01: PyTesseract_End_to_End_Invoice.ipynb

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      "name": "01: PyTesseract_End_to_End_Invoice.ipynb",
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      "include_colab_link": true
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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/gulabpatel/OCR/blob/main/01%3A%20PyTesseract_End_to_End_Invoice.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIfRTICIVBfb"
      },
      "source": [
        "Use OpenCV or NLTK kind of library to read text (OCR) from it and categories all images in the categories like Home utility, Grocery, Shopping, Vehicle expenditure, Entertainment and Investment. \n",
        "1. Extract the text from the images and store it in categorical wise CSV file. Each category \n",
        "have separate CSV file.\n",
        "2. Each CSV file store the information like date of bill, organization name which generate \n",
        "the bill, bill amount and tax information(if any) \n",
        "3. Show table which is showing the expenses details category wise and display them in \n",
        "pie chart. \n",
        " 4. Give suggestion to user, based on his expenditure which lead to increase the saving. \n",
        "\n",
        "Video walkthrough the code : https://www.youtube.com/watch?v=qfO9gqqMWxU\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WY8eS9nyVBff"
      },
      "source": [
        "18BCE168\n",
        "SHAILI PATEL\n",
        "2S404\n",
        "C DIVISION"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LoFWW6B-Q7vy"
      },
      "source": [
        "!sudo apt install tesseract-ocr"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3ESOwFgAZhTn"
      },
      "source": [
        "pip install -U git+https://github.com/madmaze/pytesseract.git"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W9LYS5G0VBff"
      },
      "source": [
        "from PIL import Image\n",
        "import pytesseract\n",
        "import argparse\n",
        "import cv2\n",
        "import os\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import csv\n",
        "import nltk\n",
        "import re\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.corpus import wordnet "
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L277CFlkVBfg"
      },
      "source": [
        "read the below dummy model as i have elaborated every step after the code.\n",
        "the below code shows how i created 7 csv files .i have gave demo of one bill in this notbook."
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "hWua7agHVBfh"
      },
      "source": [
        "#lets make a protopye of first qustion\n",
        "#to scan 100 images \n",
        "\n",
        "''' #question 1\n",
        "    for i in range(0,100):\n",
        "     image=cv2.imread('bill{}.png'.format(i),0)\n",
        "\n",
        "     text=(pytesseract.image_to_string(image)).lower()\n",
        "     print(text)\n",
        "     \n",
        "     use the operaations to create list which contains date,organisation name and amount\n",
        "     store it in a list\n",
        "     \n",
        "    #create a csv file\n",
        "    file=['entertainment','investment','shopping','grocery','transport','home','others']\n",
        "    for i in file:\n",
        "    with open('{}.csv',format(file[i]), 'a', newline='') as csvfile:\n",
        "        w = csv.writer(csvfile, delimiter=',',quoting=csv.QUOTE_MINIMAL)\n",
        "        w.writerow(['date','organisation','amount'])\n",
        "    #question 2->appening the contents\n",
        "    #after identifing the category of the file\n",
        "    #add the contents in thier respective csv file\n",
        "    row_contents = [st,head,x]\n",
        "    from csv import writer\n",
        " \n",
        "        def append_list_as_row(file, list_of_elem):\n",
        "   \n",
        "            with open(file, 'a+', newline='') as write_obj:\n",
        "       \n",
        "            csv_writer = writer(write_obj)\n",
        "        \n",
        "            csv_writer.writerow(list_of_elem)\n",
        "    append_list_as_row(filename, row_contents)\n",
        "    #this is how appending was done.now you can save the notebook to make sure that csv file is saved.\n",
        "    #and then begain with the data analysis part.\n",
        "    \n",
        "        \n",
        "    '''\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2sG96N5VBfh"
      },
      "source": [
        "here is demo of one bill that i scaned.i downloaded few bills from internet as those category bills were not available at home."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VY-QDnxyVBfi"
      },
      "source": [
        "#question one\n",
        "#lets read an image   ###!wget https://github.com/gulabpatel/OCR/blob/main/dataset/bill3.png\n",
        "image=cv2.imread('/content/bill3.png',0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJw6KqshVBfj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "090aa349-b75e-434f-8218-2c2d1d200f41"
      },
      "source": [
        "#convert it into text\n",
        "text=(pytesseract.image_to_string(image)).lower()\n",
        "print(text)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "ying thai kitchen\n",
            "2220 queen anne ave n\n",
            "seattle wa 98109\n",
            "« (206) 285-8424 fax. (206) 285-8427\n",
            "‘uw .yingthaikitchen.com\n",
            "welcome to ying thai kitchen restaurant,\n",
