L1_Introduction_to_Python.ipynb

{
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      "source": [
        "<a href=\"https://colab.research.google.com/github/anjitagargi/2-player-dice-game/blob/main/L1_Introduction_to_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQvziazJUa20"
      },
      "source": [
        "# CSB310: Artificial Intelligence  \n",
        "\n",
        "**Instructor** : [Dr. Chandra Prakash]\n",
        "\n",
        "* For more information visit the [class website](https://cprakash86.wordpress.com/csb-310-ai-2023/).\n",
        "\n",
        "\n",
        "#  LAB 1 : PYTHON PROGRAMMING\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXBnCMHVybJS"
      },
      "source": [
        "\n",
        "# PART 1.1: Introduction to Python  Programming"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VeaOGJKWybMp"
      },
      "source": [
        "* Welcome to your second lab session on Python!  \n",
        "\n",
        "* This tutorial contains some basic functions and syntax of Python, at the end of the tutorial some programming exercises are given to help you test your understanding on Python programming.\n",
        "\n",
        "* Here we give you a very brief overview of Python programming with concepts and functions that will be used later in lab sessions and assignments.\n",
        "\n",
        "* For detailed description of each concepts covered here, you may refer the official websites of Python (https://www.python.org/) and NumPy (http://www.numpy.org/)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11vvuoDEUa3B"
      },
      "source": [
        "#  Python\n",
        "\n",
        "Python is an interpreted, high-level, general-purpose programming language. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant whitespace. Its language constructs and object-oriented approach aim to help programmers write clear, logical code for small and large-scale projects.  Python has become a common language for machine learning research and is the primary language for TensorFlow.\n",
        "\n",
        "Python 3.0, released in 2008, was a significant revision of the language that is not entirely backward-compatible, and much Python 2 code does not run unmodified on Python 3.  This course makes use of Python 3.  Furthermore, TensorFlow is not compatible with versions of Python earlier than 3. A non-profit organization, the Python Software Foundation (PSF), manages and directs resources for Python development. On January 1, 2020, the PSF discontinued the Python 2 language and no longer provides security patches and other improvements. Python interpreters are available for many operating systems.\n",
        "\n",
        "\n",
        "Python 3.x is the programming language that will be used for this class.  Python, as a programming language, has the widest support for deep learning.  The three most popular frameworks for deep learning in Python are:\n",
        "\n",
        "* [TensorFlow](https://www.tensorflow.org/) (Google)\n",
        "* [MXNet](https://github.com/dmlc/mxnet) (Amazon)\n",
        "* [CNTK](https://cntk.ai/) (Microsoft)\n",
        "\n",
        "Some references on popular programming languages for AI/Data Science:\n",
        "\n",
        "* [Popular Programming Languages for AI](https://en.wikipedia.org/wiki/List_of_programming_languages_for_artificial_intelligence)\n",
        "* [Popular Programming Languages for Data Science](http://www.kdnuggets.com/2014/08/four-main-languages-analytics-data-mining-data-science.html)\n",
        "\n",
        "\n",
        "# Python  Resources\n",
        "\n",
        "* [Google CoLab](https://colab.research.google.com/) - Free web-based platform that includes Python, Juypter Notebooks, and TensorFlow [[Cite:GoogleTensorFlow]](http://download.tensorflow.org/paper/whitepaper2015.pdf).  No setup needed.\n",
        "* [Python Anaconda](https://www.continuum.io/downloads) - Python distribution that includes many data science packages, such as Numpy, Scipy, Scikit-Learn, Pandas, and much more.\n",
        "* [Juypter Notebooks](http://jupyter.org/) - Easy to use environment that combines Python, Graphics and Text.\n",
        "* [TensorFlow](https://www.tensorflow.org/) - Google's mathematics package for deep learning.\n",
        ".\n",
        "\n",
        "\n",
        "# Software Installation\n",
        "This class is technically oriented.  You needs to be compile and execute Python code for Machine  learning Techniques. There are two options for you to accomplish this:\n",
        "\n",
        "* Install Python, TensorFlow and some IDE (Jupyter, TensorFlow, and others)\n",
        "* Use Google CoLab\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "The first module of this course provide an introduction to some aspects of the Python programming language.  However, entire course focus on Python.  This module one  will not cover every detail of this language.  The reader is encouraged to consult additional sources on the Python language.\n",
        "\n",
        "Let's begin by printing Hello World."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BpFOiG3UUa3C"
      },
      "source": [
        "# Google CoLab Instructions\n",
        "\n",
        "The following code ensures that Google CoLab is running the correct version of TensorFlow."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "V1sp1cpbUa3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "4aa874a4-87e5-41fa-e8a8-3177c3c28d46"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab only includes TensorFlow 2.x; %tensorflow_version has no effect.\n",
            "Hello World\n",
            "Note: using Google CoLab\n"
          ]
        }
      ],
      "source": [
        "try:\n",
        "    from google.colab import drive\n",
        "    %tensorflow_version 2.x\n",
        "    COLAB = True\n",
        "    print(\"Hello World\")\n",
        "    print(\"Note: using Google CoLab\")\n",
        "except:\n",
        "    print(\"Hello World\")\n",
        "    print(\"Note: not using Google CoLab\")\n",
        "    COLAB = False"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "8OzQ41YDUa3D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "a46e8338-a4c3-4640-c486-8e18674291ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello World\n"
          ]
        }
      ],
      "source": [
        "# Single line comment (this has no effect on your program)\n",
        "print(\"Hello World\") # Say hello"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4mk9egs9ybPz"
      },
      "source": [
        "Python is a programming language that lets you work quickly\n",
        "and integrate systems more effectively. Some features of python programming are:\n",
        "\n",
        "* A variety of basic data types are available: numbers (floating point, complex, and unlimited-length long integers), strings (both ASCII and Unicode), lists, and dictionaries.\n",
        "* Python supports object-oriented programming with classes and multiple inheritances.\n",
        "* Code can be grouped into modules and packages.\n",
        "* The language supports raising and catching exceptions, resulting in cleaner error handling.\n",
        "* Data types are strongly and dynamically typed. Mixing incompatible types (e.g. attempting to add a string and a number) causes an exception to be raised, so errors are caught sooner.\n",
        "* Python contains advanced programming features such as generators and list comprehensions.\n",
        "* Python's automatic memory management frees you from having to manually allocate and free memory in your code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z4T10_boyzFB"
      },
      "source": [
