GenderBias.ipynb

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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Gender Bias Detection"
      ],
      "metadata": {
        "id": "3WaJ34_ctYqL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNLVn3ahhPVw",
        "outputId": "c7612ed6-6fd3-49c4-d63e-3ef7ee63d0c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Building wheels for collected packages: language-tags\n",
            "  Building wheel for language-tags (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for language-tags: filename=language_tags-1.0.0-py2.py3-none-any.whl size=209134 sha256=fcca5869a802bd7d54e023e73ed0738bb5d249f7159595629761287745f4e065\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/64/76/e98e455286bb97a4b7d1081b02a4b4fa2ff26f5700392d87a8\n",
            "Successfully built language-tags\n",
            "Installing collected packages: stopwordsiso, parameterized, nltk, language-tags, stanza, genbit\n",
            "  Attempting uninstall: nltk\n",
            "    Found existing installation: nltk 3.8.1\n",
            "    Uninstalling nltk-3.8.1:\n",
            "      Successfully uninstalled nltk-3.8.1\n",
            "Successfully installed genbit-2.2.0.0 language-tags-1.0.0 nltk-3.6.6 parameterized-0.7.4 stanza-1.2 stopwordsiso-0.6.1\n"
          ]
        }
      ],
      "source": [
        "!pip install genbit"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')\n",
        "!ln -s \"/gdrive/My Drive/Internship\" \"/content\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "glKuTDf2d1zZ",
        "outputId": "81f3140c-f68f-404e-8be4-1b27e6b41ddb"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import glob\n",
        "import genbit\n",
        "from genbit.genbit_metrics import GenBitMetrics\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from scipy import stats\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "uvHFR2uugQxe"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "language_code = \"EN\"\n",
        "genbit_metrics_object = GenBitMetrics(language_code, context_window=5, distance_weight=0.95, percentile_cutoff=80)"
      ],
      "metadata": {
        "id": "0So9X86fhaAT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "371efb46-7849-4b25-f830-50a3813a6b36"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.2.0.json: 128kB [00:00, 38.3MB/s]                    \n",
            "INFO:stanza:Downloading these customized packages for language: en (English)...\n",
            "========================\n",
            "| Processor | Package  |\n",
            "------------------------\n",
            "| tokenize  | combined |\n",
            "| lemma     | combined |\n",
            "========================\n",
            "\n",
            "Downloading http://nlp.stanford.edu/software/stanza/1.2.0/en/tokenize/combined.pt: 100%|██████████| 640k/640k [00:00<00:00, 1.16MB/s]\n",
            "Downloading http://nlp.stanford.edu/software/stanza/1.2.0/en/lemma/combined.pt: 100%|██████████| 3.86M/3.86M [00:00<00:00, 4.04MB/s]\n",
            "INFO:stanza:Finished downloading models and saved to /usr/local/lib/python3.10/dist-packages/genbit/stanza_lemmatizers.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genbit_score = []\n",
        "percentage_of_female_gender_definition_words = []\n",
        "percentage_of_male_gender_definition_words = []\n",
        "avg_bias_ratio = []\n",
        "freq_of_female_gender_definition_words = []\n",
        "freq_of_male_gender_definition_words = []\n",
        "names = []\n",
        "captions = []"
      ],
      "metadata": {
        "id": "k_xG52tJvSsF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd /content/Internship/Captions"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R3D1cBl0ah-N",
        "outputId": "55c8af9a-d1f3-4d55-82ca-954aa97aa72c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/gdrive/My Drive/Internship/Captions\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "file_list = glob.glob('*.txt')"
      ],
      "metadata": {
        "id": "OWMLpA25ef4z"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "file_contents = {}\n",
        "\n",
        "for file_path in file_list:\n",
        "    with open(file_path, 'r') as file:\n",
        "        file_contents[file_path] = file.read()"
      ],
      "metadata": {
        "id": "3-F0VHwZeiCe"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for name in file_contents:\n",
        "  names.append(name)\n",
        "  file_contents[name] = file_contents[name].replace(\"\\n\", \" \")\n",
        "  captions.append(file_contents[name])\n",
        "  test_text = [file_contents[name]]\n",
        "  genbit_metrics_object = GenBitMetrics(language_code, context_window=5, distance_weight=0.95, percentile_cutoff=80)\n",
        "  genbit_metrics_object.add_data(test_text, tokenized=False)\n",
        "  metrics = genbit_metrics_object.get_metrics(output_statistics=True, output_word_list=True)\n",
        "  genbit_score.append(metrics[\"genbit_score\"])\n",
        "  percentage_of_female_gender_definition_words.append(metrics[\"percentage_of_female_gender_definition_words\"])\n",
        "  percentage_of_male_gender_definition_words.append(metrics[\"percentage_of_male_gender_definition_words\"])\n",
        "  avg_bias_ratio.append(metrics[\"additional_metrics\"][\"avg_bias_ratio\"])\n",
        "  freq_of_female_gender_definition_words.append(metrics[\"statistics\"][\"freq_of_female_gender_definition_words\"])\n",
        "  freq_of_male_gender_definition_words.append(metrics[\"statistics\"][\"freq_of_male_gender_definition_words\"])"
      ],
      "metadata": {
        "id": "NrfN9H2Be5L4"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame()\n",
        "df[\"Name\"] = names\n",
        "df[\"Transcript\"] = captions\n",
        "df[\"Genbit Score\"] = genbit_score\n",
        "df[\"% Female Words\"] = percentage_of_female_gender_definition_words\n",
        "df[\"% Male Words\"] = percentage_of_male_gender_definition_words\n",
        "df[\"Avg Bias Ratio\"] = avg_bias_ratio\n",
        "df[\"Freq of Female Words\"] = freq_of_female_gender_definition_words\n",
        "df[\"Freq of Male Words\"] = freq_of_male_gender_definition_words"
      ],
      "metadata": {
        "id": "WbTTIk2QfRHr"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.sort_values(by=['Name']).reset_index().drop([\"index\"], axis=1)"
      ],
      "metadata": {
        "id": "9EuzwdmQhoQ-"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "xLeF5CIYhcnC",
        "outputId": "8809772b-a129-4cf7-d9a8-129323e9e3ba"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Name                                         Transcript  \\\n",
              "0     Baxter 1.txt  it's kind of a beautiful wasteland  there's ju...   \n",
              "1    Baxter 10.txt  Be honest.  Be vulnerable.  Be yourself.  Talk...   \n",
              "2     Baxter 2.txt  If we're from here, from SoCal, we have heart ...   \n",
              "3     Baxter 3.txt  hello and welcome to Baxter's guide to  pickin...   \n",
              "4     Baxter 4.txt  growing a full beard is more than a lack  of s...   \n",
              "..             ...                                                ...   \n",
              "195        W 5.txt  Bring a spellbinding look to life with a fresh...   \n",
              "196        W 6.txt  This festive season, #BeTheSameOrBeW.  Be the ...   \n",
              "197        W 7.txt  The length of the kurta is no longer debatable...   \n",
              "198        W 8.txt  Check out the exclusive #UNUSUALS Range by W F...   \n",
              "199        W 9.txt  Bring your eye makeup game to life with a spri...   \n",
              "\n",
              "     Genbit Score  % Female Words  % Male Words  Avg Bias Ratio  \\\n",
              "0        0.000000             0.0          0.00        0.000000   \n",
              "1        0.000000             0.0          0.00        0.000000   \n",
              "2        0.577149             0.0          0.25        0.619709   \n",
              "3        0.000000             0.0          0.00        0.000000   \n",
              "4        0.151080             0.0          1.00        0.130214   \n",
              "..            ...             ...           ...             ...   \n",
              "195      0.000000             0.0          0.00        0.000000   \n",
              "196      0.911303             1.0          0.00       -0.989265   \n",
              "197      0.000000             0.0          0.00        0.000000   \n",
              "198      0.572519             1.0          0.00       -0.667829   \n",
              "199      0.000000             0.0          0.00        0.000000   \n",
              "\n",
              "     Freq of Female Words  Freq of Male Words  \n",
              "0                       0                   0  \n",
              "1                       0                   0  \n",
              "2                       0                   1  \n",
              "3                       0                   0  \n",
              "4                       0                   3  \n",
              "..                    ...                 ...  \n",
              "195                     0                   0  \n",
              "196                     3                   0  \n",
              "197                     0                   0  \n",
              "198                     1                   0  \n",
              "199                     0                   0  \n",
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              "[200 rows x 8 columns]"
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              "  <div id=\"df-dbc36400-34ac-4ca5-87de-584c29a86c51\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Transcript</th>\n",
              "      <th>Genbit Score</th>\n",
              "      <th>% Female Words</th>\n",
              "      <th>% Male Words</th>\n",
              "      <th>Avg Bias Ratio</th>\n",
              "      <th>Freq of Female Words</th>\n",
              "      <th>Freq of Male Words</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Baxter 1.txt</td>\n",
