Friends_EDA_Analysis.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bchandayo/gtk/blob/master/Friends_EDA_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "source": [
        "\n",
        "# IMPORTANT: RUN THIS CELL IN ORDER TO IMPORT YOUR KAGGLE DATA SOURCES\n",
        "# TO THE CORRECT LOCATION (/kaggle/input) IN YOUR NOTEBOOK,\n",
        "# THEN FEEL FREE TO DELETE THIS CELL.\n",
        "# NOTE: THIS NOTEBOOK ENVIRONMENT DIFFERS FROM KAGGLE'S PYTHON\n",
        "# ENVIRONMENT SO THERE MAY BE MISSING LIBRARIES USED BY YOUR\n",
        "# NOTEBOOK.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "from tempfile import NamedTemporaryFile\n",
        "from urllib.request import urlopen\n",
        "from urllib.parse import unquote, urlparse\n",
        "from urllib.error import HTTPError\n",
        "from zipfile import ZipFile\n",
        "import tarfile\n",
        "import shutil\n",
        "\n",
        "CHUNK_SIZE = 40960\n",
        "DATA_SOURCE_MAPPING = 'friends-tv-series-screenplay-script:https%3A%2F%2Fstorage.googleapis.com%2Fkaggle-data-sets%2F873061%2F1487503%2Fbundle%2Farchive.zip%3FX-Goog-Algorithm%3DGOOG4-RSA-SHA256%26X-Goog-Credential%3Dgcp-kaggle-com%2540kaggle-161607.iam.gserviceaccount.com%252F20240211%252Fauto%252Fstorage%252Fgoog4_request%26X-Goog-Date%3D20240211T024816Z%26X-Goog-Expires%3D259200%26X-Goog-SignedHeaders%3Dhost%26X-Goog-Signature%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'\n",
        "\n",
        "KAGGLE_INPUT_PATH='/kaggle/input'\n",
        "KAGGLE_WORKING_PATH='/kaggle/working'\n",
        "KAGGLE_SYMLINK='kaggle'\n",
        "\n",
        "!umount /kaggle/input/ 2> /dev/null\n",
        "shutil.rmtree('/kaggle/input', ignore_errors=True)\n",
        "os.makedirs(KAGGLE_INPUT_PATH, 0o777, exist_ok=True)\n",
        "os.makedirs(KAGGLE_WORKING_PATH, 0o777, exist_ok=True)\n",
        "\n",
        "try:\n",
        "  os.symlink(KAGGLE_INPUT_PATH, os.path.join(\"..\", 'input'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "try:\n",
        "  os.symlink(KAGGLE_WORKING_PATH, os.path.join(\"..\", 'working'), target_is_directory=True)\n",
        "except FileExistsError:\n",
        "  pass\n",
        "\n",
        "for data_source_mapping in DATA_SOURCE_MAPPING.split(','):\n",
        "    directory, download_url_encoded = data_source_mapping.split(':')\n",
        "    download_url = unquote(download_url_encoded)\n",
        "    filename = urlparse(download_url).path\n",
        "    destination_path = os.path.join(KAGGLE_INPUT_PATH, directory)\n",
        "    try:\n",
        "        with urlopen(download_url) as fileres, NamedTemporaryFile() as tfile:\n",
        "            total_length = fileres.headers['content-length']\n",
        "            print(f'Downloading {directory}, {total_length} bytes compressed')\n",
        "            dl = 0\n",
        "            data = fileres.read(CHUNK_SIZE)\n",
        "            while len(data) > 0:\n",
        "                dl += len(data)\n",
        "                tfile.write(data)\n",
        "                done = int(50 * dl / int(total_length))\n",
        "                sys.stdout.write(f\"\\r[{'=' * done}{' ' * (50-done)}] {dl} bytes downloaded\")\n",
        "                sys.stdout.flush()\n",
        "                data = fileres.read(CHUNK_SIZE)\n",
        "            if filename.endswith('.zip'):\n",
        "              with ZipFile(tfile) as zfile:\n",
        "                zfile.extractall(destination_path)\n",
        "            else:\n",
        "              with tarfile.open(tfile.name) as tarfile:\n",
        "                tarfile.extractall(destination_path)\n",
        "            print(f'\\nDownloaded and uncompressed: {directory}')\n",
        "    except HTTPError as e:\n",
        "        print(f'Failed to load (likely expired) {download_url} to path {destination_path}')\n",
        "        continue\n",
        "    except OSError as e:\n",
        "        print(f'Failed to load {download_url} to path {destination_path}')\n",
        "        continue\n",
        "\n",
        "print('Data source import complete.')\n"
      ],
      "metadata": {
        "id": "CSNa7EuiENgy"
      },
      "cell_type": "code",
      "outputs": [],
      "execution_count": null
    },
    {
      "metadata": {
        "id": "qBX0sgJ6ENg0"
      },
      "cell_type": "markdown",
      "source": [
        "# Introductory EDA on Friends"
      ]
    },
    {
      "metadata": {
        "id": "yhQamyvTENg1"
      },
      "cell_type": "markdown",
      "source": [
        "## 1. Import Libraries"
      ]
    },
    {
      "metadata": {
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "1RTLL-RbENg2"
      },
      "cell_type": "code",
      "source": [
        "import re\n",
        "import os\n",
        "import nltk\n",
        "import spacy\n",
        "import random\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from PIL import Image\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "from nltk import word_tokenize\n",
        "from nltk.util import ngrams"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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          "status": "completed"
        },
        "tags": [],
        "id": "MAWWepMWENg2"
      },
      "cell_type": "markdown",
      "source": [
        "In this notebook we will produce a basic analysis for Friends transcripts and hopefully get a few insights. So, pour yourself a damn fine cup of coffee and bear with me!"