            "\n",
            " \n",
            "\n",
            " \n",
            "\n",
            " \n",
            "\n",
            "order#:17 table 2\n",
            "date: 7/4/2013 7:28 pm\n",
            "\n",
            "server: jack (1.4)\n",
            "44 ginger lover $9.50\n",
            "[pork] [24#]\n",
            "\n",
            "brown rice $2.00\n",
            "total 2 iten(s) $11.50\n",
            "sales tax $1.09\n",
            "grand total $12.59\n",
            "tip guide\n",
            "\n",
            "tek=$1.89, 18%=62.27, 20%=82.52\n",
            "thank you very much,\n",
            "cone back again\n",
            "\f\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FIFtas6rVBfk",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8acd1918-1e01-45ff-a478-9a3e4d343d7b"
      },
      "source": [
        "#identify the date\n",
        "\n",
        "match=re.findall(r'\\d+[/.-]\\d+[/.-]\\d+', text)\n",
        "\n",
        "st=\" \"\n",
        "st=st.join(match)\n",
        "print(st)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7/4/2013\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "o7hgBG94VBfk"
      },
      "source": [
        "#output\n",
        "7/4/2013"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "R2RxUQ77VBfl"
      },
      "source": [
        "some times the format of the date wont be the same for all the bills.\n",
        "so if the date is ->1/03/17\n",
        "so wee need to change yy to yyyy format\n",
        "for that\n",
        "the below code will work\n",
        "\n",
        "#formatting the date if required\n",
        "l=st.split('/')\n",
        "add='20'\n",
        "if len(l[2])==2:\n",
        "         add=add+l[2]\n",
        "         l[2]=add\n",
        "d='/'\n",
        "d=d.join(l)\n",
        "st=d\n",
        "output->01/07/2017"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c0MhTp-lVBfl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acfd31f0-7807-4a1d-97fa-d8dd98722c22"
      },
      "source": [
        "nltk.download('punkt',quiet=True)\n",
        "nltk.download('wordnet',quiet=True)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waKhky-OVBfm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1e18cbfb-33b1-4e31-cb97-9a2c1bcea6fe"
      },
      "source": [
        "#lets try to extract title\n",
        "sent_tokens = nltk.sent_tokenize(text)\n",
        "#print(sent_tokens)\n",
        "sent_tokens[0].splitlines()[0]\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'ying thai kitchen'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "8DswlNiKVBfm"
      },
      "source": [
        "#output:\n",
        "'ying thai kitchen'"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "WwCfFp_4VBfm"
      },
      "source": [
        "the other way of extracting title\n",
        "#title.split('\\n')\n",
        "for i in title.split('\\n'):\n",
        "    break\n",
        "head=i\n",
        "print(head)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W8UR8awBVBfn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f47343f-01d1-4c10-8459-e2db07d546be"
      },
      "source": [
        "#lets find the price of the category.\n",
        "price=re.findall(r'[\\$\\£\\€](\\d+(?:\\.\\d{1,2})?)',text)\n",
        "price = list(map(float,price)) \n",
        "print(max(price))\n",
        "x=max(price)  "
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "12.59\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-nYLDGKDVBfo"
      },
      "source": [
        "output:\n",
        "12.59"
      ]
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "4exM3wkoVBfp"
      },
      "source": [
        "while scanning i found that  prices of five to six bills were not able to scan,so i used other trick \n",
        "which is given below:\n",
        "\n",
        "code:\n",
        "\n",
        "for i in range(len(new_words)):\n",
        "    if new_words[i]=='grand' and new_words[i+1]=='total':\n",
        "        break\n",
        "price=new_words[i+2]\n",
        "print(price) \n",
        "x=price"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oHPI3DX9VBfq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d2851169-aa7f-4bfd-d9c3-2f2d1c681897"
      },
      "source": [
        "#till here we have extracted date,title and amount.\n",
        "#now its time to categorise bill whether it is shopping or grocery like wise\n",
        "#so i will first tokenise the text and search for key words\n",
        "print(word_tokenize(text))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ying', 'thai', 'kitchen', '2220', 'queen', 'anne', 'ave', 'n', 'seattle', 'wa', '98109', '«', '(', '206', ')', '285-8424', 'fax', '.', '(', '206', ')', '285-8427', '‘', 'uw', '.yingthaikitchen.com', 'welcome', 'to', 'ying', 'thai', 'kitchen', 'restaurant', ',', 'order', '#', ':17', 'table', '2', 'date', ':', '7/4/2013', '7:28', 'pm', 'server', ':', 'jack', '(', '1.4', ')', '44', 'ginger', 'lover', '$', '9.50', '[', 'pork', ']', '[', '24', '#', ']', 'brown', 'rice', '$', '2.00', 'total', '2', 'iten', '(', 's', ')', '$', '11.50', 'sales', 'tax', '$', '1.09', 'grand', 'total', '$', '12.59', 'tip', 'guide', 'tek=', '$', '1.89', ',', '18', '%', '=62.27', ',', '20', '%', '=82.52', 'thank', 'you', 'very', 'much', ',', 'cone', 'back', 'again']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18XEC6sEVBfr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "feefe38a-0395-40a1-ba82-5b0c07964b69"
      },
      "source": [