              "      <td>it's kind of a beautiful wasteland  there's ju...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Baxter 10.txt</td>\n",
              "      <td>Be honest.  Be vulnerable.  Be yourself.  Talk...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Baxter 2.txt</td>\n",
              "      <td>If we're from here, from SoCal, we have heart ...</td>\n",
              "      <td>0.577149</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.619709</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Baxter 3.txt</td>\n",
              "      <td>hello and welcome to Baxter's guide to  pickin...</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Baxter 4.txt</td>\n",
              "      <td>growing a full beard is more than a lack  of s...</td>\n",
              "      <td>0.151080</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.130214</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <th>195</th>\n",
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              "      <td>Bring your eye makeup game to life with a spri...</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df.to_excel('/content/results.xlsx')"
      ],
      "metadata": {
        "id": "m7Gqv_tXidgZ"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Metrics:\n",
        "\n",
        "avg_bias_ratio: The average of the bias ratio scores per token. Formally, this is the average over all words w of log( c[w,M] / c[w,F] ).\n",
        "\n",
        "avg_bias_conditional: The average of the bias conditional ratios per token. Similarly, this is the average over all words w of log( p(w|M) / p(w|F) ).\n",
        "avg_bias_ratio_absolute. The average of the absolute bias ratio scores per token. Similar to avg_bias_ratio, this is the average over all w of | log( c[w,M] / c[w,F] ) |.\n",
        "\n",
        "avg_bias_conditional_absolute (genbit_score): The average of the absolute bias conditional ratios per token [note: this score is most commonly used as the key bias score in the literature]. Formally, this is the average over all w of | log( p(w|M) / p(w|F) ) |.\n",
        "\n",
        "std_dev_bias_ratio: Standard deviation of the bias ratio scores. This is the standard deviation that corresponds to avg_bias_ratio.\n",
        "\n",
        "std_dev_bias_conditional: Standard deviation of the bias conditional scores. This is the standard deviation that corresponds to avg_bias_conditional.\n",
        "\n",
        "percentage_of_female_gender_definition_words: The percentage of gendered (male, female and non-binary) words in the corpus that belong to the list of female gendered words\n",
        "\n",
        "percentage_of_male_gender_definition_words: The percentage of gendered (male, female and non-binary) words in the corpus that belong to the list of male gendered words\n",
        "\n",
        "percentage_of_non_binary_gender_definition_words: The percentage of gendered (male, female and non-binary words in the corpus that belong to the list of non-binary gendered words)\n",
        "\n",
        "Statistics:\n",
        "\n",
        "frequency_cutoff: The percentile frequency cutoff value. Any co-occurrence counts that are above this frequency will be used in calculating the metrics\n",
        "num_words_considered: the count of words that were included in calculating the metrics\n",
        "\n",
        "freq_of_female_gender_definition_words: The number of times any of the female gendered words occur in the corpus\n",
        "\n",
        "freq_of_male_gender_definition_words: The number of times any of the male gendered words occur in the corpus\n",
        "\n",
        "freq_of_non_binary_gender_definition_words: The number of times any of the non-binary gendered words occur in the corpus\n",
        "\n",
        "jsd: The Jensen-Shannon divergence between the word probabilities conditioned on male and female gendered words. This is JSD( p(w|M) || p(w|F) ), where JSD(q||p) is the average KL divergence between q and m, and between p and m, where m is the average distribution (p+q)/2.\n",
        "\n",
        "Token Based Metrics\n",
        "\n",
        "frequency: The frequency of the token (the number of times it appears in the document): c[w,.]\n",
        "\n",
        "male_count: the number of times the word occurs within context of a male gendered word: c[w,M]\n",
        "\n",
        "female_count: the number of times the word occurs within context of a female gendered word: c[w,F]\n",
        "\n",
        "non_binary_count: the number of times the word occurs within context of a male gendered word: c[w,NB]\n",
        "\n",
        "female_conditional_prob: the conditional probability of the token occurring in context with a female gendered word: p(w|F)\n",
        "\n",
        "male_conditional_prob: the conditional probability of the token occurring in context with a male gendered word: p(w|M)\n",
        "\n",
        "bias_ratio: log(male_count / female_count) the more positive the value, the more biased the word is towards being associated with male gendered word, the more negative the value the more biased the word is towards being associated with female gendered. Each value is log( c[w,M] / c[w,F] ). A value of zero means that this word is equally likely to appear co-occurring with words in the female list as the male list (positive indicates more co-occurrence with male words; negative indicates more co-occurrence with female words).\n",
        "\n",
        "non_binary_bias_ratio: log((male_count + female_count) / non_binary_count) the more positive the value, the more biased the word is towards being associated with a binary gendered word. A value of zero means the word has non-binary gender bias associated with it\n",
        "\n",
        "bias_conditional_ratio: log( male_cond_prob/female_cond_prob ) the more positive the value, the more biased the word is towards being associated with male gendered word, the more negative the value the more biased the word is towards being associated with female gendered. Each value is log( p(w|M) / p(w|F) ). A value of zero means that the probability of this word co-occurring with words in the female list is equal to the probability of co-occurring with words in the male list (positive indicates more likely co-occurrence with male words; negative indicates more likely co-occurrence with female words).\n",
        "\n",
        "non_binary_bias_conditional_ratio: log( (male_cond_prob+female_cond_prob) / non_binary_cond_prob ) the more positive the value, the more biased the word is towards being associated with binary gendered words; the more negative the value the more biased the word is towards being associated with a non-binary gendered word. A value of zero means the word has no gender bias associated with it.\n"
      ],
      "metadata": {
        "id": "bD8R7ElqgxB9"
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    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/Internship/Gender_Bias_Metrics_Transcripts.csv\")"
      ],
      "metadata": {
        "id": "bbrhB-R7k2of"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
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      "source": [
        "df"
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        "outputId": "186056a0-9d6a-4e13-8550-bae9f5c45917"
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      "execution_count": 15,
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              "     Unnamed: 0        Brand  \\\n",
              "0             0       Baxter   \n",
              "1             1       Baxter   \n",
              "2             2       Baxter   \n",
              "3             3       Baxter   \n",
              "4             4       Baxter   \n",
              "..          ...          ...   \n",
              "195         195  W For Women   \n",
              "196         196  W For Women   \n",
              "197         197  W For Women   \n",
              "198         198  W For Women   \n",
              "199         199  W For Women   \n",
              "\n",
              "                                                 Title  \\\n",
              "0    Baxter of California Further Explores A Life L...   \n",
              "1    Baxter of California. Grooming to Live Life True.   \n",
              "2    How to Choose the Right Pomade for Short Hair ...   \n",
              "3          Full Beard Grooming by Baxter of California   \n",
              "4    True Angeleno: Henry | Life Lived True - Baxte...   \n",
              "..                                                 ...   \n",
              "195  Introduce Your Lips to a Variety of On-Trend C...   \n",
              "196  Introducing Festive Collection’22 | Be The Sam...   \n",
              "197                        Introducing Stiletto Kurtas   \n",
              "198                     W Presents the #UNUSUALS Range   \n",
              "199          W Beauty: Must Haves for Eyes that Speaks   \n",
              "\n",
              "                                            Transcript Group    Views  Likes  \\\n",
              "0    it's kind of a beautiful wasteland  there's ju...     M  2095817     39   \n",
              "1    Be honest.\\nBe vulnerable.\\nBe yourself.\\nTalk...     M  1903293     42   \n",
              "2    If we're from here, from SoCal, we have heart ...     M  1718496     30   \n",
              "3    hello and welcome to Baxter's guide to\\n\\npick...     M   926669   2200   \n",
              "4    growing a full beard is more than a lack\\n\\nof...     M   466250     12   \n",
              "..                                                 ...   ...      ...    ...   \n",
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              "196  This festive season, #BeTheSameOrBeW.\\n\\nBe th...     F   199029     77   \n",
              "197  The length of the kurta is no longer debatable...     F    81136     10   \n",
              "198  Check out the exclusive #UNUSUALS Range by W F...     F    72151     14   \n",
              "199  Bring your eye makeup game to life with a spri...     F    57709      1   \n",
