Copy_of_Basic_python_programs.ipynb

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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Lakshmi-krishna-vr/Computational-Algebra/blob/main/Copy_of_Basic_python_programs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "## 1. Program to find largest of three numbers\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "M71fbCtRpBXZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "# Python program to find the largest number among the three input numbers\n",
        "\"\"\"\n",
        "num1 = 40\n",
        "num2 = 24\n",
        "num3 = 32\n",
        "#uncomment following lines to take three numbers from user\n",
        "#num1 = float(input(\"Enter first number: \"))\n",
        "#num2 = float(input(\"Enter second number: \"))\n",
        "#num3 = float(input(\"Enter third number: \"))\n",
        "if (num1 >= num2) and (num1 >= num3):\n",
        "   largest = num1\n",
        "elif (num2 >= num1) and (num2 >= num3):\n",
        "   largest = num2\n",
        "else:\n",
        "   largest = num3\n",
        "print(\"The largest number is\", largest)"
      ],
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        "id": "IP0Rp8zNpGEL",
        "outputId": "8047ee28-19ab-4d21-ecaf-0e3703b509b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The largest number is 40\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Printing multiplication table"
      ],
      "metadata": {
        "id": "yiWkBjCAR_0-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2.Printing multiplication table"
      ],
      "metadata": {
        "id": "fmdKJp3y6pj3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "GXVl7OjcR71f",
        "outputId": "5a0f9c3b-c922-4552-e1e2-abeaaa37c6f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Display multiplication table of? 45\n",
            "1 x 45 = 45\n",
            "2 x 45 = 90\n",
            "3 x 45 = 135\n",
            "4 x 45 = 180\n",
            "5 x 45 = 225\n",
            "6 x 45 = 270\n",
            "7 x 45 = 315\n",
            "8 x 45 = 360\n",
            "9 x 45 = 405\n",
            "10 x 45 = 450\n"
          ]
        }
      ],
      "source": [
        "\"\"\"\n",
        "# Multiplication table (from 1 to 10) in Python\n",
        "\"\"\"\n",
        "#num = 12\n",
        "\n",
        "# To take input from the user\n",
        "num = int(input(\"Display multiplication table of? \"))\n",
        "\n",
        "# Iterate 10 times from i = 1 to 10\n",
        "for i in range(1, 11):\n",
        "   print(i, 'x', num, '=', num*i)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**Task:** Get an integer less than 12 from the user and display its's ,multiplication table."
      ],
      "metadata": {
        "id": "NLCPCHIQx7I6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "# To take input from the user\n",
        "'''\n",
        "num = int(input(\"enter the limit upto which you need the multiplication table \"))\n",
        "for n in range(1,num+1):\n",
        "\n",
        "  print(\"multiplication table\",n)\n",
        "# Iterate 10 times from i = 1 to 10\n",
        "for i in range(1, 11):\n",
        "  print(i, 'x', num, '=', num*i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "-TWleoH9ySsk",
        "outputId": "e1a727b8-92f6-4bd5-9f55-58620d14be8d"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "enter the limit upto which you need the multiplication table 9\n",
            "multiplication table 1\n",
            "multiplication table 2\n",
            "multiplication table 3\n",
            "multiplication table 4\n",
            "multiplication table 5\n",
            "multiplication table 6\n",
            "multiplication table 7\n",
            "multiplication table 8\n",
            "multiplication table 9\n",
            "1 x 9 = 9\n",
            "2 x 9 = 18\n",
            "3 x 9 = 27\n",
            "4 x 9 = 36\n",
            "5 x 9 = 45\n",
            "6 x 9 = 54\n",
            "7 x 9 = 63\n",
            "8 x 9 = 72\n",
            "9 x 9 = 81\n",
            "10 x 9 = 90\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check for sign of a number"
      ],
      "metadata": {
        "id": "yo27A0UgSLMG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "\"\"\"\n",
        "num = float(input(\"Enter a number: \"))\n",
        "if num > 0:\n",
        "   print(\"Positive number\")\n",
        "elif num == 0:\n",
        "   print(\"Zero\")\n",
        "else:\n",
        "   print(\"Negative number\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qagw5dYPSOtO",
        "outputId": "177c27da-cbe3-4003-b2ba-b43786e058eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a number: 34\n",
            "Positive number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find surface area and volume of a cylinder\n",
        "\n",
        "Let $r$ is the base radius and $h$ is the height of a right circular cylinder.\n",
        "$$SA=2\\pi r(r+h)$$\n",
        "\n",
        "$$V=\\pi r^2 h$$"
      ],
      "metadata": {
        "id": "Rr--yGIkVoaw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#from math import pi\n",
        "pi=3.14\n",
        "r=2.6\n",
        "h=1.6\n",
        "SA=2*pi*r*(r+h)\n",
        "V=pi*r**2*h\n",
        "print((\"Surface Area is {} and Volume is {}\").format(SA,V))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NP5Yahw5Wazn",
        "outputId": "f08fa1cb-0879-4d50-b6aa-87cfd698ba01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Surface Area is 68.57760000000002 and Volume is 33.96224\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find area of a circle with radius $r$ using user input"
      ],
      "metadata": {
        "id": "sSr3xEtuXdw-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "program to find area of a circle\n",
        "\"\"\"\n",
        "r=float(input(\"Enter the radius: \"))\n",
        "print(\"Area: \",pi*r**2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2joezx-KXljx",
        "outputId": "5eab0c01-0061-478a-a647-c97f69f28e71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the radius: 6.3\n",
            "Area:  124.6266\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find the area of a rectangle with lenght $l$ and breadth $b$"
      ],
      "metadata": {
        "id": "VRlXLlz9YD6N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "l=2;b=5.2\n",
        "Area=l*b\n",
        "print(\"Area of the Rectangle is : \",Area)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tE_hVFL2YK2F",
        "outputId": "fecd8378-eb1d-4c74-9692-3611652c3068"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Area of the Rectangle is :  10.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Replacing a word with another word  in a sentence\n",
        "\n",
        "Use the `replace()` method in `python`\n",
        "\n",
        ">**Syntax:**\n",
        "\n",
        "```\n",
        "str.replace(old,new)\n",
        "```\n",
        "\n"
      ],
      "metadata": {