        "**1.1 Your first Python Code: Print a string** - print() function displays the output on the console."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1UDU5x3kyyH1",
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          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3412a890-a53f-41dd-90d7-8129e12f5df2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Welcome to the NIT Delhi!\n"
          ]
        }
      ],
      "source": [
        "print ('Welcome to the NIT Delhi!')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N38MkQ-xUa3F"
      },
      "outputs": [],
      "source": [
        "a=5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZPID1kVXUa3F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bcbc52f-9b3d-444b-fc92-0a9227ec9165"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XT2W2yJXaZg8"
      },
      "outputs": [],
      "source": [
        "a=5*4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1-tL-CtjaZg8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e4591aa1-ddf7-420a-d760-17ee1406faba"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "a"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PJNZDDwdUa3G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d87613e-490f-4844-ff24-94aafc01faaa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Print\n",
            "Multiple\n",
            "Lines\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"\"\"Print\n",
        "Multiple\n",
        "Lines\n",
        "\"\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2j3-IPe5oya"
      },
      "source": [
        "**1.2 Input and assignment** - input() function takes user's  input.\n",
        "\n",
        "**Input Function**\n",
        "\n",
        "* You can use an the input function, just as you might have used 'scanf' in C.\n",
        "\n",
        "* When the input function is called, the program flow will be stopped until you give an input and end it using the return key.\n",
        "\n",
        "* The text of the optional parameter, i.e., the prompt, will be printed on the screen.\n",
        "\n",
        "* The input of the user will be interpreted. If you input an integer value, the input function will return this integer value. If you input a list, the function will return the list.\n",
        "\n",
        "**Let's have a look at the following example:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Ljnp0BsJyZ38",
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          "height": 0
        },
        "outputId": "201e5255-c937-4ed6-b2b1-a42bb56d9069"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What is your name ? \n",
            "Anjita\n",
            "Enter the current year.  3\n",
            "Welcome Anjita to the PYTHON PROGRAMMING @ AI 3 Course!\n"
          ]
        }
      ],
      "source": [
        "name = input('What is your name ? \\n')\n",
        "year = input(\"Enter the current year.  \")\n",
        "print ('Welcome ' + name + ' to the PYTHON PROGRAMMING @ AI '+year +' Course!')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NEmn_MWb53LV"
      },
      "source": [
        "**Important note: Python is indent sensitive.**\n",
        "\n",
        "* Python is case sensitive.\n",
        "\n",
        "* Python is also indent sensitive.\n",
        "\n",
        "* If you observe the 'for loop' and the 'if-else' statement, there is indent within the loop.\n",
        "\n",
        "* For example if you run the following code, it will show \"IndentationError\".\n",
        "\n",
        "* Run the cell to see the error and correct the code by putting indent."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "agJja9zTyaBG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e916690c-ccf4-449a-fcb7-3c69faffb9bf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 \n",
            "\n",
            "10 \n",
            "\n",
            "20 \n",
            "\n",
            "30 \n",
            "\n",
            "40 \n",
            "\n",
            "50 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "for number in [1,10,20,30,40,50]:\n",
        "    print(number,\"\\n\")\n",
        "\n",
        "# Identify the Error and resolve"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gorYQrJk58p3"
      },
      "source": [
        "\n",
        "\n",
        "*  As you can see above it is giving IndentationError\n",
        "*  We can overcome this error by providing a single/multiple space or tab seperation\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "aYPoQu3kyaET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e07c9443-700d-4ac1-ee97-b174dc94bc02"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1 \n",
            "\n",
            "10 \n",
            "\n",
            "20 \n",
            "\n",
            "30 \n",
            "\n",
            "40 \n",
            "\n",
            "50 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "for number in [1,10,20,30,40,50]:\n",
        "    print(number,\"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qw3_1UvI6xKg"
      },
      "source": [
        "**1.3 If Else Statement** -\n",
        "\n",
        "An if-else statement has the following syntax.\n",
        "\n",
        "**Syntax**\n",
        "\n",
        "if expression:\n",
        "\n",
        "> statement(s)\n",
        "\n",
        "else:\n",
        "\n",
        "> statement(s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bbPVHbs8yaJB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6e4c21f-a92f-42f7-b7a0-0c6816bbacd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "number :5\n",
            "Positive number\n"
          ]
        }
      ],
      "source": [
        "num = input(\"number :\")\n",
        "if int(num) > 0:\n",
        "    print(\"Positive number\")\n",
        "elif int(num) == 0:\n",
        "    print(\"Zero\")\n",
        "else:\n",
        "    print(\"Negative number\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hf3g1ZRRSb-c"
      },
      "source": [
        "**1.4 Loops in Python** - We can iterate loop using various types. Here we are using built-in enumerate function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Prt7cplYqkPh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "465f7bdf-57e3-42f8-968a-c111bebc28f7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Centrally Funded Technical Institute:\n",
            "1 : NIT Delhi\n",
            "2 : JMI Delhi\n",
            "3 : IIT Delhi\n",
            "4 : IIITM Gwalior \n"
          ]
        }
      ],
      "source": [
        "names = ['NIT Delhi', 'JMI Delhi', 'IIT Delhi', 'IIITM Gwalior ']\n",
        "print('Centrally Funded Technical Institute:')\n",
        "for i, name in enumerate(names):\n",
        "    print (\"{iteration} : {name}\".format(iteration=i+1, name=name))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A3sEf7rfS8H6"
      },
      "source": [
        "**1.5 While Loop** - Same as other programming languages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lSwKDLmzxM4v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0130f765-ed8b-4988-e82f-61d7af2c07ac"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Centrally Funded Technical Institute:\n",
            "1 : NIT Delhi\n",
            "2 : JMI Delhi\n",
            "3 : IIT Delhi\n",
            "4 : IIITM Gwalior \n"
          ]
        }
      ],
      "source": [
        "names = ['NIT Delhi', 'JMI Delhi', 'IIT Delhi', 'IIITM Gwalior ']\n",
        "value=0\n",
        "print('Centrally Funded Technical Institute:')\n",
        "while value<len(names):\n",
        "    print(\"{iteration} : {name}\".format(iteration=value+1, name=names[value]))\n",
        "    value+=1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hJSLjsVxT-6T"
      },
      "source": [
