10_File_Handling_+_Serialization_&_Deserialization.ipynb

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  "metadata": {
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      "authorship_tag": "ABX9TyP84+WeQ2M5FmFNKftVDSXJ",
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AzadMehedi/Python/blob/main/10_File_Handling_%2B_Serialization_%26_Deserialization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8qypjqqWf4Dw"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Some Theory\n",
        "\n",
        "##### Types of data used for I/O:\n",
        "- Text - '12345' as a sequence of unicode chars\n",
        "- Binary - 12345 as a sequence of bytes of its binary equivalent\n",
        "\n",
        "##### Hence there are 2 file types to deal with\n",
        "- Text files - All program files are text files\n",
        "- Binary Files - Images,music,video,exe files"
      ],
      "metadata": {
        "id": "WzAGmXL45eIa"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7qheD-W85fSo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### How File I/O is done in most programming languages\n",
        "\n",
        "- Open a file\n",
        "- Read/Write data\n",
        "- Close the file"
      ],
      "metadata": {
        "id": "8JDok8Nd5hs6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Writing to a file"
      ],
      "metadata": {
        "id": "_kC3muQc5n-1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# case 1 - if the file is not present\n",
        "f = open('sample.txt','w')\n",
        "f.write('Hello World')\n",
        "f.close()\n",
        "# since file is closed this will not work\n",
        "f.write('hello')"
      ],
      "metadata": {
        "id": "MmsAU7Si5ilg",
        "outputId": "d7595ae7-be1e-4e1c-9cff-5eefb38b0bb1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-79a9b31ee332>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m# since file is closed this will not work\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hello'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# write multiple strings\n",
        "f = open('sample1.txt','w')\n",
        "f.write('Hello World')\n",
        "f.write('\\nHow are you?')\n",
        "f.close()"
      ],
      "metadata": {
        "id": "bgQKA2ga8ssx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# case 2 - if the file is already present\n",
        "f = open('sample.txt','w')\n",
        "f.write('Salman khan')\n",
        "f.close()"
      ],
      "metadata": {
        "id": "JC5o9Y1R-Mf8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Problem with w mode\n",
        "# introducing append mode\n",
        "f = open('sample1.txt','a')   # using 'a' is allow us to write new things without removing previous.\n",
        "f.write('\\nI am fine.')\n",
        "f.close()"
      ],
      "metadata": {
        "id": "_DNizt9Z-rTu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# write lines\n",
        "L = 'My name is Mehedi Hasan.','\\nI studied CSE','\\nI have a dream'\n",
        "f = open('/content/sample.txt','w')\n",
        "f.writelines(L)\n",
        "f.close()"
      ],
      "metadata": {
        "id": "aXgQl6tKBJsz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reading from files\n",
        "# -> using read()\n",
        "f = open('/content/sample.txt', 'r')\n",
        "s = f.read()\n",
        "print(s)\n",
        "f.close( )"
      ],
      "metadata": {
        "id": "nXjA0iK8C_cQ",
        "outputId": "0dd5a9e1-71f6-4fb6-b150-7aa057de30d9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is Mehedi Hasan.\n",
            "I studied CSE\n",
            "I have a dream\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reading 10 characters\n",
        "# -> using read()\n",
        "f = open('/content/sample.txt', 'r')\n",
        "s = f.read(10)\n",
        "print(s)\n",
        "f.close( )"
      ],
      "metadata": {
        "id": "Wgf3aBb7IXi-",
        "outputId": "a5374d32-aa06-47b2-c55e-25fc6a7dd6ef",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# readline() -> to read line by line\n",
        "f = open('/content/sample.txt','r')\n",
        "print(f.readline(), end='')\n",
        "print(f.readline(),end='')\n",
        "f.close()"
      ],
      "metadata": {
        "id": "N05lZRZrIjB_",
        "outputId": "6a0d6c9d-c634-4340-c629-141dee79e40e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is Mehedi Hasan.\n",
            "I studied CSE\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reading entire using readline\n",
        "f = open('/content/sample.txt','r')\n",
        "while True:\n",
        "\n",
        "  data = f.readline()\n",
        "  if data == '':\n",
        "    break\n",
        "  else:\n",