        "id": "SP246J1XZHbk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# program to replace a word by another word in a sentence\n",
        "\n",
        "text = 'hand ball is a hand full of hands'\n",
        "\n",
        "# replace 'hand' with 'foot'\n",
        "replaced_text = text.replace('hand ', 'foot ')\n",
        "print(replaced_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QfIPj2_SZM6n",
        "outputId": "49f71787-5fc9-4245-cd7e-769bff7b0b08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "foot ball is a foot full of foots\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to remove a word from a sentence\n",
        "\n",
        "We will use the string method `remove()`.\n",
        "\n",
        ">**Syntax:**\n",
        "\n",
        "```\n",
        "str.remove(word to be removed)\n",
        "```"
      ],
      "metadata": {
        "id": "kpxkra3ZjgzQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# program to remove a word from a sentence\n",
        "text = input(\"Enter the String: \")\n",
        "word = input(\"Enter a Word to Delete: \")\n",
        "\n",
        "text = text.replace(word, \"\")\n",
        "print(\"The updated sentence is:\\n\")\n",
        "print(text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-bTOChg4kxRr",
        "outputId": "8dbdb8b0-d752-4567-d531-85028c329020"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the String: He is my friend\n",
            "Enter a Word to Delete: my\n",
            "The updated sentence is:\n",
            "\n",
            "He is  friend\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# program to remove a word from a sentence\n",
        "text = input(\"Enter the String: \")\n",
        "word = input(\"Enter a Word to Delete: \")\n",
        "\n",
        "text = text.remove(word)\n",
        "print(\"The updated sentence is:\\n\")\n",
        "print(text)"
      ],
      "metadata": {
        "id": "ccujKR0l7qwZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to split a sentence into words\n",
        "\n",
        "use the `split()` method\n",
        ">**Syntax:**\n",
        "```\n",
        "str.split()\n",
        "```"
      ],
      "metadata": {
        "id": "NRg-zTGOlVKG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "wordlist = text.split()\n",
        "wordlist"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjXgR_vmlqRd",
        "outputId": "4f9ed9a9-bcb1-4164-9955-ef8744a9b64c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['You', 'are', 'girl']"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check if a list of words is in a long string at least one"
      ],
      "metadata": {
        "id": "EAo8docqnlRg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# program to check atleat one occurance of keywords\n",
        "\n",
        "keyword_list = ['motorcycle', 'bike', 'bicycle', 'dirtbike', \"long\"]\n",
        "all_text = 'The bicycle is just as good company as most husbands and, when it gets old and shabby, a woman can dispose of it and get a new one without shocking the entire community'\n",
        "if set(keyword_list).intersection(all_text.split()):\n",
        "     print(\"Found One\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RnmT51ISnsVu",
        "outputId": "14d2a745-882a-4fd6-8a27-365a81f815c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found One\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## program to check a number is even or odd\n"
      ],
      "metadata": {
        "id": "WG6KupkbovWM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter the Number: \"))\n",
        "if num%2==0:\n",
        "    print(\"\\n\",num,\"is an Even Number\")\n",
        "else:\n",
        "    print(\"\\n\",num,\"is an Odd Number\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b_-2wp13o0Vo",
        "outputId": "a5ecea89-d08d-4987-858a-65d91d441f94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Number: 22\n",
            "\n",
            " 22 is an Even Number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check whether an entered one is an alphabet or not"
      ],
      "metadata": {
        "id": "kEo5cMDdpRlz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = input(\"Enter a Character: \")\n",
        "if c>='a' and c<='z':\n",
        "    print(\"\\nIt is an alphabet\")\n",
        "elif c>='A' and c<='z':\n",
        "    print(\"\\nIt is an alphabet\")\n",
        "else:\n",
        "    print(\"\\nIt is not an alphabet!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofx_FoHKpX_K",
        "outputId": "6a334ad9-1d22-43f0-bcb7-83982aa5121e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a Character: 12\n",
            "\n",
            "It is not an alphabet!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check whether an entered letter is a vowel or not"
      ],
      "metadata": {
        "id": "C4_oMf3apqzg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = input(\"Enter the Character: \")\n",
        "v={'a','e','i','o','u','A','E','I','O','U'}\n",
        "if c in v:\n",
        "    print(\"\\nIt is a Vowel\")\n",
        "else:\n",
        "    print(\"\\nIt is a Consonant\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRAi0VdtpzPi",
        "outputId": "a60612ef-fb92-4bac-92cd-2e7daa784e30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Character: A\n",
            "\n",
            "It is a Vowel\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**Another method**\n",
        "\n",
        "using `or` expression"
      ],
      "metadata": {
        "id": "iz3TO1SAqXKh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "c = input(\"Enter the Character: \")\n",
        "\n",
        "if c=='a' or c=='e' or c=='i' or c=='o' or c=='u':\n",
        "    print(\"\\nIt is a Vowel\")\n",
        "elif c=='A' or c=='E' or c=='I' or c=='O' or c=='U':\n",
        "    print(\"\\nIt is a Vowel\")\n",
        "else:\n",
        "    print(\"\\nIt is a Consonant\")"
      ],
      "metadata": {
        "id": "cBPy2QN5qawv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check leap year"
      ],
      "metadata": {
        "id": "xWyNWqflqt6T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y = int(input(\"Enter the Year: \"))\n",
        "\n",
        "if y%4==0 and y%100!=0:\n",
        "    print(\"\\nIt is a Leap Year\")\n",
        "elif y%400==0:\n",
        "    print(\"\\nIt is a Leap Year\")\n",
        "else:\n",
        "    print(\"\\nIt is not a Leap Year\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08_q45cmqwj_",
        "outputId": "bd7494cd-aeae-4f74-b66d-a5ea231d117a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Year: 1980\n",
            "\n",
            "It is a Leap Year\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Programs with iteratives"
      ],
      "metadata": {
        "id": "vz2ZLvryun46"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check whether a number is a perfect number or not\n",
        "\n",
        "A number $n$ is called a perfect number if sum of all its divisors gives $n$."