              "\n",
              "    Comments          Date  Genbit Score  % Female Words  % Male Words  \\\n",
              "0          3  May 17, 2018      0.000000             0.0          0.00   \n",
              "1          1  Aug 16, 2017      0.000000             0.0          0.00   \n",
              "2          3  Apr 26, 2019      0.577149             0.0          0.25   \n",
              "3        164   Nov 6, 2013      0.000000             0.0          0.00   \n",
              "4          0  Aug 16, 2017      0.151080             0.0          1.00   \n",
              "..       ...           ...           ...             ...           ...   \n",
              "195        2  Jan 17, 2022      0.000000             0.0          0.00   \n",
              "196        0  Sep 19, 2022      0.911303             1.0          0.00   \n",
              "197        1  Apr 18, 2016      0.000000             0.0          0.00   \n",
              "198        1  Jan 20, 2016      0.572519             1.0          0.00   \n",
              "199        0  Jan 27, 2022      0.000000             0.0          0.00   \n",
              "\n",
              "     Avg Bias Ratio  Freq of Female Words  Freq of Male Words  \n",
              "0          0.000000                     0                   0  \n",
              "1          0.000000                     0                   0  \n",
              "2          0.619709                     0                   1  \n",
              "3          0.000000                     0                   0  \n",
              "4          0.130214                     0                   3  \n",
              "..              ...                   ...                 ...  \n",
              "195        0.000000                     0                   0  \n",
              "196       -0.989265                     3                   0  \n",
              "197        0.000000                     0                   0  \n",
              "198       -0.667829                     1                   0  \n",
              "199        0.000000                     0                   0  \n",
              "\n",
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              "      <td>Baxter</td>\n",
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              "      <td>466250</td>\n",
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          "metadata": {},
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    {
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      "source": [
        "df = df.drop([\"Unnamed: 0\"], axis = 1)"
      ],
      "metadata": {
        "id": "bS2i63Eh9MXH"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
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        "df"
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        "id": "lKYiL4jf_T-y",
        "outputId": "68b11fed-7b3c-4336-d7bf-801b0bd01fe7"
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      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           Brand                                              Title  \\\n",
              "0         Baxter  Baxter of California Further Explores A Life L...   \n",
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              "..           ...                                                ...   \n",
              "195  W For Women  Introduce Your Lips to a Variety of On-Trend C...   \n",
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              "197  W For Women                        Introducing Stiletto Kurtas   \n",
              "198  W For Women                     W Presents the #UNUSUALS Range   \n",
              "199  W For Women          W Beauty: Must Haves for Eyes that Speaks   \n",
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              "4    growing a full beard is more than a lack\\n\\nof...     M   466250     12   \n",
              "..                                                 ...   ...      ...    ...   \n",
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              "196  This festive season, #BeTheSameOrBeW.\\n\\nBe th...     F   199029     77   \n",
              "197  The length of the kurta is no longer debatable...     F    81136     10   \n",
              "198  Check out the exclusive #UNUSUALS Range by W F...     F    72151     14   \n",
              "199  Bring your eye makeup game to life with a spri...     F    57709      1   \n",
              "\n",
              "    Comments          Date  Genbit Score  % Female Words  % Male Words  \\\n",
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              "199        0  Jan 27, 2022      0.000000             0.0          0.00   \n",
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              "0          0.000000                     0                   0  \n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Baxter</td>\n",
              "      <td>Full Beard Grooming by Baxter of California</td>\n",
              "      <td>hello and welcome to Baxter's guide to\\n\\npick...</td>\n",
              "      <td>M</td>\n",
              "      <td>926669</td>\n",
              "      <td>2200</td>\n",
              "      <td>164</td>\n",
              "      <td>Nov 6, 2013</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Baxter</td>\n",
              "      <td>True Angeleno: Henry | Life Lived True - Baxte...</td>\n",
              "      <td>growing a full beard is more than a lack\\n\\nof...</td>\n",
              "      <td>M</td>\n",
              "      <td>466250</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>Aug 16, 2017</td>\n",
              "      <td>0.151080</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.130214</td>\n",
              "      <td>0</td>\n",
              "      <td>3</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>W For Women</td>\n",
              "      <td>Introduce Your Lips to a Variety of On-Trend C...</td>\n",
              "      <td>Bring a spellbinding look to life with a fresh...</td>\n",
              "      <td>F</td>\n",
              "      <td>227956</td>\n",
              "      <td>22</td>\n",
              "      <td>2</td>\n",
              "      <td>Jan 17, 2022</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>W For Women</td>\n",
              "      <td>Introducing Festive Collection’22 | Be The Sam...</td>\n",
              "      <td>This festive season, #BeTheSameOrBeW.\\n\\nBe th...</td>\n",
              "      <td>F</td>\n",
              "      <td>199029</td>\n",
              "      <td>77</td>\n",
              "      <td>0</td>\n",
              "      <td>Sep 19, 2022</td>\n",
              "      <td>0.911303</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.989265</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>W For Women</td>\n",
              "      <td>Introducing Stiletto Kurtas</td>\n",
              "      <td>The length of the kurta is no longer debatable...</td>\n",
              "      <td>F</td>\n",
              "      <td>81136</td>\n",
              "      <td>10</td>\n",
              "      <td>1</td>\n",
              "      <td>Apr 18, 2016</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>W For Women</td>\n",
              "      <td>W Presents the #UNUSUALS Range</td>\n",
              "      <td>Check out the exclusive #UNUSUALS Range by W F...</td>\n",
              "      <td>F</td>\n",
              "      <td>72151</td>\n",
              "      <td>14</td>\n",
              "      <td>1</td>\n",
              "      <td>Jan 20, 2016</td>\n",
              "      <td>0.572519</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>-0.667829</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>W For Women</td>\n",
              "      <td>W Beauty: Must Haves for Eyes that Speaks</td>\n",
              "      <td>Bring your eye makeup game to life with a spri...</td>\n",
              "      <td>F</td>\n",
              "      <td>57709</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Jan 27, 2022</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-972616fe-e60e-48e1-865a-a140ae9b2398')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-972616fe-e60e-48e1-865a-a140ae9b2398 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-972616fe-e60e-48e1-865a-a140ae9b2398');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])"
      ],
      "metadata": {
        "id": "QasY-aVtAnyY"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Days Old'] = datetime.now() - df['Date']"
      ],
      "metadata": {
        "id": "OgVWgGtSDkke"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Days Old'] = df['Days Old'].dt.days"
      ],
      "metadata": {
        "id": "pSatPEcdD2LA"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Days Old']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uIlpzJUFEGlM",
        "outputId": "f375ffd2-1ddc-47ae-a1a1-be515853e649"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      1884\n",
              "1      2158\n",
              "2      1540\n",
              "3      3537\n",
              "4      2158\n",
              "       ... \n",
              "195     543\n",
              "196     298\n",
              "197    2643\n",
              "198    2732\n",
              "199     533\n",
              "Name: Days Old, Length: 200, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bT4qZvWOG0JM",
        "outputId": "084c2d24-434e-4fee-a483-a7d9bbd82236"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 15 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
            "---  ------                --------------  -----         \n",
            " 0   Brand                 200 non-null    object        \n",
            " 1   Title                 200 non-null    object        \n",
            " 2   Transcript            200 non-null    object        \n",
            " 3   Group                 200 non-null    object        \n",
            " 4   Views                 200 non-null    object        \n",
            " 5   Likes                 200 non-null    int64         \n",
            " 6   Comments              200 non-null    object        \n",
