Numpy Intro.ipynb

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     "text": [
      "[1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "a=np.array([1,2,3,4])\n",
    "print(a)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 13,
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     "text": [
      "[ 3  5  6  4  9 10]\n",
      "[ 30  50  60  40  90 100]\n",
      "[ 6 10 12  8 18 20]\n",
      "(6,)\n",
      "int32\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "b=np.array([3,5,6,4,9,10])\n",
    "print(b)\n",
    "\n",
    "c=b*10\n",
    "print(c)\n",
    "\n",
    "c=b+b\n",
    "print(c)\n",
    "\n",
    "c=b.shape\n",
    "print(c)\n",
    "c=b.dtype\n",
    "print(c)"
   ]
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   "cell_type": "code",
   "execution_count": 21,
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      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "data=[0,1,8.4,6,7,10]\n",
    "arr=np.array(data)\n",
    "arr\n",
    "\n",
    "data1=[[1,2,3,4],[5,3,9,10]]\n",
    "arr1=np.array(data1)\n",
    "arr1\n",
    "arr1.ndim\n",
    "arr1.shape\n",
    "arr1.dtype\n",
    "arr.dtype\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
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     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
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     "execution_count": 30,
     "metadata": {},
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   ],
   "source": [
    "import numpy as np\n",
    "np.zeros(10)\n",
    "np.zeros((2,3))\n",
    "np.empty((3,5))\n",
    "\n",
    "np.arange(17)\n",
    "np.eye(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.        , 0.5       , 0.33333333],\n",
       "        [0.25      , 0.2       , 0.16666667],\n",
       "        [0.16666667, 0.14285714, 0.125     ]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "arr=np.array([[[1,2,3],[4,5,6],[6,7,8]]])\n",
    "arr\n",
    "arr*arr\n",
    "arr*0.5\n",
    "1/arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([], shape=(1, 0, 3), dtype=int32)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "c=np.arange(10)\n",
    "c[5:9]\n",
    "\n",
    "arr=np.array([[[1,2,3],[4,3,2]],[[4,5,7],[5,8,3]]])\n",
    "arr[0:1,1:0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Aveen', 'Saad', 'Faraz'], dtype='<U5')"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "names=np.array(['Aveen','Saad','Faraz'])\n",
    "names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data=np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "data\n",
    "data[2]\n",
    "data[:2,1:]\n",
    "data[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.12275345],\n",
       "       [ 0.10550945],\n",
       "       [ 0.99959744],\n",
       "       [ 0.6312845 ]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data=np.random.randn(7,4)\n",
    "da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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