Whatsapp_Group_Chat_Analysis_for_iOS.ipynb

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  "metadata": {
    "colab": {
      "name": "Whatsapp Group Chat Analysis for iOS.ipynb",
      "provenance": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyPPgvz+NJw6rZKeFrq+Oc6X",
      "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/kurasaiteja/Whatsapp-Analysis/blob/master/Whatsapp_Group_Chat_Analysis_for_iOS.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ct_E1ZPzBmkL",
        "colab_type": "text"
      },
      "source": [
        "# Whatsapp Group Chat Data Analysis using python."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0eOmerDBQc_",
        "colab_type": "text"
      },
      "source": [
        "Hey there! This a project which would keep evolving with time.\n",
        "\n",
        "A typical data science project would involve 5 steps. The first step is Data Collection. In this case Data collection involves\n",
        "\n",
        "Opening Whatsapp group chat.\n",
        "Go to Settings.\n",
        "Click on Export Chat.\n",
        "Click on without media."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_1Enh9VIYPnw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "f8c93ce2-93de-4517-c86a-223608979c53"
      },
      "source": [
        "!pip install emoji"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: emoji in /usr/local/lib/python3.6/dist-packages (0.5.4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-Jq5_jkMBE8I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import re\n",
        "import regex\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import emoji\n",
        "import plotly.express as px\n",
        "from collections import Counter\n",
        "import matplotlib.pyplot as plt\n",
        "from os import path\n",
        "from PIL import Image\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "% matplotlib inline"
      ],
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_IVxlHe_vr6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "277368c8-71bb-4826-b065-c92de2f22749"
      },
      "source": [
        "! git clone https://github.com/amueller/word_cloud.git\n",
        "% cd word_cloud\n",
        "! pip install ."
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "/content/word_cloud/word_cloud/word_cloud/word_cloud\n",
            "Processing /content/word_cloud/word_cloud/word_cloud/word_cloud\n",
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            "Building wheels for collected packages: wordcloud\n",
            "  Building wheel for wordcloud (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wordcloud: filename=wordcloud-1.6.0.post90+gbda4195-cp36-cp36m-linux_x86_64.whl size=338624 sha256=ced3d23d2e9c96ce7a052f954f659f648b2ffc41eca2f093ddfb96681af4f519\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-kuqr5p3l/wheels/4a/a2/76/e7f6c5039f8f8c93c078f38cedf6c90ee95019d9483f181ed7\n",
            "Successfully built wordcloud\n",
            "Installing collected packages: wordcloud\n",
            "  Found existing installation: wordcloud 1.6.0.post90+gbda4195\n",
            "    Uninstalling wordcloud-1.6.0.post90+gbda4195:\n",
            "      Successfully uninstalled wordcloud-1.6.0.post90+gbda4195\n",
            "Successfully installed wordcloud-1.6.0.post90+gbda4195\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "wordcloud"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "igJDg7pI_wW7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nI43wvlgBvoX",
        "colab_type": "text"
      },
      "source": [
        "# **Data Preparation:**\n",
        "\n",
        "\n",
        "This plain text file will have to be parsed and tokenized in a meaningful manner in order to be served (stored) in a Pandas dataframe.\n",
        "\n",
        "Let us consider just a single line from the text (which we will call “raw text”) and see how we can extract relevant columns from it:\n",
        "\n",
        "18/06/17, 9:47 PM - Teja: Why do you have 2 numbers?\n",
        "\n",
        "In our sample line of text, our main objective is to automatically break down the raw message into 4 tokens.\n",
        "\n",
        "{Date}, {Time} - {Author}: {Message}\n",
        "\n",
        "{18/06/17}, {9:47 PM} - {Teja}: {Why do you have 2 numbers?}\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ccX8HRn5BL89",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def startsWithDateAndTime(s):\n",
        "    pattern = '^\\[([0-9]+)([\\/-])([0-9]+)([\\/-])([0-9]+)[,]? ([0-9]+):([0-9][0-9]):([0-9][0-9])[ ]?(AM|PM|am|pm)?\\]'\n",
        "    result = re.match(pattern, s)\n",
        "    if result:\n",
        "        return True\n",
        "    return False"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9E_Pu7LLWZyR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bc2a5a29-0ee6-427b-8813-278d040dfc74"
      },
      "source": [
        "startsWithDateAndTime(\"[07-03-19 6:46:53] Gowth Bhuj: Ok\")"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qLR8M2dhBz7h",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def FindAuthor(s):\n",
        "    patterns = [\n",
        "        '([\\w]+):',                        # First Name\n",
        "        '([\\w]+[\\s]+[\\w]+):',              # First Name + Last Name\n",
        "        '([\\w]+[\\s]+[\\w]+[\\s]+[\\w]+):',    # First Name + Middle Name + Last Name\n",
        "        '([+]\\d{2} \\d{5} \\d{5}):',         # Mobile Number (India)\n",
        "        '([+]\\d{2} \\d{3} \\d{3} \\d{4}):',   # Mobile Number (US)\n",
        "        '([\\w]+)[\\u263a-\\U0001f999]+:',    # Name and Emoji              \n",
        "    ]\n",
        "    pattern = '^' + '|'.join(patterns)\n",
        "    result = re.match(pattern, s)\n",
        "    if result:\n",
        "        return True\n",
        "    return False"
      ],