        "**1.6. Defining Function** - Just write def function_name(parameters)\n",
        "\n",
        "Just as in C, Python also provides various types of functions. It gives you many built-in functions like print(), etc. but you can also create your own functions. These functions are called *user-defined functions.*\n",
        "\n",
        "In the following some simple examples of functions are given."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u_6VVQXRT9k2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25568040-2b02-4634-a1dc-bc52357b779e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Addition is : 2\n"
          ]
        }
      ],
      "source": [
        "def add(x,y):\n",
        "    print ('Addition is :', x+y)\n",
        "\n",
        "add(12,-10)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Write a function for multiplicatin :\n",
        "\n",
        "## WRITE YOUR CODE HERE\n",
        "def mul(x,y):\n",
        "    print ('Multiplication is :', x*y)\n",
        "\n",
        "mul(12,-10)\n"
      ],
      "metadata": {
        "id": "uNF1_N4P-zyO",
        "outputId": "2fecc265-7105-4afe-a0a9-f0edd811cf19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Multiplication is : -120\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1NzXKSDaUZcQ"
      },
      "source": [
        "**1.7 Dictionary** - Dictionary is an associative array of python\n",
        "\n",
        "As given in ( https://docs.python.org/3/tutorial/datastructures.html ) :\n",
        "\n",
        "\n",
        "\"Dictionary is a useful data type built into Python, similar to “associative memories” or “associative arrays” in some languages. A dictionary is indexed by keys, Unlike a sequence, which is indexed by a range of numbers.  A Key can be any immutable type, strings and numbers can always be keys.\n",
        "\n",
        "So a dictionary can be considered as an unordered set of key: value pairs, with the requirement that the keys are unique (within one dictionary). Each key is separated from its value by a colon (:), the items are separated by commas, and the entire unordered ke:value pair in enclosed within curly braces.\n",
        "\n",
        "A dictionary can be initialized to be an empty dictionary by using a pair of braces : {}. Placing a comma-separated list of key:value pairs within the braces adds initial key:value pairs to the dictionary; this is also the way dictionaries are written on output.\n",
        "\n",
        "Tuples can be used as keys if they contain only strings, numbers, or tuples; if a tuple contains any mutable object either directly or indirectly, it cannot be used as a key. You can’t use lists as keys, since lists can be modified in place using index assignments, slice assignments, or methods like append() and extend().\n",
        "\n",
        "* The main operations on a dictionary are storing a value with some key and extracting the value given the key.\n",
        "\n",
        "* To delete a key:value pair you can use 'del'. If you store a value using a key that is already in use, then the old value associated with that key is overwritten.\n",
        "* Use list(d.keys()) to obtain a list of all the keys used in the dictionary, in arbitrary order.\n",
        "* Use sorted(d.keys())  if you wanted it to be in a sorted order.  \n",
        "* To check whether a single key is in the dictionary, use the 'in' keyword.\n",
        "\n",
        "Here are some examples of code pieces using dictionaries:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "F0z7IcxXSvAm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "92e1a577-5047-4e0f-bae2-4a93eaa859db"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "apple :14\n",
            "banana :15\n",
            "Total Bill : 2150.00 rupees\n"
          ]
        }
      ],
      "source": [
        "prices = {'apple': 100, 'banana': 50}\n",
        "my_purchase = {'apple': input('apple :'),'banana': input('banana :')}\n",
        "grocery_bill = sum(prices[fruit] * int(my_purchase[fruit])\n",
        "                   for fruit in my_purchase)\n",
        "print ('Total Bill : %.2f' % grocery_bill +' rupees')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umqGQbOAU3SE"
      },
      "source": [
        "**1.8 List** -\n",
        "\n",
        "Python offers a range of compound datatypes often referred to as sequences.\n",
        "\n",
        "List is one of the most frequently used datatypes used in Python.\n",
        "\n",
        "* A list is created by placing all the items (elements) inside a square bracket [ ], separated by commas.\n",
        "* It can have any number of items and they may be of different types (integer, float, string etc.).\n",
        "\n",
        "**Accessing the elements of the list**\n",
        "\n",
        "You can use the index operator [ ] to access an item in a list. Index starts from 0. So, a list having 5 elements will have indices ranging from 0 to 4."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PylfrjKtUd_v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3af43d2e-8473-4194-d72e-f04b0f815149"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Python', 'Ruby', 'Groovy', 'Lisp']"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "dynamic_languages = ['Python', 'Ruby', 'Groovy']\n",
        "dynamic_languages.append('Lisp')\n",
        "dynamic_languages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SCyEYR8iU9tZ"
      },
      "source": [
        "**1.9 Exception Handling** - try and except blocks are used for exception handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "DXWtpTcbU6VB",
        "outputId": "a9e6ce90-c9d1-47e3-cd44-5f6d0e8c38e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Please enter a number: h\n",
            "Oops!  That was no valid number.  Try again...\n",
            "Please enter a number: 4\n"
          ]
        }
      ],
      "source": [
        "\n",
        "while True:\n",
        "    try:\n",
        "        x = int(input(\"Please enter a number: \"))\n",
        "        break\n",
        "    except ValueError:\n",
        "        print(\"Oops!  That was no valid number.  Try again...\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4nvsLqecoOoP"
      },
      "source": [
        "# Python Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eCkZm4lxoQJB"
      },
      "outputs": [],
      "source": [
        "import math"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import math"
      ],
      "metadata": {
        "id": "fVz8V73DA1NI"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "lSiEHau3oV8S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "5ecd8aa3-1fa7-4f52-ebe0-ea3861d7a9e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.0\n"
          ]
        }
      ],
      "source": [
        "a = math.sqrt(100)\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "3yZAgHD1oX2H",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "f6b051ad-da44-4178-8dc3-edb669cb32d9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10.0\n"
          ]
        }
      ],
      "source": [
        "a = math.pow(100, 0.5)\n",
        "print(a)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "pdPODnnzoaWP",
        "outputId": "034396cc-c1df-4090-a4a2-431a7f7de525",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Factorial of 100 is 933262154439441526816992388562667004907159682643816214685929638952175999932299156089414639761565182862536979208272237582511852109168640000000000000000000000\n"
          ]
        }
      ],
      "source": [
        "x = 100\n",
        "y = 1\n",
        "for i in range(1, x):\n",
        "  y *= i\n",
        "print('Factorial of', x, 'is', y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "2Wc0vvPxopEH"
      },
      "outputs": [],
      "source": [