      ],
      "metadata": {
        "id": "wIpKRZf8rSFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter the Number:\"))\n",
        "sum = 0\n",
        "for i in range(1, num):\n",
        "  if num%i==0:\n",
        "    sum = sum+i\n",
        "if num==sum:\n",
        "  print(\"It is a Perfect Number\")\n",
        "else:\n",
        "  print(\"It is not a Perfect Number\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0iMMskGnrg67",
        "outputId": "63ff8e32-eb97-4829-acc9-291370d73222"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Number:6\n",
            "It is a Perfect Number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Program to list all perfect number below a limit\n",
        "num = int(input(\"Enter the limit:\"))\n",
        "print(\"Perfect numbers are:\")\n",
        "print(\"***\"*10)\n",
        "pn=[]\n",
        "for n in range(1,num):\n",
        "  sum = 0\n",
        "  for i in range(1, n):\n",
        "    if n%i==0:\n",
        "      sum = sum+i\n",
        "  if n==sum:\n",
        "    pn.append(n)\n",
        "print(pn)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S5VM67CIrvS8",
        "outputId": "3ca7b591-6fc1-46f0-fa36-46a0fe613351"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the limit:500\n",
            "Perfect numbers are:\n",
            "******************************\n",
            "[6, 28, 496]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to test whether reversal of a number is same as itself"
      ],
      "metadata": {
        "id": "azoRFThntacI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter the Number: \"))\n",
        "rev = 0\n",
        "orig = num\n",
        "while num>0:\n",
        "    rem = num%10\n",
        "    rev = rem + (rev*10)\n",
        "    num = num//10\n",
        "\n",
        "if orig==rev:\n",
        "    print(\"\\nThe Number is Equal to Its Reverse\")\n",
        "else:\n",
        "    print(\"\\nThe Number is not Equal to Its Reverse\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FaadzWp8tgfb",
        "outputId": "270ffb79-8604-4800-c848-8f6ae0841510"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Number: 151\n",
            "\n",
            "The Number is Equal to Its Reverse\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## program to count the number of digits in a number"
      ],
      "metadata": {
        "id": "E0ygCvykuNkH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter the Number: \"))\n",
        "tot = 0\n",
        "\n",
        "while num:\n",
        "  num = num//10\n",
        "  tot = tot+1\n",
        "\n",
        "print(\"\\nTotal Digit: \")\n",
        "print(tot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKiuShhFr1HD",
        "outputId": "89c87bd2-5b3a-4788-a0a4-0a0a5d258cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter the Number: 12345\n",
            "\n",
            "Total Digit: \n",
            "5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to find sum of digits in a number"
      ],
      "metadata": {
        "id": "LCpDPbkxuv0K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter a Number\"))\n",
        "sum = 0\n",
        "while num>0:\n",
        "    rem = num%10\n",
        "    sum = sum+rem\n",
        "    num = num//10\n",
        "print(\"\\nSum of Digits of Given Number: \", sum)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Uy1GdK4u0Ik",
        "outputId": "f083d3e5-3daa-420c-9789-81d009ec29d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a Number7589\n",
            "\n",
            "Sum of Digits of Given Number:  29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to find the sum of first and last digit of a number"
      ],
      "metadata": {
        "id": "u0mGKD8zvTE2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter a Number: \"))\n",
        "\n",
        "count = 0\n",
        "while num!=0:\n",
        "    if count==0:\n",
        "        last = num%10\n",
        "        count = count+1\n",
        "    rem = num%10\n",
        "    num = int(num/10)\n",
        "\n",
        "sum = rem + last\n",
        "print(\"\\nSum of first and last digit =\", sum)"
      ],
      "metadata": {
        "id": "CSriuS_ivY6A"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to print first and last digit of a number"
      ],
      "metadata": {
        "id": "smVhW0qbvOO0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = int(input(\"Enter a Number: \"))\n",
        "org=num\n",
        "count = 0\n",
        "while num!=0:\n",
        "    if count==0:\n",
        "        last = num%10\n",
        "        count = count+1\n",
        "    rem = num%10\n",
        "    num = num//10\n",
        "FL=[rem,last]\n",
        "print(\"\\nThe first and last digit of \", org ,\" are: \", FL)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1_Z5qGQUvgou",
        "outputId": "e8dc0673-cac2-4aac-d6a9-35d197aa410f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter a Number: 786\n",
            "\n",
            "The first and last digit of  786  are:  [7, 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Python program to display even and odd members in a tuple"
      ],
      "metadata": {
        "id": "R-_QxAxvzqHJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "Python Program:\n",
        " Using slice [] operator to iterate over a specific parts of a tuple to print odd and even members\n",
        "\"\"\"\n",
        "even=[];odd=[]\n",
        "tuple_created = (\"p\", \"r\", \"o\", \"g\", \"r\", \"a\", \"m\", \"i\", \"z\")\n",
        "for char in tuple_created[ :  : 2]:\n",
        "   even.append(char)\n",
        "for char in tuple_created[1 :  : 2]:\n",
        "   odd.append(char)\n",
        "print(\"Even members: \",even,\"\\nOdd members: \",odd)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vuDNoBYXzweI",
        "outputId": "7fe481bc-89db-4c3c-9b41-f36b5d264546"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Even members:  ['p', 'o', 'r', 'm', 'z'] \n",
            "Odd members:  ['r', 'g', 'a', 'i']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to create  a book shop stock register using python dictionary"
      ],
      "metadata": {
        "id": "Fdk3KUDi2uIB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n = eval(input('Num books: '))\n",
        "books = {}\n",
        "for i in range(n):\n",
        "    titlez = input(\"Enter Title: \")\n",
        "    ncopy = eval(input(\"Num of copies: \"))\n",
        "    books[titlez] = ncopy\n",
        "\n",
        "prob = input('Sell a book; enter YES or NO: ')\n",
        "if prob == 'YES' or 'yes':\n",
        "    choice = input('Enter book title: ')\n",
        "    if choice in books:\n",
        "        init_num = books[choice]\n",
        "        init_num -= 1\n",
        "        books[choice] = init_num\n",
        "        print(books)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "raSpV8kR284D",
        "outputId": "94f03302-78c8-4ee9-cc82-5309d222e499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num books: 2\n",
            "Enter Title: lalgebra\n",
            "Num of copies: 3\n",
            "Enter Title: calculus\n",
            "Num of copies: 4\n",
            "Sell a book; enter YES or NO: Yes\n",
            "Enter book title: calculus\n",
            "{'lalgebra': 3, 'calculus': 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**To remove a book from the stock**"
      ],
      "metadata": {
        "id": "Va8mV5v43jqF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "to_rem = input('Want to remove a book from the stock? YES or NO ')\n",
        "if to_rem == 'YES' or 'yes':\n",
        "    choice = input('Enter book title: ')\n",
        "    if choice in books:\n",
        "        books.pop(choice)\n",
        "        print(books)"
      ],
      "metadata": {
        "id": "hJ7pwxJH3rFS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "121d5599-767b-472d-b730-eef20fd87bb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Want to remove a book from the stock? YES or NO yes\n",