      ]
    },
    {
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          "end_time": "2020-09-15T20:48:36.121939",
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          "status": "completed"
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        "tags": [],
        "id": "6m7Bgnc2ENg2"
      },
      "cell_type": "markdown",
      "source": [
        "## 2. Data loading and preprocessing"
      ]
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:36.166032Z",
          "iopub.status.busy": "2020-09-15T20:48:36.165259Z",
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        },
        "tags": [],
        "trusted": true,
        "id": "8xGJjmV7ENg3"
      },
      "cell_type": "code",
      "source": [
        "# count the number of episodes in each season\n",
        "ep_num = len([name for name in os.listdir('../input/friends-tv-series-screenplay-script')])\n",
        "\n",
        "\n",
        "print(\"Friends Season consists of {} episodes.\".format(ep_num))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "execution": {
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        "trusted": true,
        "id": "JfLM7jWCENg3"
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      "cell_type": "code",
      "source": [
        "import glob\n",
        "texts = \"\"\n",
        "folder_name = \"../input/friends-tv-series-screenplay-script/\"\n",
        "for f in glob.glob(folder_name + '/*.txt'):\n",
        "    temp = open(f,'r')\n",
        "    texts += temp.read()\n",
        "    temp.close()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:36.284047Z",
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        "tags": [],
        "trusted": true,
        "id": "rYOY_3hVENg5"
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      "cell_type": "code",
      "source": [
        "len(texts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
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        "tags": [],
        "trusted": true,
        "id": "oJNC8YHhENg5"
      },
      "cell_type": "code",
      "source": [
        "text = re.sub('[^A-Za-z]+', ' ', texts)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:36.415365Z",
          "iopub.status.busy": "2020-09-15T20:48:36.414647Z",
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        "tags": [],
        "trusted": true,
        "id": "0J6HtyqyENg5"
      },
      "cell_type": "code",
      "source": [
        "# adding screenplay notes to stopwords\n",
        "nlp = spacy.load(\"en\")\n",
        "nlp.Defaults.stop_words |= {\"d\",\"ll\",\"m\",\"re\",\"s\",\"ve\", \"t\", \"oh\", \"uh\", \"na\", \"okay\",\n",
        "                           \"didn\",\"don\",\"gon\",\"j\",\"hm\",\"um\",\"dr\",\"room\",\"int\", \"ext\",\n",
        "                           \"cut\", \"day\", \"night\", \"theme\", \"tune\",\"music\", \"ends\",\"view\",\"opening credits scene\",\n",
        "                            \"commercial break scene\", \"hey hey hey\", \"hey\", \"closing credits scene\",\"scene\",\n",
        "                            \"closeup\", 'freshly', 'squeezed', 'fade'}\n",
        "stopwords = nlp.Defaults.stop_words"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
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          "status": "completed"
        },
        "tags": [],
        "id": "wuOZRqjjENg6"
      },
      "cell_type": "markdown",
      "source": [
        "## 3. Most frequent words"
      ]
    },
    {
      "metadata": {
        "execution": {
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      "cell_type": "code",
      "source": [
        "# function to find and plot frequent words\n",
        "def plot_words(words,title,color=\"#114d1e\"):\n",
        "    counts = {}\n",
        "    for i in range(len(words)):\n",
        "        counts[words[i][0]] = words[i][1]\n",
        "    plt.figure(figsize=(8,6))\n",
        "    plt.title(title, fontsize=14)\n",
        "    plt.barh(range(len(counts)), list(counts.values()), color=color, align=\"center\")\n",
        "    plt.yticks(range(len(counts)), list(counts.keys()), fontsize=12)\n",
        "    plt.gca().invert_yaxis()\n",
        "    plt.show()\n",
        "\n",
        "def plot_ngrams(ngrams,title,color=\"#7a2822\"):\n",
        "    counts = {}\n",
        "    for i in range(len(ngrams)):\n",
        "        counts[\" \".join(ngrams[i][0])] = ngrams[i][1]\n",
        "    plt.figure(figsize=(8,6))\n",
        "    plt.title(title, fontsize=14)\n",
        "    plt.barh(range(len(counts)), list(counts.values()), color=color,align=\"center\")\n",
        "    plt.yticks(range(len(counts)), list(counts.keys()), fontsize=12)\n",
        "    plt.gca().invert_yaxis()\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
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          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "n5K3-qq_ENg7"
      },
      "cell_type": "code",
      "source": [
        "all_words = nltk.tokenize.word_tokenize(text.lower())\n",
        "all_words_no_stop = nltk.FreqDist(w.lower() for w in all_words if w not in stopwords)\n",
        "plot_words(all_words_no_stop.most_common(10), \"Top 10 frequent words\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
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        "tags": [],
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      "cell_type": "markdown",
      "source": [
        "Unsuprisingly, these are the **names of the main characters**.\n",
        "\n",
        "Now let's get the most frequent bigrams and bigrams, i.e. the sequences of two and three neighbouring words respectively."