        "y = math.factorial(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "M7amwM7YoxLv"
      },
      "outputs": [],
      "source": [
        "import math as m"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "EPPb0q4xo0yX"
      },
      "outputs": [],
      "source": [
        "y = m.factorial(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "lyMXPcaeo2Aw"
      },
      "outputs": [],
      "source": [
        "from math import factorial"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "azItJu7Ao5bb"
      },
      "outputs": [],
      "source": [
        "y = factorial(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "5YrGXzyYo6lb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7ed761f6-65f1-40cf-db50-91e705b36f48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Elapsed time in secs without own function 0.001611948013305664\n",
            "Elapsed time in secs with own function 0.0002288818359375\n"
          ]
        }
      ],
      "source": [
        "import time\n",
        "\n",
        "vals = list(range(1, 100))\n",
        "tic = time.time()\n",
        "for x in vals:\n",
        "  y = 1\n",
        "  for i in range(1, x):\n",
        "    y *= i\n",
        "toc = time.time()\n",
        "print('Elapsed time in secs without own function', toc - tic)\n",
        "\n",
        "tic = time.time()\n",
        "for x in vals:\n",
        "  y = math.factorial(x)\n",
        "toc = time.time()\n",
        "print('Elapsed time in secs with own function', toc - tic)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "u1CRIBPtpP7S",
        "outputId": "951af2f3-7b5c-4bd5-9e43-434049e1d2ec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello NITD\n"
          ]
        }
      ],
      "source": [
        "!echo 'def hello():' > my_first_module.py\n",
        "!echo '    print(\"hello, i am living in a different file!!!\")' >> my_first_module.py\n",
        "\n",
        "## Add one more line as to print Hello NITD!'\n",
        "!echo 'Hello NITD'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "3q0hPJ8IpwYX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "306c1c00-bc4a-4c31-8daa-2546bd5ea31d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def hello():\n",
            "    print(\"hello, i am living in a different file!!!\")\n"
          ]
        }
      ],
      "source": [
        "!cat my_first_module.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "6ItBO96rp1O0"
      },
      "outputs": [],
      "source": [
        "import my_first_module"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "HAPgfIjMqEhz",
        "outputId": "4f75fa85-4d91-477a-8885-e53a3667fe50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello, i am living in a different file!!!\n"
          ]
        }
      ],
      "source": [
        "my_first_module.hello()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "iiJNMrLGqGm0"
      },
      "outputs": [],
      "source": [
        "from my_first_module import hello"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "t7idGE7-qJUV",
        "outputId": "cde301d5-4e33-44ed-ce43-63c8a22d4771",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hello, i am living in a different file!!!\n"
          ]
        }
      ],
      "source": [
        "hello()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gU7QTkRy3SMz"
      },
      "source": [
        "# File handling"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "UEjTlzPt3ceh"
      },
      "outputs": [],
      "source": [
        "file = open('mobile_cleaned.csv', 'r')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "K0cZlXDS3nUA"
      },
      "outputs": [],
      "source": [
        "s = file.readline()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "id": "bJVzrBHWOiwH",
        "outputId": "c2fd3f7c-dfe3-4396-ac0c-3f644b45c748",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "nARRi5LD3pU2",
        "outputId": "6441a2cc-d6b9-49c9-9eed-7c4f8d6dccad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PhoneId,Pixel Density,Screen Size,Weight,RAM,Processor_frequency,Screen to Body Ratio (calculated),Height,Internal Memory,Capacity,Resolution,SIM 2_2G,SIM 2_3G,SIM 2_4G,SIM 2_Other,Num_cores_312,Num_cores_Deca,Num_cores_Dual,Num_cores_Hexa,Num_cores_Octa,Num_cores_Other,Num_cores_Quad,Num_cores_Tru-Octa,Brand_10.or,Brand_Apple,Brand_Asus,Brand_Billion,Brand_Blackberry,Brand_Comio,Brand_Coolpad,Brand_Do,Brand_Gionee,Brand_Google,Brand_HTC,Brand_Honor,Brand_Huawei,Brand_InFocus,Brand_Infinix,Brand_Intex,Brand_Itel,Brand_Jivi,Brand_Karbonn,Brand_LG,Brand_Lava,Brand_LeEco,Brand_Lenovo,Brand_Lephone,Brand_Lyf,Brand_Meizu,Brand_Micromax,Brand_Mobiistar,Brand_Moto,Brand_Motorola,Brand_Nokia,Brand_Nubia,Brand_OPPO,Brand_OnePlus,Brand_Oppo,Brand_Panasonic,Brand_Razer,Brand_Realme,Brand_Reliance,Brand_Samsung,Brand_Sony,Brand_Spice,Brand_Tecno,Brand_Ulefone,Brand_VOTO,Brand_Vivo,Brand_Xiaomi,Brand_Xiaomi Poco,Brand_Yu,Brand_iVooMi,os_name_Android,os_name_Blackberry,os_name_KAI,os_name_Nokia,os_name_Other,os_name_Tizen,os_name_iOS,\"SIM Slot(s)_Dual SIM, GSM+CDMA\",\"SIM Slot(s)_Dual SIM, GSM+GSM\",\"SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE\",\"SIM Slot(s)_Single SIM, GSM\",Sim1_2G,Sim1_3G,Sim1_4G,Rating\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(s)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "iqGdV0ej3qTx",
        "outputId": "84866693-d566-4f2d-b90f-ec339f62742e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['PhoneId', 'Pixel Density', 'Screen Size', 'Weight', 'RAM', 'Processor_frequency', 'Screen to Body Ratio (calculated)', 'Height', 'Internal Memory', 'Capacity', 'Resolution', 'SIM 2_2G', 'SIM 2_3G', 'SIM 2_4G', 'SIM 2_Other', 'Num_cores_312', 'Num_cores_Deca', 'Num_cores_Dual', 'Num_cores_Hexa', 'Num_cores_Octa', 'Num_cores_Other', 'Num_cores_Quad', 'Num_cores_Tru-Octa', 'Brand_10.or', 'Brand_Apple', 'Brand_Asus', 'Brand_Billion', 'Brand_Blackberry', 'Brand_Comio', 'Brand_Coolpad', 'Brand_Do', 'Brand_Gionee', 'Brand_Google', 'Brand_HTC', 'Brand_Honor', 'Brand_Huawei', 'Brand_InFocus', 'Brand_Infinix', 'Brand_Intex', 'Brand_Itel', 'Brand_Jivi', 'Brand_Karbonn', 'Brand_LG', 'Brand_Lava', 'Brand_LeEco', 'Brand_Lenovo', 'Brand_Lephone', 'Brand_Lyf', 'Brand_Meizu', 'Brand_Micromax', 'Brand_Mobiistar', 'Brand_Moto', 'Brand_Motorola', 'Brand_Nokia', 'Brand_Nubia', 'Brand_OPPO', 'Brand_OnePlus', 'Brand_Oppo', 'Brand_Panasonic', 'Brand_Razer', 'Brand_Realme', 'Brand_Reliance', 'Brand_Samsung', 'Brand_Sony', 'Brand_Spice', 'Brand_Tecno', 'Brand_Ulefone', 'Brand_VOTO', 'Brand_Vivo', 'Brand_Xiaomi', 'Brand_Xiaomi Poco', 'Brand_Yu', 'Brand_iVooMi', 'os_name_Android', 'os_name_Blackberry', 'os_name_KAI', 'os_name_Nokia', 'os_name_Other', 'os_name_Tizen', 'os_name_iOS', '\"SIM Slot(s)_Dual SIM', ' GSM+CDMA\"', '\"SIM Slot(s)_Dual SIM', ' GSM+GSM\"', '\"SIM Slot(s)_Dual SIM', ' GSM+GSM', ' Dual VoLTE\"', '\"SIM Slot(s)_Single SIM', ' GSM\"', 'Sim1_2G', 'Sim1_3G', 'Sim1_4G', 'Rating\\n']\n"
          ]
        }
      ],
      "source": [
        "print(s.split(','))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "XWU2nyzf30Xr"
      },
      "outputs": [],
      "source": [
        "file.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "AWmQKP-t36LK",
