Untitled18.ipynb

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  "metadata": {
    "colab": {
      "name": "Untitled18.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "11IWMgnTGZXG_DTgHIw-tLner40wKZTPn",
      "authorship_tag": "ABX9TyOFqUM5lSjHGEUxnkszgs8t",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nikhilkumarreddy/jio-analysis/blob/master/Untitled18.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFCSK_ZTxR-l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "d22cdc1c-30fc-4a6f-c6cd-12aafbef2532"
      },
      "source": [
        "!pip install PyPDF2 "
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting PyPDF2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b4/01/68fcc0d43daf4c6bdbc6b33cc3f77bda531c86b174cac56ef0ffdb96faab/PyPDF2-1.26.0.tar.gz (77kB)\n",
            "\r\u001b[K     |████▎                           | 10kB 19.4MB/s eta 0:00:01\r\u001b[K     |████████▌                       | 20kB 1.8MB/s eta 0:00:01\r\u001b[K     |████████████▊                   | 30kB 2.3MB/s eta 0:00:01\r\u001b[K     |█████████████████               | 40kB 2.6MB/s eta 0:00:01\r\u001b[K     |█████████████████████▏          | 51kB 2.1MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▍      | 61kB 2.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 71kB 2.6MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 81kB 2.3MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: PyPDF2\n",
            "  Building wheel for PyPDF2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for PyPDF2: filename=PyPDF2-1.26.0-cp36-none-any.whl size=61086 sha256=caf4e2b616942366a097a2e9a6d30866a3c8cd6d7d41d9f313a3c77a5ba54997\n",
            "  Stored in directory: /root/.cache/pip/wheels/53/84/19/35bc977c8bf5f0c23a8a011aa958acd4da4bbd7a229315c1b7\n",
            "Successfully built PyPDF2\n",
            "Installing collected packages: PyPDF2\n",
            "Successfully installed PyPDF2-1.26.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7vrr1ufmO3P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import time\n",
        "import PyPDF2\n",
        "import re\n",
        "import os\n",
        "import matplotlib.pyplot as plt "
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xnyDvNeoba0M",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6687f9e0-c2b3-48d9-c6d4-95bb61ff66dd"
      },
      "source": [
        "\n",
        "def data(ab): \n",
        "  dataa = ''\n",
        "  for i in ab:\n",
        "    if i == '.':\n",
        "      x = ab.index(i)\n",
        "      #print(x)\n",
        "  dataa = ab[:x]+ab[x:x+4]\n",
        "  return float(dataa)\n",
        "  \n",
        "\n",
        "print(data('1064.8251064.8251064.8250.0000.0'))\n",
        "\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1064.825\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y9Y5-psTJFt5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "016ca49c-9f9d-47a0-c92a-e6d12350c251"
      },
      "source": [
        "\n",
        "start = time.process_time()\n",
        "total = ''\n",
        "dict ={ } \n",
        "\n",
        "\n",
        "for file in os.listdir(\"/content/drive/My Drive/jio/jio1\"):\n",
        "\n",
        "    if file.endswith(\".pdf\"):\n",
        "        FileName = os.path.join(\"/content/drive/My Drive/jio/jio1\", file)\n",
        "        pdf = open(FileName, \"rb\")\n",
        "           \n",
        "        Pdf = PyPDF2.PdfFileReader(pdf)\n",
        "     \n",
        "        NumPages = Pdf.getNumPages()\n",
        "        \n",
        "        for i in range(0, NumPages):\n",
        "\n",
        "          PageObj = Pdf.getPage(i)\n",
        "          total = PageObj.extractText()\n",
        "          \n",
        "          string = total\n",
        "          pattern = 'JIO'\n",
        "          finall = [match.start() for match in re.finditer(pattern, string)] \n",
        "\n",
        "#find the date \n",
        "          for j in finall:\n",
        "            abc = ''\n",
        "            for i in range(j-36, j):\n",
        "              abc +=total[i]\n",
        "              dates = abc[:9]\n",
        "#find the data used\n",
        "\n",