            "Enter book title: 5\n",
            "{'Calculus': 4, 'Linear Algebra': 5, 'Thomas calculus': 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**To add a book to the stock**"
      ],
      "metadata": {
        "id": "NjTb4dV94hOp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "to_add = input('Want to add a book to the stock? YES or NO ')\n",
        "if to_add == 'YES' or 'yes':\n",
        "    choice = input('Enter book title: ')\n",
        "    nc=eval(input('Number of copies:'))\n",
        "    books[choice]=nc\n",
        "print(books)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CRKVHdhx4qbW",
        "outputId": "df1587fd-5179-4e90-a120-307f172d4626"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Want to add a book to the stock? YES or NO yes\n",
            "Enter book title: Thomas calculus\n",
            "Number of copies:3\n",
            "{'Calculus': 4, 'Linear Algebra': 5, '5': 2, 'Thomas calculus': 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Program to check validity of an email ID by verifying its structure"
      ],
      "metadata": {
        "id": "-3p4ILRKdYUz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def verify(s):\n",
        "   pat = \"^[a-zA-Z0-9-._]+@[a-zA-Z0-9.]+\\.[a-z]{2,3}$\"\n",
        "   if re.match(pat,s):\n",
        "      return True\n",
        "   return False\n"
      ],
      "metadata": {
        "id": "c2ugMruLdhWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "s = \"siju.swamy@gihub.ac.in\"\n",
        "print(verify(s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2NvtttcdsLK",
        "outputId": "86d822c5-437c-405c-a9ab-2f1921b4d3e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Factorial"
      ],
      "metadata": {
        "id": "cbImEuNxSbT-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Python program to find the factorial of a number provided by the user.\n",
        "\n",
        "# change the value for a different result\n",
        "num = 7\n",
        "\n",
        "# To take input from the user\n",
        "#num = int(input(\"Enter a number: \"))\n",
        "\n",
        "factorial = 1\n",
        "\n",
        "# check if the number is negative, positive or zero\n",
        "if num < 0:\n",
        "   print(\"Sorry, factorial does not exist for negative numbers\")\n",
        "elif num == 0:\n",
        "   print(\"The factorial of 0 is 1\")\n",
        "else:\n",
        "   for i in range(1,num + 1):\n",
        "       factorial = factorial*i\n",
        "   print(\"The factorial of\",num,\"is\",factorial)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1dJehoOMSdNu",
        "outputId": "f7a904ca-465f-4a75-da2b-b6bb24102c14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The factorial of 7 is 5040\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Factorial with recursion"
      ],
      "metadata": {
        "id": "JDmlGF7NSpV2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Python program to find the factorial of a number provided by the user\n",
        "# using recursion\n",
        "\n",
        "def factorial(x):\n",
        "    \"\"\"This is a recursive function\n",
        "    to find the factorial of an integer\"\"\"\n",
        "\n",
        "    if x == 1:\n",
        "        return 1\n",
        "    else:\n",
        "        # recursive call to the function\n",
        "        return (x * factorial(x-1))\n",
        "\n",
        "\n",
        "# change the value for a different result\n",
        "num = 7\n",
        "\n",
        "# to take input from the user\n",
        "# num = int(input(\"Enter a number: \"))\n",
        "\n",
        "# call the factorial function\n",
        "result = factorial(num)\n",
        "print(\"The factorial of\", num, \"is\", result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HqPTf1N7SidW",
        "outputId": "fa00490d-99c9-43c3-fa05-f5afe6f218fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The factorial of 7 is 5040\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Check prime"
      ],
      "metadata": {
        "id": "qjSAwtSeS09e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Program to check if a number is prime or not\n",
        "\n",
        "num = 29\n",
        "\n",
        "# To take input from the user\n",
        "#num = int(input(\"Enter a number: \"))\n",
        "\n",
        "# define a flag variable\n",
        "flag = False\n",
        "\n",
        "if num == 1:\n",
        "    print(num, \"is not a prime number\")\n",
        "elif num > 1:\n",
        "    # check for factors\n",
        "    for i in range(2, num):\n",
        "        if (num % i) == 0:\n",
        "            # if factor is found, set flag to True\n",
        "            flag = True\n",
        "            # break out of loop\n",
        "            break\n",
        "\n",
        "    # check if flag is True\n",
        "    if flag:\n",
        "        print(num, \"is not a prime number\")\n",
        "    else:\n",
        "        print(num, \"is a prime number\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aJH993DVS3zu",
        "outputId": "43f395c0-26bc-44f9-f4a6-0dccaf885746"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "29 is a prime number\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## prime check- method 2"
      ],
      "metadata": {
        "id": "g0x-F1X0S_mm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num = 407\n",
        "\n",
        "# To take input from the user\n",
        "#num = int(input(\"Enter a number: \"))\n",
        "\n",
        "if num == 1:\n",
        "    print(num, \"is not a prime number\")\n",
        "elif num > 1:\n",
        "   # check for factors\n",
        "   for i in range(2,num):\n",
        "       if (num % i) == 0:\n",
        "           print(num,\"is not a prime number\")\n",
        "           print(i,\"times\",num//i,\"is\",num)\n",
        "           break\n",
        "   else:\n",
        "       print(num,\"is a prime number\")\n",
        "\n",
        "# if input number is less than\n",
        "# or equal to 1, it is not prime\n",
        "else:\n",
        "   print(num,\"is not a prime number\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pcDcKDCGTDvm",
        "outputId": "fcd77a81-2227-4e17-e0cd-d0d4fd5e8e64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "407 is not a prime number\n",
            "11 times 37 is 407\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Display prime numbers in a range"
      ],
      "metadata": {
        "id": "AaE8XbGMTJwG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Python program to display all the prime numbers within an interval\n",
        "\n",
        "lower = 900\n",
        "upper = 1000\n",
        "\n",
        "print(\"Prime numbers between\", lower, \"and\", upper, \"are:\")\n",
        "\n",
        "for num in range(lower, upper + 1):\n",
        "   # all prime numbers are greater than 1\n",
        "   if num > 1:\n",
        "       for i in range(2, num):\n",
        "           if (num % i) == 0:\n",
        "               break\n",
        "       else:\n",
        "           print(num)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ofYjMnRJTNyG",
        "outputId": "4cc472da-92f5-4b69-8885-ef62c580f493"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prime numbers between 900 and 1000 are:\n",
            "907\n",
            "911\n",
            "919\n",
            "929\n",
            "937\n",
            "941\n",
            "947\n",
            "953\n",
            "967\n",
            "971\n",
            "977\n",
            "983\n",
            "991\n",
            "997\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## check amstrong number"
      ],
      "metadata": {
        "id": "ECgPlR3yTVhN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Program to check Armstrong numbers in a certain interval\n",
        "\n",