        "#we will remove punctuation\n",
        "tokenizer = nltk.RegexpTokenizer(r\"\\w+\")\n",
        "new_words = tokenizer.tokenize(text)\n",
        "print(new_words)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ying', 'thai', 'kitchen', '2220', 'queen', 'anne', 'ave', 'n', 'seattle', 'wa', '98109', '206', '285', '8424', 'fax', '206', '285', '8427', 'uw', 'yingthaikitchen', 'com', 'welcome', 'to', 'ying', 'thai', 'kitchen', 'restaurant', 'order', '17', 'table', '2', 'date', '7', '4', '2013', '7', '28', 'pm', 'server', 'jack', '1', '4', '44', 'ginger', 'lover', '9', '50', 'pork', '24', 'brown', 'rice', '2', '00', 'total', '2', 'iten', 's', '11', '50', 'sales', 'tax', '1', '09', 'grand', 'total', '12', '59', 'tip', 'guide', 'tek', '1', '89', '18', '62', '27', '20', '82', '52', 'thank', 'you', 'very', 'much', 'cone', 'back', 'again']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D-UU3D5tVBfr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "149bca2c-e702-4351-cfbe-529e20b724d1"
      },
      "source": [
        "#stop_words = set(nltk.corpus.stopwords.words('english')) \n",
        "nltk.download('stopwords')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fskgZEYcVBfs"
      },
      "source": [
        "#there are stop words like a ,an,the etc which are not required\n",
        "#so we need to filter them\n",
        "stop_words = set(nltk.corpus.stopwords.words('english')) "
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hFTsUV4dVBft",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b0ff1b68-0993-4942-eb2b-91cca0ec2784"
      },
      "source": [
        "#there is the filetred list\n",
        "filtered_list=[w for w in new_words if w not in stop_words ]\n",
        "print(filtered_list)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['ying', 'thai', 'kitchen', '2220', 'queen', 'anne', 'ave', 'n', 'seattle', 'wa', '98109', '206', '285', '8424', 'fax', '206', '285', '8427', 'uw', 'yingthaikitchen', 'com', 'welcome', 'ying', 'thai', 'kitchen', 'restaurant', 'order', '17', 'table', '2', 'date', '7', '4', '2013', '7', '28', 'pm', 'server', 'jack', '1', '4', '44', 'ginger', 'lover', '9', '50', 'pork', '24', 'brown', 'rice', '2', '00', 'total', '2', 'iten', '11', '50', 'sales', 'tax', '1', '09', 'grand', 'total', '12', '59', 'tip', 'guide', 'tek', '1', '89', '18', '62', '27', '20', '82', '52', 'thank', 'much', 'cone', 'back']\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NygJGbn1VBfu"
      },
      "source": [
        "in the next six blocks we will make list of 6 categories.these list will contain few words which are relatable to the category of bill.for instance suppose the above bill of the restuarant would include words like kitchen,food or restuarant.so we will compare it list category and likewise allot the rescpective category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QyzwOOAdVBfu"
      },
      "source": [
        "#entertainment\n",
        "entertainment = [] \n",
        "for syn in wordnet.synsets(\"entertainment\"): \n",
        "    for l in syn.lemmas(): \n",
        "        entertainment.append(l.name()) \n",
        "        \n",
        "l=['happy','restaurant','food','kitchen','hotel','room','park','movie','cinema','popcorn','combo meal']\n",
        "entertainment=entertainment+l\n"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8td5RQaVBfu"
      },
      "source": [
        "#home utility\n",
        "home_utility=[] \n",
        "for syn in wordnet.synsets(\"home\"): \n",
        "    for l in syn.lemmas(): \n",
        "         home_utility.append(l.name()) \n",
        "l2=['internet','telephone','elecricity','meter','wifi','broadband','consumer','reading','gas','water','postpaid','prepaid']\n",
        "home_utility+=l2"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S0KljiS6VBfu"
      },
      "source": [
        "#grocery\n",
        " \n",
        "grocery=[] \n",
        "for syn in wordnet.synsets(\"grocery\"): \n",
        "    for l in syn.lemmas(): \n",
        "         grocery.append(l.name())\n",
        "l3=['bigbasket','milk','atta','sugar','suflower','oil','bread','vegetabe','fruit','salt','paneer']\n",
        "grocery+=l3\n"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-T927njVBfu"
      },
      "source": [
        "#investment\n",
        "investment=[] \n",
        "for syn in wordnet.synsets(\"investment\"): \n",
        "    for l in syn.lemmas(): \n",
        "         investment.append(l.name()) \n",
        "l1=['endowment','grant','loan','applicant','income','expenditure','profit','interest','expense','finance','property','money','fixed','deposit','kissan','vikas']\n",
        "investment=investment+l1"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RbTuEd_cVBfv"
      },
      "source": [
        "#travel and transportation\n",
        "transport=[]\n",
        "for syn in wordnet.synsets(\"car\"): \n",
        "    for l in syn.lemmas(): \n",
        "         transport.append(l.name()) \n",
        "l4=['cab','ola','uber','autorickshaw','railway','air','emirates','aerofloat','taxi','booking','road','highway']\n",
        "transport+=l4"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "59w321RDVBfv"
      },
      "source": [
        "#shopping\n",
        "shopping=[]\n",
        "for syn in wordnet.synsets(\"dress\"): \n",
        "    for l in syn.lemmas(): \n",
        "         shopping.append(l.name()) \n",
        "l4=['iphone','laptop','saree','max','pantaloons','westside','vedic','makeup','lipstick','cosmetics','mac','facewash','heels','crocs','footwear','purse']\n",