            " 7   Date                  200 non-null    datetime64[ns]\n",
            " 8   Genbit Score          200 non-null    float64       \n",
            " 9   % Female Words        200 non-null    float64       \n",
            " 10  % Male Words          200 non-null    float64       \n",
            " 11  Avg Bias Ratio        200 non-null    float64       \n",
            " 12  Freq of Female Words  200 non-null    int64         \n",
            " 13  Freq of Male Words    200 non-null    int64         \n",
            " 14  Days Old              200 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(4), int64(4), object(6)\n",
            "memory usage: 23.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Views\"] == '110,167', 'Views'] = '110167'\n",
        "df.loc[df[\"Views\"] == '17,340', 'Views'] = '17340'\n",
        "df.loc[df[\"Views\"] == '232,897', 'Views'] = '232897'\n",
        "df.loc[df[\"Views\"] == '5,409,260', 'Views'] = '5409260'\n",
        "df.loc[df[\"Views\"] == '207,259', 'Views'] = '207259'"
      ],
      "metadata": {
        "id": "xuSLnRr2HdRJ"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Comments\"] == '5,261', 'Comments'] = '5261'\n",
        "df.loc[df[\"Comments\"] == 'Sep 9, 2017', 'Comments'] = '2607'"
      ],
      "metadata": {
        "id": "MBAbyk7_KG-G"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Views\"] = df[\"Views\"].astype(int)\n",
        "df[\"Likes\"] = df[\"Likes\"].astype(int)\n",
        "df[\"Comments\"] = df[\"Comments\"].astype(int)"
      ],
      "metadata": {
        "id": "198oMFmEG6Bc"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Length\"] = df[\"Transcript\"].str.len()\n",
        "df[\"Group\"] = df[\"Group\"].map({'M': 1, 'F': 2})"
      ],
      "metadata": {
        "id": "_mmKbdsisJP1"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = pd.pivot_table(df, index='Brand', aggfunc='sum')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3kKlQ8garNL",
        "outputId": "9e1de105-f5c1-4a9d-98f3-4532263a9ed7"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-27-50e37cb6d585>:1: FutureWarning: pivot_table dropped a column because it failed to aggregate. This behavior is deprecated and will raise in a future version of pandas. Select only the columns that can be aggregated.\n",
            "  new_df = pd.pivot_table(df, index='Brand', aggfunc='sum')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[\"Group\"] = new_df[\"Group\"]/10"
      ],
      "metadata": {
        "id": "9NctJ2TPcD10"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df[\"Group\"] = new_df[\"Group\"].astype(int)"
      ],
      "metadata": {
        "id": "uqxj-XwscRa8"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "ZEU4MDFjcIu6",
        "outputId": "1f6d945a-45a9-4317-826c-08714f33082d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   % Female Words  % Male Words  Avg Bias Ratio  Comments  \\\n",
              "Brand                                                                       \n",
              "Baxter                   0.500000      2.000000        1.169384       175   \n",
              "Beardo                   2.333333      3.083333        0.437618       147   \n",
              "Chanel                   5.105976      1.649393       -4.420704     16840   \n",
              "Dollar Shave Club        0.365642      1.905749        1.272304      9483   \n",
              "Fenty Beauty             3.130279      2.063792       -2.401192     15867   \n",
              "Gillette                 0.660088      6.388870        7.233313     26530   \n",
              "Hugo Boss                1.714286      1.142857       -0.509593       481   \n",
              "Lancome                  3.138462      1.000000       -3.021117       274   \n",
              "Loreal                   0.666667      1.333333        0.328819       864   \n",
              "Lumin                    1.857143      3.508929        3.084114       114   \n",
              "Maybelline               0.611111      1.944444       -0.338240      1881   \n",
              "Old Spice                2.079437      5.639610        6.176935     48815   \n",
              "Peter England            0.041667      3.095238        1.389284       932   \n",
              "Rare Beauty              1.757751      0.558764       -1.318778     10558   \n",
              "Sephora                  4.503324      0.833552       -5.215806      1949   \n",
              "Shein                    3.072460      0.670380       -4.324021        42   \n",
              "Taylor Stitch            0.000000      0.645833        0.245962        29   \n",
              "The Man Company          0.244403      7.478483        6.972233       205   \n",
              "Veet                     2.000000      0.000000       -1.370610      1349   \n",
              "W For Women              3.000000      0.000000       -2.992753         8   \n",
              "\n",
              "                   Days Old  Freq of Female Words  Freq of Male Words  \\\n",
              "Brand                                                                   \n",
              "Baxter                22149                     7                   6   \n",
              "Beardo                12045                     9                  12   \n",
              "Chanel                30926                   102                  25   \n",
              "Dollar Shave Club     12403                     6                  24   \n",
              "Fenty Beauty          14791                    42                  37   \n",
              "Gillette               7667                     9                  52   \n",
              "Hugo Boss             12390                    16                   6   \n",
              "Lancome               10718                    18                   1   \n",
              "Loreal                 7023                     2                   3   \n",
              "Lumin                  6637                     6                  36   \n",
              "Maybelline            12370                     3                  10   \n",
              "Old Spice             17856                    18                  48   \n",
              "Peter England         29658                     1                  10   \n",
              "Rare Beauty            5400                    58                  53   \n",
              "Sephora                9790                    74                  27   \n",
              "Shein                  1313                   142                  71   \n",
              "Taylor Stitch          2506                     0                   8   \n",
              "The Man Company       10620                   131                 205   \n",
              "Veet                   9896                     2                   0   \n",
              "W For Women           10577                     5                   0   \n",
              "\n",
              "                   Genbit Score  Group  Length   Likes      Views  \n",
              "Brand                                                              \n",
              "Baxter                 2.705264      1    7624    2406    8162420  \n",
              "Beardo                 5.539433      1    4034   11842   46461777  \n",
              "Chanel                 6.612798      2   15799  488000   81585458  \n",
              "Dollar Shave Club      1.752471      1   24063  153066   36210921  \n",
              "Fenty Beauty           3.282830      2   36965  515600   20319966  \n",
              "Gillette               6.695955      1    8416   96168   70545754  \n",
              "Hugo Boss              2.974110      1    6135   10255   13607459  \n",
              "Lancome                3.913216      2    9412    6642    8929492  \n",
              "Loreal                 0.869210      2    4156    7958  375508207  \n",
              "Lumin                  6.130032      1   11062     359    7896601  \n",
              "Maybelline             1.203574      2    8124   22251   87071351  \n",
              "Old Spice              5.795288      1    7847  455545  360968497  \n",
              "Peter England          1.433252      1   10479   29904   36133468  \n",
              "Rare Beauty            1.837463      2   50433  296000    7995623  \n",
              "Sephora                6.734489      2   23373   45335   23995615  \n",
              "Shein                  4.613016      2   79847     302      18568  \n",
              "Taylor Stitch          0.272442      1   13603     175      10165  \n",
              "The Man Company        6.728635      1   37409    6159   15905558  \n",
              "Veet                   1.150549      2    4081   21515   26982618  \n",
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              "      <td>0.660088</td>\n",
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              "      <th>Lancome</th>\n",
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              "      <td>-3.021117</td>\n",
              "      <td>274</td>\n",
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              "      <th>Loreal</th>\n",
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              "      <td>21515</td>\n",
              "      <td>26982618</td>\n",
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              "      <td>8</td>\n",
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              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.sort_values(by='Genbit Score', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "cbXT3hbddpB4",
        "outputId": "a56359cb-e905-49cd-a04f-5ab3b62add6b"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   % Female Words  % Male Words  Avg Bias Ratio  Comments  \\\n",
              "Brand                                                                       \n",
              "Sephora                  4.503324      0.833552       -5.215806      1949   \n",
              "The Man Company          0.244403      7.478483        6.972233       205   \n",