      ]
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:38.053284Z",
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        "tags": [],
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      },
      "cell_type": "code",
      "source": [
        "bigram = nltk.FreqDist(nltk.bigrams(w.lower() for w in all_words if w not in stopwords))\n",
        "plot_ngrams(bigram.most_common(10), \"Top 10 frequent bigrams.\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:38.338784Z",
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        "tags": [],
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        "id": "KGDcR6r-ENg7"
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      "cell_type": "code",
      "source": [
        "trigrams = nltk.FreqDist(nltk.trigrams(w.lower() for w in all_words if w not in stopwords))\n",
        "plot_ngrams(trigrams.most_common(10), \"Top 10 frequent trigrams.\", \"#2b2e2b\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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        "tags": [],
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      "cell_type": "markdown",
      "source": [
        "What will change if we remove the names?"
      ]
    },
    {
      "metadata": {
        "execution": {
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        "tags": [],
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        "id": "tXfl5W8TENg9"
      },
      "cell_type": "code",
      "source": [
        "characters = [\n",
        "'monica','rachel','ross','joey','chandler','phoebe','central perk',\"opening credits scene\",\n",
        "\"commercial break scene\", \"hey hey hey\", \"hey\", \"closing credits scene\",\"scene\"]\n",
        "\n",
        "# unique names only\n",
        "names = set(\" \".join(set(characters)).lower().split())\n",
        "\n",
        "nlp.Defaults.stop_words |= names"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
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          "exception": false,
          "start_time": "2020-09-15T20:48:38.686948",
          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "ljeQ9EUgENg9"
      },
      "cell_type": "code",
      "source": [
        "no_names = nltk.FreqDist(w.lower() for w in all_words if w not in stopwords)\n",
        "plot_words(no_names.most_common(10), \"Top 10 frequent words except for names\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:38.964316Z",
          "iopub.status.busy": "2020-09-15T20:48:38.948727Z",
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          "shell.execute_reply": "2020-09-15T20:48:39.161067Z"
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          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "cPTczmA8ENg-"
      },
      "cell_type": "code",
      "source": [
        "no_names_bigram = nltk.FreqDist(nltk.bigrams(w.lower() for w in all_words if w not in stopwords))\n",
        "plot_ngrams(no_names_bigram.most_common(10), \"Top 10 frequent bigrams except for names\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:39.237386Z",
          "iopub.status.busy": "2020-09-15T20:48:39.221786Z",
          "iopub.status.idle": "2020-09-15T20:48:39.434192Z",
          "shell.execute_reply": "2020-09-15T20:48:39.433538Z"
        },
        "papermill": {
          "duration": 0.249363,
          "end_time": "2020-09-15T20:48:39.434311",
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          "start_time": "2020-09-15T20:48:39.184948",
          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "gFny9ai9ENg-"
      },
      "cell_type": "code",
      "source": [
        "no_names_trigram = nltk.FreqDist(nltk.trigrams(w.lower() for w in all_words if w not in stopwords))\n",
        "plot_ngrams(no_names_trigram.most_common(10), \"Top 10 frequent trigrams except for names\", \"#2b2e2b\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "papermill": {
          "duration": 0.02383,
          "end_time": "2020-09-15T20:48:39.48252",
          "exception": false,
          "start_time": "2020-09-15T20:48:39.45869",
          "status": "completed"
        },
        "tags": [],
        "id": "641SxKVeENg-"
      },
      "cell_type": "markdown",
      "source": [
        "## Word Cloud for Season 1"
      ]
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:48:39.539341Z",
          "iopub.status.busy": "2020-09-15T20:48:39.538571Z",
          "iopub.status.idle": "2020-09-15T20:51:14.089718Z",
          "shell.execute_reply": "2020-09-15T20:51:14.090274Z"
        },
        "papermill": {
