution_count": 70,
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    {
     "name": "stdout",
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     "text": [
      "x [ 0  5 10 15 20 25 30 35 40 45] (10,)\n",
      "y [50 55 60 65 70 75 80 85 90 95] (10,)\n",
      "x.reshaped (10, 1)\n",
      "x.reshaped\n",
      " [[ 0]\n",
      " [ 5]\n",
      " [10]\n",
      " [15]\n",
      " [20]\n",
      " [25]\n",
      " [30]\n",
      " [35]\n",
      " [40]\n",
      " [45]]\n"
     ]
    }
   ],
   "source": [
    "# so, separate x and y variables\n",
    "\n",
    "x = data[:, 0]   #(10,)\n",
    "y = data[:, 1]\n",
    "\n",
    "print(\"x\", x, x.shape)\n",
    "print(\"y\", y, y.shape)\n",
    "\n",
    "# reshape (10,) -> (10,1)\n",
    "# important for tensorflow input !\n",
    "x = np.reshape(x, (-1,1))\n",
    "\n",
    "print(\"x.reshaped\", x.shape)\n",
    "print(\"x.reshaped\\n\", x)"
   ],
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