              "Gillette                 0.660088      6.388870        7.233313     26530   \n",
              "Chanel                   5.105976      1.649393       -4.420704     16840   \n",
              "Lumin                    1.857143      3.508929        3.084114       114   \n",
              "Old Spice                2.079437      5.639610        6.176935     48815   \n",
              "Beardo                   2.333333      3.083333        0.437618       147   \n",
              "Shein                    3.072460      0.670380       -4.324021        42   \n",
              "Lancome                  3.138462      1.000000       -3.021117       274   \n",
              "Fenty Beauty             3.130279      2.063792       -2.401192     15867   \n",
              "Hugo Boss                1.714286      1.142857       -0.509593       481   \n",
              "Baxter                   0.500000      2.000000        1.169384       175   \n",
              "W For Women              3.000000      0.000000       -2.992753         8   \n",
              "Rare Beauty              1.757751      0.558764       -1.318778     10558   \n",
              "Dollar Shave Club        0.365642      1.905749        1.272304      9483   \n",
              "Peter England            0.041667      3.095238        1.389284       932   \n",
              "Maybelline               0.611111      1.944444       -0.338240      1881   \n",
              "Veet                     2.000000      0.000000       -1.370610      1349   \n",
              "Loreal                   0.666667      1.333333        0.328819       864   \n",
              "Taylor Stitch            0.000000      0.645833        0.245962        29   \n",
              "\n",
              "                   Days Old  Freq of Female Words  Freq of Male Words  \\\n",
              "Brand                                                                   \n",
              "Sephora                9790                    74                  27   \n",
              "The Man Company       10620                   131                 205   \n",
              "Gillette               7667                     9                  52   \n",
              "Chanel                30926                   102                  25   \n",
              "Lumin                  6637                     6                  36   \n",
              "Old Spice             17856                    18                  48   \n",
              "Beardo                12045                     9                  12   \n",
              "Shein                  1313                   142                  71   \n",
              "Lancome               10718                    18                   1   \n",
              "Fenty Beauty          14791                    42                  37   \n",
              "Hugo Boss             12390                    16                   6   \n",
              "Baxter                22149                     7                   6   \n",
              "W For Women           10577                     5                   0   \n",
              "Rare Beauty            5400                    58                  53   \n",
              "Dollar Shave Club     12403                     6                  24   \n",
              "Peter England         29658                     1                  10   \n",
              "Maybelline            12370                     3                  10   \n",
              "Veet                   9896                     2                   0   \n",
              "Loreal                 7023                     2                   3   \n",
              "Taylor Stitch          2506                     0                   8   \n",
              "\n",
              "                   Genbit Score  Group  Length   Likes      Views  \n",
              "Brand                                                              \n",
              "Sephora                6.734489      2   23373   45335   23995615  \n",
              "The Man Company        6.728635      1   37409    6159   15905558  \n",
              "Gillette               6.695955      1    8416   96168   70545754  \n",
              "Chanel                 6.612798      2   15799  488000   81585458  \n",
              "Lumin                  6.130032      1   11062     359    7896601  \n",
              "Old Spice              5.795288      1    7847  455545  360968497  \n",
              "Beardo                 5.539433      1    4034   11842   46461777  \n",
              "Shein                  4.613016      2   79847     302      18568  \n",
              "Lancome                3.913216      2    9412    6642    8929492  \n",
              "Fenty Beauty           3.282830      2   36965  515600   20319966  \n",
              "Hugo Boss              2.974110      1    6135   10255   13607459  \n",
              "Baxter                 2.705264      1    7624    2406    8162420  \n",
              "W For Women            2.596337      2    2289    1256   15395193  \n",
              "Rare Beauty            1.837463      2   50433  296000    7995623  \n",
              "Dollar Shave Club      1.752471      1   24063  153066   36210921  \n",
              "Peter England          1.433252      1   10479   29904   36133468  \n",
              "Maybelline             1.203574      2    8124   22251   87071351  \n",
              "Veet                   1.150549      2    4081   21515   26982618  \n",
              "Loreal                 0.869210      2    4156    7958  375508207  \n",
              "Taylor Stitch          0.272442      1   13603     175      10165  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>% Female Words</th>\n",
              "      <th>% Male Words</th>\n",
              "      <th>Avg Bias Ratio</th>\n",
              "      <th>Comments</th>\n",
              "      <th>Days Old</th>\n",
              "      <th>Freq of Female Words</th>\n",
              "      <th>Freq of Male Words</th>\n",
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              "      <th>Brand</th>\n",
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              "      <th>Sephora</th>\n",
              "      <td>4.503324</td>\n",
              "      <td>0.833552</td>\n",
              "      <td>-5.215806</td>\n",
              "      <td>1949</td>\n",
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              "      <th>The Man Company</th>\n",
              "      <td>0.244403</td>\n",
              "      <td>7.478483</td>\n",
              "      <td>6.972233</td>\n",
              "      <td>205</td>\n",
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              "      <td>131</td>\n",
              "      <td>205</td>\n",
              "      <td>6.728635</td>\n",
              "      <td>1</td>\n",
              "      <td>37409</td>\n",
              "      <td>6159</td>\n",
              "      <td>15905558</td>\n",
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              "      <th>Gillette</th>\n",
              "      <td>0.660088</td>\n",
              "      <td>6.388870</td>\n",
              "      <td>7.233313</td>\n",
              "      <td>26530</td>\n",
              "      <td>7667</td>\n",
              "      <td>9</td>\n",
              "      <td>52</td>\n",
              "      <td>6.695955</td>\n",
              "      <td>1</td>\n",
              "      <td>8416</td>\n",
              "      <td>96168</td>\n",
              "      <td>70545754</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Chanel</th>\n",
              "      <td>5.105976</td>\n",
              "      <td>1.649393</td>\n",
              "      <td>-4.420704</td>\n",
              "      <td>16840</td>\n",
              "      <td>30926</td>\n",
              "      <td>102</td>\n",
              "      <td>25</td>\n",
              "      <td>6.612798</td>\n",
              "      <td>2</td>\n",
              "      <td>15799</td>\n",
              "      <td>488000</td>\n",
              "      <td>81585458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lumin</th>\n",
              "      <td>1.857143</td>\n",
              "      <td>3.508929</td>\n",
              "      <td>3.084114</td>\n",
              "      <td>114</td>\n",
              "      <td>6637</td>\n",
              "      <td>6</td>\n",
              "      <td>36</td>\n",
              "      <td>6.130032</td>\n",
              "      <td>1</td>\n",
              "      <td>11062</td>\n",
              "      <td>359</td>\n",
              "      <td>7896601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Old Spice</th>\n",
              "      <td>2.079437</td>\n",
              "      <td>5.639610</td>\n",
              "      <td>6.176935</td>\n",
              "      <td>48815</td>\n",
              "      <td>17856</td>\n",
              "      <td>18</td>\n",
              "      <td>48</td>\n",
              "      <td>5.795288</td>\n",
              "      <td>1</td>\n",
              "      <td>7847</td>\n",
              "      <td>455545</td>\n",
              "      <td>360968497</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beardo</th>\n",
              "      <td>2.333333</td>\n",
              "      <td>3.083333</td>\n",
              "      <td>0.437618</td>\n",
              "      <td>147</td>\n",
              "      <td>12045</td>\n",
              "      <td>9</td>\n",
              "      <td>12</td>\n",
              "      <td>5.539433</td>\n",
              "      <td>1</td>\n",
              "      <td>4034</td>\n",
              "      <td>11842</td>\n",
              "      <td>46461777</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Shein</th>\n",
              "      <td>3.072460</td>\n",
              "      <td>0.670380</td>\n",
              "      <td>-4.324021</td>\n",
              "      <td>42</td>\n",
              "      <td>1313</td>\n",
              "      <td>142</td>\n",
              "      <td>71</td>\n",
              "      <td>4.613016</td>\n",
              "      <td>2</td>\n",
              "      <td>79847</td>\n",
              "      <td>302</td>\n",
