PY0101EN-5-1-Numpy1D.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center>\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\"  />\n",
    "</center>\n",
    "\n",
    "# 1D Numpy in Python\n",
    "\n",
    "Estimated time needed: **30** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "After completing this lab you will be able to:\n",
    "\n",
    "*   Import and use the `numpy` library\n",
    "*   Perform operations with `numpy`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Table of Contents</h2>\n",
    "<div class=\"alert alert-block alert-info\" style=\"margin-thttps://op/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01: 20px\">\n",
    "    <ul>\n",
    "        <li><a href=\"https://pre/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Preparation</a></li>\n",
    "        <li>\n",
    "            <a href=\"https://numpy/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">What is Numpy?</a>\n",
    "            <ul>\n",
    "                <li><a href=\"https://type/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Type</a></li>\n",
    "                <li><a href=\"https://val/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Assign Value</a></li>\n",
    "                <li><a href=\"https://slice/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Slicing</a></li>\n",
    "                <li><a href=\"https://list/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Assign Value with List</a></li>\n",
    "                <li><a href=\"https://other/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Other Attributes</a></li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li>\n",
    "            <a href=\"op\">Numpy Array Operations</a>\n",
    "            <ul>\n",
    "                <li><a href=\"https://add/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Array Addition</a></li>\n",
    "                <li><a href=\"https://multi/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Array Multiplication</a></li>\n",
    "                <li><a href=\"https://prod/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Product of Two Numpy Arrays</a></li>\n",
    "                <li><a href=\"https://dot/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Dot Product</a></li>\n",
    "                <li><a href=\"https://cons/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Adding Constant to a Numpy Array</a></li>\n",
    "            </ul>\n",
    "        </li>\n",
    "        <li><a href=\"https://math/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Mathematical Functions</a></li>\n",
    "        <li><a href=\"https://lin/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Linspace</a></li>\n",
    "    </ul>\n",
    "\n",
    "</div>\n",
    "\n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"pre\">Preparation</h2>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Import the libraries\n",
    "\n",
    "import time \n",
    "import sys\n",
    "import numpy as np \n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Plotting functions\n",
    "\n",
    "def Plotvec1(u, z, v):\n",
    "    \n",
    "    ax = plt.axes()\n",
    "    ax.arrow(0, 0, *u, head_width=0.05, color='r', head_length=0.1)\n",
    "    plt.text(*(u + 0.1), 'u')\n",
    "    \n",
    "    ax.arrow(0, 0, *v, head_width=0.05, color='b', head_length=0.1)\n",
    "    plt.text(*(v + 0.1), 'v')\n",
    "    ax.arrow(0, 0, *z, head_width=0.05, head_length=0.1)\n",
    "    plt.text(*(z + 0.1), 'z')\n",
    "    plt.ylim(-2, 2)\n",
    "    plt.xlim(-2, 2)\n",
    "\n",
    "def Plotvec2(a,b):\n",
    "    ax = plt.axes()\n",
    "    ax.arrow(0, 0, *a, head_width=0.05, color ='r', head_length=0.1)\n",
    "    plt.text(*(a + 0.1), 'a')\n",
    "    ax.arrow(0, 0, *b, head_width=0.05, color ='b', head_length=0.1)\n",
    "    plt.text(*(b + 0.1), 'b')\n",
    "    plt.ylim(-2, 2)\n",
    "    plt.xlim(-2, 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a Python List as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a python list\n",
    "\n",
    "a = [\"0\", 1, \"two\", \"3\", 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the data via an index:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumOneList.png\" width=\"660\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access each element using a square bracket as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a[0]: 0\n",
      "a[1]: 1\n",
      "a[2]: two\n",
      "a[3]: 3\n",
      "a[4]: 4\n"
     ]
    }
   ],
   "source": [