        "shopping+=l4"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWro-qHqVBfv"
      },
      "source": [
        "#here we will check that the bill belongs to which category\n",
        "#we will make that category true.\n",
        "for word in filtered_list:\n",
        "    if word in entertainment:\n",
        "        e=True\n",
        "        break\n",
        "    elif word in investment:\n",
        "        inv=True\n",
        "        break\n",
        "    elif word in grocery:\n",
        "        g=True\n",
        "        break\n",
        "    elif word in shopping:\n",
        "        s=True\n",
        "        break\n",
        "    elif word in transport:\n",
        "        t=True\n",
        "        break\n",
        "    elif word in home_utility:\n",
        "        h=True\n",
        "        break\n",
        "            "
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "ktJS_Fy4VBfx"
      },
      "source": [
        "#this is how i created all the csv files.\n",
        "'''with open('entertainment.csv', 'a', newline='') as csvfile:\n",
        "    spamwriter = csv.writer(csvfile, delimiter=',',quoting=csv.QUOTE_MINIMAL)\n",
        "    spamwriter.writerow(['date','organisation','amount'])'''\n",
        "   \n",
        "   "
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jUsDM6ByVBfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cebda72a-ef8f-4b25-86ce-c6771061abdc"
      },
      "source": [
        "#question 2\n",
        "#this code the category in which the bill belongs to\n",
        "#if e is true then entertainment categrory and we will ,ake filename as entertainment.csv using\n",
        "#formatting\n",
        "if(e):\n",
        "    print(\"entertainment category\")\n",
        "    filename='{}.csv'.format('entertainment')\n",
        "    #df=pd.read_csv('entertainment.csv')\n",
        "elif(inv):\n",
        "    print(\"investment category\")\n",
        "    filename='{}.csv'.format('investment')\n",
        "    #df=pd.read_csv('investment.csv')\n",
        "elif(s):\n",
        "    print(\"shopping category\")\n",
        "    filename='{}.csv'.format('shopping')\n",
        "    #df=pd.read_csv('shopping.csv')\n",
        "elif(g):\n",
        "    print(\"grocery category\")\n",
        "    filename='{}.csv'.format('grocery')\n",
        "    #df=pd.read_csv('grocery.csv')\n",
        "elif(t):\n",
        "    print(\"transport category\")\n",
        "    filename='{}.csv'.format('transport')\n",
        "    #df=pd.read_csv('transport.csv')\n",
        "elif(h):\n",
        "    print(\"home utility category\")\n",
        "    filename='{}.csv'.format('home')\n",
        "    #df=pd.read_csv('home.csv')\n",
        "else:\n",
        "    print(\"others\")\n",
        "    filename='{}.csv'.format('others')\n",
        "    #df=pd.read_csv('others.csv')\n",
        "        \n",
        "        \n"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "entertainment category\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WS159l7VBfy"
      },
      "source": [
        "#add the contents in thier respective csv file\n",
        "row_contents = [st, head, x]\n",
        "from csv import writer\n",
        " \n",
        "def append_list_as_row(file, list_of_elem):\n",
        "   \n",
        "    with open(file, 'a+', newline='') as write_obj:\n",
        "       \n",
        "        csv_writer = writer(write_obj)\n",
        "        \n",
        "        csv_writer.writerow(list_of_elem)\n",
        "append_list_as_row(filename, row_contents)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "raw",
      "metadata": {
        "id": "AGb4_QcCVBfy"
      },
      "source": [
        "this was the end of question one and two where we read image,converted into text ,extracted data and stored in the respective category.\n",
        "now we will move on to third and fourth question that is all about data analysis\n",
        "woohooo this is my favourite part."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgxH4iHnVBfy"
      },
      "source": [
        "#after this make sure you save it\n",
        "entertainment=pd.read_csv('entertainment.csv')\n",
        "investment=pd.read_csv('investment.csv')\n",
        "shopping=pd.read_csv('shopping.csv')\n",
        "grocery=pd.read_csv('grocery.csv')\n",
        "transport=pd.read_csv('transport.csv')\n",
        "other=pd.read_csv('others.csv')\n",
        "home=pd.read_csv('home.csv')"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES8_PZNyVBfz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "bf593fe7-3f7c-4450-bc8d-b56612eb1b21"
      },
      "source": [
        "entertainment['Date']= pd.to_datetime(entertainment.Date)\n",
        "investment['Date']=pd.to_datetime(investment.Date)\n",
        "shopping['Date']=pd.to_datetime(shopping.Date)\n",
        "grocery['Date']=pd.to_datetime(grocery.Date)\n",
        "transport['Date']=pd.to_datetime(transport.Date)\n",
        "other['Date']=pd.to_datetime(other.Date)\n",