        "            abcd = ''\n",
        "            for k in range(j+6 , j+15):\n",
        "              abcd += total[k]\n",
        "            datas = data(abcd)\n",
        "\n",
        "#push it into dictionary and calculates the data \n",
        "            if dates in dict:\n",
        "              dict[dates] = dict.get(dates) + datas\n",
        " \n",
        "            else:\n",
        "              dict[dates] = datas\n",
        "                \n",
        "\n",
        "#calculate the average data used                  \n",
        "print(\"average DATA used per DAY =\" , sum(dict.values())/len(dict))   \n",
        "\n",
        "print(time.process_time() - start)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "average DATA used per DAY = 1676.8522\n",
            "0.5253394100000004\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEBnHiEfe-tg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 222
        },
        "outputId": "d9956e0e-f484-46b5-c193-aaf685f83e90"
      },
      "source": [
        "total"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Jio Number : 8639387079Mr. Bhoge Anandareddy  Statement Date : 28-Aug-2020No.DateNumberTotal UsageFree UsageBilled UsageAmount(Nos)(Nos)(Nos)(`)(A)(B)(C = A-B)1307-MAR-20 19:25:259182978379151100.001407-MAR-20 19:24:119182978379151100.001507-MAR-20 19:23:569182978379151100.001607-MAR-20 19:21:529182978379151100.001707-MAR-20 19:21:059182978379151100.001807-MAR-20 19:20:379182978379151100.001907-MAR-20 19:20:299182978379151100.002007-MAR-20 19:20:159182978379151100.002107-MAR-20 09:59:119178160302881100.002207-MAR-20 09:58:589178160302881100.002307-MAR-20 09:58:569178160302881100.002405-MAR-20 21:42:239178160302881100.002505-MAR-20 21:42:179178160302881100.002605-MAR-20 21:42:139178160302881100.002705-MAR-20 20:57:179178160302881100.002805-MAR-20 20:50:529178160302881100.002905-MAR-20 20:50:369178160302881100.003005-MAR-20 20:50:319178160302881100.003105-MAR-20 20:50:289178160302881100.003205-MAR-20 20:49:469178160302881100.003305-MAR-20 20:49:379178160302881100.003405-MAR-20 20:44:199178160302881100.003505-MAR-20 20:43:579178160302881100.003605-MAR-20 20:43:289178160302881100.003705-MAR-20 20:43:029178160302881100.003804-MAR-20 20:21:219178160302881100.003904-MAR-20 20:21:179178160302881100.004004-MAR-20 20:20:529178160302881100.004104-MAR-20 20:20:449178160302881100.004204-MAR-20 20:20:429178160302881100.004304-MAR-20 20:18:449178160302881100.004404-MAR-20 20:18:269178160302881100.004504-MAR-20 20:18:239178160302881100.004604-MAR-20 20:18:029178160302881100.004704-MAR-20 15:24:019189195060691100.004804-MAR-20 15:23:509189195060691100.004903-MAR-20 22:41:319178160302881100.005002-MAR-20 13:42:159178160302881100.00-Subtotal50505000.00Usage in India Total50505000.00SMS Total50505000.00Page 10 of 10'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4UKVSqWT2rBj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "ea415c5d-c495-4c36-82f7-826ccee8c0a3"
      },
      "source": [
        "pdf = open('/content/drive/My Drive/jio/jio1/Statement1598629561193.pdf', \"rb\")\n",
        "Pdf = PyPDF2.PdfFileReader(pdf)\n",
        "NumPages = Pdf.getNumPages()\n",
        "PageObj = Pdf.getPage(1)\n",
        "total = PageObj.extractText()   \n",
        "print(total.find('/day'))\n",
        "print(total[3509-1])\n",
        "\n"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "3509\n",
            "b\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jg2svfnvi0yx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "400d0fcb-3c71-4e09-bf7a-2beb8b3494e1"
      },
      "source": [
        "def data_plan(total):\n",
        "  plan_name = ''\n",
        "  day = total.find('/day')\n",
        "\n",
        "  for i in range(day-4 , day):\n",
        "    plan_name += total[i]\n",
        "\n",
        "  return plan_name\n",
        "    \n",
        "print(data_plan(total))"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2 Gb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PC3KvhvANfv4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keys = dict.keys()\n",
        "values = dict.values()\n",
