Whatsapp_Group_Chat_Analysis.ipynb

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    "colab": {
      "name": "Whatsapp_Group_Chat_Analysis.ipynb",
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        "HHt0guBu8upV"
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      "toc_visible": true,
      "include_colab_link": true
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    "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.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": 54
        },
        "outputId": "0a1b2538-6a64-483c-ae26-d02d5bcc78c5"
      },
      "source": [
        "!pip install emoji"
      ],
      "execution_count": 329,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: emoji in /usr/local/lib/python3.6/dist-packages (0.6.0)\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",
        "import datetime\n",
        "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n",
        "% matplotlib inline"
      ],
      "execution_count": 330,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_IVxlHe_vr6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "outputId": "ade96648-1909-4f6e-e98d-8a1825a020c5"
      },
      "source": [
        "! git clone https://github.com/amueller/word_cloud.git\n",
        "% cd word_cloud\n",
        "! pip install ."
      ],
      "execution_count": 331,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'word_cloud'...\n",
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            "remote: Compressing objects: 100% (134/134), done.\u001b[K\n",
            "remote: Total 5853 (delta 85), reused 120 (delta 52), pack-reused 5667\u001b[K\n",
            "Receiving objects: 100% (5853/5853), 107.06 MiB | 34.63 MiB/s, done.\n",
            "Resolving deltas: 100% (3420/3420), done.\n",
            "/content/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud\n",
            "Processing /content/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud/word_cloud\n",
            "Requirement already satisfied: numpy>=1.6.1 in /usr/local/lib/python3.6/dist-packages (from wordcloud==1.6.0.post90+gbda4195) (1.18.5)\n",
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            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib->wordcloud==1.6.0.post90+gbda4195) (1.15.0)\n",
            "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=338629 sha256=e7c5b557001a41bed132ad51d8cf139743900f57b440dee419f2f149348912d7\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-7tr9d_qb/wheels/f5/6c/dc/a8ba0219e200b2cda5078137dd0135059a3accd97f169528e8\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": 331,
      "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 startsWithDateAndTimeAndroid(s):\n",
        "    pattern = '^([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\n",
        "\n",
        "def startsWithDateAndTimeios(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": 332,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EE7_sfSBXPU2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def FindAuthor(s):\n",
        "  s=s.split(\":\")\n",
        "  if len(s)==2:\n",
        "    return True\n",
        "  else:\n",
        "    return False"
      ],
      "execution_count": 333,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s6WSoM1vB1zy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def getDataPointAndroid(line):   \n",
        "    splitLine = line.split(' - ') \n",
        "    dateTime = splitLine[0]\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",
        "\n",
        "def getDataPointios(line):\n",
        "\tsplitLine = line.split('] ')\n",
        "\tdateTime = splitLine[0]\n",
        "\tif ',' in dateTime:\n",
        "\t\tdate, time = dateTime.split(',')\n",
        "\telse:\n",
        "\t\tdate, time = dateTime.split(' ')\n",
        "\tmessage = ' '.join(splitLine[1:])\n",
        "\tif FindAuthor(message):\n",
        "\t\tsplitMessage = message.split(':')\n",
        "\t\tauthor = splitMessage[0]\n",
        "\t\tmessage = ' '.join(splitMessage[1:])\n",
        "\telse:\n",
        "\t\tauthor = None\n",
        "\tif time[5]==\":\":\n",
        "\t\ttime = time[:5]+time[-3:]\n",
        "\telse:\n",
        "\t\tif 'AM' in time or 'PM' in time:\n",
        "\t\t\ttime = time[:6]+time[-3:]\n",
        "\t\telse:\n",
        "\t\t\ttime = time[:6]\n",
        "\treturn date, time, author, message\n"
      ],
      "execution_count": 334,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TScVgf-ofSOq",
        "colab_type": "text"
      },
      "source": [
        "If you get a **Value Error** after running the below cell , it may be due to different format of date."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4CDoWlm6fc1O",
        "colab_type": "text"
      },
      "source": [
        "Try replacing %d-%m-%Y with %m-%d-%Y and replacing %d-%m-%y with %m-%d-%y and similarly for / too."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hfQQarnrXNsI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "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\")"
      ],
      "execution_count": 335,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JFjvlDAwcYdc",
        "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"
      ],
      "execution_count": 336,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IWQisHLQfxpd",
        "colab_type": "text"
      },
      "source": [
        "Change file path in the below cell"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PgfGiHyGB68i",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "519e85ae-8633-40b4-8bf8-a29371cfe438"
      },
      "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",
        "    device=''\n",
        "    first=fp.readline()\n",
        "    print(first)\n",
        "    if '[' in first:\n",
        "      device='ios'\n",
        "    else:\n",
        "      device=\"android\"\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",
        "        if device==\"ios\":\n",
        "          line = line.strip()\n",
        "          if startsWithDateAndTimeios(line):\n",
        "            if len(messageBuffer) > 0:\n",
        "              parsedData.append([date, time, author, ' '.join(messageBuffer)])\n",
        "            messageBuffer.clear()\n",
        "            date, time, author, message = getDataPointios(line)\n",
        "            messageBuffer.append(message)\n",
        "          else:\n",