    "# Print each element\n",
    "\n",
    "print(\"a[0]:\", a[0])\n",
    "print(\"a[1]:\", a[1])\n",
    "print(\"a[2]:\", a[2])\n",
    "print(\"a[3]:\", a[3])\n",
    "print(\"a[4]:\", a[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"numpy\">What is Numpy?</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A numpy array is similar to a list. It's usually fixed in size and each element is of the same type. We can cast a list to a numpy array by first importing `numpy`:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import numpy library\n",
    "\n",
    "import numpy as np "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then cast the list as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "a = np.array([0, 1, 2, 3, 4])\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each element is of the same type, in this case integers:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumOneNp.png\" width=\"500\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with lists, we can access each element via a square bracket:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a[0]: 0\n",
      "a[1]: 1\n",
      "a[2]: 2\n",
      "a[3]: 3\n",
      "a[4]: 4\n"
     ]
    }
   ],
   "source": [
    "# Print each element\n",
    "\n",
    "print(\"a[0]:\", a[0])\n",
    "print(\"a[1]:\", a[1])\n",
    "print(\"a[2]:\", a[2])\n",
    "print(\"a[3]:\", a[3])\n",
    "print(\"a[4]:\", a[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"type\">Type</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we check the type of the array we get <b>numpy.ndarray</b>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of the array\n",
    "\n",
    "type(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As numpy arrays contain data of the same type, we can use the attribute \"dtype\" to obtain the data type of the array’s elements. In this case, it's a 64-bit integer:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of the values stored in numpy array\n",
    "\n",
    "a.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create a numpy array with real numbers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "b = np.array([3.1, 11.02, 6.2, 213.2, 5.2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we check the type of the array we get <b>numpy.ndarray</b>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the type of array\n",
    "\n",
    "type(b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we examine the attribute <code>dtype</code> we see float 64, as the elements are not integers:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the value type\n",
    "\n",
    "b.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"val\">Assign value</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the value of the array. Consider the array <code>c</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20,  1,  2,  3,  4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create numpy array\n",
    "\n",
    "c = np.array([20, 1, 2, 3, 4])\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the first element of the array to 100 as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100,   1,   2,   3,   4])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the first element to 100\n",
    "\n",
    "c[0] = 100\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can change the 5th element of the array to 0 as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100,   1,   2,   3,   0])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the 5th element to 0\n",
    "\n",
    "c[4] = 0\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"slice\">Slicing</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like lists, we can slice the numpy array. We can select the elements from 1 to 3 and assign it to a new numpy array <code>d</code> as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Slicing the numpy array\n",
    "\n",
    "d = c[1:4]\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assign the corresponding indexes to new values as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100,   1,   2, 300, 400])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the fourth element and fifth element to 300 and 400\n",
    "\n",
    "c[3:5] = 300, 400\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"list\">Assign Value with List</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can use a list to select more than one specific index.\n",
    "The list `select` contains several values:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create the index list\n",