        "outputId": "40128ba3-fe2a-4c8d-89b0-de83a603f752",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PhoneId,Pixel Density,Screen Size,Weight,RAM,Processor_frequency,Screen to Body Ratio (calculated),Height,Internal Memory,Capacity,Resolution,SIM 2_2G,SIM 2_3G,SIM 2_4G,SIM 2_Other,Num_cores_312,Num_cores_Deca,Num_cores_Dual,Num_cores_Hexa,Num_cores_Octa,Num_cores_Other,Num_cores_Quad,Num_cores_Tru-Octa,Brand_10.or,Brand_Apple,Brand_Asus,Brand_Billion,Brand_Blackberry,Brand_Comio,Brand_Coolpad,Brand_Do,Brand_Gionee,Brand_Google,Brand_HTC,Brand_Honor,Brand_Huawei,Brand_InFocus,Brand_Infinix,Brand_Intex,Brand_Itel,Brand_Jivi,Brand_Karbonn,Brand_LG,Brand_Lava,Brand_LeEco,Brand_Lenovo,Brand_Lephone,Brand_Lyf,Brand_Meizu,Brand_Micromax,Brand_Mobiistar,Brand_Moto,Brand_Motorola,Brand_Nokia,Brand_Nubia,Brand_OPPO,Brand_OnePlus,Brand_Oppo,Brand_Panasonic,Brand_Razer,Brand_Realme,Brand_Reliance,Brand_Samsung,Brand_Sony,Brand_Spice,Brand_Tecno,Brand_Ulefone,Brand_VOTO,Brand_Vivo,Brand_Xiaomi,Brand_Xiaomi Poco,Brand_Yu,Brand_iVooMi,os_name_Android,os_name_Blackberry,os_name_KAI,os_name_Nokia,os_name_Other,os_name_Tizen,os_name_iOS,\"SIM Slot(s)_Dual SIM, GSM+CDMA\",\"SIM Slot(s)_Dual SIM, GSM+GSM\",\"SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE\",\"SIM Slot(s)_Single SIM, GSM\",Sim1_2G,Sim1_3G,Sim1_4G,Rating\n",
            "\n"
          ]
        }
      ],
      "source": [
        "with open('mobile_cleaned.csv', 'r') as file:\n",
        "  print(file.readline())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "losKznsX4EPL",
        "outputId": "2cf88a15-3c38-493e-da66-ffa1b5fbaa97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PhoneId,Pixel Density,Screen Size,Weight,RAM,Processor_frequency,Screen to Body Ratio (calculated),Height,Internal Memory,Capacity,Resolution,SIM 2_2G,SIM 2_3G,SIM 2_4G,SIM 2_Other,Num_cores_312,Num_cores_Deca,Num_cores_Dual,Num_cores_Hexa,Num_cores_Octa,Num_cores_Other,Num_cores_Quad,Num_cores_Tru-Octa,Brand_10.or,Brand_Apple,Brand_Asus,Brand_Billion,Brand_Blackberry,Brand_Comio,Brand_Coolpad,Brand_Do,Brand_Gionee,Brand_Google,Brand_HTC,Brand_Honor,Brand_Huawei,Brand_InFocus,Brand_Infinix,Brand_Intex,Brand_Itel,Brand_Jivi,Brand_Karbonn,Brand_LG,Brand_Lava,Brand_LeEco,Brand_Lenovo,Brand_Lephone,Brand_Lyf,Brand_Meizu,Brand_Micromax,Brand_Mobiistar,Brand_Moto,Brand_Motorola,Brand_Nokia,Brand_Nubia,Brand_OPPO,Brand_OnePlus,Brand_Oppo,Brand_Panasonic,Brand_Razer,Brand_Realme,Brand_Reliance,Brand_Samsung,Brand_Sony,Brand_Spice,Brand_Tecno,Brand_Ulefone,Brand_VOTO,Brand_Vivo,Brand_Xiaomi,Brand_Xiaomi Poco,Brand_Yu,Brand_iVooMi,os_name_Android,os_name_Blackberry,os_name_KAI,os_name_Nokia,os_name_Other,os_name_Tizen,os_name_iOS,\"SIM Slot(s)_Dual SIM, GSM+CDMA\",\"SIM Slot(s)_Dual SIM, GSM+GSM\",\"SIM Slot(s)_Dual SIM, GSM+GSM, Dual VoLTE\",\"SIM Slot(s)_Single SIM, GSM\",Sim1_2G,Sim1_3G,Sim1_4G,Rating\n",
            "0,403,6.26,182,4,1.8,80.68,157.9,64,4000,20,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,4.5\n",
            "1,271,6.2,168,3,1.8,80.85,156.2,32,4230,8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,1,4.5\n",
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            "446,531,6.2,173,6,2.3,83.7,159.5,128,3500,8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.5\n",
            "447,294,5.0,149,3,1.3,67.02,142.3,8,2600,5,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.2\n",
            "449,218,4.5,132,1,1.3,63.93,132.0,8,1850,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,3.8\n",
            "450,201,5.34,161,1,1.28,72.35,149.94639393939397,8,2700,5,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.7\n",
            "451,294,5.6,162,4,1.6,75.33,149.9,64,3000,16,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.2\n",
            "452,424,5.2,165,4,2.6,68.04,147.9,32,4000,8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.1\n",
            "453,424,5.2,161,3,2.15,70.75,146.0,64,2900,13,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.0\n",
            "454,294,5.0,161,2,1.3,65.96,145.4,16,2400,8,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.9\n",
            "456,401,5.5,155,2,1.5,68.91,154.8,16,2500,5,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.7\n",
            "457,294,5.0,149,1,1.2,68.73,141.5,16,2070,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,4.2\n",
            "458,401,5.5,160,3,1.4,70.39,154.3,32,3000,13,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.0\n",
            "460,294,5.0,155,2,1.0,66.19,142.7,16,2000,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.6\n",
            "461,401,5.5,183,2,1.7,67.95,158.0,16,2600,13,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.6\n",
            "462,268,6.0,161,3,1.4,75.91,160.4,32,4000,13,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.4\n",
            "465,220,5.0,141,1,1.2,67.2,142.7,16,2420,5,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.8\n",
            "466,401,5.5,165,4,1.8,69.95,156.5,64,3000,13,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.9\n",
            "468,282,5.2,160,3,1.4,68.52,149.7,16,3000,8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.1\n",
            "470,218,4.5,156,1,1.3,63.93,132.0,8,1800,2,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,3.6\n",
            "472,401,5.5,153,3,1.8,74.21,151.1,32,3000,8,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,1,4.3\n",
            "\n"
          ]
        }
      ],
      "source": [
        "with open('mobile_cleaned.csv', 'r') as file:\n",
        "  print(file.read())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "Z7ZiqBi-4NAw",
        "outputId": "df13d189-7cf1-44a3-f012-67296453698d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "\n",
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            "4.0\n",
            "\n",
            "-------\n",
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            "3.5\n",
            "\n",
            "-------\n",
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            "3.0\n",
            "\n",
            "-------\n",
            "408\n",
            "267\n",
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            "4.2\n",
            "\n",
            "-------\n",
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            "8\n",
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            "\n",
            "-------\n",
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            "\n",
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            "\n",
            "-------\n",
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            "\n",
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            "\n",
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            "\n",
            "-------\n",
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            "\n",
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            "4.0\n",
            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "4.0\n",
            "\n",
            "-------\n",
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            "3.7\n",
            "\n",
            "-------\n",
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            "4.6\n",
            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "\n",
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            "4.4\n",
            "\n",
            "-------\n",
            "436\n",
            "401\n",
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            "177\n",
            "3\n",
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            "4.3\n",
            "\n",
            "-------\n",
            "438\n",
            "295\n",
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            "4.1\n",
            "\n",
            "-------\n",
            "439\n",
            "294\n",
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            "3.9\n",
            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "3.9\n",
            "\n",
            "-------\n",
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            "4.0\n",
            "\n",
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            "4.4\n",
            "\n",
            "-------\n",
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            "4.5\n",