              "      <td>18568</td>\n",
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              "      <th>Lancome</th>\n",
              "      <td>3.138462</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-3.021117</td>\n",
              "      <td>274</td>\n",
              "      <td>10718</td>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>3.913216</td>\n",
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              "      <td>9412</td>\n",
              "      <td>6642</td>\n",
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              "      <th>Fenty Beauty</th>\n",
              "      <td>3.130279</td>\n",
              "      <td>2.063792</td>\n",
              "      <td>-2.401192</td>\n",
              "      <td>15867</td>\n",
              "      <td>14791</td>\n",
              "      <td>42</td>\n",
              "      <td>37</td>\n",
              "      <td>3.282830</td>\n",
              "      <td>2</td>\n",
              "      <td>36965</td>\n",
              "      <td>515600</td>\n",
              "      <td>20319966</td>\n",
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              "      <th>Hugo Boss</th>\n",
              "      <td>1.714286</td>\n",
              "      <td>1.142857</td>\n",
              "      <td>-0.509593</td>\n",
              "      <td>481</td>\n",
              "      <td>12390</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "      <td>2.974110</td>\n",
              "      <td>1</td>\n",
              "      <td>6135</td>\n",
              "      <td>10255</td>\n",
              "      <td>13607459</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Baxter</th>\n",
              "      <td>0.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.169384</td>\n",
              "      <td>175</td>\n",
              "      <td>22149</td>\n",
              "      <td>7</td>\n",
              "      <td>6</td>\n",
              "      <td>2.705264</td>\n",
              "      <td>1</td>\n",
              "      <td>7624</td>\n",
              "      <td>2406</td>\n",
              "      <td>8162420</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>W For Women</th>\n",
              "      <td>3.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-2.992753</td>\n",
              "      <td>8</td>\n",
              "      <td>10577</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>2.596337</td>\n",
              "      <td>2</td>\n",
              "      <td>2289</td>\n",
              "      <td>1256</td>\n",
              "      <td>15395193</td>\n",
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              "    <tr>\n",
              "      <th>Rare Beauty</th>\n",
              "      <td>1.757751</td>\n",
              "      <td>0.558764</td>\n",
              "      <td>-1.318778</td>\n",
              "      <td>10558</td>\n",
              "      <td>5400</td>\n",
              "      <td>58</td>\n",
              "      <td>53</td>\n",
              "      <td>1.837463</td>\n",
              "      <td>2</td>\n",
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              "      <td>296000</td>\n",
              "      <td>7995623</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dollar Shave Club</th>\n",
              "      <td>0.365642</td>\n",
              "      <td>1.905749</td>\n",
              "      <td>1.272304</td>\n",
              "      <td>9483</td>\n",
              "      <td>12403</td>\n",
              "      <td>6</td>\n",
              "      <td>24</td>\n",
              "      <td>1.752471</td>\n",
              "      <td>1</td>\n",
              "      <td>24063</td>\n",
              "      <td>153066</td>\n",
              "      <td>36210921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Peter England</th>\n",
              "      <td>0.041667</td>\n",
              "      <td>3.095238</td>\n",
              "      <td>1.389284</td>\n",
              "      <td>932</td>\n",
              "      <td>29658</td>\n",
              "      <td>1</td>\n",
              "      <td>10</td>\n",
              "      <td>1.433252</td>\n",
              "      <td>1</td>\n",
              "      <td>10479</td>\n",
              "      <td>29904</td>\n",
              "      <td>36133468</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Maybelline</th>\n",
              "      <td>0.611111</td>\n",
              "      <td>1.944444</td>\n",
              "      <td>-0.338240</td>\n",
              "      <td>1881</td>\n",
              "      <td>12370</td>\n",
              "      <td>3</td>\n",
              "      <td>10</td>\n",
              "      <td>1.203574</td>\n",
              "      <td>2</td>\n",
              "      <td>8124</td>\n",
              "      <td>22251</td>\n",
              "      <td>87071351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Veet</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1.370610</td>\n",
              "      <td>1349</td>\n",
              "      <td>9896</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1.150549</td>\n",
              "      <td>2</td>\n",
              "      <td>4081</td>\n",
              "      <td>21515</td>\n",
              "      <td>26982618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Loreal</th>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>0.328819</td>\n",
              "      <td>864</td>\n",
              "      <td>7023</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0.869210</td>\n",
              "      <td>2</td>\n",
              "      <td>4156</td>\n",
              "      <td>7958</td>\n",
              "      <td>375508207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Taylor Stitch</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.645833</td>\n",
              "      <td>0.245962</td>\n",
              "      <td>29</td>\n",
              "      <td>2506</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.272442</td>\n",
              "      <td>1</td>\n",
              "      <td>13603</td>\n",
              "      <td>175</td>\n",
              "      <td>10165</td>\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9e9ca7cb-2098-44fe-90d4-3fb507ca4076 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9e9ca7cb-2098-44fe-90d4-3fb507ca4076');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.to_excel(\"/content/Internship/Company_wise_metrics.xlsx\")"
      ],
      "metadata": {
        "id": "kj3wYTN-cYep"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Masculine Brands Selected: Old Spice, The Man Company and Gillette <br>\n",
        "#### Feminine Brands Selected: Shein, Chanel and Sephora"
      ],
      "metadata": {
        "id": "wak_77McdSIX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"/content/Internship/Results_New.csv\")"
      ],
      "metadata": {
        "id": "3SETH3Kjewel"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZ7idfnZe-Uk",
        "outputId": "fc26df11-088c-44dd-8cc9-82f5578acfc2"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 9 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Brand       150 non-null    object\n",
            " 1   Title       150 non-null    object\n",
            " 2   Link        150 non-null    object\n",
            " 3   Transcript  150 non-null    object\n",
            " 4   Group       150 non-null    object\n",
            " 5   Views       150 non-null    int64 \n",
            " 6   Likes       150 non-null    int64 \n",
            " 7   Comment     150 non-null    int64 \n",
            " 8   Date        150 non-null    object\n",
            "dtypes: int64(3), object(6)\n",
            "memory usage: 10.7+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Transcript\"] = df[\"Transcript\"].astype(str)"
      ],
      "metadata": {
        "id": "jeil7ZO_5JZZ"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Group\"] = df[\"Group\"].astype(str)"
      ],
      "metadata": {
        "id": "VjdmYGsIvHU3"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transcripts = np.array(df[\"Transcript\"])"
      ],
      "metadata": {
        "id": "11zkZJLf4SIj"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "titles = np.array(df[\"Title\"])"
      ],
      "metadata": {
        "id": "cuks2t0bBtGM"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "genbit_score = []\n",
        "percentage_of_female_gender_definition_words = []\n",
        "percentage_of_male_gender_definition_words = []\n",
        "avg_bias_ratio = []\n",
        "freq_of_female_gender_definition_words = []\n",
        "freq_of_male_gender_definition_words = []\n",
        "title_genbit = []\n",
        "title_avg_bias_ratio = []"
      ],
      "metadata": {
        "id": "1r_l2WRbeV6Z"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(transcripts)):\n",
        "  transcripts[i] = transcripts[i].replace(\"\\n\", \" \")\n",
        "  test_text = [transcripts[i]]\n",
        "  genbit_metrics_object = GenBitMetrics(language_code, context_window=5, distance_weight=0.95, percentile_cutoff=80)\n",
        "  genbit_metrics_object.add_data(test_text, tokenized=False)\n",
        "  metrics = genbit_metrics_object.get_metrics(output_statistics=True, output_word_list=True)\n",
        "  genbit_score.append(metrics[\"genbit_score\"])\n",
        "  percentage_of_female_gender_definition_words.append(metrics[\"percentage_of_female_gender_definition_words\"])\n",
        "  percentage_of_male_gender_definition_words.append(metrics[\"percentage_of_male_gender_definition_words\"])\n",
        "  avg_bias_ratio.append(metrics[\"additional_metrics\"][\"avg_bias_ratio\"])\n",
        "  freq_of_female_gender_definition_words.append(metrics[\"statistics\"][\"freq_of_female_gender_definition_words\"])\n",
        "  freq_of_male_gender_definition_words.append(metrics[\"statistics\"][\"freq_of_male_gender_definition_words\"])\n",
        "  test_text = [titles[i]]\n",
        "  genbit_metrics_object = GenBitMetrics(language_code, context_window=5, distance_weight=0.95, percentile_cutoff=80)\n",
        "  genbit_metrics_object.add_data(test_text, tokenized=False)\n",