          "duration": 154.58399,
          "end_time": "2020-09-15T20:51:14.090447",
          "exception": false,
          "start_time": "2020-09-15T20:48:39.506457",
          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "79SYuG02ENg-"
      },
      "cell_type": "code",
      "source": [
        "# the mask image taken from http://www.designcenterassoc.com/wp-content/uploads/2017/11/Friends-PNG-HD-e1509653607131.png\n",
        "# cooper_mask = np.array(Image.open('../input/temporary/Friends-PNG-HD-e1509653607131.png'))\n",
        "\n",
        "def color_func(word, font_size, position, orientation, random_state=None,\n",
        "                    **kwargs):\n",
        "    return \"hsl(0, 100%, 27%)\"\n",
        "\n",
        "wc = WordCloud(background_color=\"white\", max_words=1000,\n",
        "               stopwords=stopwords, contour_width=4, contour_color='steelblue')\n",
        "\n",
        "wc.generate(\" \".join(all_words_no_stop.keys()))\n",
        "\n",
        "plt.figure(figsize=(18, 10))\n",
        "plt.imshow(wc.recolor(color_func=color_func, random_state=3),interpolation=\"bilinear\")\n",
        "plt.axis(\"off\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "papermill": {
          "duration": 0.030457,
          "end_time": "2020-09-15T20:51:14.152161",
          "exception": false,
          "start_time": "2020-09-15T20:51:14.121704",
          "status": "completed"
        },
        "tags": [],
        "id": "ZiXW2rbkENg-"
      },
      "cell_type": "markdown",
      "source": [
        "## How many times coffee was mentioned?"
      ]
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:51:14.219318Z",
          "iopub.status.busy": "2020-09-15T20:51:14.218359Z",
          "iopub.status.idle": "2020-09-15T20:51:14.22252Z",
          "shell.execute_reply": "2020-09-15T20:51:14.223056Z"
        },
        "papermill": {
          "duration": 0.040496,
          "end_time": "2020-09-15T20:51:14.223245",
          "exception": false,
          "start_time": "2020-09-15T20:51:14.182749",
          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "4UACr5j7ENg-"
      },
      "cell_type": "code",
      "source": [
        "\"Well, exactly {} times\".format(all_words_no_stop['coffee'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "papermill": {
          "duration": 0.037801,
          "end_time": "2020-09-15T20:51:14.292175",
          "exception": false,
          "start_time": "2020-09-15T20:51:14.254374",
          "status": "completed"
        },
        "tags": [],
        "id": "DGMXGKwkENg_"
      },
      "cell_type": "markdown",
      "source": [
        "## And what about a famous How you doin?"
      ]
    },
    {
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-09-15T20:51:14.378494Z",
          "iopub.status.busy": "2020-09-15T20:51:14.37752Z",
          "iopub.status.idle": "2020-09-15T20:51:14.381235Z",
          "shell.execute_reply": "2020-09-15T20:51:14.381862Z"
        },
        "papermill": {
          "duration": 0.050722,
          "end_time": "2020-09-15T20:51:14.382024",
          "exception": false,
          "start_time": "2020-09-15T20:51:14.331302",
          "status": "completed"
        },
        "tags": [],
        "trusted": true,
        "id": "84vzRY-hENg_"
      },
      "cell_type": "code",
      "source": [
        "\"It was mentioned {} times throughout all episodes\".format(all_words_no_stop['doin'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "metadata": {
        "papermill": {
          "duration": 0.035184,
          "end_time": "2020-09-15T20:51:14.456057",
          "exception": false,
          "start_time": "2020-09-15T20:51:14.420873",
          "status": "completed"
        },
        "tags": [],
        "id": "Tk2t3ig2ENg_"
      },
      "cell_type": "markdown",
      "source": [
        "## 4. What's next?\n",
        "It would be great to do some sentiment analysis on the scripts."
      ]
    },
    {
      "metadata": {
        "trusted": true,
        "id": "FwWNQLjHENg_"
      },
      "cell_type": "code",
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ],
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        "name": "ipython",
        "version": 3
      },
      "name": "python",
      "mimetype": "text/x-python"
    },
    "colab": {
      "name": "Friends EDA Analysis",
      "provenance": [],
      "include_colab_link": true
    }
  },
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  "nbformat_minor": 0
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