    "\n",
    "select = [0, 2, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the list as an argument in the brackets. The output is the elements corresponding to the particular indexes:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100,   2, 300])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use List to select elements\n",
    "\n",
    "d = c[select]\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can assign the specified elements to a new value. For example, we can assign the values to 100 000 as follows:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100000,      1, 100000, 100000,    400])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign the specified elements to new value\n",
    "\n",
    "c[select] = 100000\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"other\">Other Attributes</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's review some basic array attributes using the array <code>a</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "a = np.array([0, 1, 2, 3, 4])\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attribute <code>size</code> is the number of elements in the array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the size of numpy array\n",
    "\n",
    "a.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next two attributes will make more sense when we get to higher dimensions but let's review them. The attribute <code>ndim</code> represents the number of array dimensions, or the rank of the array. In this case, one:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the number of dimensions of numpy array\n",
    "\n",
    "a.ndim"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The attribute <code>shape</code> is a tuple of integers indicating the size of the array in each dimension:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5,)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the shape/size of numpy array\n",
    "\n",
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "a = np.array([1, -1, 1, -1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the mean of numpy array\n",
    "\n",
    "mean = a.mean()\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the standard deviation of numpy array\n",
    "\n",
    "standard_deviation=a.std()\n",
    "standard_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  2,  3,  4,  5])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "b = np.array([-1, 2, 3, 4, 5])\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the biggest value in the numpy array\n",
    "\n",
    "max_b = b.max()\n",
    "max_b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the smallest value in the numpy array\n",
    "\n",
    "min_b = b.min()\n",
    "min_b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"op\">Numpy Array Operations</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"add\">Array Addition</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the numpy array <code>u</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "u = np.array([1, 0])\n",
    "u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the numpy array <code>v</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v = np.array([0, 1])\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can add the two arrays and assign it to z:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy Array Addition\n",
    "\n",
    "z = u + v\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The operation is equivalent to vector addition:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dadP+ZuZsYDa0X2VUX2lS+T7zbx/lM/+2tuoyBo7TT4fFi9sPF3U8bHTkkTBsWNXVDSplBMJqoON9+BOAtj1oI2kwuummqitQoYyrjBYCkyNiUkQMB2YCc3ZpMwf4XHG10QnAJs8fSFJjqXuGkJnbIuLrwN3AEOBXmflERJxZbL8cmAtMB1qBLcAX6u1XklSuUm5My8y5tP/R7/je5R2WE/haGX1JknqHD7eTJAEGgiSpYCBIkgADQZJUMBAkSYCBIEkqGAiSJMBAkCQVDARJEmAgSJIKBoIkCTAQJEkFA0GSBBgIkqSCgSBJAgwESVLBQJAkAQaCJKlQ11doRsRBwA3AEcAK4BOZ+VKNdiuAzcB2YFtmNtfTrySpfPXOEM4F7s/MycD9xXpnpmXmVMNAkhpTvYEwA7i6WL4a+Fid+5MkVaTeQDgkM9cAFD/HdNIugXsiYlFEzNrdDiNiVkS0RETLunXr6ixPktRdXZ5DiIj7gENrbDqvB/28JzPbImIMcG9EPJmZ82s1zMzZwGyA5ubm7EEfkqQ6dBkImXlKZ9si4oWIGJuZayJiLLC2k320FT/XRsStwHFAzUCQJFWj3kNGc4DPF8ufB27ftUFEjIiIkf9YBj4ELK2zX0lSyeoNhIuAD0bE08AHi3UiYlxEzC3aHAI8HBFLgD8Dd2bmXXX2K0kqWV33IWTmeuADNd5vA6YXy88C/1RPP5Kk3uedypIkwECQJBUMBEkSYCBIkgoGgiQJMBAkSQUDQZIEGAiSpIKBIEkCDARJUsFAkCQBBoIkqWAgSJIAA0GSVDAQJEmAgSBJKhgIkiTAQJAkFeoKhIg4IyKeiIgdEdG8m3anRsRfI6I1Is6tp09JUu+od4awFPhXYH5nDSJiCPBT4DRgCvCpiJhSZ7+SpJINrefDmbkcICJ21+w4oDUzny3aXg/MAJbV07