        "            line= (line.encode('ascii', 'ignore')).decode(\"utf-8\")\n",
        "            if startsWithDateAndTimeios(line):\n",
        "              if len(messageBuffer) > 0:\n",
        "                parsedData.append([date, time, author, ' '.join(messageBuffer)])\n",
        "              messageBuffer.clear()\n",
        "              date, time, author, message = getDataPointios(line)\n",
        "              messageBuffer.append(message)\n",
        "            else:\n",
        "              messageBuffer.append(line)\n",
        "        else:\n",
        "          line = line.strip()\n",
        "          if startsWithDateAndTimeAndroid(line):\n",
        "            if len(messageBuffer) > 0:\n",
        "              parsedData.append([date, time, author, ' '.join(messageBuffer)])\n",
        "            messageBuffer.clear()\n",
        "            date, time, author, message = getDataPointAndroid(line)\n",
        "            messageBuffer.append(message)\n",
        "          else:\n",
        "            messageBuffer.append(line)"
      ],
      "execution_count": 337,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8A3Qswc2ZkdF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "if device =='android':\n",
        "        df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message'])\n",
        "        df[\"Date\"] = pd.to_datetime(df[\"Date\"])\n",
        "        df = df.dropna()\n",
        "        df[\"emoji\"] = df[\"Message\"].apply(split_count)\n",
        "        URLPATTERN = r'(https?://\\S+)'\n",
        "        df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()\n",
        "else:\n",
        "        df = pd.DataFrame(parsedData, columns=['Date', 'Time', 'Author', 'Message']) # Initialising a pandas Dataframe.\n",
        "        df = df.dropna()\n",
        "        df[\"Date\"] = df[\"Date\"].apply(dateconv)\n",
        "        df[\"Date\"] = pd.to_datetime(df[\"Date\"],format='%Y-%m-%d')\n",
        "        df[\"emoji\"] = df[\"Message\"].apply(split_count)\n",
        "        URLPATTERN = r'(https?://\\S+)'\n",
        "        df['urlcount'] = df.Message.apply(lambda x: re.findall(URLPATTERN, x)).str.len()"
      ],
      "execution_count": 338,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UxnSCcaGCPam",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "outputId": "d428cf4f-eda6-401e-cc4a-a68785603761"
      },
      "source": [
        "df.head(2)"
      ],
      "execution_count": 339,
      "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>0</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:45 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>Arey</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:45 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>Adhe chedham aa inka?</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date      Time  ... emoji urlcount\n",
              "0 2019-03-07   6:45 PM  ...    []        0\n",
              "1 2019-03-07   6:45 PM  ...    []        0\n",
              "\n",
              "[2 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 339
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a1Gk-sfwDj3d",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "06d2a8b8-4d7c-4aa2-de02-226b5922ed1d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 340,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34827 entries, 0 to 35088\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Date      34827 non-null  datetime64[ns]\n",
            " 1   Time      34827 non-null  object        \n",
            " 2   Author    34827 non-null  object        \n",
            " 3   Message   34827 non-null  object        \n",
            " 4   emoji     34827 non-null  object        \n",
            " 5   urlcount  34827 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(1), object(4)\n",
            "memory usage: 1.9+ 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": "31383f55-ae59-4a02-f226-d26ba5eea9e1"
      },
      "source": [
        "df.Author.unique()"
      ],
      "execution_count": 341,
      "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": 341
        }
      ]
    },
    {
      "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": 238
        },
        "outputId": "755d46d8-55df-405f-d17b-987cf5e485bf"
      },
      "source": [
        "df = df.dropna()\n",
        "df.info()"
      ],
      "execution_count": 342,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34827 entries, 0 to 35088\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Date      34827 non-null  datetime64[ns]\n",
            " 1   Time      34827 non-null  object        \n",
            " 2   Author    34827 non-null  object        \n",
            " 3   Message   34827 non-null  object        \n",
            " 4   emoji     34827 non-null  object        \n",
            " 5   urlcount  34827 non-null  int64         \n",
            "dtypes: datetime64[ns](1), int64(1), object(4)\n",
            "memory usage: 1.9+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FzPSOF7yDosE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "fbe00eef-3ae6-4c00-9fa4-c195b9be4568"
      },
      "source": [
        "df.Author.unique()"
      ],
      "execution_count": 343,
      "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": 343
        }
      ]
    },
    {
      "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": "1c1169ee-40f9-4e21-c33f-c767416b90b9"
      },
      "source": [
        "total_messages = df.shape[0]\n",
        "print(total_messages)"
      ],
      "execution_count": 344,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "34827\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": "a8c0e01e-8fb4-4992-a2f8-6f973a39c588"
      },
      "source": [
        "media_messages = df[df['Message'] == '<Media omitted>'].shape[0]\n",
        "print(media_messages)"
      ],
      "execution_count": 345,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ukq2MWKPUHWV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a4eb3c75-407b-42c7-b3ca-a25b1d22a23a"
      },
      "source": [
        "emojis = sum(df['emoji'].str.len())\n",
        "print(emojis)"
      ],
      "execution_count": 346,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "6570\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": 347,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xa0oY2BWX6X_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "links = np.sum(df.urlcount)"