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6WSoM1vB1zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDataPoint(line):   \n",
        "    splitLine = line.split('] ')\n",
        "    dateTime = splitLine[0]\n",
        "    if ',' in dateTime:\n",
        "      date, time = dateTime.split(',') \n",
        "    else:\n",
        "      date, time = dateTime.split(' ') \n",
        "    message = ' '.join(splitLine[1:])\n",
        "    if FindAuthor(message): \n",
        "        splitMessage = message.split(': ') \n",
        "        author = splitMessage[0] \n",
        "        message = ' '.join(splitMessage[1:])\n",
        "    else:\n",
        "        author = None\n",
        "    return date, time, author, message\n"
      ],
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgfGiHyGB68i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "parsedData = [] # List to keep track of data so it can be used by a Pandas dataframe\n",
        "conversationPath = '/content/_chat 2.txt' \n",
        "with open(conversationPath, encoding=\"utf-8\") as fp:\n",
        "    fp.readline() # Skipping first line of the file because contains information related to something about end-to-end encryption\n",
        "    fp.readline()\n",
        "    fp.readline()\n",
        "    messageBuffer = [] \n",
        "    date, time, author = None, None, None\n",
        "    while True:\n",
        "        line = fp.readline()\n",
        "        if not line: \n",
        "            break \n",
        "        line = line.strip()\n",
        "        if startsWithDateAndTime(line): \n",
        "            if len(messageBuffer) > 0: \n",
        "                parsedData.append([date, time, author, ' '.join(messageBuffer)]) \n",
        "            messageBuffer.clear() \n",
        "            date, time, author, message = getDataPoint(line) \n",
        "            messageBuffer.append(message) \n",
        "        else:\n",
        "          line= (line.encode('ascii', 'ignore')).decode(\"utf-8\")\n",
        "          if startsWithDateAndTime(line): \n",
        "            if len(messageBuffer) > 0: \n",
        "                parsedData.append([date, time, author, ' '.join(messageBuffer)]) \n",
        "            messageBuffer.clear() \n",
        "            date, time, author, message = getDataPoint(line) \n",
        "            messageBuffer.append(message) \n",
        "          else:\n",
        "            messageBuffer.append(line)"
      ],
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OtdpF5Y5Al5Q",
        "colab_type": "text"
      },
      "source": [
        "### If date time is in format Day-Month-year run this code."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8dVEi09eB8qK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import datetime\n",
        "def dateconv(date):\n",
        "  year=''\n",
        "  if '-' in date:\n",
        "    year = date.split('-')[2]\n",
        "    if len(year) == 4:\n",
        "      return datetime.datetime.strptime(date, \"[%d-%m-%Y\").strftime(\"%Y-%m-%d\")\n",
        "    elif len(year) ==2:\n",
        "      return datetime.datetime.strptime(date, \"[%d-%m-%y\").strftime(\"%Y-%m-%d\")\n",
        "  elif '/' in date:\n",
        "    year = date.split('/')[2]\n",
        "    if len(year) == 4:\n",
        "      return datetime.datetime.strptime(date, \"[%d/%m/%Y\").strftime(\"%Y-%m-%d\")\n",
        "    if len(year) ==2:\n",
        "      return datetime.datetime.strptime(date, \"[%d/%m/%y\").strftime(\"%Y-%m-%d\")\n",
        "df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message']) # Initialising a pandas Dataframe.\n",
        "df[\"Date\"] = df[\"Date\"].apply(dateconv)\n",
        "df[\"Date\"] = pd.to_datetime(df[\"Date\"])"
      ],
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxnSCcaGCPam",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "2bc100c7-ac9e-4215-ad88-b88e23d97677"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Author</th>\n",
              "      <th>Message</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:45:58 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>Adhe chedham aa inka?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:46:15 PM</td>\n",
              "      <td>Gowtham Vce</td>\n",
              "      <td>Emaina ideas unte chep</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date         Time             Author                 Message\n",
              "0 2019-03-07   6:45:58 PM  Aakarsh Yelisetty   Adhe chedham aa inka?\n",
              "1 2019-03-07   6:46:15 PM        Gowtham Vce  Emaina ideas unte chep"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1Gk-sfwDj3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "c548e364-eb1d-434d-b37c-57d56dbf39b0"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 35088 entries, 0 to 35087\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype         \n",
            "---  ------   --------------  -----         \n",
            " 0   Date     35088 non-null  datetime64[ns]\n",
            " 1   Time     35088 non-null  object        \n",
            " 2   Author   35070 non-null  object        \n",
            " 3   Message  35088 non-null  object        \n",
            "dtypes: datetime64[ns](1), object(3)\n",
            "memory usage: 1.1+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGfkDZ5iCgqO",
        "colab_type": "text"
      },
      "source": [
        "## Printing Participants"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EEttBBKZCQfU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "9aadcfb1-8e8c-49bf-a1af-e4c2eb939aa0"
      },
      "source": [
        "df.Author.unique()"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Aakarsh Yelisetty', 'Gowtham Vce', 'Vishal Vce', 'Sai Teja', None,\n",
              "       'Manikanta'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TzqAPs6aCogE",
        "colab_type": "text"