        "lower = 100\n",
        "upper = 2000\n",
        "\n",
        "for num in range(lower, upper + 1):\n",
        "\n",
        "   # order of number\n",
        "   order = len(str(num))\n",
        "\n",
        "   # initialize sum\n",
        "   sum = 0\n",
        "\n",
        "   temp = num\n",
        "   while temp > 0:\n",
        "       digit = temp % 10\n",
        "       sum += digit ** order\n",
        "       temp //= 10\n",
        "\n",
        "   if num == sum:\n",
        "       print(num)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Osy2PPiGTUt9",
        "outputId": "156142b7-c37a-4e5c-e340-6905f08495b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "153\n",
            "370\n",
            "371\n",
            "407\n",
            "1634\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## check for anagrams"
      ],
      "metadata": {
        "id": "hkXtBisdTqA-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "str1 = \"Race\"\n",
        "str2 = \"Care\"\n",
        "\n",
        "# convert both the strings into lowercase\n",
        "str1 = str1.lower()\n",
        "str2 = str2.lower()\n",
        "\n",
        "# check if length is same\n",
        "if(len(str1) == len(str2)):\n",
        "\n",
        "    # sort the strings\n",
        "    sorted_str1 = sorted(str1)\n",
        "    sorted_str2 = sorted(str2)\n",
        "\n",
        "    # if sorted char arrays are same\n",
        "    if(sorted_str1 == sorted_str2):\n",
        "        print(str1 + \" and \" + str2 + \" are anagram.\")\n",
        "    else:\n",
        "        print(str1 + \" and \" + str2 + \" are not anagram.\")\n",
        "\n",
        "else:\n",
        "    print(str1 + \" and \" + str2 + \" are not anagram.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y06GUt-KTue-",
        "outputId": "4b35699b-7a68-44d4-cdfa-e22b3b3c2c61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "race and care are anagram.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## capitalize first word"
      ],
      "metadata": {
        "id": "A_4BJ_OrT8O-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_string = \"programiz is List\"\n",
        "\n",
        "print(my_string[0].upper() + my_string[1:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzDbDL2ST--W",
        "outputId": "bb2ecdb7-ec85-4b1c-d4e1-f43fe6defcbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Programiz is Lit\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## alphabet counting-1 with loop"
      ],
      "metadata": {
        "id": "r-fAFrn9UJ0G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "count = 0\n",
        "\n",
        "my_string = \"Programiz\"\n",
        "my_char = \"r\"\n",
        "\n",
        "for i in my_string:\n",
        "    if i == my_char:\n",
        "        count += 1\n",
        "\n",
        "print(count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6mVeWfvqUMtW",
        "outputId": "c5f23934-c89b-48a1-efc7-7eef3b6a438a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## alphabet count with `.count()` function"
      ],
      "metadata": {
        "id": "DDZysEVUUTzO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_string = \"Programiz\"\n",
        "my_char = \"r\"\n",
        "\n",
        "print(my_string.count(my_char))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oT7hqG6vUZA2",
        "outputId": "14cc9011-57d5-40d7-b207-08c17b881c31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## counting occurance in a list"
      ],
      "metadata": {
        "id": "-9WftgBIUhoe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "freq = ['a', 1, 'a', 4, 3, 2, 'a'].count('a')\n",
        "print(freq)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeOt_mjAUkcH",
        "outputId": "0995cf5d-65f3-46c8-f3e0-110b45cf3637"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## counting number of vowels"
      ],
      "metadata": {
        "id": "q_JEDbybUp4n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Program to count the number of each vowels\n",
        "\n",
        "# string of vowels\n",
        "vowels = 'aeiou'\n",
        "\n",
        "ip_str = 'Hello, have you tried our tutorial section yet?'\n",
        "\n",
        "# make it suitable for caseless comparisions\n",
        "ip_str = ip_str.casefold()\n",
        "\n",
        "# make a dictionary with each vowel a key and value 0\n",
        "count = {}.fromkeys(vowels,0)\n",
        "\n",
        "# count the vowels\n",
        "for char in ip_str:\n",
        "   if char in count:\n",
        "       count[char] += 1\n",
        "\n",
        "print(count)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6p6ar8-pUsqv",
        "outputId": "cfbdd111-9c51-40af-8ffb-1f5ba3a365d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'a': 2, 'e': 5, 'i': 3, 'o': 5, 'u': 3}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## program to count, lines, words and special characters stored in a file"
      ],
      "metadata": {
        "id": "wt897tqWCbfP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('File1.txt', 'w') as file2:\n",
        "\n",
        "    # write contents to the test2.txt file\n",
        "    file2.write('Programming is Fun in 100 days.\\n')\n",
        "    file2.write('Programiz for beginners')\n",
        "    file2.close()"
      ],
      "metadata": {
        "id": "UERg9y3EMnKl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Python implementation to compute\n",
        "# number of characters, words, spaces\n",
        "# and lines in a file\n",
        "\n",
        "# Function to count number\n",
        "# of characters, words, spaces\n",
        "# and lines in a file\n",
        "def counter(fname):\n",
        "\n",
        "\t# variable to store total word count\n",
        "\tnum_words = 0\n",
        "\n",
        "\t# variable to store total line count\n",
        "\tnum_lines = 0\n",
        "\n",
        "\t# variable to store total character count\n",
        "\tnum_charc = 0\n",
        "\n",
        "\t# variable to store total space count\n",
        "\tnum_spaces = 0\n",
        "\n",
        "\t# opening file using with() method\n",
        "\t# so that file gets closed\n",
        "\t# after completion of work\n",
        "\twith open(fname, 'r') as f:\n",
        "\n",
        "\t\t# loop to iterate file\n",
        "\t\t# line by line\n",
        "\t\tfor line in f:\n",
        "\n",
        "\t\t\t# incrementing value of\n",
        "\t\t\t# num_lines with each\n",
        "\t\t\t# iteration of loop to\n",
        "\t\t\t# store total line count\n",
        "\t\t\tnum_lines += 1\n",
        "\n",
        "\t\t\t# declaring a variable word\n",
        "\t\t\t# and assigning its value as Y\n",
        "\t\t\t# because every file is\n",
        "\t\t\t# supposed to start with\n",
        "\t\t\t# a word or a character\n",
        "\t\t\tword = 'Y'\n",
        "\n",
        "\t\t\t# loop to iterate every\n",
        "\t\t\t# line letter by letter\n",
        "\t\t\tfor letter in line:\n",
        "\n",
        "\t\t\t\t# condition to check\n",
        "\t\t\t\t# that the encountered character\n",
        "\t\t\t\t# is not white space and a word\n",
        "\t\t\t\tif (letter != ' ' and word == 'Y'):\n",
        "\n",
        "\t\t\t\t\t# incrementing the word\n",
        "\t\t\t\t\t# count by 1\n",
        "\t\t\t\t\tnum_words += 1\n",
        "\n",
        "\t\t\t\t\t# assigning value N to\n",
        "\t\t\t\t\t# variable word because until\n",
        "\t\t\t\t\t# space will not encounter\n",
        "\t\t\t\t\t# a word can not be completed\n",
        "\t\t\t\t\tword = 'N'\n",
        "\n",
        "\t\t\t\t# condition to check\n",
        "\t\t\t\t# that the encountered character\n",
        "\t\t\t\t# is a white space\n",
        "\t\t\t\telif (letter == ' '):\n",
        "\n",
        "\t\t\t\t\t# incrementing the space\n",
        "\t\t\t\t\t# count by 1\n",
        "\t\t\t\t\tnum_spaces += 1\n",
        "\n",