            "\n",
            "-------\n",
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            "\n",
            "-------\n",
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            "0\n",
            "3.8\n",
            "\n",
            "-------\n",
            "450\n",
            "201\n",
            "5.34\n",
            "161\n",
            "1\n",
            "1.28\n",
            "72.35\n",
            "149.94639393939397\n",
            "8\n",
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            "1\n",
            "3.7\n",
            "\n",
            "-------\n",
            "451\n",
            "294\n",
            "5.6\n",
            "162\n",
            "4\n",
            "1.6\n",
            "75.33\n",
            "149.9\n",
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            "1\n",
            "4.2\n",
            "\n",
            "-------\n",
            "452\n",
            "424\n",
            "5.2\n",
            "165\n",
            "4\n",
            "2.6\n",
            "68.04\n",
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            "1\n",
            "4.1\n",
            "\n",
            "-------\n",
            "453\n",
            "424\n",
            "5.2\n",
            "161\n",
            "3\n",
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            "70.75\n",
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            "1\n",
            "4.0\n",
            "\n",
            "-------\n",
            "454\n",
            "294\n",
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            "1\n",
            "3.9\n",
            "\n",
            "-------\n",
            "456\n",
            "401\n",
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            "68.91\n",
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            "3.7\n",
            "\n",
            "-------\n",
            "457\n",
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            "4.2\n",
            "\n",
            "-------\n",
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            "1\n",
            "4.0\n",
            "\n",
            "-------\n",
            "460\n",
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            "1\n",
            "3.6\n",
            "\n",
            "-------\n",
            "461\n",
            "401\n",
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            "67.95\n",
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            "1\n",
            "3.6\n",
            "\n",
            "-------\n",
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            "4.4\n",
            "\n",
            "-------\n",
            "465\n",
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            "5.0\n",
            "141\n",
            "1\n",
            "1.2\n",
            "67.2\n",
            "142.7\n",
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            "3.8\n",
            "\n",
            "-------\n",
            "466\n",
            "401\n",
            "5.5\n",
            "165\n",
            "4\n",
            "1.8\n",
            "69.95\n",
            "156.5\n",
            "64\n",
            "3000\n",
            "13\n",
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            "3.9\n",
            "\n",
            "-------\n",
            "468\n",
            "282\n",
            "5.2\n",
            "160\n",
            "3\n",
            "1.4\n",
            "68.52\n",
            "149.7\n",
            "16\n",
            "3000\n",
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            "4.1\n",
            "\n",
            "-------\n",
            "470\n",
            "218\n",
            "4.5\n",
            "156\n",
            "1\n",
            "1.3\n",
            "63.93\n",
            "132.0\n",
            "8\n",
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            "3.6\n",
            "\n",
            "-------\n",
            "472\n",
            "401\n",
            "5.5\n",
            "153\n",
            "3\n",
            "1.8\n",
            "74.21\n",
            "151.1\n",
            "32\n",
            "3000\n",
            "8\n",
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            "1\n",
            "4.3\n",
            "\n",
            "-------\n"
          ]
        }
      ],
      "source": [
        "with open('mobile_cleaned.csv', 'r') as file:\n",
        "  for line in file:\n",
        "    for word in line.split(','):\n",
        "      print(word)\n",
        "    print('-------')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "kaekxExq4Yme"
      },
      "outputs": [],
      "source": [
        "with open('my_first_file_output.txt', 'w') as file:\n",
        "  file.write('hello world from python code')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gB2XHSeU4rWN"
      },
      "outputs": [],
      "source": [
        "!cat my_first_file_output.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pjefe-RlWdma"
      },
      "source": [
        "**1.10- NumPy** -\n",
        "**\"NumPy is a general-purpose fundamental package for scientific computing with Python. It contains various features including these important ones:\n",
        "\n",
        ">A powerful N-dimensional array object\n",
        "\n",
        ">Sophisticated (broadcasting) functions\n",
        "\n",
        ">Tools for integrating C/C++ and Fortran code\n",
        "\n",
        ">Useful linear algebra, Fourier transform, and random number capabilities\n",
        "\n",
        "Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.\"\n",
        "\n",
        " * Source: Official website of NumPy (www.numpy.org)\n",
        "\n",
        " **Let us consider some examples to understand this powerful package.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "JJdnijyeXofj",
        "outputId": "6d978c20-1796-4357-ca1b-f69ec3ba38c8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 5  7  9 12 12 17]\n",
            "[12 15 18 21 21 27]\n",
            "No. of dimensions:  1\n",
            "Shape of array:  (6,)\n",
            "Reshaped array:  [[1 2 3]\n",
            " [5 5 8]]\n",
            "Shape of array:  (2, 3)\n",
            "Size of array:  6\n",
            "Array stores elements of type:  int64\n"
          ]
        }
      ],
      "source": [
        "# First you need to import the NumPy Library\n",
        "import numpy as np\n",
        "\n",
        "b1 = np.array([1,2,3,5,5,8])    #Declaring a NumPy Array\n",
        "b2 = np.array([4,5,6,7,7,9])\n",
        "\n",
        "print(b1+b2)\n",
        "\n",
        "print(b2 * 3)\n",
        "print(\"No. of dimensions: \", b1.ndim)  # Rows in array, considered as a matrix.\n",
        "# Printing shape of array\n",
        "print(\"Shape of array: \", b1.shape)  # Dimension\n",
        "\n",
        "#reshaping an array\n",
        "r_b1=b1.reshape(2,3)\n",
        "\n",
        "print(\"Reshaped array: \", r_b1)\n",
        "print(\"Shape of array: \", r_b1.shape)\n",
        "\n",
        "# Printing size (total number of elements) of array\n",
        "print(\"Size of array: \", b1.size) # elements in a row or column elements.\n",
        "\n",
        "# Printing the datatype of elements in array\n",
        "print(\"Array stores elements of type: \", b1.dtype)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "llsiqU1LYANy"
      },
      "source": [
        "**1.11 Operations on Arrays** -\n",
        "You can create arrays in NumPy in various ways.\n",
        "\n",
        "**Array creation:**\n",
        "You can create arrays in NumPy in various ways.\n",
        "\n",
        "For example, you can create an array from a regular Python list or tuple using the array function.\n",