      },
      "source": [
        "**None** which is the first element in array indicates few messages which do not have authors like,\n",
        "\n",
        "\n",
        "*   Group was created.\n",
        "*   Teja was added and so on.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IcM2IhXmC0zY",
        "colab_type": "text"
      },
      "source": [
        "Let us remove the messages created by None"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RA4b5nSGC6Kr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "30337485-d848-4c16-e88f-a33eaabb239b"
      },
      "source": [
        "df = df.dropna()\n",
        "df.info()"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 35070 entries, 0 to 35087\n",
            "Data columns (total 4 columns):\n",
            " #   Column   Non-Null Count  Dtype         \n",
            "---  ------   --------------  -----         \n",
            " 0   Date     35070 non-null  datetime64[ns]\n",
            " 1   Time     35070 non-null  object        \n",
            " 2   Author   35070 non-null  object        \n",
            " 3   Message  35070 non-null  object        \n",
            "dtypes: datetime64[ns](1), object(3)\n",
            "memory usage: 1.3+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzPSOF7yDosE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "da08ca42-8664-4770-84d2-4d9a96cec923"
      },
      "source": [
        "df.Author.unique()"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Aakarsh Yelisetty', 'Gowtham Vce', 'Vishal Vce', 'Sai Teja',\n",
              "       'Manikanta'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z-32IxysDuts",
        "colab_type": "text"
      },
      "source": [
        "We successfully removed None author."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdG_QS-DFgMt",
        "colab_type": "text"
      },
      "source": [
        "# Group Wise Stats."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W-RYhorEOfZF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9cb9990a-0f07-4f5f-85cf-c205934f067b"
      },
      "source": [
        "total_messages = df.shape[0]\n",
        "print(total_messages)"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "35070\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xKDrfBB2Ovfa",
        "colab_type": "text"
      },
      "source": [
        "Let us now find out the total Media Messages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8kZeRLADzxn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "eac6566f-6b9d-4b44-daae-064f3f110db4"
      },
      "source": [
        "media_messages = df[df['Message'] == 'image omitted'].shape[0]\n",
        "print(media_messages)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "361\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ftegdpLrDgF6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_count(text):\n",
        "\n",
        "    emoji_list = []\n",
        "    data = regex.findall(r'\\X', text)\n",
        "    for word in data:\n",
        "        if any(char in emoji.UNICODE_EMOJI for char in word):\n",
        "            emoji_list.append(word)\n",
        "\n",
        "    return emoji_list\n",
        "\n",
        "df[\"emoji\"] = df[\"Message\"].apply(split_count)"
      ],
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ukq2MWKPUHWV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8cb29f0c-e230-4e2e-9449-03807170072c"
      },
      "source": [
        "emojis = sum(df['emoji'].str.len())\n",
        "print(emojis)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6572\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Bw_Lbr3XzMq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "URLPATTERN = r'(https?://\\S+)'\n",
        "df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()\n"
      ],
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa0oY2BWX6X_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "links = np.sum(df.urlcount)"
      ],
      "execution_count": 129,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZdFz-0UYaTs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "4d6c693e-7228-4b7d-d275-347d777cb959"
      },
      "source": [
        "print(\"Group Wise Stats\")\n",
        "print(\"Messages:\",total_messages)\n",
        "print(\"Media:\",media_messages)\n",
        "print(\"Emojis:\",emojis)\n",
        "print(\"Links:\",links)"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Group Wise Stats\n",
            "Messages: 35070\n",
            "Media: 361\n",
            "Emojis: 6572\n",
            "Links: 254\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vj3J0Bb-usX4",
        "colab_type": "text"
      },
      "source": [
        "## Let us Separate the media messages and text messages and sticker messages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ9Cd-_8uxT4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "media_messages_df = df[(df['Message'] == 'image omitted')|(df['Message'] == 'video omitted')]"
      ],
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7dTGeFOTono",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "58b8d517-dbd2-497e-a057-95711c6e2573"
      },
      "source": [
        "sticker_messages_df = df[df['Message'] == 'sticker omitted']\n",
        "sticker_messages_df"
      ],
      "execution_count": 132,
      "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>Time</th>\n",
              "      <th>Author</th>\n",
              "      <th>Message</th>\n",
              "      <th>emoji</th>\n",
              "      <th>urlcount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>34250</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>1:34:29 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34267</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>1:45:31 PM</td>\n",