        "\t\t\t\t\t# assigning value Y to\n",
        "\t\t\t\t\t# variable word because after\n",
        "\t\t\t\t\t# white space a word\n",
        "\t\t\t\t\t# is supposed to occur\n",
        "\t\t\t\t\tword = 'Y'\n",
        "\n",
        "\t\t\t\t# loop to iterate every\n",
        "\t\t\t\t# letter character by\n",
        "\t\t\t\t# character\n",
        "\t\t\t\tfor i in letter:\n",
        "\n",
        "\t\t\t\t\t# condition to check\n",
        "\t\t\t\t\t# that the encountered character\n",
        "\t\t\t\t\t# is not white space and not\n",
        "\t\t\t\t\t# a newline character\n",
        "\t\t\t\t\tif(i !=\" \" and i !=\"\\n\"):\n",
        "\n",
        "\t\t\t\t\t\t# incrementing character\n",
        "\t\t\t\t\t\t# count by 1\n",
        "\t\t\t\t\t\tnum_charc += 1\n",
        "\n",
        "\t# printing total word count\n",
        "\tprint(\"Number of words in text file: \",\n",
        "\t\tnum_words)\n",
        "\n",
        "\t# printing total line count\n",
        "\tprint(\"Number of lines in text file: \",\n",
        "\t\tnum_lines)\n",
        "\n",
        "\t# printing total character count\n",
        "\tprint('Number of characters in text file: ',\n",
        "\t\tnum_charc)\n",
        "\n",
        "\t# printing total space count\n",
        "\tprint('Number of spaces in text file: ',\n",
        "\t\tnum_spaces)\n",
        "\n",
        "# Driver Code:\n",
        "if __name__ == '__main__':\n",
        "\tfname = 'File1.txt'\n",
        "\ttry:\n",
        "\t\tcounter(fname)\n",
        "\texcept:\n",
        "\t\tprint('File not found')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "73X_rqTUCl0V",
        "outputId": "6872b068-7ab4-4f07-b0a1-5d53b6426f0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of words in text file:  9\n",
            "Number of lines in text file:  2\n",
            "Number of characters in text file:  47\n",
            "Number of spaces in text file:  7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Plot a graph $y=f(x)$"
      ],
      "metadata": {
        "id": "EfaV1mdrNmJX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "WOI7MnaCN2Ml"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_cords = range(-50,50)\n",
        "y_cords = [x*x for x in x_cords]\n",
        "\n",
        "plt.plot(x_cords, y_cords)\n",
        "plt.title(r\"power of a variable $y=x^2$\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "UGswhkd7Nq4a",
        "outputId": "8aeecd6e-dc64-427a-a03e-a000b88d629e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## create a bar chart showing area of countries\n"
      ],
      "metadata": {
        "id": "N40chQUcPj8d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "from matplotlib.pyplot import pie, axis, show\n",
        "\n",
        "df = pd.DataFrame([[1,'Africa',11.7],   [2, 'Asia', 1.9],    [3, 'Europe', 9.4], [4, 'North America', 3.3],[5, 'Occania', 6.9],[6, 'South America', 7.9],[7, 'Soviet Union', 7.9]], columns=['ID',  'country',  'Area'])"
      ],
      "metadata": {
        "id": "-ypaWChwPoRM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a140E2rTeI4N",
        "outputId": "bcc130a1-c482-4eba-f0b7-aeff622683de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7 entries, 0 to 6\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   ID       7 non-null      int64  \n",
            " 1   country  7 non-null      object \n",
            " 2   Area     7 non-null      float64\n",
            "dtypes: float64(1), int64(1), object(1)\n",
            "memory usage: 296.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "order = df.groupby('country').mean().sort_values('Area').index\n",
        "# read a titanic.csv file\n",
        "# country v / s area barplot\n",
        "chart=sns.barplot(x = 'country', y = 'Area', data = df,order=order)\n",
        "chart.set_xticklabels(chart.get_xticklabels(), rotation=45,horizontalalignment='right')\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 328
        },
        "id": "rtEEqNbKQUQy",
        "outputId": "ff79952c-b164-4e79-b787-491b991bcb6e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Create a histogram of the following class interval\n",
        "\n",
        "**Type-1:** Create the class interval and plot the histogram."
      ],
      "metadata": {
        "id": "VshkS69zhOXk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import numpy as np\n",
        "selected_data =[10,12,21,13,23,24,32,13,24,35,32,31,34,8,6,7,5,3,4,9,14,26,40]\n",
        "bins=[0,10,20,30,40]\n",
        "df=pd.DataFrame(selected_data,index=range(23),columns=[\"data\"])\n",
        "df[\"Interval\"]=pd.cut(df[\"data\"],bins)\n",
        "table=pd.crosstab(index=df['Interval'],columns=[\"frequency\"])\n"
      ],
      "metadata": {
        "id": "BOLfR3Mderh-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OMrJq_Elrlnp",
        "outputId": "e7d7917b-b802-4345-c58c-8d7eb504fdef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "col_0     frequency\n",
              "Interval           \n",
              "(0, 10]           8\n",
              "(10, 20]          4\n",
              "(20, 30]          5\n",
              "(30, 40]          6"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>col_0</th>\n",
              "      <th>frequency</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Interval</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>(0, 10]</th>\n",
              "      <td>8</td>\n",
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              "    <tr>\n",
              "      <th>(10, 20]</th>\n",
              "      <td>4</td>\n",
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              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>(30, 40]</th>\n",
              "      <td>6</td>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-dd90fc71-6216-4303-827f-d72de09cf414 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dd90fc71-6216-4303-827f-d72de09cf414');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df,x='data',bins=bins, kde = True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "dv6tHqSPvdcc",
        "outputId": "417de8a7-2ecb-4f81-b5d6-22f43dc4012b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "Z204Klnvrb64"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Type 2:** Directly plot the histogram\n",
        "\n",
        ">Problem:\n",
        "Plot the histogram of the following data\n",
        "\\begin{array}{|l|c|c|c|c|}\\hline\n",
        "Height (in\\,\\, cm):&135-140&140-145&145-150&150-155\\\\\\hline\n",
        "No.of\\,\\, students:&4&12&16&8\\\\\\hline\n",
        "\\end{array}\n"
      ],
      "metadata": {
        "id": "aBKReB7LFAmt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data={\"135-140\":4,\"140-145\":12,\"145-150\":16,\"150-155\":8}"
      ],
      "metadata": {
        "id": "4Uv7pLzmF_9t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "sns.barplot(x=list(data.keys()),y=list(data.values()))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "YFg9hzmrGfhV",
        "outputId": "4eedbdcb-ae49-44e3-a125-0ce68e97eb9a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**Task:** Following table contains the population and murder rates ( ie., unit of murders per 100000 people per year) for different states in US. Compute the mean,median, variance and expected number of murders in US based on the data.\n",
        "$\\begin{array}{|l|l|r|r|}\\hline&        State &  Population &  Murder \\\\\\hline 1&      Alabama &     4779736 &     5.7 \\\\\\hline 2 &       Alaska &      710231 &     5.6 \\\\\\hline 3 &      Arizona &     6392017 &     4.7 \\\\\\hline 4 &    Arkansans &     2915918 &     5.6 \\\\\\hline 5 &   California &    37253956 &     4.4 \\\\\\hline 6 &     Colorado &     5029196 &     2.8 \\\\\\hline 7 &  Coenctiecut &     3574097 &     2.4 \\\\\\hline 8 &     Delaware &      897934 &     5.8 \\\\\\hline \\end{array}$"
      ],
      "metadata": {