        "home['Date']=pd.to_datetime(home.Date)\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-38-8faa0768f631>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mentertainment\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mentertainment\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDate\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      2\u001b[0m \u001b[0;31m# investment['Date']=pd.to_datetime(investment.Date)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# shopping['Date']=pd.to_datetime(shopping.Date)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# grocery['Date']=pd.to_datetime(grocery.Date)\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# transport['Date']=pd.to_datetime(transport.Date)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5139\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\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   5140\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5141\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5142\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5143\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'Date'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n4tgUSIpVBfz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "outputId": "62f76004-61d3-49e0-c641-7308348766ad"
      },
      "source": [
        "#question3\n",
        "entertainment.head()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [date, organisation, amount]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khQnBcHoVBfz",
        "outputId": "4b1c54f6-4c61-4aaa-a19d-dd3e1005b139"
      },
      "source": [
        "investment.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015-11-08</td>\n",
              "      <td>shriram transport finance company limited</td>\n",
              "      <td>25000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date                               organisation  amount\n",
              "0 2015-11-08  shriram transport finance company limited   25000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNZr200qVBf0",
        "outputId": "56665c23-6045-4cc8-845e-845d0e0358d6"
      },
      "source": [
        "shopping.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-27</td>\n",
              "      <td>gauri garments</td>\n",
              "      <td>2400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018-02-19</td>\n",
              "      <td>thakur</td>\n",
              "      <td>3000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-06-20</td>\n",
              "      <td>paytm</td>\n",
              "      <td>507</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-27</td>\n",
              "      <td>max</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-02-19</td>\n",
              "      <td>pantaloons</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date    organisation  amount\n",
              "0 2019-01-27  gauri garments    2400\n",
              "1 2018-02-19          thakur    3000\n",
              "2 2019-06-20           paytm     507\n",
              "3 2020-01-27             max     500\n",
              "4 2019-02-19      pantaloons     700"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVEjkUChVBf0",
        "outputId": "8dc450b8-e707-45b0-c270-2787b3196483"
      },
      "source": [
        "grocery.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-10-02</td>\n",
              "      <td>dmart</td>\n",
              "      <td>1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-03-15</td>\n",
              "      <td>hind</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-01-14</td>\n",
              "      <td>osia</td>\n",
              "      <td>1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-09-15</td>\n",
              "      <td>osia</td>\n",
              "      <td>700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-20</td>\n",
              "      <td>bigbasket</td>\n",
              "      <td>400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date organisation  amount\n",
              "0 2020-10-02        dmart    1500\n",
              "1 2020-03-15         hind     500\n",
              "2 2020-01-14         osia    1500\n",
              "3 2019-09-15         osia     700\n",
              "4 2020-01-20    bigbasket     400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LGOswOpPVBf1",
        "outputId": "a280fe98-af79-4bd5-8712-cbd41120f50b"
      },
      "source": [
        "transport.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-08-13</td>\n",
              "      <td>ola</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-08-15</td>\n",
              "      <td>ola</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-08-16</td>\n",
              "      <td>uber</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2018-01-01</td>\n",
              "      <td>dipam motors</td>\n",
              "      <td>1000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-03-03</td>\n",
              "      <td>honda</td>\n",
              "      <td>5000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  organisation  amount\n",
              "0 2019-08-13           ola     200\n",
              "1 2019-08-15           ola     150\n",
              "2 2019-08-16          uber      60\n",
              "3 2018-01-01  dipam motors    1000\n",
              "4 2020-03-03         honda    5000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xx-Wu8-DVBf1",
        "outputId": "0c871476-2e2e-4564-c2ac-df767ea5dd5e"
      },
      "source": [
        "other.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-13</td>\n",
              "      <td>vision</td>\n",
              "      <td>1500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-02-15</td>\n",
              "      <td>new era</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-12-25</td>\n",
              "      <td>yukti</td>\n",