              "      <td>Vishal Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34270</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>2:55:59 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34294</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>9:30:23 PM</td>\n",
              "      <td>Vishal Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34304</th>\n",
              "      <td>2020-07-07</td>\n",
              "      <td>9:50:03 PM</td>\n",
              "      <td>Sai Teja</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35059</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>1:10:38 AM</td>\n",
              "      <td>Manikanta</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35070</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>1:13:46 AM</td>\n",
              "      <td>Gowtham Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35073</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>1:14:09 AM</td>\n",
              "      <td>Gowtham Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35076</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>1:14:43 AM</td>\n",
              "      <td>Gowtham Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35079</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>1:17:46 AM</td>\n",
              "      <td>Gowtham Vce</td>\n",
              "      <td>sticker omitted</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>82 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date         Time  ... emoji urlcount\n",
              "34250 2020-07-07   1:34:29 PM  ...    []        0\n",
              "34267 2020-07-07   1:45:31 PM  ...    []        0\n",
              "34270 2020-07-07   2:55:59 PM  ...    []        0\n",
              "34294 2020-07-07   9:30:23 PM  ...    []        0\n",
              "34304 2020-07-07   9:50:03 PM  ...    []        0\n",
              "...          ...          ...  ...   ...      ...\n",
              "35059 2020-07-10   1:10:38 AM  ...    []        0\n",
              "35070 2020-07-10   1:13:46 AM  ...    []        0\n",
              "35073 2020-07-10   1:14:09 AM  ...    []        0\n",
              "35076 2020-07-10   1:14:43 AM  ...    []        0\n",
              "35079 2020-07-10   1:17:46 AM  ...    []        0\n",
              "\n",
              "[82 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8_bqcU6bu6ev",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages_df = df.drop(media_messages_df.index)\n",
        "messages_df = messages_df.drop(sticker_messages_df.index)"
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZflAweBoGFY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "0eb44261-2fb8-4e75-aeca-00d10203b0d2"
      },
      "source": [
        "messages_df.info()"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34624 entries, 0 to 35087\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Date      34624 non-null  datetime64[ns]\n",
            " 1   Time      34624 non-null  object        \n",
            " 2   Author    34624 non-null  object        \n",
            " 3   Message   34624 non-null  object        \n",
            " 4   emoji     34624 non-null  object        \n",
            " 5   urlcount  34624 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(1), object(4)\n",
            "memory usage: 1.8+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P1irwAJZvU0r",
        "colab_type": "text"
      },
      "source": [
        "It might be interesting to count the number of letters and words used by each author in each message. So, let us add 2 new columns to the data frame called “Letter_Count” and “Word_Count”, using the following code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wd5Bu3fOvKWg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages_df['Letter_Count'] = messages_df['Message'].apply(lambda s : len(s))\n",
        "messages_df['Word_Count'] = messages_df['Message'].apply(lambda s : len(s.split(' ')))\n",
        "messages_df[\"MessageCount\"]=1"
      ],
      "execution_count": 135,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Dn_glCLPhp8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "44a50429-030d-4600-ffc6-52419a7b8508"
      },
      "source": [
        "messages_df.tail(2)"
      ],
      "execution_count": 136,
      "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>Time</th>\n",
              "      <th>Author</th>\n",
              "      <th>Message</th>\n",
              "      <th>emoji</th>\n",
              "      <th>urlcount</th>\n",
              "      <th>Letter_Count</th>\n",
              "      <th>Word_Count</th>\n",
              "      <th>MessageCount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>35086</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>12:23:09 PM</td>\n",
              "      <td>Sai Teja</td>\n",
              "      <td>Bathuku gurinchi cheptadu</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35087</th>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>12:33:04 PM</td>\n",
              "      <td>Vishal Vce</td>\n",
              "      <td>Yo chusta</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>9</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date          Time  ... Word_Count MessageCount\n",
              "35086 2020-07-10   12:23:09 PM  ...          3            1\n",
              "35087 2020-07-10   12:33:04 PM  ...          2            1\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8c5r6rbGQnAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages_df[\"emojicount\"]= df['emoji'].str.len()"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LVLnOAKzvasf",
        "colab_type": "text"
      },
      "source": [
        "#Author wise stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4VF29K-svWEQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 697
        },
        "outputId": "51371073-579c-44c4-cbdf-1486397ba8c7"
      },
      "source": [
        "# Creates a list of unique Authors - ['Manikanta', 'Teja Kura', .........]\n",