        "id": "JFXUGIgEHZtk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "State=['Alabama', 'Alaska', 'Arizona', 'Arkansans', 'California', 'Colorado', 'Coenctiecut', 'Delaware']\n",
        "Population=[4779736,710231,6392017,2915918,37253956,5029196,3574097,897934]\n",
        "Murder=[5.7,5.6,4.7,5.6,4.4,2.8,2.4,5.8]"
      ],
      "metadata": {
        "id": "hfsHnePfQRY5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dfm=pd.DataFrame({\"State\":State,\"Population\":Population,\"Murder\":Murder})\n"
      ],
      "metadata": {
        "id": "0c6da0MkKfAb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dfm['Population'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZOENMVuOK9MT",
        "outputId": "3579e8cb-6011-40a5-eff9-54d7458d139d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7694135.625"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfm[['Population','Murder']].var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d0LY93qKTOFs",
        "outputId": "b0b4bfd9-c9c8-4fa7-9336-72e025df9cb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Population    1.465491e+14\n",
              "Murder        1.825000e+00\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dfm[['Population','Murder']].median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YfnlvuijUKDu",
        "outputId": "220bb169-fb49-451f-912f-65b8901b90e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Population    4176916.50\n",
              "Murder              5.15\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calculating the Expected number of murders in US based on the data\n",
        "$$E(X)=\\sum\\limits_{n=0}^N x_nP(x_n)$$"
      ],
      "metadata": {
        "id": "8aghWoeTVBI7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "np.dot(dfm['Population'],(dfm['Murder']/dfm['Murder'].sum()).T)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VS0IkwbFUQ5j",
        "outputId": "63ec5bc8-c3d0-4b13-a710-ef63f6bbc3fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7280497.694594595"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ">**Verification:** The relative proportion defines a probability function.\n",
        "\n",
        "1. $Prop(M)\\geq 0$\n",
        "2. $\\sum Prop(M)=1$"
      ],
      "metadata": {
        "id": "fqcSrFN8VTav"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dfm['Murder']/dfm['Murder'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CpJAuoQ_VeNq",
        "outputId": "67b0537f-0152-40a7-8136-a8020656ca54"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.154054\n",
              "1    0.151351\n",
              "2    0.127027\n",
              "3    0.151351\n",
              "4    0.118919\n",
              "5    0.075676\n",
              "6    0.064865\n",
              "7    0.156757\n",
              "Name: Murder, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.sum(dfm['Murder']/dfm['Murder'].sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NU_dvHt0TNuq",
        "outputId": "48a76380-5581-4b66-c404-f1af68adeb4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##  Find the standard deviation and coefficient of variation for the following frequency distribution\n",
        "\n",
        "\\begin{array}{|c|c|c|c|c|c|c|c|c|}\\hline\n",
        "Class:&0-10&10-20&20-30&30-40&40-50&50-60&60-70&70-80\\\\\\hline\n",
        "Frequency:&5&10&20&40&30&20&10&5\\\\\\hline\n",
        "\\end{array}"
      ],
      "metadata": {
        "id": "t-OeMMw9Vte4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## creating data for calculation\n",
        "midx=[5,15,25,35,45,55,65,75]\n",
        "freqs=[5,10,20,40,30,20,10,5]"
      ],
      "metadata": {
        "id": "4v7IqrXEYZr9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def descriptives_from_agg(midx, freqs):\n",
        "    midx = np.array(midx)\n",
        "    freqs = np.array(freqs)\n",
        "    count = freqs.sum()\n",
        "    fx = midx * freqs\n",
        "    mean = fx.sum() / count\n",
        "    variance = ((freqs * midx**2).sum() / count) - mean**2\n",
        "    variance = count / (count - 1) * variance  # dof correction for sample variance\n",
        "    std = np.sqrt(variance)\n",
        "    minimum = np.min(midx)\n",
        "    maximum = np.max(midx)\n",
        "    CV=std/mean *100\n",
        "    idx = ['count', 'mean','variance', 'std', 'min','max', 'CV']\n",
        "    result = pd.Series([count, mean, variance, std, minimum, maximum,CV], index=idx)\n",
        "    return result"
      ],
      "metadata": {
        "id": "80iH1lynXq6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## calling function\n",
        "descriptives_from_agg(midx, freqs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UFa7Sua6ZFEl",
        "outputId": "7491c7fc-7c1a-41cb-9a52-4b2d6cac78a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count       140.000000\n",
              "mean         39.642857\n",
              "variance    255.267215\n",
              "std          15.977084\n",
              "min           5.000000\n",
              "max          75.000000\n",
              "CV           40.302554\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Binomial problem"
      ],
      "metadata": {
        "id": "KoGbcyRFbgZS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import binom\n",
        "n = 6\n",
        "p = 0.25\n",
        "x = np.arange(0, n+1)\n"
      ],
      "metadata": {
        "id": "HBYlR5Tebi_o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "binomial_pmf = binom.pmf(x,n,p)\n",
        "print(binomial_pmf)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m5BfjMlIbq6D",
        "outputId": "590ec513-d412-4b83-d7b4-3b4be92f0b36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1.77978516e-01 3.55957031e-01 2.96630859e-01 1.31835937e-01\n",
            " 3.29589844e-02 4.39453125e-03 2.44140625e-04]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "binom.pmf(4,n,p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D6_ILj64b73N",
        "outputId": "88758594-10ee-401d-cde1-03acda34ece3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.03295898437499997"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "1-binom.cdf(0,n,p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lyEiMLr9cDa1",
        "outputId": "8217d6b9-42fd-4a22-ffed-b167cf94d3e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.822021484375"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 18. Poisson distribution"
      ],
      "metadata": {
        "id": "h_xslw0bc2q2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import poisson\n",
        "k=6\n",
        "mu=3.4\n",
        "#calculate probability\n",
        "poisson.pmf(k,mu)\n",
        "\n",
        "0.100819"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GXYeTcLpc66j",
        "outputId": "28a789a3-1825-4821-b56b-388f48e2c592"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.100819"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 19. Testing of independence using $\\chi^2$"
      ],
      "metadata": {
        "id": "DtzUzknydMlw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import scipy.stats as stats\n"
      ],
      "metadata": {
        "id": "LkaAAniZduqb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "crosstab=np.array([[60, 54, 46, 41],[40, 44, 53,57]])"
      ],
      "metadata": {
        "id": "bAi6efcyd0Cc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "stats.chi2_contingency(crosstab)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7LmhwLqud0kP",
        "outputId": "fbf661ed-6e2e-48d2-b8db-019dae257889"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8.006066246262538,\n",
              " 0.045886500891747214,\n",
              " 3,\n",
              " array([[50.88607595, 49.86835443, 50.37721519, 49.86835443],\n",
              "        [49.11392405, 48.13164557, 48.62278481, 48.13164557]]))"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:** Here the test statistics, $\\chi^2(3)=8.006$ with p-value $0.045< 0.05$. So the nnull hypotheis that, the distribution is independent is rejected."