        "    print(data, end='')\n",
        "\n",
        "f.close()"
      ],
      "metadata": {
        "id": "e9ATH3YTIjJe",
        "outputId": "2e8bedf3-9b0f-450c-9f28-1fa97e78e177",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is Mehedi Hasan.\n",
            "I studied CSE\n",
            "I have a dream"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Using Context Manager (With)\n",
        "\n",
        "- It's a good idea to close a file after usage as it will free up the resources\n",
        "- If we dont close it, garbage collector would close it\n",
        "- with keyword closes the file as soon as the usage is over"
      ],
      "metadata": {
        "id": "grikRwDVMFB9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# with\n",
        "with open('/content/sample1.txt','w') as f:\n",
        "  f.write('Selmon vai')\n",
        "\n"
      ],
      "metadata": {
        "id": "yJCCSkWVK5Gd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f.write('hello')  # not working because uses of 'with', it close the file after work is done"
      ],
      "metadata": {
        "id": "7m3wXBSca1NO",
        "outputId": "43a41ebe-07b0-4adf-a857-fdc13a4751c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-11-ea620e87a41c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'hello'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# not working because uses of 'with', it close the file after work is done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m: I/O operation on closed file."
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# tell function - to know where is what is the next situation\n",
        "# seek function - to drug and drop the access as wish\n",
        "with open('sample.txt','r') as f:\n",
        "  print(f.read(10))\n",
        "  print(f.tell()) \n",
        "  f.seek(0)\n",
        "  print(f.read())\n",
        "  print(f.tell())\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "m0gX0Snua_Sz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39130ef0-65de-4c10-eed3-9cbd709aa950"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "My name is\n",
            "10\n",
            "My name is Mehedi Hasan.\n",
            "I studied CSE\n",
            "I have a dream\n",
            "53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "f = open('text.txt','w')\n",
        "f.write('Hello, My name is Mehedi Azad. I lived in Noyatola mogbazar dhaka. i studied B.Sc. in computer science') \n",
        "f.close()"
      ],
      "metadata": {
        "id": "wyBE199uKgco"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tell and seek function\n",
        "with open('text.txt','r') as f:\n",
        "  print(f.read(10))\n",
        "  print(f.tell())\n",
        "  f.seek(15)\n",
        "  print(f.read(10))\n",
        "  print(f.tell())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "py3pyQMrNuWu",
        "outputId": "e3c891db-3217-4193-80dc-3f8c765bd3c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hello, My \n",
            "10\n",
            "is Mehedi \n",
            "25\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# seek during write\n",
        "with open('text.txt','w') as f:\n",
        "  f.write('Hello')\n",
        "  f.seek(0)\n",
        "  f.write('X')"
      ],
      "metadata": {
        "id": "jIZlczs0NyZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Problems with working in text mode\n",
        "\n",
        "- can't work with binary files like images\n",
        "- not good for other data types like int/float/list/tuples"
      ],
      "metadata": {
        "id": "y9HmNifzVyB-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "with open('screenshot.jpg','r') as f:\n",
        "  f.read()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "qxkbDH1TVzhl",
        "outputId": "b5a7a767-f3bb-48f7-b367-4a2b74f5435c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "UnicodeDecodeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-cc1d52bcfce9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'screenshot.jpg'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.8/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# working with binary file\n",
        "with open('screenshot.png','rb') as f:           # rb = read binary\n",
        "  with open('screenshot_copy.png', 'wb') as wf:\n",
        "    wf.write(f.read())"
      ],
      "metadata": {
        "id": "5nPHKvwEWc4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# working with other data type\n",
        "with open('text.txt','w') as f:\n",
        "  f.write(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "fuNtKPxbZErg",