        "plt.bar(keys, values)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WFW2E-NbDce9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "acd6d18c-2663-4d2f-9203-3c4a789497c4"
      },
      "source": [
        "\n",
        "import re\n",
        "\n",
        "string = total\n",
        "pattern = 'JIO'\n",
        "\n",
        "finall = [match.start() for match in re.finditer(pattern, string)]\n",
        "print(finall)\n",
        "print(len(finall))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[]\n",
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4zLRMT-Dy3Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dict ={ } \n",
        "\n",
        "\n",
        "\n",
        "for j in finall:\n",
        "\n",
        "  abc = ''\n",
        "  for i in range(j-36, j):\n",
        "    abc +=total[i]\n",
        "    dates = abc[:9]\n",
        "  print(dates)\n",
        "   \n",
        "\n",
        "\n",
        "  abcd = ''\n",
        "  for k in range(j+6 , j+15):\n",
        "    abcd += total[k]\n",
        "\n",
        "  datas = data(abcd)\n",
        "  print(datas)\n",
        "\n",
        "\n",
        "  if dates in dict:\n",
        "    dict[dates] = dict.get(dates) + datas\n",
        "  else:\n",
        "    dict[dates] = datas\n",
        "\n",
        "\n",
        "print(sum(dict.values())/len(dict))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4PPl9Od6ZiPj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from datetime import datetime\n",
        "\n",
        "rdered_data = sorted(data.items(), key = lambda x:datetime.strptime(x[0], '%d-%m-%Y'), reverse=True)\n",
        "\n",
        "print(ordered_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xNhZjwqUmyNY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bbd670fe-3c26-4af8-f944-62caeb31ae5c"
      },
      "source": [
        "print(sum(dict.values())/len(dict))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2525.0438851351355\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_al7Ft3U7zDh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f634437f-23df-4360-b924-291c3d4e9ec0"
      },
      "source": [
        "# gives the start date and end date\n",
        "\n",
        "\n",
        "num = total.find('from')\n",
        "\n",
        "def dates(num):\n",
        "  \n",
        "  start = ''\n",
        "  a = num + 5\n",
        "  b = a + 11\n",
        "\n",
        "  for i in range(a , b ):\n",
        "    start += total[i]\n",
        "\n",
        "  end =''\n",
        "  a = b + 4\n",
        "  b = a + 11\n",
        "  for i in range(a , b):\n",
        "    end += total[i]\n",
        "    \n",
        "  \n",
        "  return start , end\n",
        "\n",
        "  \n",
        "print(dates(num))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('Number : 86', '7079Mr. Bho')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRlwlyN9dF3W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zAb1lrHJc6O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "def date_time(finall):\n",
        "  for j in finall:\n",
        "\n",
        "    abc = ''\n",
        "    for i in range(j-36, j):\n",
        "      abc +=total[i]\n",
        "      dates = abc[:9]\n",
        "    #print(dates)\n",
        "    \n",
        "\n",
        "\n",
        "    abcd = ''\n",
        "    for k in range(j+6 , j+15):\n",
        "      abcd += total[k]\n",
        "\n",
        "    datas = data(abcd)\n",
        "    #print(datas)\n",
        "\n",
        "    if dates in dict:\n",
        "       dict[dates] = dict.get(dates) + datas\n",
        "\n",
        "    else:\n",
        "        dict[dates] = datas\n",
        "\n",
        "    return dict\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mX1pbSabkLkm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "80307d6f-2ebf-474a-eaf7-aeeb704b3b69"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
        "labels = 'Frogs', 'Hogs' \n",
        "sizes = [45, 60, ]\n",
        "explode = (0, 0.1)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
        "\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax1.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iRa5ORHukMLy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}