        "\n",
        "The type of the resulting array is deduced from the type of the elements in the sequences.\n",
        "\n",
        "Often, we need to declare arrays whose sizes are known but elements are initially unknown. Hence, NumPy offers many functions to create arrays with initial placeholder content. This minimizes the necessity of growing arrays, which is generally an expensive operation.\n",
        " * Examples: *np.zeros, np.ones, np.full, np.empty*, etc.\n",
        "\n",
        "To create sequences of numbers, NumPy provides a function analogous to range that returns arrays instead of lists.\n",
        "\n",
        "**arange:** returns evenly spaced values within a given interval. In this, step size is specified.\n",
        "\n",
        "**linspace:** returns evenly spaced values within a given interval. Number of elements are returned.\n",
        "\n",
        "**Reshaping array:** The reshape method is used to reshape an array. If you have an array (a1, a2, a3, …, aN) and you want to reshape and convert it into another array of shape (b1, b2, b3, …, bM), you can do it easily using the reshape method. But the only precondition is that\n",
        "a1 x a2 x a3 … x aN = b1 x b2 x b3 … x bM . (i.e. , the total number of elements in the array should be the same, or the original size of array should remain unchanged.)\n",
        "\n",
        "**Flatten array:** The flatten method is used to convert an array  into one dimension. It accepts order argument. Default value is ‘C’ (for row-major order), and you can use ‘F’ to use the flatten method for column major order.\n",
        "\n",
        "* Let us see some examples."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "4C9FEFTTXqmq",
        "outputId": "ee85e8b5-55b7-4850-884f-39b72e998c60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A :  [[1. 2. 4.]\n",
            " [5. 8. 7.]]\n",
            "B :  [[0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]\n",
            " [0. 0. 0. 0.]]\n",
            "C :  [[6.+0.j 6.+0.j 6.+0.j]\n",
            " [6.+0.j 6.+0.j 6.+0.j]\n",
            " [6.+0.j 6.+0.j 6.+0.j]]\n",
            "D :  [[ 1.62434536 -0.61175641]\n",
            " [-0.52817175 -1.07296862]]\n",
            "E :  [[0.39676747 0.53881673]\n",
            " [0.41919451 0.6852195 ]]\n",
            "F :  [[14 13  5  7]\n",
            " [10 10 12  4]]\n"
          ]
        }
      ],
      "source": [
        "# array creation\n",
        "import numpy as np\n",
        "\n",
        "# Creating array from a list with type float\n",
        "A = np.array([[1, 2, 4], [5, 8, 7]], dtype = 'float')\n",
        "print(\"A : \",A)\n",
        "# Create a 3X4 array with all zeros. Please note, we have used double paranthesis.\n",
        "B = np.zeros((3, 4))\n",
        "print(\"B : \",B)\n",
        "# Create an arrary of complex numbers\n",
        "C = np.full((3, 3), 6, dtype = 'complex')\n",
        "print(\"C : \",C)\n",
        "# Create an array with random values\n",
        "np.random.seed(1) # A seed is set to ensure that the results are consistent if you use this array in future computations also.\n",
        "\n",
        "D = np.random.randn(2, 2)\n",
        "print(\"D : \",D)\n",
        "E = np.random.random((2, 2))\n",
        "print(\"E : \",E)\n",
        "F = np.random.randint(3, 15, size=(2, 4))\n",
        "print(\"F : \",F)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G020X2ujYIYR"
      },
      "source": [
        "**1.12 Array Reshaping** -"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "rDjzwPlIYEgI",
        "outputId": "79830437-fdb0-400b-cfb5-e55ba868b757",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Original array:\n",
            " [[1 2 3 4]\n",
            " [5 6 7 8]\n",
            " [9 1 2 3]]\n",
            "Reshaped array:\n",
            " [[[1 2 3]\n",
            "  [4 5 6]]\n",
            "\n",
            " [[7 8 9]\n",
            "  [1 2 3]]]\n",
            "\n",
            "Original array:\n",
            " [[1 2 3]\n",
            " [4 5 6]]\n",
            "Fattened array:\n",
            " [1 2 3 4 5 6]\n",
            "Column Fattened array:\n",
            " [[1]\n",
            " [2]\n",
            " [3]\n",
            " [4]\n",
            " [5]\n",
            " [6]]\n"
          ]
        }
      ],
      "source": [
        "# Reshaping 3X4 array to 2X2X3 array\n",
        "A = np.array([[1, 2, 3, 4],\n",
        "                [5, 6, 7, 8],\n",
        "                [9, 1, 2, 3]])\n",
        "\n",
        "new_A = A.reshape(2, 2, 3)\n",
        "\n",
        "# Flatten array\n",
        "B = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "flat_B= B.flatten()\n",
        "column_flat_B = flat_B.reshape(-1, 1)\n",
        "\n",
        "print (\"\\nOriginal array:\\n\", A)\n",
        "print (\"Reshaped array:\\n\", new_A)\n",
        "print (\"\\nOriginal array:\\n\", B)\n",
        "print (\"Fattened array:\\n\", flat_B)\n",
        "print (\"Column Fattened array:\\n\", column_flat_B)\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5BftDB4oYP1x"
      },
      "source": [
        "**1.13 Broadcasting** -\n",
        "\n",
        "The term broadcasting refers to managing arrays of different shapes during arithmetic operations in NumPy.\n",
        "\n",
        "Arithmetic operations on arrays are usually performed on corresponding elements, based on the law of matrices \\ arrays.\n",
        "\n",
        "NumPy provides a way by which dimension mismatch is taken care of. When operating on two arrays, NumPy compares their shapes element-wise. It starts with the trailing dimensions, and works its way forward. Two dimensions are compatible when they are equal, or one of them is 1\n",
        "\n",
        "For example, if you want to add an array 'b' of size (4,1) to a matrix 'A' of size (4,3), then the values in each row of 'b' are broadcasted and added in each column of the matrix 'A'. Let us see how it is done."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z-G5jSNg1g2Z"
      },
      "source": [
        "**1.14 Python Classes-**\n",
        "\n",
        "Classes provide a means of bundling data and functionality together. Creating a new class creates a new type of object, allowing new instances of that type to be made. Each class instance can have attributes attached to it for maintaining its state. Class instances can also have methods (defined by its class) for modifying its state.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "ETDYzg4a1iAN"
      },
      "outputs": [],
      "source": [
        "class MobilePhone:\n",
        "  \"\"\"This is a sample class to illustrate how Python classes work\"\"\"\n",
        "  def __init__(self, name, is_android = False, screen_size = 4.3):\n",
        "    self.name = name\n",
        "    self.is_android = is_android\n",
        "    self.screen_size = screen_size\n",
        "    self.rating = -1\n",
        "\n",
        "  def has_rating(self):\n",
        "    return self.rating > -1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "vkHApjaE1kod"
      },
      "outputs": [],
      "source": [
        "new_phone = MobilePhone('iPhone 5s')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "L4KjarI-1mPH",
        "outputId": "9d45e0ac-e8ee-4ee6-f679-dd2dc59d0b3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "__main__.MobilePhone"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "type(new_phone)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "id": "aw6De6Fi1nmp",
        "outputId": "29dc4942-ed77-4fb2-9427-885b5692c2b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iPhone 5s False 4.3\n"
          ]
        }
      ],
      "source": [