ckqVx9cQ5hPLCqw/rq4r2aImJWRLRERMu6det6vThJUrsuZwgRcR9waI1N52Xm7d3oo9b0ITtrnJmzgdkAzc3NnbaTJJWry0DIzFPq7GM1cFiH9QlAW537lCSVrC8OGS0EJkfEpIgYDswE5vRBv5KkHqj3stOPR8Rq4ETgzoi4u3h/XETMBcjMbcDXgbuB5cCNmflEfWVLkspW71VGtwK31ni/DZjeYX0uMLeeviRJvcs7lSVJgIEgSSoYCJIkwECQJBUMBEkSYCBIkgoGgiQJMBAkSQUDQZIEGAiSpIKBIEkCDARJUsFAkCQBBoIkqWAgSJIAA0GSVDAQJEmAgSBJKtT7ncpnRMQTEbEjIpp3025FRDweEY9GREs9fUqSekdd36kMLAX+Ffh5N9pOy8wX6+xPktRL6gqEzFwOEBHlVCNJqkxfnUNI4J6IWBQRs/qoT0lSD3Q5Q4iI+4BDa2w6LzNv72Y/78nMtogYA9wbEU9m5vxO+psFzAKYOHFiN3cvSapXl4GQmafU20lmthU/10bErcBxQM1AyMzZwGyA5ubmrLdvSVL39Poho4gYEREj/7EMfIj2k9GSpAZS72WnH4+I1cCJwJ0RcXfx/riImFs0OwR4OCKWAH8G7szMu+rpV5JUvnqvMroVuLXG+23A9GL5WeCf6ulHktT7vFNZkgQYCJKkgoEgSQIMBElSwUCQJAEGgiSpYCBIkgADQZJUMBAkSYCBIEkqGAiSJMBAkCQVDARJEmAgSJIKBoIkCTAQJEkFA0GSBBgIkqSCgSBJAuoMhIj4vxHxZEQ8FhG3RsQBnbQ7NSL+GhGtEXFuPX1KknpHvTOEe4G3Z+Y7gKeA/7Vrg4gYAvwUOA2YAnwqIqbU2a8kqWR1BUJm3pOZ24rVBcCEGs2OA1oz89nMfAO4HphRT7+SpPINLXFfXwRuqPH+eGBVh/XVwPGd7SQiZgGzitXXI2JpaRX2jlHAi1UX0Q3WWS7rLJd1lue/7ekHuwyEiLgPOLTGpvMy8/aizXnANuA3tXZR473srL/MnA3MLvbbkpnNXdVYpf5QI1hn2ayzXNZZnoho2dPPdhkImXlKF51/Hvgw8IHMrPWHfjVwWIf1CUBbT4qUJPW+eq8yOhU4B/hoZm7ppNlCYHJETIqI4cBMYE49/UqSylfvVUaXAiOBeyPi0Yi4HCAixkXEXIDipPPXgbuB5cCNmflEN/c/u876+kJ/qBGss2zWWS7rLM8e1xi1j/JIkgYb71SWJAEGgiSp0DCB0F8egxERZ0TEExGxIyI6vfwsIlZExOPFuZU9vgxsT/WgzqrH86CIuDcini5+HthJu0rGs6vxiXaXFNsfi4hj+6q2HtR4ckRsKsbu0Yg4v69rLOr4VUSs7ezeokYYy6KOruqsfDwj4rCI+ENELC/+P/9WjTY9H8/MbIgX8CFgaLH8A+AHNdoMAZ4B3goMB5YAU/q4zqNpv/HjAaB5N+1WAKMqHM8u62yQ8fwhcG6xfG6t/+5VjWd3xgeYDsyj/X6bE4BHGrDGk4E7qvpd7FDHScCxwNJOtlc6lj2os/LxBMYCxxbLI2l/dFDdv5sNM0PIfvIYjMxcnpl/7cs+90Q366x8PIv+ri6WrwY+1sf97053xmcGcE22WwAcEBFjG6zGhpCZ84ENu2lS9VgC3aqzcpm5JjMXF8ubab+Cc/wuzXo8ng0TCLv4Iu3Jtqtaj8HYdRAaRQL3RMSi4nEcjagRxvOQzFwD7b/kwJhO2lUxnt0Zn6rHsLv9nxgRSyJiXkQc0zel9VjVY9kTDTOeEXEE8E7gkV029Xg8y3yWUZf6+jEYe6o7dXbDezKzLSLG0H6fxpPFvzxKU0KdlY9nD3bT6+NZQ3fGp0/GcDe60/9i4PDMfCUipgO3AZN7u7A9UPVYdlfDjGdE7AfcDHw7M1/edXONj+x2PPs0ELKfPAajqzq7uY+24ufaiLiV9ql9qX/ASqiz8vGMiBciYmxmrimms2s72Uevj2cN3Rmfqh/N0mX/Hf9QZObciLgsIkZlZqM9pK3qseyWRhnPiBhGexj8JjNvqdGkx+PZMIeMYgA9BiMiRkTEyH8s037CvBGf2toI4zkH+Hyx/HngTTObCsezO+MzB/hccUXHCcCmfxwC6yNd1hgRh0ZEFMvH0f7//fo+rLG7qh7LbmmE8Sz6vwJYnpn/3kmzno9nlWfKdzkj3kr78a5Hi9flxfvjgLm7nDl/ivYrK86roM6P0568rwMvAHfvWiftV3wsKV5PNGqdDTKeBwP3A08XPw9qpPGsNT7AmcCZxXLQ/gVQzwCPs5srzyqs8evFuC2h/YKNf+7rGos6rgPWAFuL380vNdpYdrPOyscTeC/th38e6/A3c3q94+mjKyRJQAMdMpIkVctAkCQBBoIkqWAgSJIAA0GSVDAQJEmAgSBJKvx/c5+2+a6YW2wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot numpy arrays\n",
    "\n",
    "Plotvec1(u, z, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"multi\">Array Multiplication</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the vector numpy array <code>y</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "y = np.array([1, 2])\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can multiply every element in the array by 2:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 4])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Numpy Array Multiplication\n",
    "\n",
    "z = 2 * y\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is equivalent to multiplying a vector by a scaler:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"prod\">Product of Two Numpy Arrays</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following array <code>u</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "u = np.array([1, 2])\n",
    "u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following array <code>v</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 2])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a numpy array\n",
    "\n",
    "v = np.array([3, 2])\n",
    "v"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The product of the two numpy arrays <code>u</code> and <code>v</code> is given by:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the production of two numpy arrays\n",
    "\n",
    "z = u * v\n",
    "z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"dot\">Dot Product</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dot product of the two numpy arrays <code>u</code> and <code>v</code> is given by:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the dot product\n",
    "\n",
    "np.dot(u, v)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 id=\"cons\">Adding Constant to a Numpy Array</h3>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the following array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3, -1])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a constant to numpy array\n",