        "l = messages_df.Author.unique()\n",
        "\n",
        "for i in range(len(l)):\n",
        "  # Filtering out messages of particular user\n",
        "  req_df= messages_df[messages_df[\"Author\"] == l[i]]\n",
        "  # req_df will contain messages of only one particular user\n",
        "  print(f'Stats of {l[i]} -')\n",
        "  # shape will print number of rows which indirectly means the number of messages\n",
        "  print('Messages Sent', req_df.shape[0])\n",
        "  #Word_Count contains of total words in one message. Sum of all words/ Total Messages will yield words per message\n",
        "  words_per_message = (np.sum(req_df['Word_Count']))/req_df.shape[0]\n",
        "  print('Words per message', words_per_message)\n",
        "  #media conists of media messages\n",
        "  media = media_messages_df[media_messages_df['Author'] == l[i]].shape[0]\n",
        "  print('Media Messages Sent', media)\n",
        "  #sticker conists of stickers messages\n",
        "  stickers = sticker_messages_df[sticker_messages_df['Author'] == l[i]].shape[0]\n",
        "  print('Sticker Messages Sent', stickers)\n",
        "  # emojis conists of total emojis\n",
        "  emojis = sum(req_df['emoji'].str.len())\n",
        "  print('Emojis Sent', emojis)\n",
        "  #links consist of total links\n",
        "  links = sum(req_df[\"urlcount\"])   \n",
        "  print('Links Sent', links)   \n",
        "  print()"
      ],
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stats of Aakarsh Yelisetty -\n",
            "Messages Sent 5615\n",
            "Words per message 3.8016028495102403\n",
            "Media Messages Sent 78\n",
            "Sticker Messages Sent 22\n",
            "Emojis Sent 1370\n",
            "Links Sent 79\n",
            "\n",
            "Stats of Gowtham Vce -\n",
            "Messages Sent 5997\n",
            "Words per message 2.836251459062865\n",
            "Media Messages Sent 31\n",
            "Sticker Messages Sent 15\n",
            "Emojis Sent 1817\n",
            "Links Sent 15\n",
            "\n",
            "Stats of Vishal Vce -\n",
            "Messages Sent 9699\n",
            "Words per message 3.3620991854830393\n",
            "Media Messages Sent 33\n",
            "Sticker Messages Sent 31\n",
            "Emojis Sent 1408\n",
            "Links Sent 55\n",
            "\n",
            "Stats of Sai Teja -\n",
            "Messages Sent 7821\n",
            "Words per message 3.9914333205472445\n",
            "Media Messages Sent 152\n",
            "Sticker Messages Sent 13\n",
            "Emojis Sent 800\n",
            "Links Sent 54\n",
            "\n",
            "Stats of Manikanta -\n",
            "Messages Sent 5492\n",
            "Words per message 4.2731245447924255\n",
            "Media Messages Sent 70\n",
            "Sticker Messages Sent 1\n",
            "Emojis Sent 1177\n",
            "Links Sent 51\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FsyiweIveiPh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages_df=messages_df[messages_df[\"urlcount\"]==0]"
      ],
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cGTpyoNbTCLI",
        "colab_type": "text"
      },
      "source": [
        "# Most used Emoji in Group"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "odOdNXtMTGFI",
        "colab_type": "text"
      },
      "source": [
        "# Emoji Stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUuhfOUVzwoO",
        "colab_type": "text"
      },
      "source": [
        "### Unique emojis used in group"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L_0BHwJ0zvoG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "18329fd6-8c83-457e-cd13-25620fd64935"
      },
      "source": [
        "total_emojis_list = list(set([a for b in messages_df.emoji for a in b]))\n",
        "total_emojis = len(total_emojis_list)\n",
        "print(total_emojis)"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "178\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTZMMc2cz5Bk",
        "colab_type": "text"
      },
      "source": [
        "### Most used emoji"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yxVBAJ_JotaS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "e55e1cb5-40c3-4798-e33e-1e34d2531d92"
      },
      "source": [
        "total_emojis_list = list([a for b in messages_df.emoji for a in b])\n",
        "emoji_dict = dict(Counter(total_emojis_list))\n",
        "emoji_dict = sorted(emoji_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "print(emoji_dict)"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('😂', 2682), ('🤣', 1431), ('\\U0001f97a', 384), ('😒', 208), ('🔥', 79), ('🙄', 78), ('😞', 64), ('🤦🏻\\u200d♂️', 61), ('\\U0001f973', 61), ('🤕', 53), ('🤙🏻', 46), ('😮', 45), ('😪', 44), ('🙂', 44), ('😭', 43), ('😢', 43), ('🤧', 40), ('🤦🏻\\u200d♂', 37), ('😅', 35), ('😨', 34), ('😕', 34), ('😔', 34), ('😏', 31), ('🙃', 29), ('😎', 29), ('😱', 28), ('\\U0001f975', 26), ('🤥', 26), ('🤢', 23), ('🙏', 23), ('☹️', 23), ('💥', 23), ('\\U0001f92f', 22), ('\\U0001f928', 20), ('😖', 19), ('👏🏻', 18), ('😣', 18), ('😆', 18), ('😑', 17), ('🙏🏻', 17), ('😁', 16), ('😟', 16), ('🤷🏻\\u200d♂️', 16), ('👍🏻', 16), ('😳', 15), ('🍃', 14), ('\\U0001f974', 13), ('😲', 13), ('\\U0001f929', 11), ('🍻', 11), ('\\U0001f971', 11), ('😤', 10), ('🕺🏻', 10), ('\\U0001f970', 10), ('😍', 10), ('😐', 9), ('😓', 9), ('😴', 9), ('🤔', 8), ('👀', 8), ('\\U0001f92e', 8), ('👍', 8), ('\\U0001f9d0', 8), ('🤷🏻\\u200d♂', 8), ('😦', 8), ('\\U0001f92d', 7), ('🤐', 7), ('😶', 7), ('😬', 7), ('😡', 7), ('👗', 7), ('😵', 7), ('🙁', 7), ('\\U0001f9b8\\u200d♂️', 7), ('💯', 7), ('🙆🏻\\u200d♂', 6), ('😹', 6), ('😇', 6), ('😌', 5), ('🇮🇳', 5), ('👌🏻', 5), ('🕺', 5), ('🌿', 5), ('😯', 4), ('💓', 4), ('🤘🏻', 4), ('😄', 4), ('❤️', 4), ('😊', 4), ('👅', 4), ('😃', 4), ('💦', 4), ('🙌🏻', 4), ('\\U0001f92c', 4), ('\\U0001f92b', 4), ('😷', 4), ('👁️', 4), ('✌', 3), ('💃🏻', 3), ('🙀', 3), ('✋🏻', 3), ('🤒', 3), ('🥇', 