        "print(new_phone.name, new_phone.is_android, new_phone.screen_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "id": "ngQoF2tn27D3"
      },
      "outputs": [],
      "source": [
        "new_phone.screen_size = 4"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "vPOH3OsF2-HW",
        "outputId": "baeb589c-bb8b-4b2a-f25c-fdf187e1e821",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "new_phone.has_rating()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "u4_kG8F73OoA"
      },
      "outputs": [],
      "source": [
        "new_phone.rating = 3.9"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "9uU7BylG3U5K",
        "scrolled": true,
        "outputId": "4b646c16-9919-4d7b-c1b7-568542b63433",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'This is a sample class to illustrate how Python classes work'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "MobilePhone.__doc__"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "id": "qNUICpHc3c2w"
      },
      "outputs": [],
      "source": [
        "class iPhone(MobilePhone):\n",
        "  def __init__(self, name):\n",
        "    MobilePhone.__init__(self, name, False, 4)\n",
        "\n",
        "  def __str__(self):\n",
        "    return self.name + \" \" + str(self.is_android) + \" \" + str(self.screen_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "id": "6rvMwJsL3rgO"
      },
      "outputs": [],
      "source": [
        "new_iphone = iPhone('iPhone 5s')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "7Gj-7qNG3t-J",
        "outputId": "67897922-1b3b-4c0a-ac16-f0bd62d110be",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ],
      "source": [
        "new_iphone.is_android"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "id": "MadGk2Po3viS",
        "outputId": "16791e02-cd91-4181-f0da-8635fd3f6907",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "iPhone 5s False 4\n"
          ]
        }
      ],
      "source": [
        "print(new_iphone)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "yLInEQr0YMIm",
        "outputId": "12f6514e-7272-4c62-e581-c09532abcb29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3 3 3]\n",
            " [3 3 3]\n",
            " [3 3 3]]\n",
            "[[2 2 2]\n",
            " [2 2 2]\n",
            " [2 2 2]]\n"
          ]
        }
      ],
      "source": [
        "a = np.array([[1,1,1],[1,1,1], [1,1,1]])\n",
        "b = np.array([2,2,2])\n",
        "c = a + b\n",
        "c1= a*b #element wise multiplication. This will also be broadcasted accordingly.\n",
        "\n",
        "print(c)\n",
        "print(c1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "88t-d8w_YjXN"
      },
      "source": [
        "**1.15 Files I/O** -\n",
        "The file object provides a set of methods to read and write on files. Some examples are given below.  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "hW4J1gEHYgQm",
        "outputId": "62df9b83-eae7-4ca6-872f-cb07009a6d3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Name of the file:  sample.txt\n",
            "Read String from file is :  Welcome to the couse on Machine Learning at NIT Delhi.\n"
          ]
        }
      ],
      "source": [
        "# File open, read and write.\n",
        "\n",
        "file1 = open(\"sample.txt\", \"w+\")\n",
        "file1.write(\"Welcome to the couse on Machine Learning at NIT Delhi.\");\n",
        "file1.close()\n",
        "\n",
        "# Open a file\n",
        "file_read = open(\"sample.txt\", \"r\")\n",
        "str = file_read.read(80);\n",
        "print(\"Name of the file: \", file_read.name)\n",
        "print(\"Read String from file is : \", str)\n",
        "# Close opend files\n",
        "file_read.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gzgIiCSgYaxp"
      },
      "source": [
        "**1.16 Using matplotlib** -\n",
        "Matplotlib is a Python 2D plotting library.\n",
        "\n",
        "Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application servers, and four graphical user interface toolkits ( source: https://matplotlib.org/ , https://matplotlib.org/citing.html)\n",
        "\n",
        "Here we give a simple example of its use."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "d8bAlzxeYXaE",
        "outputId": "c07de53f-5f1e-467b-acf1-fbdc172c7560",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Computes x and y coordinates for\n",
        "# points on sine and cosine curves\n",
        "x = np.arange(0, 3 * np.pi, 0.1)\n",
        "y_sin = np.sin(x)\n",
        "y_cos = np.cos(x)\n",
        "\n",
        "# Plot the points using matplotlib\n",
        "plt.plot(x, y_sin)\n",
        "plt.plot(x, y_cos)\n",
        "plt.xlabel('x axis label')\n",
        "plt.ylabel('y axis label')\n",
        "plt.title('Sine and Cosine')\n",
        "plt.legend(['Sine', 'Cosine'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOkH3IOqYvIB"
      },
      "source": [
        "**1.17 Using Images** -"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "FC4kQ2PKYnbn",
        "outputId": "ccc7fbe3-8cb3-43a9-93f8-b3f4033137a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 409
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Dimensions :  (129, 129, 3)\n",
            "Resized Dimensions :  (103, 103, 3)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import matplotlib.image as mpimg\n",
        "img=mpimg.imread('NITD.jpeg')\n",
        "\n",
        "print('Original Dimensions : ',img.shape)\n",
        "\n",
        "scale_percent = 80 # percent of original size\n",
        "width = int(img.shape[1] * scale_percent / 100)\n",
        "height = int(img.shape[0] * scale_percent / 100)\n",
        "dim = (width, height)\n",
        "# resize image\n",
        "resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n",
        "\n",
        "print('Resized Dimensions : ',resized.shape)\n",
        "plt.figure(figsize=(4,10))\n",
        "imgplot = plt.imshow(resized)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qZg7BjxPUa3W"
      },
      "outputs": [],
      "source": [
        "# In case any error in the above cell , use either 1 or 2 as per your python environment\n",
        "\n",
        "# 1. pip install opencv-python\n",
        "# 2. pip install cv2\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DvQPEyqCUa3W"
      },
      "source": [
        "# Pythonic Way of Coding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "id": "-Tovl7kQUa3W",
        "outputId": "58e1f5a6-d90b-46c4-cc7d-c0148d3d927e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello\n",
            "I\n",
            "am\n",
            "in\n",
            "Delhi\n"
          ]
        }
      ],
      "source": [
        "#this is bad example and not pythonic\n",
        "data = [\"Hello\", \"I\", \"am\", \"in\", \"Delhi\"]\n",
        "for i in range(len(data)):\n",
        "    print(data[i])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "20KXR2JOUa3W",
        "outputId": "afed87fc-88d3-46c0-d5ad-583c6ac62c70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello\n",
            "I\n",
            "am\n",
            "in\n",
            "Delhi\n"
          ]
        }
      ],
      "source": [
        "#this is good example and more pythonic\n",
        "data = [\"Hello\", \"I\", \"am\", \"in\", \"Delhi\"]\n",
        "for word in data:\n",
        "    print(word)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hQwDBWHpUa3X"
      },
      "source": [
        "Reference:\n",
        "    - Machine Learning for Computer Vision2020, E&ICT"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3LmqIqPbaZhc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Learning of this course\n",
        "\n"
      ],
      "metadata": {
        "id": "ozjutnpeSiGy"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}