      ],
      "execution_count": 348,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SZdFz-0UYaTs",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "ef3a4706-af1e-4054-fd3e-0ce0e874f8bd"
      },
      "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": 349,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Group Wise Stats\n",
            "Messages: 34827\n",
            "Media: 0\n",
            "Emojis: 6570\n",
            "Links: 88\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vj3J0Bb-usX4",
        "colab_type": "text"
      },
      "source": [
        "## Let us Separate the media messages and text messages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ9Cd-_8uxT4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "link_messages= df[df['urlcount']>0]\n",
        "deleted_messages=df[(df[\"Message\"] == \" You deleted this message\")| (df[\"Message\"] == \" This message was deleted.\")|(df[\"Message\"] == \" You deleted this message.\")]\n",
        "media_messages_df = df[(df['Message'] == ' <Media omitted>')|(df['Message'] == ' image omitted')|(df['Message'] == ' video omitted')|(df['Message'] == ' sticker omitted')]\n",
        "messages_df = df.drop(media_messages_df.index)\n",
        "messages_df = messages_df.drop(deleted_messages.index)\n",
        "messages_df = messages_df.drop(link_messages.index)"
      ],
      "execution_count": 350,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZflAweBoGFY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "77e6a274-9efe-472e-b9cb-a7a51fe33480"
      },
      "source": [
        "messages_df.info()"
      ],
      "execution_count": 351,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 34348 entries, 0 to 35088\n",
            "Data columns (total 6 columns):\n",
            " #   Column    Non-Null Count  Dtype         \n",
            "---  ------    --------------  -----         \n",
            " 0   Date      34348 non-null  datetime64[ns]\n",
            " 1   Time      34348 non-null  object        \n",
            " 2   Author    34348 non-null  object        \n",
            " 3   Message   34348 non-null  object        \n",
            " 4   emoji     34348 non-null  object        \n",
            " 5   urlcount  34348 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": 352,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Dn_glCLPhp8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 182
        },
        "outputId": "8c92bad0-1998-48b7-8e76-bc6d4d4c8357"
      },
      "source": [
        "messages_df.head(2)"
      ],
      "execution_count": 353,
      "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>0</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:45 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>Arey</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-03-07</td>\n",
              "      <td>6:45 PM</td>\n",
              "      <td>Aakarsh Yelisetty</td>\n",
              "      <td>Adhe chedham aa inka?</td>\n",
              "      <td>[]</td>\n",
              "      <td>0</td>\n",
              "      <td>22</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Date      Time  ... Word_Count MessageCount\n",
              "0 2019-03-07   6:45 PM  ...          2            1\n",
              "1 2019-03-07   6:45 PM  ...          5            1\n",
              "\n",
              "[2 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 353
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8c5r6rbGQnAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "messages_df[\"emojicount\"]= df['emoji'].str.len()"
      ],
      "execution_count": 354,
      "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": 612
        },
        "outputId": "bddd8913-44db-4c8f-8bca-7d9961166c24"
      },
      "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",
        "  # 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(link_messages[link_messages['Author'] == l[i]][\"urlcount\"])   \n",
        "  print('Links Sent', links)   \n",
        "  print()"
      ],
      "execution_count": 355,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stats of Aakarsh Yelisetty -\n",
            "Messages Sent 5544\n",
            "Words per message 4.681637806637807\n",
            "Media Messages Sent 100\n",
            "Emojis Sent 1369\n",
            "Links Sent 35\n",
            "\n",
            "Stats of Gowtham Vce -\n",
            "Messages Sent 5979\n",
            "Words per message 3.7991302893460444\n",
            "Media Messages Sent 46\n",
            "Emojis Sent 1817\n",
            "Links Sent 1\n",
            "\n",
            "Stats of Vishal Vce -\n",
            "Messages Sent 9653\n",
            "Words per message 4.3061224489795915\n",
            "Media Messages Sent 64\n",
            "Emojis Sent 1408\n",
            "Links Sent 26\n",
            "\n",
            "Stats of Sai Teja -\n",
            "Messages Sent 7732\n",
            "Words per message 4.942188308329023\n",
            "Media Messages Sent 165\n",
            "Emojis Sent 799\n",
            "Links Sent 17\n",
            "\n",
            "Stats of Manikanta -\n",
            "Messages Sent 5440\n",
            "Words per message 5.254044117647059\n",
            "Media Messages Sent 71\n",
            "Emojis Sent 1175\n",
            "Links Sent 9\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": 34
        },
        "outputId": "2f8d3761-cf54-4efb-9c0d-687d0482b60e"
      },
      "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": 356,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "177\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": 54
        },
        "outputId": "0b2d9d73-b699-4a6c-b1b5-08862d9b806f"
      },
      "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": 357,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[('😂', 2682), ('🤣', 1431), ('\\U0001f97a', 383), ('😒', 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), ('💇\\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": 419
        },
        "outputId": "92b1b1ff-6f17-45ef-a926-b70b2a8428e4"
      },
      "source": [
        "emoji_df = pd.DataFrame(emoji_dict, columns=['emoji', 'count'])\n",
        "emoji_df"
      ],
      "execution_count": 358,
      "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>383</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>172</th>\n",
              "      <td>👋</td>\n",