      ],
      "metadata": {
        "id": "FpiqEcfReSlO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 20.  Coefficient of correlation"
      ],
      "metadata": {
        "id": "OrOx_qfyeyzr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Hand=np.array([17,15,19,17,21]); Height=np.array([150,154,169,172,175])"
      ],
      "metadata": {
        "id": "ofK0JGsKfXDZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pearsons_coefficient = np.corrcoef(Hand, Height)\n",
        "print(\"The pearson's coeffient of the Hand and Height inputs are: \\n\" ,pearsons_coefficient)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VinOiQcAfAIA",
        "outputId": "1f1c01a8-938c-488f-e4a5-c3828b28dbcc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The pearson's coeffient of the Hand and Height inputs are: \n",
            " [[1.         0.72131472]\n",
            " [0.72131472 1.        ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 21. t-test"
      ],
      "metadata": {
        "id": "qnFl-9LSgSC1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mu, sigma = 22, 3 # mean and standard deviation\n",
        "s = np.random.normal(mu, sigma, 1000)"
      ],
      "metadata": {
        "id": "1X8UFra2gUwo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t_statistic, p = stats.ttest_1samp(a=s, popmean=20,alternative='two-sided')\n",
        "if p > 0.05:\n",
        " print('Probably equal')\n",
        "else:\n",
        " print('Probably not equal')\n",
        "print(t_statistic , p)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l__YreNWg7sX",
        "outputId": "ca3b4d04-0bd3-49f7-956b-bf33b869f0d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Probably not equal\n",
            "21.281656793515168 3.53432168940205e-83\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 22. Missing data"
      ],
      "metadata": {
        "id": "Mqy3h51lgR8_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 23. Obtain a regression model to given data"
      ],
      "metadata": {
        "id": "Gon43ILdkLiN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## reading data\n",
        "JS=np.array([34,23,19,43,56,47,32,16,55,25])\n",
        "SBP=np.array([124,128,157,133,116,125,147,167,110,156])\n",
        "dfl=pd.DataFrame({\"Js\":JS,\"SBP\":SBP})"
      ],
      "metadata": {
        "id": "2R53knemkj5y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from statsmodels.formula.api import ols\n",
        "import statsmodels.api as sm\n",
        "model = ols('SBP ~ JS', data=dfl).fit()\n"
      ],
      "metadata": {
        "id": "t5hLr_b_kQ8l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 504
        },
        "id": "mrhoPWmMlOrJ",
        "outputId": "cdbda53c-2a53-4cee-96ef-e598a2ccdaac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/scipy/stats/stats.py:1541: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=10\n",
            "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                    SBP   R-squared:                       0.719\n",
              "Model:                            OLS   Adj. R-squared:                  0.684\n",
              "Method:                 Least Squares   F-statistic:                     20.47\n",
              "Date:                Tue, 24 Jan 2023   Prob (F-statistic):            0.00194\n",
              "Time:                        07:01:42   Log-Likelihood:                -36.893\n",
              "No. Observations:                  10   AIC:                             77.79\n",
              "Df Residuals:                       8   BIC:                             78.39\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "Intercept    175.4198      9.300     18.863      0.000     153.974     196.865\n",
              "JS            -1.1177      0.247     -4.524      0.002      -1.687      -0.548\n",
              "==============================================================================\n",
              "Omnibus:                        4.926   Durbin-Watson:                   1.123\n",
              "Prob(Omnibus):                  0.085   Jarque-Bera (JB):                2.280\n",
              "Skew:                          -1.168   Prob(JB):                        0.320\n",
              "Kurtosis:                       3.098   Cond. No.                         102.\n",
              "==============================================================================\n",
              "\n",
              "Notes:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>           <td>SBP</td>       <th>  R-squared:         </th> <td>   0.719</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.684</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   20.47</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Tue, 24 Jan 2023</td> <th>  Prob (F-statistic):</th>  <td>0.00194</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>07:01:42</td>     <th>  Log-Likelihood:    </th> <td> -36.893</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    10</td>      <th>  AIC:               </th> <td>   77.79</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>     8</td>      <th>  BIC:               </th> <td>   78.39</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Intercept</th> <td>  175.4198</td> <td>    9.300</td> <td>   18.863</td> <td> 0.000</td> <td>  153.974</td> <td>  196.865</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>JS</th>        <td>   -1.1177</td> <td>    0.247</td> <td>   -4.524</td> <td> 0.002</td> <td>   -1.687</td> <td>   -0.548</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td> 4.926</td> <th>  Durbin-Watson:     </th> <td>   1.123</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.085</td> <th>  Jarque-Bera (JB):  </th> <td>   2.280</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td>-1.168</td> <th>  Prob(JB):          </th> <td>   0.320</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td> 3.098</td> <th>  Cond. No.          </th> <td>    102.</td>\n",
              "</tr>\n",
              "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "aov_table = sm.stats.anova_lm(model, typ=2)\n",
        "aov_table"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "XeTP6vFvlNFp",
        "outputId": "09f5f1df-5cdc-4a1d-9b41-1836f8f13b72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               sum_sq   df          F    PR(>F)\n",
              "JS        2398.602083  1.0  20.468117  0.001939\n",
              "Residual   937.497917  8.0        NaN       NaN"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th>sum_sq</th>\n",
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              "      <th>PR(&gt;F)</th>\n",
              "    </tr>\n",
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              "      <th>JS</th>\n",
              "      <td>2398.602083</td>\n",
              "      <td>1.0</td>\n",
              "      <td>20.468117</td>\n",
              "      <td>0.001939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Residual</th>\n",
              "      <td>937.497917</td>\n",
              "      <td>8.0</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "        buttonEl.style.display =\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def anova_table(aov):\n",
        "    aov['mean_sq'] = aov[:]['sum_sq']/aov[:]['df']\n",
        "\n",
        "    aov['eta_sq'] = aov[:-1]['sum_sq']/sum(aov['sum_sq'])\n",
        "\n",
        "    aov['omega_sq'] = (aov[:-1]['sum_sq']-(aov[:-1]['df']*aov['mean_sq'][-1]))/(sum(aov['sum_sq'])+aov['mean_sq'][-1])\n",
        "\n",
        "    cols = ['sum_sq', 'df', 'mean_sq', 'F', 'PR(>F)', 'eta_sq', 'omega_sq']\n",
        "    aov = aov[cols]\n",
        "    return aov\n",
        "\n",
        "anova_table(aov_table)"
      ],
      "metadata": {
        "id": "6hftGGcLmZJs",
        "outputId": "327e2533-bf5e-4d9a-9baa-fde86ccdd3ce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               sum_sq   df      mean_sq          F    PR(>F)    eta_sq  \\\n",
              "JS        2398.602083  1.0  2398.602083  20.468117  0.001939  0.718984   \n",
              "Residual   937.497917  8.0   117.187240        NaN       NaN       NaN   \n",
              "\n",
              "          omega_sq  \n",
              "JS         0.66065  \n",
              "Residual       NaN  "
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          "metadata": {},
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}