    "\n",
    "u = np.array([1, 2, 3, -1]) \n",
    "u"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding the constant 1 to each element in the array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 0])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add the constant to array\n",
    "\n",
    "u + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The process is summarised in the following animation:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0101EN-SkillsNetwork/labs/Module%205/images/NumOneAdd.gif\" width=\"500\" />\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"math\">Mathematical Functions</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the value of <code>pi</code> in numpy as follows :\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.141592653589793"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The value of pi\n",
    "\n",
    "np.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can create the following numpy array in Radians:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the numpy array in radians\n",
    "\n",
    "x = np.array([0, np.pi/2 , np.pi])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply the function <code>sin</code> to the array <code>x</code> and assign the values to the array <code>y</code>; this applies the sine function to each element in the array:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0000000e+00, 1.0000000e+00, 1.2246468e-16])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the sin of each elements\n",
    "\n",
    "y = np.sin(x)\n",
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"lin\">Linspace</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A useful function for plotting mathematical functions is <code>linspace</code>.   Linspace returns evenly spaced numbers over a specified interval. We specify the starting point of the sequence and the ending point of the sequence. The parameter \"num\" indicates the Number of samples to generate, in this case 5:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2., -1.,  0.,  1.,  2.])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Makeup a numpy array within [-2, 2] and 5 elements\n",
    "\n",
    "np.linspace(-2, 2, num=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we change the parameter <code>num</code> to 9, we get 9 evenly spaced numbers over the interval from -2 to 2:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-2. , -1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a numpy array within [-2, 2] and 9 elements\n",
    "\n",
    "np.linspace(-2, 2, num=9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the function <code>linspace</code> to generate 100 evenly spaced samples from the interval 0 to 2π:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a numpy array within [0, 2π] and 100 elements \n",
    "\n",
    "x = np.linspace(0, 2*np.pi, num=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can apply the sine function to each element in the array <code>x</code> and assign it to the array <code>y</code>:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Calculate the sine of x list\n",
    "\n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5cdd263a90>]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the result\n",
    "\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2 id=\"quiz\">Quiz on 1D Numpy Array</h2>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Implement the following vector subtraction in numpy: u-v\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1 -1]\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "\n",
    "u = np.array([1, 0])\n",
    "v = np.array([0, 1])\n",
    "w = u-v\n",
    "print(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "u - v\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Multiply the numpy array z with -2:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[4 8]\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "\n",
    "z = np.array([2, 4])\n",
    "p = 2*z\n",
    "print(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "-2 * z\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider the list <code>\\[1, 2, 3, 4, 5]</code> and <code>\\[1, 0, 1, 0, 1]</code>. Cast both lists to a numpy array then multiply them together:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 3 0 5]\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "a = np.array([1, 2, 3, 4, 5])\n",
    "b = np.array([1, 0, 1, 0, 1])\n",
    "print(a*b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "a = np.array([1, 2, 3, 4, 5])\n",