              "      <td>1200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date organisation  amount\n",
              "0 2020-01-13       vision    1500\n",
              "1 2019-02-15      new era     500\n",
              "2 2019-12-25        yukti    1200"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q35NZMePVBf2",
        "outputId": "817bb2c1-8648-408e-a411-2df4d24b8a4c"
      },
      "source": [
        "#lets do some statistics\n",
        "entertainment.shape\n",
        "#3->columns 11->rows\n",
        "#similarliy do for others"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "45ELO-IIVBf2",
        "outputId": "60cd0398-9a47-4cf5-bf13-6902c93bdb09"
      },
      "source": [
        "#getting statistical info\n",
        "entertainment.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1267.780909</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2254.159505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.590000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>195.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>400.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>750.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7346.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            amount\n",
              "count    11.000000\n",
              "mean   1267.780909\n",
              "std    2254.159505\n",
              "min      12.590000\n",
              "25%     195.000000\n",
              "50%     400.000000\n",
              "75%     750.000000\n",
              "max    7346.000000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xZl9VOTwVBf2",
        "outputId": "15466d66-ed6c-4476-a2bd-d913da891c09"
      },
      "source": [
        "#oh my 7346 is too much,lets see where have i used this amount\n",
        "entertainment[entertainment['amount']==entertainment['amount'].max()]\n",
        "#dubai trip was awesome!!!:)\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-10-10</td>\n",
              "      <td>burj khalifa</td>\n",
              "      <td>7346.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date  organisation  amount\n",
              "5 2019-10-10  burj khalifa  7346.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkIoTMxLVBf2",
        "outputId": "ad7260fc-6628-41df-86ef-02c1755f7354"
      },
      "source": [
        "#lets check the data type of the data set -entertainment\n",
        "entertainment.dtypes\n",
        "#everything looks fine\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date             object\n",
              "organisation     object\n",
              "amount          float64\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OKIH5tX0VBf3",
        "outputId": "ea757fe9-fac7-40c9-9c44-56e21dbab66a"
      },
      "source": [
        "#now lets check for missing values\n",
        "entertainment.isnull().any()\n",
        "#woo hooo there are no missing values\n",
        "#if True was displayed then we would have to use some pandas function to get rid of that\n",
        "#eg df.dropna() or .fillna() etc.."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date            False\n",
              "organisation    False\n",
              "amount          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHOu0WkhVBf3",
        "outputId": "56415941-11f5-422c-ccfb-6eab2218f878"
      },
      "source": [
        "#lets check for other data frames as well\n",
        "investment.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date            False\n",
              "organisation    False\n",
              "amount          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p-bHbjhFVBf3",
        "outputId": "e052703f-1d85-4bd1-a835-7cca22fb24aa"
      },
      "source": [
        "shopping.isnull()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>organisation</th>\n",
              "      <th>amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Date  organisation  amount\n",
              "0  False         False   False\n",
              "1  False         False   False\n",
              "2  False         False   False\n",
              "3  False         False   False\n",
              "4  False         False   False\n",
              "5  False         False   False\n",
              "6  False         False   False"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "id": "PE_VCybIVBf3",
        "outputId": "d493b19c-b27d-47f6-8706-ceb0c2d224d8"
      },
      "source": [
        "#similary we can check for the other category\n",
        "grocery.isnull().any()\n",
        "transport.isnull().any()\n",
        "other.isnull().any()\n",
        "home.isnull().any()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date            False\n",
              "organisation    False\n",
              "amount          False\n",
              "dtype: bool"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mGg6mFScVBf3",
        "outputId": "aa177d37-0b58-4b96-d34f-840bb622625b"
      },
      "source": [
        "#since everything looks fine we will move to the next important step \n",
        "#DATA ANALYSIS\n",
        "plt.figure(figsize=(24,5))\n",
        "plt.bar(entertainment['organisation'],entertainment['amount'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 11 artists>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gRU3w198VBf6",
        "outputId": "d8dca1f5-afdd-4eb1-e26a-083b368eb204"
      },
      "source": [
        "#lets make it more readble and analyzable\n",