        "outputId": "29bb5ced-a55b-4bef-c2b2-ce938ae2aaa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-28-f7ec8edd825a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# working with other data type\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'text.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m   \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: write() argument must be str, not int"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('text.txt','w') as f:\n",
        "  f.write('5')"
      ],
      "metadata": {
        "id": "AbjtlJKuZqJz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('sample.txt','r') as f:\n",
        "  print(f.read() + 5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 184
        },
        "id": "k713EUlLZrLn",
        "outputId": "b33b034c-1b23-4b7c-fee4-927ca3c67323"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-32-6d6a05f94ee4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sample.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open('text.txt','r') as f:\n",
        "  print(int(f.read()) + 5)  # have to use type casting"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b3NIGAbfZ0CL",
        "outputId": "63562ed7-8972-4c7d-d5d6-87ab616ce5cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# more complex data type\n",
        "d = {\n",
        "    'name': 'Evan',\n",
        "    'age': 26,\n",
        "     'gender': 'Male'\n",
        "}\n",
        "with open('text.txt','w') as f:\n",
        "  f.write(d)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "-bzyd-Tqa2x8",
        "outputId": "55a8d981-3cc1-4efd-fb30-7b5b9794dab8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-36-73c5b09b2e35>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m }\n\u001b[1;32m      7\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'text.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m   \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: write() argument must be str, not dict"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "d = {\n",
        "    'name': 'Evan',\n",
        "    'age': 26,\n",
        "     'gender': 'Male'\n",
        "}\n",
        "with open('sample.txt','w') as f:\n",
        "  f.write(str(d))        # have to use type casting"
      ],
      "metadata": {
        "id": "Gvkj7f4FbfES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('sample.txt','r') as f:\n",
        "  print(dict(f.read()))\n",
        "  # print(type(f.read))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 201
        },
        "id": "9RhJvyVLb2m2",
        "outputId": "47d2bb11-2b8b-40fa-bfca-3014c569d9e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-49-e193059e15d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'sample.txt'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m   \u001b[0;31m# print(type(f.read))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: dictionary update sequence element #0 has length 1; 2 is required"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Serialization and Deserialization\n",
        "\n",
        "- **Serialization** - process of converting python data types to JSON format\n",
        "- **Deserialization** - process of converting JSON to python data types\n",
        "\n",
        "#### What is JSON?\n",
        "\n",
        "\n",
        "![image.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "8UONCLIMdhI3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "######JSON - javascript on notation.\n",
        "- It's kind of universal data formate understood by every language "
      ],
      "metadata": {
        "id": "qP5UJs_kfMva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# serialization using json module\n",
        "# list\n",
        "\n",
        "import json\n",
        "L = [1,2,3,4]\n",
        "with open('demo.json','w') as f:\n",
        "  json.dump(L,f)             # dumping file, file handler object"
      ],
      "metadata": {
        "id": "J4-EeKYkcJot"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# dict\n",
        "d = {\n",
        "    'name': 'Evan',\n",
        "    'age': 26,\n",
        "     'gender': 'Male'\n",
        "}\n",
        "with open('demo.json','w') as f:\n",
        "  json.dump(d,f, indent=4)    # indent for better visualization"
      ],
      "metadata": {
        "id": "xKw6U7flkjdB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# deserialization\n",
        "import json\n",
        "with open('demo.json', 'r') as f:\n",
        "  print(json.load(f))       # only file handler object\n",
        "  \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tGoVYyelAr9",
        "outputId": "8a884572-7366-476d-bce5-39337f99a22b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'name': 'Evan', 'age': 26, 'gender': 'Male'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# serialize and deserialize tuple\n",