    "b = np.array([1, 0, 1, 0, 1])\n",
    "a * b\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the list <code>\\[-1, 1]</code> and <code>\\[1, 1]</code> to numpy arrays <code>a</code> and <code>b</code>. Then, plot the arrays as vectors using the fuction <code>Plotvec2</code> and find their dot product:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "s = np.array([-1, 1])\n",
    "t = np.array([1, 1])\n",
    "Plotvec2(s,t)\n",
    "print(np.dot(s,t))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "a = np.array([-1, 1])\n",
    "b = np.array([1, 1])\n",
    "Plotvec2(a, b)\n",
    "print(\"The dot product is\", np.dot(a,b))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the list <code>\\[1, 0]</code> and <code>\\[0, 1]</code> to numpy arrays <code>a</code> and <code>b</code>. Then, plot the arrays as vectors using the function <code>Plotvec2</code> and find their dot product:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "a = np.array([1, 0])\n",
    "b = np.array([1, 1])\n",
    "Plotvec2(a,b)\n",
    "print(np.dot(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "a = np.array([1, 0])\n",
    "b = np.array([0, 1])\n",
    "Plotvec2(a, b)\n",
    "print(\"The dot product is\", np.dot(a, b))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the list <code>\\[1, 1]</code> and <code>\\[0, 1]</code> to numpy arrays <code>a</code> and <code>b</code>. Then plot the arrays as vectors using the fuction <code>Plotvec2</code> and find their dot product:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "a = np.array([1, 1])\n",
    "b = np.array([1, 1])\n",
    "Plotvec2(a,b)\n",
    "print(np.dot(a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "a = np.array([1, 1])\n",
    "b = np.array([0, 1])\n",
    "Plotvec2(a, b)\n",
    "print(\"The dot product is\", np.dot(a, b))\n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why are the results of the dot product for <code>\\[-1, 1]</code> and <code>\\[1, 1]</code> and the dot product for <code>\\[1, 0]</code> and <code>\\[0, 1]</code> zero, but not zero for the dot product for <code>\\[1, 1]</code> and <code>\\[0, 1]</code>? <p><i>Hint: Study the corresponding figures, pay attention to the direction the arrows are pointing to.</i></p>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (1017003717.py, line 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/tmp/ipykernel_67/1017003717.py\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    The vectors used for questions 4 and 5 are perpendicular. Therefore, the dot product is zero.\u001b[0m\n\u001b[0m              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# Write your code below and press Shift+Enter to execute\n",
    "The vectors used for questions 4 and 5 are perpendicular. Therefore, the dot product is zero."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<details><summary>Click here for the solution</summary>\n",
    "\n",
    "```python\n",
    "The vectors used for question 4 and 5 are perpendicular. As a result, the dot product is zero. \n",
    "\n",
    "```\n",
    "\n",
    "</details>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<h2>The last exercise!</h2>\n",
    "<p>Congratulations, you have completed your first lesson and hands-on lab in Python. \n",
    "<hr>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Author\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/joseph-s-50398b136/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\" target=\"_blank\">Joseph Santarcangelo</a>\n",
    "\n",
    "## Other contributors\n",
    "\n",
    "<a href=\"https://www.linkedin.com/in/jiahui-mavis-zhou-a4537814a?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkPY0101ENSkillsNetwork19487395-2021-01-01\">Mavis Zhou</a>\n",
    "\n",
    "## Change Log\n",
    "\n",
    "| Date (YYYY-MM-DD) | Version | Changed By | Change Description                 |\n",
    "| ----------------- | ------- | ---------- | ---------------------------------- |\n",
    "| 2022-01-10        | 2.1     | Malika     | Removed the readme for GitShare    |\n",
    "| 2020-08-26        | 2.0     | Lavanya    | Moved lab to course repo in GitLab |\n",
    "|                   |         |            |                                    |\n",
    "|                   |         |            |                                    |\n",
    "\n",
    "<hr/>\n",
    "\n",
    "## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n"
   ]
  }
 ],
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