              "      <td>1</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>177 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "    emoji  count\n",
              "0       😂   2682\n",
              "1       🤣   1431\n",
              "2       🥺    383\n",
              "3       😒    208\n",
              "4       🔥     79\n",
              "..    ...    ...\n",
              "172     👋      1\n",
              "173     😗      1\n",
              "174     🍀      1\n",
              "175    🖕🏻      1\n",
              "176     😝      1\n",
              "\n",
              "[177 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 358
        }
      ]
    },
    {
      "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": 542
        },
        "outputId": "ede3b1e1-567c-4640-8913-42c189792a94"
      },
      "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": 359,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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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": 1000
        },
        "outputId": "9abe4e02-9bfc-4689-f8cd-dbd9bc953d09"
      },
      "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": 360,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Emoji Distribution for Aakarsh Yelisetty\n"
          ],
          "name": "stdout"
        },
        {
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              "        </div>\n",
              "</body>\n",
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            ]
          },
          "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": 361,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "819m2jtvY2zA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "c70f0ba2-5d55-445c-d7a3-c0989db28dd0"
      },
      "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": 362,
      "outputs": [
        {
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              "    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": "-4CmeKkd6hQa",
        "colab_type": "text"
      },
      "source": [
        "### **Number of messages as times move on**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lW0yRqyl5odQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "outputId": "79d51040-04a5-4b60-c649-347384d5d711"
      },
      "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": 363,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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        "fig.update_xaxes(nticks=20)\n",
        "fig.show()"
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      "execution_count": 364,
      "outputs": [
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              "\n",
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
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            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": 562
        },
        "outputId": "47fce696-a428-4237-cece-b0719e47e2a0"
      },
      "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": 365,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "                        })\n",
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          },
          "metadata": {
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    {
      "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": 296
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        "outputId": "4dd2ee2a-961f-4ae9-e71a-15ce1b785323"
      },
      "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": 366,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Time')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 366
        },
        {
          "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": 500
        },
        "outputId": "1e928981-e4da-4701-d444-20e907b147ba"
      },
      "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": 367,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2019-07-26    720\n",
            "2020-06-26    457\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": 367
        },
        {
          "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": 367,
      "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": 367,
      "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": 224
        },
        "outputId": "c498ca5e-c394-463e-93d6-6decf7ec8cc1"
      },
      "source": [
        "messages_df.iloc[messages_df['Word_Count'].argmax()]"
      ],
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                                          2019-04-25 00:00:00\n",
              "Time                                                      9:59 PM\n",
              "Author                                          Aakarsh Yelisetty\n",
              "Message          Hello IT III Year, Section A-B students,  Ple...\n",
              "emoji                                                          []\n",
              "urlcount                                                        0\n",
              "Letter_Count                                                 2704\n",
              "Word_Count                                                    498\n",
              "MessageCount                                                    1\n",
              "emojicount                                                      0\n",
              "Name: 1260, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 368
        }
      ]
    },
    {
      "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": "f7fa2710-98a9-4edd-dcd6-ca5be23a10b5"
      },
      "source": [
        "text = \" \".join(review for review in messages_df.Message)\n",
        "print (\"There are {} words in all the messages.\".format(len(text)))"
      ],
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "There are 744871 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": "25e4c44c-ad0a-4e14-e987-2922aebb22af"
      },
      "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": 370,
      "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": "CZ91tFdEa70Y",
        "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": 371,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zrX8D218Siw5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 37
        },
        "outputId": "a5c13633-6e5e-4cf6-f9cf-3cea8a334aca"
      },
      "source": [
        "\"\""
      ],
      "execution_count": 372,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "''"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 372
        }
      ]
    }
  ]
}