3), ('😋', 3), ('🙉', 3), ('✊🏻', 3), ('🔪', 3), ('🙋🏻\\u200d♂', 3), ('🥃', 3), ('😩', 3), ('\\U0001f92a', 3), ('😰', 3), ('😧', 3), ('👌', 3), ('👨🏻\\u200d💻', 2), ('☕', 2), ('🍺', 2), ('🎩', 2), ('🤷🏼\\u200d♂', 2), ('\\U0001f91f', 2), ('😫', 2), ('💴', 2), ('\\U0001f9e1', 2), ('💸', 2), ('❤', 2), ('🍆', 2), ('✅', 2), ('🦉', 2), ('🖐🏻', 2), ('🤦\\u200d♂', 2), ('🙇🏾\\u200d♂', 2), ('🏴', 2), ('✊', 2), ('😿', 2), ('😀', 2), ('🙅\\u200d♂', 1), ('👽', 1), ('😼', 1), ('👎🏻', 1), ('👫', 1), ('💀', 1), ('☃️', 1), ('🙅🏻\\u200d♂️', 1), ('👊🏻', 1), ('🤤', 1), ('✌🏻', 1), ('\\U0001f91f🏻', 1), ('👨\\u200d🎓', 1), ('🤗', 1), ('💰', 1), ('💵', 1), ('💇\\u200d♂', 1), ('👠', 1), ('🙊', 1), ('🙈', 1), ('🏅', 1), ('🤝', 1), ('💣', 1), ('💋', 1), ('🌟', 1), ('🙎🏻\\u200d♂️', 1), ('🖕', 1), ('🤝🏻', 1), ('😠', 1), ('🍿', 1), ('🚀', 1), ('☺️', 1), ('🤘', 1), ('🤙', 1), ('\\U0001f9b8', 1), ('🐣', 1), ('🐥', 1), ('😥', 1), ('👋', 1), ('😗', 1), ('🍀', 1), ('🖕🏻', 1), ('😝', 1)]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dxg5R_7tuWh3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "225ecb78-bacb-41eb-b294-6a06d6ce2a01"
      },
      "source": [
        "emoji_df = pd.DataFrame(emoji_dict, columns=['emoji', 'count'])\n",
        "emoji_df"
      ],
      "execution_count": 142,
      "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>emoji</th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>😂</td>\n",
              "      <td>2682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>🤣</td>\n",
              "      <td>1431</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>🥺</td>\n",
              "      <td>384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>😒</td>\n",
              "      <td>208</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>🔥</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>👋</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>😗</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>🍀</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>🖕🏻</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>😝</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>178 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    emoji  count\n",
              "0       😂   2682\n",
              "1       🤣   1431\n",
              "2       🥺    384\n",
              "3       😒    208\n",
              "4       🔥     79\n",
              "..    ...    ...\n",
              "173     👋      1\n",
              "174     😗      1\n",
              "175     🍀      1\n",
              "176    🖕🏻      1\n",
              "177     😝      1\n",
              "\n",
              "[178 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59PN-N8E2kHh",
        "colab_type": "text"
      },
      "source": [
        "We can see '😂 '   dominates the list."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VXqqFzEF2pu0",
        "colab_type": "text"
      },
      "source": [
        "### Emoji distribution visualisation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7mP0VQAuq_3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2cbb4233-f635-40ff-a73c-10000f69a724"
      },
      "source": [
        "import plotly.express as px\n",
        "fig = px.pie(emoji_df, values='count', names='emoji')\n",
        "fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "fig.show()"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<head><meta charset=\"utf-8\" /></head>\n",
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              "                <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
              "        <script src=\"https://cdn.plot.ly/plotly-latest.min.js\"></script>    \n",
              "            <div id=\"2cd97ea1-b1e1-44c2-9c59-66418411b64f\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                \n",
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          "metadata": {
            "tags": []
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yquiysmD2tlc",
        "colab_type": "text"
      },
      "source": [
        "### Person Wise Emoji Distribution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lEjIpwTSuovI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "8f13a8f3-01cd-4771-97cf-94d36fa97799"
      },
      "source": [
        "# Creates a list of unique Authors - ['Manikanta', 'Teja Kura', .........]\n",
        "l = messages_df.Author.unique()\n",
        "for i in range(len(l)):\n",
        "  dummy_df = messages_df[messages_df['Author'] == l[i]]\n",
        "  total_emojis_list = list([a for b in dummy_df.emoji for a in b])\n",
        "  emoji_dict = dict(Counter(total_emojis_list))\n",
        "  emoji_dict = sorted(emoji_dict.items(), key=lambda x: x[1], reverse=True)\n",
        "  print('Emoji Distribution for', l[i])\n",
        "  author_emoji_df = pd.DataFrame(emoji_dict, columns=['emoji', 'count'])\n",
        "  fig = px.pie(author_emoji_df, values='count', names='emoji')\n",
        "  fig.update_traces(textposition='inside', textinfo='percent+label')\n",
        "  fig.show()"
      ],
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Emoji Distribution for Aakarsh Yelisetty\n"
          ],
          "name": "stdout"
        },
        {
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          "metadata": {
            "tags": []
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        {
          "output_type": "stream",
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            "Emoji Distribution for Gowtham Vce\n"
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              "                        {\"responsive\": true}\n",
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              "                            \n",
              "var gd = document.getElementById('f73deef6-c218-4c4b-99ab-2965d9011376');\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TJkWMAFG5Xmy",
        "colab_type": "text"
      },
      "source": [
        "# Some more Stats"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWtv-fi36bMr",
