Numpy Basics.ipynb

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  {
   "cell_type": "code",
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   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d15f64db",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = list()\n",
    "lst.extend([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9db3dc9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "49999797",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array(lst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8b6952ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0a87eb39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## It is a One-Dimensional Array\n",
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9dfd2517",
   "metadata": {},
   "outputs": [],
   "source": [
    "list1 = [1,2,3,4]\n",
    "list2 = [5,6,7,8]\n",
    "list3 = [1,4,6,7]\n",
    "nd_array = np.array([list1,list2,list3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "729a3f11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8],\n",
       "       [1, 4, 6, 7]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1bbcb2b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 2-D Array with 3 rows and 4 columns\n",
    "nd_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "187a8edf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 1],\n",
       "       [4, 6, 7]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Reshped 3x4 array to 4x3 array (4 rows and 3 columns)\n",
    "nd_array = nd_array.reshape(4,3)\n",
    "nd_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9ebe781d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nd_array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf02b751",
   "metadata": {},
   "source": [
    "## Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "320bcc11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 1],\n",
       "       [4, 6, 7]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Gives all the elements in the array\n",
    "nd_array[:,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "55ebfdf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [4, 5]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## The first (0:2) tells that I need row elements in 0 to 2nd row\n",
    "## The second (0:2) tells that I need column elements in 0 to 2nd column\n",
    "nd_array[0:2,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "87b7ea9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##If I need to get [[1,2,3],[4,5,6]]\n",
    "nd_array[0:2,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8b7e8cf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [4, 5],\n",
       "       [7, 8]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## If I need to get [[1,2],[4,5],[7,8]]\n",
    "nd_array[0:3,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "515ebbb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4, 6, 8])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## np.arange(start,stop,step)\n",
    "arr = np.arange(0,10,step=2)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dd4da722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   2,   4, 100, 100])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[3:] = 100\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "68e292e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.        ,  1.47368421,  1.94736842,  2.42105263,  2.89473684,\n",
       "        3.36842105,  3.84210526,  4.31578947,  4.78947368,  5.26315789,\n",
       "        5.73684211,  6.21052632,  6.68421053,  7.15789474,  7.63157895,\n",
       "        8.10526316,  8.57894737,  9.05263158,  9.52631579, 10.        ])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Return evenly spaced numbers over a specified interval\n",
    "## np.linspace(start,stop,number of points)\n",
    "np.linspace(1,10,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5c557484",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   2 100 100 100]\n",
      "[  0   2 100 100 100]\n"
     ]
    }
   ],
   "source": [
    "## In assign operation, new array will not be created. The reference will be shared. \n",
    "## To confirm this, lets change the value of the arr and check if it is reflected in arr1\n",
    "arr1 = arr\n",
    "## arr1 = [0,2,3,100,100]\n",
    "arr[2] = 100\n",
    "## arr = [0,2,100,100,100]\n",
    "print(arr)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "60a22d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0, 2, 100, 100, 100]\n",
      "[0, 2, 3, 100, 100]\n"
     ]
    }
   ],
   "source": [
    "## In assign operation, new array will not be created. The reference will be shared. \n",
    "## To confirm this, lets change the value of the arr and check if it is reflected in arr1\n",
    "## In order to prevent this scenario, we use arr.copy function. \n",
    "## Copy function creates another memory space to store the newly created array\n",
    "arr = [0,2,3,100,100]\n",
    "arr1 = arr.copy()\n",
    "## arr1 = [0,2,3,100,100]\n",
    "arr[2] = 100\n",
    "## arr = [0,2,100,100,100]\n",
    "print(arr)\n",
    "print(arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "393f3b55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True, False, False],\n",
       "       [False, False, False],\n",
       "       [False, False,  True],\n",
       "       [False, False, False]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## conditions in array\n",
    "## <,*,/\n",
    "nd_array<2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "67f067d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "ae4c7b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(4,dtype=int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bd55e8cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.71551792, 0.66260427, 0.44725188, 0.93475876, 0.39812573],\n",
       "       [0.00742282, 0.95557679, 0.08775562, 0.25116444, 0.34445486]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Distribute data randomly\n",
    "np.random.rand(2,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e0d58bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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