numpy_exercise_2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NumPy Exercise\n",
    "\n",
    "![](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1: Create a 4X2 integer array and Prints its attributes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: element must be a type of unsigned int16. And print the following Attributes: –\n",
    "\n",
    "    The shape of an array.\n",
    "    Array dimensions.\n",
    "    The Length of each element of the array in bytes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here\n",
    "arr = np.arange(8,dtype = 'int16')\n",
    "arr = arr.reshape(4,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int16')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2: Create a 5X2 integer array from a range between 100 to 200 such that the difference between each element is 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here\n",
    "arr = np.linspace(100,200,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100.        , 111.11111111],\n",
       "       [122.22222222, 133.33333333],\n",
       "       [144.44444444, 155.55555556],\n",
       "       [166.66666667, 177.77777778],\n",
       "       [188.88888889, 200.        ]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(5,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3: Following is the provided numPy array. return array of items in the third column from all rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleArray = np.array([[11 ,22, 33], [44, 55, 66], [77, 88, 99]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(33, 66, 99)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Write code here\n",
    "a1 = sampleArray[0][-1]\n",
    "a2 = sampleArray[1][-1]\n",
    "a3 = sampleArray[2][-1]\n",
    "a1,a2,a3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33, 66, 99])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray[:,-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4: Following is the given numpy array return array of odd rows and even columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleArray = np.array([[3 ,6, 9, 12], [15 ,18, 21, 24],[27 ,30, 33, 36], [39 ,42, 45, 48], [51 ,54, 57, 60]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  6,  9, 12],\n",
       "       [15, 18, 21, 24],\n",
       "       [27, 30, 33, 36],\n",
       "       [39, 42, 45, 48],\n",
       "       [51, 54, 57, 60]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6, 12],\n",
       "       [30, 36],\n",
       "       [54, 60]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray[::2,1::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 6 12]\n",
      " [30 36]\n",
      " [54 60]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  5: Add the following two NumPy arrays and Modify a result array by calculating the square  of each element"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "arrayOne = np.array([[5, 6, 9], [21 ,18, 27]])\n",
    "arrayTwo = np.array([[15 ,33, 24], [4 ,7, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[20, 39, 33],\n",
       "       [25, 25, 28]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_plus_two = arrayOne + arrayTwo\n",
    "one_plus_two"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 400, 1521, 1089],\n",
       "       [ 625,  625,  784]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(one_plus_two)**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "addition of two arrays is \n",
      "\n",
      "[[20 39 33]\n",
      " [25 25 28]]\n",
      "\n",
      "Result array after calculating the square of all elements\n",
      "\n",
      "[[ 400 1521 1089]\n",
      " [ 625  625  784]]\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 6: Split the array into four equal-sized sub-arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Create an 8X3 integer array from a range between 10 to 34 such that the difference between each element is 1 and then Split the array into four equal-sized sub-arrays."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.        , 11.04347826, 12.08695652],\n",
       "       [13.13043478, 14.17391304, 15.2173913 ],\n",
       "       [16.26086957, 17.30434783, 18.34782609],\n",
       "       [19.39130435, 20.43478261, 21.47826087],\n",
       "       [22.52173913, 23.56521739, 24.60869565],\n",
       "       [25.65217391, 26.69565217, 27.73913043],\n",
       "       [28.7826087 , 29.82608696, 30.86956522],\n",
       "       [31.91304348, 32.95652174, 34.        ]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.linspace(10,34,24)\n",
    "arr.reshape(8,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10.        , 11.04347826],\n",
       "       [12.08695652, 13.13043478],\n",
       "       [14.17391304, 15.2173913 ]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub = arr.reshape(4,3,2)\n",
    "sub[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16.26086957, 17.30434783],\n",
       "       [18.34782609, 19.39130435],\n",
       "       [20.43478261, 21.47826087]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[22.52173913, 23.56521739],\n",
       "       [24.60869565, 25.65217391],\n",
       "       [26.69565217, 27.73913043]])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[28.7826087 , 29.82608696],\n",
       "       [30.86956522, 31.91304348],\n",
       "       [32.95652174, 34.        ]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sub[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 7: Sort following NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleArray = np.array([[34,43,73],[82,22,12],[53,94,66]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write code here\n",
    "sampleArray.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [12, 22, 82],\n",
       "       [53, 66, 94]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 8: Following is the 2-D array. Print max from axis 0 and min from axis 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [82, 22, 12],\n",
       "       [53, 94, 66]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray = np.array([[34,43,73],[82,22,12],[53,94,66]])\n",
    "sampleArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([82, 94, 73])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray.max(axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([34, 12, 53])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray.min(axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 9: Following is the input NumPy array delete column two and insert following new column in its place."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Hint:\n",
    "    np.delete()\n",
    "    np.insert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 73],\n",
       "       [82, 22, 12],\n",
       "       [53, 94, 66]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray = np.array([[34,43,73],[82,22,12],[53,94,66]]) \n",
    "newColumn = np.array([[10,10,10]])\n",
    "sampleArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43],\n",
       "       [82, 22],\n",
       "       [53, 94]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray = np.delete(sampleArray,2,axis = 1)\n",
    "sampleArray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 10],\n",
       "       [82, 22, 10],\n",
       "       [53, 94, 10]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray = np.insert(sampleArray,2,newColumn,axis = 1)\n",
    "sampleArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Optional Method***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "sampleArray[:,2:] = newColumn.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[34, 43, 10],\n",
       "       [82, 22, 10],\n",
       "       [53, 94, 10]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleArray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 10: Create a two 2-D array and Plot it using matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.58260257,  1.23302935,  0.40720011, -1.06636936],\n",
       "       [-0.38670354,  0.63350828,  0.10030397, -1.22903004],\n",
       "       [-0.49731849,  1.19977066,  0.0416261 ,  0.99943967],\n",
       "       [ 0.6497998 , -0.98738075,  1.33465774,  1.12278457],\n",
       "       [-0.3478929 , -1.06182548, -2.09711298, -1.11671007]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.randn(5,4)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array Dimension(s): 2\n"
     ]
    }
   ],
   "source": [
    "sampleArray = np.array([[34,43,73],[82,22,12],[53,94,66]])\n",
    "print('Array Dimension(s):',sampleArray.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.pyplot import plot,title,hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, \"sampleArray value's Line Plot\")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write code here\n",
    "plot(sampleArray)\n",
    "title('sampleArray value\\'s Line Plot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![blue_bar](https://user-images.githubusercontent.com/7065401/52071924-c003ad80-2562-11e9-8297-1c6595f8a7ff.png)\n",
    "$End$ $of$ $the$ $notebook..$"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}