        "colab_type": "text"
      },
      "source": [
        "### **Day wise Distribution**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZMTsC_eZvg1C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f(i):\n",
        "  l = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\n",
        "  return l[i];\n",
        "day_df=pd.DataFrame(messages_df[\"Message\"])\n",
        "day_df['day_of_date'] = messages_df['Date'].dt.weekday\n",
        "day_df['day_of_date'] = day_df[\"day_of_date\"].apply(f)\n",
        "day_df[\"messagecount\"] = 1\n",
        "day = day_df.groupby(\"day_of_date\").sum()\n",
        "day.reset_index(inplace=True)"
      ],
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "819m2jtvY2zA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "7e5584c4-fa7d-46f2-8905-56978ce65ae8"
      },
      "source": [
        "fig = px.line_polar(day, r='messagecount', theta='day_of_date', line_close=True)\n",
        "fig.update_traces(fill='toself')\n",
        "fig.update_layout(\n",
        "  polar=dict(\n",
        "    radialaxis=dict(\n",
        "      visible=True,\n",
        "    )),\n",
        "  showlegend=False\n",
        ")\n",
        "fig.show()"
      ],
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "<body>\n",
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              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4CmeKkd6hQa",
        "colab_type": "text"
      },
      "source": [
        "### **Number of messages as times move on**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lW0yRqyl5odQ",
        "colab_type": "code",
        "colab": {
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          "height": 0
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        "outputId": "e11cd667-8b8b-4c99-9b0e-d40dd02c5d42"
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      "source": [
        "date_df = messages_df.groupby(\"Date\").sum()\n",
        "date_df.reset_index(inplace=True)\n",
        "fig = px.line(date_df, x=\"Date\", y=\"MessageCount\")\n",
        "fig.update_xaxes(nticks=20)\n",
        "fig.show()"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "display_data",
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          "metadata": {
            "tags": []
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        }
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "93uPXu5F-rzw",
        "colab_type": "text"
      },
      "source": [
        "### **Chatter**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDUbJuQ--vaf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "273346da-4c06-48b3-f0e9-12096aa44a1d"
      },
      "source": [
        "auth = messages_df.groupby(\"Author\").sum()\n",
        "auth.reset_index(inplace=True)\n",
        "fig = px.bar(auth, y=\"Author\", x=\"MessageCount\", color='Author', orientation=\"h\",\n",
        "             color_discrete_sequence=[\"red\", \"green\", \"blue\", \"goldenrod\", \"magenta\"],\n",
        "             title=\"Explicit color sequence\"\n",
        "            )\n",
        "\n",
        "fig.show()"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "display_data",
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              "                        [{\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Author=%{y}<br>MessageCount=%{x}\", \"legendgroup\": \"Author=Aakarsh Yelisetty\", \"marker\": {\"color\": \"red\"}, \"name\": \"Author=Aakarsh Yelisetty\", \"offsetgroup\": \"Author=Aakarsh Yelisetty\", \"orientation\": \"h\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [5567], \"xaxis\": \"x\", \"y\": [\"Aakarsh Yelisetty\"], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Author=%{y}<br>MessageCount=%{x}\", \"legendgroup\": \"Author=Gowtham Vce\", \"marker\": {\"color\": \"green\"}, \"name\": \"Author=Gowtham Vce\", \"offsetgroup\": \"Author=Gowtham Vce\", \"orientation\": \"h\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [5982], \"xaxis\": \"x\", \"y\": [\"Gowtham Vce\"], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Author=%{y}<br>MessageCount=%{x}\", \"legendgroup\": \"Author=Manikanta\", \"marker\": {\"color\": \"blue\"}, \"name\": \"Author=Manikanta\", \"offsetgroup\": \"Author=Manikanta\", \"orientation\": \"h\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [5443], \"xaxis\": \"x\", \"y\": [\"Manikanta\"], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Author=%{y}<br>MessageCount=%{x}\", \"legendgroup\": \"Author=Sai Teja\", \"marker\": {\"color\": \"goldenrod\"}, \"name\": \"Author=Sai Teja\", \"offsetgroup\": \"Author=Sai Teja\", \"orientation\": \"h\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [7777], \"xaxis\": \"x\", \"y\": [\"Sai Teja\"], \"yaxis\": \"y\"}, {\"alignmentgroup\": \"True\", \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"Author=%{y}<br>MessageCount=%{x}\", \"legendgroup\": \"Author=Vishal Vce\", \"marker\": {\"color\": \"magenta\"}, \"name\": \"Author=Vishal Vce\", \"offsetgroup\": \"Author=Vishal Vce\", \"orientation\": \"h\", \"showlegend\": true, \"textposition\": \"auto\", \"type\": \"bar\", \"x\": [9656], \"xaxis\": \"x\", \"y\": [\"Vishal Vce\"], \"yaxis\": \"y\"}],\n",
              "                        {\"barmode\": \"relative\", \"legend\": {\"tracegroupgap\": 0}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, 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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('566f67ef-1698-47fb-846a-8b84e39aee01');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M6ql1uCT6nEY",
        "colab_type": "text"
      },
      "source": [
        "### **When are the group members most active?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W2HZVpwr6oyj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "54b8d1f7-040a-459e-d4ca-2ebde85b7d90"
      },
      "source": [