        "  metrics = genbit_metrics_object.get_metrics(output_statistics=True, output_word_list=True)\n",
        "  title_genbit.append(metrics[\"genbit_score\"])\n",
        "  title_avg_bias_ratio.append(metrics[\"additional_metrics\"][\"avg_bias_ratio\"])"
      ],
      "metadata": {
        "id": "nQ0uLedAeXl_"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Transcript\"] = transcripts\n",
        "df[\"Genbit Score\"] = genbit_score\n",
        "df[\"% Female Words\"] = percentage_of_female_gender_definition_words\n",
        "df[\"% Male Words\"] = percentage_of_male_gender_definition_words\n",
        "df[\"Avg Bias Ratio\"] = avg_bias_ratio\n",
        "df[\"Freq of Female Words\"] = freq_of_female_gender_definition_words\n",
        "df[\"Freq of Male Words\"] = freq_of_male_gender_definition_words\n",
        "df[\"Title GenBit Score\"] = title_genbit\n",
        "df[\"Title Avg Bias Ratio\"] = title_avg_bias_ratio"
      ],
      "metadata": {
        "id": "IyvS8NTEej0V"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Date'] = pd.to_datetime(df['Date'])\n",
        "df['Days Old'] = datetime.now() - df['Date']\n",
        "df['Days Old'] = df['Days Old'].dt.days"
      ],
      "metadata": {
        "id": "GnZ2M6CF6GsE"
      },
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Length\"] = df[\"Transcript\"].str.len()\n",
        "df[\"Group\"] = df[\"Group\"].map({'M': 1, 'F': 2})"
      ],
      "metadata": {
        "id": "wNbJEra96bSj"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hYdUb5Hl6g5c",
        "outputId": "e46451ec-5c19-48ec-bdb3-6bb15812a757"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 150 entries, 0 to 149\n",
            "Data columns (total 19 columns):\n",
            " #   Column                Non-Null Count  Dtype         \n",
            "---  ------                --------------  -----         \n",
            " 0   Brand                 150 non-null    object        \n",
            " 1   Title                 150 non-null    object        \n",
            " 2   Link                  150 non-null    object        \n",
            " 3   Transcript            150 non-null    object        \n",
            " 4   Group                 150 non-null    int64         \n",
            " 5   Views                 150 non-null    int64         \n",
            " 6   Likes                 150 non-null    int64         \n",
            " 7   Comment               150 non-null    int64         \n",
            " 8   Date                  150 non-null    datetime64[ns]\n",
            " 9   Genbit Score          150 non-null    float64       \n",
            " 10  % Female Words        150 non-null    float64       \n",
            " 11  % Male Words          150 non-null    float64       \n",
            " 12  Avg Bias Ratio        150 non-null    float64       \n",
            " 13  Freq of Female Words  150 non-null    int64         \n",
            " 14  Freq of Male Words    150 non-null    int64         \n",
            " 15  Title GenBit Score    150 non-null    float64       \n",
            " 16  Title Avg Bias Ratio  150 non-null    float64       \n",
            " 17  Days Old              150 non-null    int64         \n",
            " 18  Length                150 non-null    int64         \n",
            "dtypes: datetime64[ns](1), float64(6), int64(8), object(4)\n",
            "memory usage: 22.4+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_excel(\"/content/Internship/Transcripts.xlsx\")"
      ],
      "metadata": {
        "id": "5XaS1yMBAcA9"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "dwf3Wz-nu2ml",
        "outputId": "9784c2a9-491b-4371-ebdb-76f809209269"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Brand                                              Title  \\\n",
              "0    Old Spice  The Expert | Old Spice GentleMan’s Blend Body ...   \n",
              "1    Old Spice                             The Secret | Old Spice   \n",
              "2    Old Spice  Smells Like Romance | Old Spice Body Spray and...   \n",
              "3    Old Spice  Give Odor The Bird | Old Spice Eaglefangs Deod...   \n",
              "4    Old Spice  Tango | Old Spice Swagger Body Wash and Deodorant   \n",
              "..         ...                                                ...   \n",
              "145     Chanel  The CHANEL Spring-Summer 2023 Ready-to-Wear co...   \n",
              "146     Chanel  Literary Rendezvous at Rue Cambon: Portrait of...   \n",
              "147     Chanel  Kristen Stewart & Golshifteh Farahani in Berli...   \n",
              "148     Chanel  Sadie Sink and Lucy Boynton at the CHANEL Spri...   \n",
              "149     Chanel                         Give wonder, give CHANEL\\r   \n",
              "\n",
              "                                            Link  \\\n",
              "0    https://www.youtube.com/watch?v=TBJAJKspa0I   \n",
              "1    https://www.youtube.com/watch?v=8vbJiu40k7w   \n",
              "2    https://www.youtube.com/watch?v=564Y6lpuTDw   \n",
              "3    https://www.youtube.com/watch?v=ABGF-h6GJY0   \n",
              "4    https://www.youtube.com/watch?v=WeFl5Ypcp_E   \n",
              "..                                           ...   \n",
              "145  https://www.youtube.com/watch?v=mxJKsiU2-OE   \n",
              "146  https://www.youtube.com/watch?v=fARFkSSwE04   \n",
              "147  https://www.youtube.com/watch?v=McxrTRF0N9E   \n",
              "148  https://www.youtube.com/watch?v=CdIsut5UtKs   \n",
              "149  https://www.youtube.com/watch?v=Bx7OptSxwiQ   \n",
              "\n",
              "                                            Transcript  Group    Views  Likes  \\\n",
              "0    wow your skin is amazing oh oh thank you  I go...      1     2493    141   \n",
              "1    they are losing they need a miracle encouragem...      1     1491     75   \n",
              "2    I can smell the romance in the air   it's the ...      1   865470    158   \n",
              "3    pepper brownie pepperoni so where are we  drop...      1  2480224    157   \n",
              "4    didn't use Old Spice I think I smelled  freshn...      1    15821    166   \n",
              "..                                                 ...    ...      ...    ...   \n",
              "145  Kristen, I wanna ask you something.  What make...      2   158176   6300   \n",
              "146  She believes in experience, in the body...  Sh...      2    29787   1800   \n",
              "147  It's an enormous opportunity to have a hand  i...      2    38893   2200   \n",
              "148  Favorite part about the show?  What's always s...      2    19063    961   \n",
              "149  We copy you on the ground.  All systems runnin...      2    32620   2000   \n",
              "\n",
              "     Comment       Date  Genbit Score  % Female Words  % Male Words  \\\n",
              "0         10 2023-06-30      1.839898        0.000000      1.000000   \n",
              "1          5 2023-06-29      0.000000        0.000000      0.000000   \n",
              "2         13 2023-06-27      0.000000        0.000000      0.000000   \n",
              "3         25 2023-06-15      0.000000        0.000000      0.000000   \n",
              "4         28 2023-03-27      0.000000        0.000000      0.000000   \n",
              "..       ...        ...           ...             ...           ...   \n",
              "145     2245 2023-03-16      0.000000        0.000000      0.000000   \n",
              "146     1036 2023-03-01      1.326426        0.772093      0.046512   \n",
              "147      747 2023-02-28      0.000000        0.000000      0.000000   \n",
              "148       36 2023-01-26      0.000000        0.000000      0.000000   \n",
              "149      122 2022-11-30      0.060625        0.000000      1.000000   \n",
              "\n",
              "     Avg Bias Ratio  Freq of Female Words  Freq of Male Words  \\\n",
              "0          2.329446                     0                  12   \n",
              "1          0.000000                     0                   0   \n",
              "2          0.000000                     0                   0   \n",
              "3          0.000000                     0                   0   \n",
              "4          0.000000                     0                   0   \n",
              "..              ...                   ...                 ...   \n",
              "145        0.000000                     0                   0   \n",
              "146       -1.544781                   166                  10   \n",
              "147        0.000000                     0                   0   \n",
              "148        0.000000                     0                   0   \n",
              "149        0.000000                     0                   1   \n",
              "\n",
              "     Title GenBit Score  Title Avg Bias Ratio  Days Old  Length  \n",
              "0              0.513679              0.667829        14     783  \n",
              "1              0.000000              0.000000        15     193  \n",
              "2              0.000000              0.000000        17     298  \n",
              "3              0.000000              0.000000        29     311  \n",
              "4              0.000000              0.000000       109      59  \n",
              "..                  ...                   ...       ...     ...  \n",
              "145            0.000000              0.000000       120     678  \n",
              "146            0.000000              0.000000       135   19475  \n",
              "147            0.000000              0.000000       136    1390  \n",
              "148            0.000000              0.000000       169     733  \n",
              "149            0.000000              0.000000       226     300  \n",
              "\n",
              "[150 rows x 19 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "      <th>Brand</th>\n",
              "      <th>Title</th>\n",
              "      <th>Link</th>\n",