        "entertainment.plot(x='organisation',y='amount',kind='barh',title='entertainment expenditure')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1ffc5d60978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2andHrLqVBf8",
        "outputId": "cfbdc1a9-0e3a-48a2-a60c-dfc96c53a202"
      },
      "source": [
        "#its time to make pie chart\n",
        "#here i will make pie chart using three ways just to see which one fits the best\n",
        "labels=[]\n",
        "for i in entertainment['organisation']:\n",
        "    labels.append(i)  \n",
        "amount=[]\n",
        "for i in entertainment['amount']:\n",
        "    amount.append(i)\n",
        "plt.pie(amount, labels=labels, autopct='%1.1f%%',\n",
        "        shadow=True, startangle=90)\n",
        "\n",
        "plt.axis('equal')  \n",
        "plt.tight_layout()\n",
        "#entertainment.plot(x='organisation',y='amount',kind='barh',title='entertainment expenditure')\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": "jsHnu_9eVBf8",
        "outputId": "7b0b15e3-3e76-491d-8a70-02b8556ff793"
      },
      "source": [
        "#yucks this plot is too messy\n",
        "#lets try other way\n",
        "#this is donut plot\n",
        "# create a figure and set different background\n",
        "fig = plt.figure()\n",
        "fig.patch.set_facecolor('#B7AC9C')\n",
        "# Change color of text and make a circle\n",
        "plt.rcParams['text.color'] = 'black'\n",
        "my_circle=plt.Circle( (0,0), 0.6,color='#B7AC9C' )\n",
        "# Pieplot + circle on it\n",
        "plt.pie(amount, labels=labels)\n",
        "p=plt.gcf()\n",
        "p.gca().add_artist(my_circle)\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": "zbckUzjqVBf8",
        "outputId": "48527b08-68ee-488a-dee0-864197c5aa18"
      },
      "source": [
        "#this seems nice\n",
        "#lets do this for other category as well\n",
        "#similarly we will look at grocery and shopping\n",
        "#colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#8c564b\",\"#B7AC9C\"]\n",
        "plt.subplot(221)\n",
        "plt.pie(grocery['amount'], labels=grocery['organisation'], \n",
        "autopct='%1.1f%%', shadow=True, startangle=140,)\n",
        "plt.title(\"grocery expenditure\")\n",
        "plt.subplot(222)\n",
        "plt.pie(shopping['amount'], labels=shopping['organisation'], \n",
        "autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "plt.title(\"shopping expenditure\")\n",
        "plt.subplot(223)\n",
        "plt.pie(home['amount'], labels=home['organisation'], \n",
        "autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "plt.title(\"home expenditure\")\n",
        "plt.subplot(224)\n",
        "plt.pie(transport['amount'], labels=transport['organisation'], \n",
        "autopct='%1.1f%%', shadow=True, startangle=0)\n",
        "plt.title(\"transport expenditure\")\n",
        "#plt.legend(labels)\n",
        "plt.show()\n",
        "plt.pie(other['amount'], labels=other['organisation'], autopct='%1.1f%%', shadow=True, startangle=0)\n",
        "plt.title(\"other expenditure\")\n",
        "plt.show()\n",
        "plt.pie(investment['amount'], labels=investment['organisation'], autopct='%1.1f%%', shadow=True, startangle=0)\n",
        "plt.title(\"investment expenditure\")\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 4 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "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": "CpvPfFXfVBf9"
      },
      "source": [
        "#lets merge all the expenditure and save it to other csv\n",
        "category=['entertainment','investment','shopping','grocery','transport','home','others']\n",
        "#lets sum all the expenditure category wise\n",
        "total_entertainment=entertainment['amount'].sum()\n",
        "total_investment=investment['amount'].sum()\n",
        "total_shopping=shopping['amount'].sum()\n",
        "total_grocery=grocery['amount'].sum()\n",
        "total_transport=transport['amount'].sum()\n",
        "total_home=home['amount'].sum()\n",
        "total_others=other['amount'].sum()\n",
        "amount=[total_entertainment,total_investment,total_shopping,total_grocery,total_transport,total_home,total_others]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6NbXO-seVBf9"
      },
      "source": [
        "data={'category':category,'total':amount}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VKZALZVLVBf9"
      },
      "source": [
        "#here we created a new table which shows the total expenditure of all the category\n",
        "df = pd.DataFrame(data) "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-WoKQyRVBf9",
        "outputId": "d560f8c1-c63d-468a-a2d6-ea2b7e8e5b0e"
      },
      "source": [
        "df.head(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>entertainment</td>\n",
              "      <td>13945.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>investment</td>\n",
              "      <td>25000.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>shopping</td>\n",
              "      <td>11107.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>grocery</td>\n",
              "      <td>4800.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>transport</td>\n",
              "      <td>6558.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>home</td>\n",
              "      <td>8800.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>others</td>\n",
              "      <td>3200.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        category     total\n",