        "messages_df['Time'].value_counts().head(10).plot.barh() # Top 10 Times of the day at which the most number of messages were sent\n",
        "plt.xlabel('Number of messages')\n",
        "plt.ylabel('Time')"
      ],
      "execution_count": 149,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Time')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 149
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "xsKFMObX69zn",
        "colab_type": "text"
      },
      "source": [
        "### **The most happening day was -**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "actxbmCf6pkb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "ba1f8174-9ae7-401e-b79c-24aa6701ae9b"
      },
      "source": [
        "messages_df['Date'].value_counts().head(10).plot.barh()\n",
        "print(messages_df['Date'].value_counts())\n",
        "plt.xlabel('Number of Messages')\n",
        "plt.ylabel('Date')"
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-07-26    720\n",
            "2020-06-26    461\n",
            "2019-08-18    434\n",
            "2020-04-06    432\n",
            "2019-08-12    409\n",
            "             ... \n",
            "2019-09-09      1\n",
            "2019-11-03      1\n",
            "2019-04-15      1\n",
            "2019-04-28      1\n",
            "2019-06-05      1\n",
            "Name: Date, Length: 390, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Date')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        },
        {
          "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": "markdown",
      "metadata": {
        "id": "2y5ROAL_7FLO",
        "colab_type": "text"
      },
      "source": [
        "### **The early bird**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-fIBleWX7Mzz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_DEtbSW7Nsr",
        "colab_type": "text"
      },
      "source": [
        "### The Late Owl"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eAI-nM8Z7StQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHt0guBu8upV",
        "colab_type": "text"
      },
      "source": [
        "### **Message Having Maximum number of words**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cmb8DbXC7Qpy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "outputId": "2809c3fe-80b5-4c2f-950a-9269daf69e27"
      },
      "source": [
        "messages_df.iloc[messages_df['Word_Count'].argmax()]"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                                          2019-04-25 00:00:00\n",
              "Time                                                   9:59:12 PM\n",
              "Author                                          Aakarsh Yelisetty\n",
              "Message         Hello IT III Year, Section A-B students,  Plea...\n",
              "emoji                                                          []\n",
              "urlcount                                                        0\n",
              "Letter_Count                                                 2703\n",
              "Word_Count                                                    497\n",
              "MessageCount                                                    1\n",
              "emojicount                                                      0\n",
              "Name: 1259, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kSYaYRnu9tEQ",
        "colab_type": "text"
      },
      "source": [
        "### **Word Cloud**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T4YT-IvnBVJ4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "76591eed-43c9-435a-f2e0-f40fc6893090"
      },
      "source": [
        "text = \" \".join(review for review in messages_df.Message)\n",
        "print (\"There are {} words in all the messages.\".format(len(text)))"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 714127 words in all the messages.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HdDrDmxWOzBn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "c8e3e072-aa3d-4375-995c-d5cc903920b9"
      },
      "source": [
        "  stopwords = set(STOPWORDS)\n",
        "  stopwords.update([\"ra\", \"ga\", \"na\", \"ani\", \"em\", \"ki\", \"ah\",\"ha\",\"la\",\"eh\",\"ne\",\"le\",\"ni\",\"lo\",\"Ma\",\"Haa\",\"ni\"])\n",
        "  # Generate a word cloud image\n",
        "  wordcloud = WordCloud(stopwords=stopwords, background_color=\"white\").generate(text)\n",
        "  # Display the generated image:\n",
        "  # the matplotlib way:\n",
        "  \n",
        "  plt.figure( figsize=(10,5))\n",
        "  plt.imshow(wordcloud, interpolation='bilinear')\n",
        "  plt.axis(\"off\")\n",
        "  plt.show()"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-4eCtT48ahgH",
        "colab_type": "text"
      },
      "source": [
        "### **Author wise WordCloud**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IZYsoyySBw1x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# l = messages_df.Author.unique()\n",
        "# for i in range(len(l)):\n",
        "#   dummy_df = messages_df[messages_df['Author'] == l[i]]\n",
        "#   text = \" \".join(review for review in dummy_df.Message)\n",
        "#   stopwords = set(STOPWORDS)\n",
        "#   stopwords.update([\"ra\", \"ga\", \"na\", \"ani\", \"em\", \"ki\", \"ah\",\"ha\",\"anta\",\"kuda\",\"ante\",\"la\",\"eh\",\"Nen\",\"ne\",\"haa\",\"Haa\",\"le\"])\n",
        "#   # Generate a word cloud image\n",
        "#   print('Author name',l[i])\n",
        "#   wordcloud = WordCloud(stopwords=stopwords, background_color=\"white\").generate(text)\n",
        "#   # Display the generated image:\n",
        "#   # the matplotlib way:\n",
        "  \n",
        "#   plt.figure( figsize=(10,5))\n",
        "#   plt.imshow(wordcloud, interpolation='bilinear')\n",
        "#   plt.axis(\"off\")\n",
        "#   plt.show()"
      ],
      "execution_count": 154,
      "outputs": []
    }
  ]
}