        "\n",
        "import json\n",
        "t = (1,2,3,4,5)\n",
        "\n",
        "with open('demo.json', 'w') as f:\n",
        "  json.dump(t,f)"
      ],
      "metadata": {
        "id": "7JSOm2z1lhE1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# serialize and deserialize a nested dict\n",
        "\n",
        "import json\n",
        "d = {\n",
        "    'name': 'Mehedi'\n",
        "    'marks': [43,74,25,87]\n",
        "}\n",
        "\n",
        "with open('demo.json','w') as f:\n",
        "  json.dump(d,f)\n"
      ],
      "metadata": {
        "id": "1jvQiNWYmuxp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Serializing and Deserializing custom objects"
      ],
      "metadata": {
        "id": "0Z6U-hyZXhm2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Person:\n",
        "  \n",
        "  def __init__(self,fname,lname,age,gender):\n",
        "    self.fname = fname\n",
        "    self.lname = lname\n",
        "    self.age = age\n",
        "    self.gender = gender\n",
        "\n",
        "    # format to printed in\n",
        "    # name->Mehedi Azad    age->26     gender->make\n",
        "\n",
        "person = Person('Mehedi', 'Azad', 26, 'male') "
      ],
      "metadata": {
        "id": "WOnbGq57oHdn"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# as a string\n",
        "import json\n",
        "\n",
        "def show_object(person):\n",
        "  if isinstance(person, Person):\n",
        "    return 'name-> {} {} age-> {} gender-> {}'.format(person.fname,person.lname,person.age,person.gender)\n",
        "    \n",
        "with open('demo.json','w') as f:\n",
        "  json.dump(person, f, default= show_object)"
      ],
      "metadata": {
        "id": "pzFGMLutYiW5"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# as a dict\n",
        "import json\n",
        "\n",
        "def show_object(person):\n",
        "  if isinstance(person, Person):\n",
        "    return {'name':person.fname+' '+person.lname, 'age': person.age, 'gender':person.gender}\n",
        "    \n",
        "with open('demo.json','w') as f:\n",
        "  json.dump(person, f, default= show_object, indent=4)"
      ],
      "metadata": {
        "id": "nMv8uefRY2Np"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# deserializig\n",
        "import json\n",
        "\n",
        "with open('demo.json','r') as f:\n",
        "  d = json.load(f)\n",
        "  print(d)\n",
        "  print(type(f))"
      ],
      "metadata": {
        "id": "qKzN3PDLdp34",
        "outputId": "1879b0e3-1d81-4fe1-eed7-40ac497c17cf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'name': 'Mehedi Azad', 'age': 26, 'gender': 'male'}\n",
            "<class '_io.TextIOWrapper'>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pickling\n",
        "`Pickling` is the process whereby a Python object hierarchy is converted into a byte stream, and `unpickling` is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy."
      ],
      "metadata": {
        "id": "UoipuOUKeS18"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Person:\n",
        "  def __init__(self,name,age):\n",
        "    self.name = name\n",
        "    self.age = age\n",
        "\n",
        "  def display_info(self):\n",
        "    print('Hi, my name is',self.name, 'and i am',self.age,'years old')"
      ],
      "metadata": {
        "id": "ffU8bTV0lW0I"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p = Person('Mehedi', 26)"
      ],
      "metadata": {
        "id": "H7jSUIaWl1sD"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pickle dump\n",
        "import pickle \n",
        "with open('person.pkl','wb') as f:\n",
        "  pickle.dump(p,f)"
      ],
      "metadata": {
        "id": "AUjwc0g7eT7r"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# pickle load\n",
        "import pickle\n",
        "with open('person.pkl','rb') as f:\n",
        "  p = pickle.load(f)\n",
        "\n",
        "p.display_info()"
      ],
      "metadata": {
        "id": "-j8fLm-5mQbQ",
        "outputId": "4908e4c8-d897-4344-ed2c-28359ccd8948",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Hi, my name is Mehedi and i am 26 years old\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Both json and pickle use for serialize and deserialize.\n",
        "\n",
        "### Pickle Vs Json\n",
        "\n",
        "- Pickle lets the user to store data in binary format. JSON lets the user store data in a human-readable text format."
      ],
      "metadata": {
        "id": "JR-EOOTXnHBc"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RImBiALbm9cH"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}