              "0  entertainment  13945.59\n",
              "1     investment  25000.00\n",
              "2       shopping  11107.00\n",
              "3        grocery   4800.00\n",
              "4      transport   6558.00\n",
              "5           home   8800.00\n",
              "6         others   3200.00"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JnOVJoE7VBf-",
        "outputId": "0f132550-6c21-4bb5-84b9-19a101f93b63"
      },
      "source": [
        "#lets plot a piechart and bar to analzye where we used \n",
        "#colors = [\"#1f77b4\", \"#ff7f0e\", \"#2ca02c\", \"#d62728\", \"#8c564b\",\"#B7AC9C\"]\n",
        "\n",
        "plt.pie(df['total'], labels=df['category'], autopct='%1.1f%%', shadow=True, startangle=140)\n",
        "plt.title(\" expenditure\")\n",
        "\n",
        "df.plot(x='category',y='total',kind='barh',title='entertainment expenditure')\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": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z2-FyhGPVBf-",
        "outputId": "925ae0f7-6cc0-49b7-8424-594274623496"
      },
      "source": [
        "fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect=\"equal\"))\n",
        "wedges, texts = ax.pie(df['total'], wedgeprops=dict(width=0.5), startangle=-40)\n",
        "bbox_props = dict(boxstyle=\"square,pad=0.3\", fc=\"w\", ec=\"k\", lw=0.72)\n",
        "kw = dict(arrowprops=dict(arrowstyle=\"-\"),\n",
        "          bbox=bbox_props, zorder=0, va=\"center\")\n",
        "\n",
        "for i, p in enumerate(wedges):\n",
        "    ang = (p.theta2 - p.theta1)/2. + p.theta1\n",
        "    y = np.sin(np.deg2rad(ang))\n",
        "    x = np.cos(np.deg2rad(ang))\n",
        "    horizontalalignment = {-1: \"right\", 1: \"left\"}[int(np.sign(x))]\n",
        "    connectionstyle = \"angle,angleA=0,angleB={}\".format(ang)\n",
        "    kw[\"arrowprops\"].update({\"connectionstyle\": connectionstyle})\n",
        "    ax.annotate(category[i], xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y),\n",
        "                horizontalalignment=horizontalalignment, **kw)\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vPfzsq-jVBf-",
        "outputId": "ba879d4a-24e0-4bab-d483-3c2f7c385f5b"
      },
      "source": [
        "#wohooo we completed with the piechart\n",
        "#nowlets move to last question\n",
        "#lets make a inferance\n",
        "ordered_df = df.sort_values(by='total')\n",
        "data_sort=ordered_df\n",
        "\n",
        "data_sort.plot(x='category',y='total',kind='barh',title='entertainment expenditure')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x1ffc72762b0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ScOc4xalVBgA"
      },
      "source": [
        "#this used to calculate the percentage  of every category\n",
        "percent=[]\n",
        "for i in df['total']:\n",
        "    per=int((i/df['total'].sum())*100)\n",
        "    percent.append(per)\n",
        "\n",
        "percent.sort()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eQukg18MVBgA",
        "outputId": "ade32bd5-ceeb-415b-b1b6-9642820594e4"
      },
      "source": [
        "#question4\n",
        "#from the above graph we can conclude that we have invested maximum money\n",
        "#now lets see how much we have used in all category\n",
        "for i in range(len(df)):\n",
        "    print(\"{}%  of your expenditure in {} category\".format(percent[i],data_sort['category'].iloc[i]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "4%  of your expenditure in others category\n",
            "6%  of your expenditure in grocery category\n",
            "8%  of your expenditure in transport category\n",
            "11%  of your expenditure in home category\n",
            "15%  of your expenditure in shopping category\n",
            "18%  of your expenditure in entertainment category\n",
            "34%  of your expenditure in investment category\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kqPlvqZqVBgA"
      },
      "source": [
        "conclusion:\n",
        "Expense data provides detailed insights into entertainment expenditure such as, how much and how frequently the we went for movies and resturant.It is easier for us to cut down on these expenses.\n",
        "as we can go once a month and we also need to take care of our health. the above Data analytics determined us where to find cost-saving opportunities, validate expenses, and point out the areas to invest more.The investment has maximum as it is necessary to invest for second source of income.The grocery expense detail is not completety true as  grocery vendors in india doesnot provide bill.the data for grocery used is of supermarket that we go sometimes.the transport category is less as i dont have petrol bills,and we have our own vehical.so we usually don't take taxi.to cut the transportation cost we can use city bus.\n",
        "here are some money saving tips:\n",
        "1)evaluate your spending\n",
        "2)set a montly budget\n",
        "3)track your spending,if it goes above the limits try to cut down  or spend less the next month in that category\n",
        "4)plan out your meals for the week so that you can avoid randomly going out and spending unnesscerily\n",
        "5)cut out cable.wifi is enough ,no need for tv.with services like Hulu, Netflix and Amazon Prime, you can now watch your favorite TV shows and movies for a fraction of the cost of cable TV.\n"
      ]
    }
  ]
}