              "      <th>Transcript</th>\n",
              "      <th>Group</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Old Spice</td>\n",
              "      <td>The Expert | Old Spice GentleMan’s Blend Body ...</td>\n",
              "      <td>https://www.youtube.com/watch?v=TBJAJKspa0I</td>\n",
              "      <td>wow your skin is amazing oh oh thank you  I go...</td>\n",
              "      <td>1</td>\n",
              "      <td>2493</td>\n",
              "      <td>141</td>\n",
              "      <td>10</td>\n",
              "      <td>2023-06-30</td>\n",
              "      <td>1.839898</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.329446</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>0.513679</td>\n",
              "      <td>0.667829</td>\n",
              "      <td>14</td>\n",
              "      <td>783</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Old Spice</td>\n",
              "      <td>The Secret | Old Spice</td>\n",
              "      <td>https://www.youtube.com/watch?v=8vbJiu40k7w</td>\n",
              "      <td>they are losing they need a miracle encouragem...</td>\n",
              "      <td>1</td>\n",
              "      <td>1491</td>\n",
              "      <td>75</td>\n",
              "      <td>5</td>\n",
              "      <td>2023-06-29</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>15</td>\n",
              "      <td>193</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Old Spice</td>\n",
              "      <td>Smells Like Romance | Old Spice Body Spray and...</td>\n",
              "      <td>https://www.youtube.com/watch?v=564Y6lpuTDw</td>\n",
              "      <td>I can smell the romance in the air   it's the ...</td>\n",
              "      <td>1</td>\n",
              "      <td>865470</td>\n",
              "      <td>158</td>\n",
              "      <td>13</td>\n",
              "      <td>2023-06-27</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>17</td>\n",
              "      <td>298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Old Spice</td>\n",
              "      <td>Give Odor The Bird | Old Spice Eaglefangs Deod...</td>\n",
              "      <td>https://www.youtube.com/watch?v=ABGF-h6GJY0</td>\n",
              "      <td>pepper brownie pepperoni so where are we  drop...</td>\n",
              "      <td>1</td>\n",
              "      <td>2480224</td>\n",
              "      <td>157</td>\n",
              "      <td>25</td>\n",
              "      <td>2023-06-15</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>29</td>\n",
              "      <td>311</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Old Spice</td>\n",
              "      <td>Tango | Old Spice Swagger Body Wash and Deodorant</td>\n",
              "      <td>https://www.youtube.com/watch?v=WeFl5Ypcp_E</td>\n",
              "      <td>didn't use Old Spice I think I smelled  freshn...</td>\n",
              "      <td>1</td>\n",
              "      <td>15821</td>\n",
              "      <td>166</td>\n",
              "      <td>28</td>\n",
              "      <td>2023-03-27</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>109</td>\n",
              "      <td>59</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>145</th>\n",
              "      <td>Chanel</td>\n",
              "      <td>The CHANEL Spring-Summer 2023 Ready-to-Wear co...</td>\n",
              "      <td>https://www.youtube.com/watch?v=mxJKsiU2-OE</td>\n",
              "      <td>Kristen, I wanna ask you something.  What make...</td>\n",
              "      <td>2</td>\n",
              "      <td>158176</td>\n",
              "      <td>6300</td>\n",
              "      <td>2245</td>\n",
              "      <td>2023-03-16</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>120</td>\n",
              "      <td>678</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>146</th>\n",
              "      <td>Chanel</td>\n",
              "      <td>Literary Rendezvous at Rue Cambon: Portrait of...</td>\n",
              "      <td>https://www.youtube.com/watch?v=fARFkSSwE04</td>\n",
              "      <td>She believes in experience, in the body...  Sh...</td>\n",
              "      <td>2</td>\n",
              "      <td>29787</td>\n",
              "      <td>1800</td>\n",
              "      <td>1036</td>\n",
              "      <td>2023-03-01</td>\n",
              "      <td>1.326426</td>\n",
              "      <td>0.772093</td>\n",
              "      <td>0.046512</td>\n",
              "      <td>-1.544781</td>\n",
              "      <td>166</td>\n",
              "      <td>10</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>135</td>\n",
              "      <td>19475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>147</th>\n",
              "      <td>Chanel</td>\n",
              "      <td>Kristen Stewart &amp; Golshifteh Farahani in Berli...</td>\n",
              "      <td>https://www.youtube.com/watch?v=McxrTRF0N9E</td>\n",
              "      <td>It's an enormous opportunity to have a hand  i...</td>\n",
              "      <td>2</td>\n",
              "      <td>38893</td>\n",
              "      <td>2200</td>\n",
              "      <td>747</td>\n",
              "      <td>2023-02-28</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>136</td>\n",
              "      <td>1390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>Chanel</td>\n",
              "      <td>Sadie Sink and Lucy Boynton at the CHANEL Spri...</td>\n",
              "      <td>https://www.youtube.com/watch?v=CdIsut5UtKs</td>\n",
              "      <td>Favorite part about the show?  What's always s...</td>\n",
              "      <td>2</td>\n",
              "      <td>19063</td>\n",
              "      <td>961</td>\n",
              "      <td>36</td>\n",
              "      <td>2023-01-26</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>169</td>\n",
              "      <td>733</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>149</th>\n",
              "      <td>Chanel</td>\n",
              "      <td>Give wonder, give CHANEL\\r</td>\n",
              "      <td>https://www.youtube.com/watch?v=Bx7OptSxwiQ</td>\n",
              "      <td>We copy you on the ground.  All systems runnin...</td>\n",
              "      <td>2</td>\n",
              "      <td>32620</td>\n",
              "      <td>2000</td>\n",
              "      <td>122</td>\n",
              "      <td>2022-11-30</td>\n",
              "      <td>0.060625</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>226</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>150 rows × 19 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4455302d-5773-49cd-9aa9-ab89aead1a82')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-1916ffff-eb63-4336-b1ee-1a1f05e0ab77\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1916ffff-eb63-4336-b1ee-1a1f05e0ab77')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1916ffff-eb63-4336-b1ee-1a1f05e0ab77 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-4455302d-5773-49cd-9aa9-ab89aead1a82 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4455302d-5773-49cd-9aa9-ab89aead1a82');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_male = df[df[\"Group\"]==1]\n",
        "df_female = df[df[\"Group\"]==2]"
      ],
      "metadata": {
        "id": "nt9xsgqb9ah0"
      },
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_female.Brand.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BlQvRPKGAT2f",
        "outputId": "4169a9ee-69ff-4743-f6f9-844a0def8a32"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Shein', 'Sephora', 'Chanel'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_male.Brand.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CI2aRGH__KYr",
        "outputId": "54a6617d-47fc-46d7-deaf-9a066e97e95e"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Old Spice', 'The Man Company', 'Gillette'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Independent T Test"
      ],
      "metadata": {
        "id": "CMq8S2CIk4L3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metrics = [\"Views\", \"Likes\", \"Comments\", \"GenBit Score\", \"% Female Words\", \"% Male Words\", \"Avg Bias Ratio\", \"Freq Female Words\", \"Freq Male Words\", \"Title GenBit Score\", \"Title Avg Bias Ratio\"]\n",
        "var_m = []\n",
        "var_f = []\n",
        "t_statistic = []\n",
        "p_value = []"
      ],
      "metadata": {
        "id": "-wFypiuO8Q8t"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Views"
      ],
      "metadata": {
        "id": "DmqVjJ-wAf34"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m1 = np.array(df_male[\"Views\"])\n",
        "f1 = np.array(df_female[\"Views\"])"
      ],
      "metadata": {
        "id": "Y6ZvOoUdlIGd"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m1), np.var(f1)"
      ],
      "metadata": {
        "id": "IrYO_HlllBQ4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "87c88f14-105e-45f2-a300-a7ee8f0688b1"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(141436807733007.88, 369982186695.75055)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m1))\n",
        "var_f.append(np.var(f1))"
      ],
      "metadata": {
        "id": "6oy1Xy6e8yrj"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m1)/ np.var(f1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xsDOp6IfGnWE",
        "outputId": "139e132b-45cb-42c1-df70-822b16057d08"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "382.28004703728175"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m1, b=f1, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8_m9JQIsLYWz",
        "outputId": "0ed051be-7ffe-425d-b061-7b56f77e3f12"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=2.7397433663755324, pvalue=0.00690523942448511)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result1 = stats.ttest_ind(a=m1, b=f1, equal_var=True)\n",
        "t_statistic.append(result1[0])\n",
        "p_value.append(result1[1])"
      ],
      "metadata": {
        "id": "gPrhsN3m9W8d"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Likes"
      ],
      "metadata": {
        "id": "kr79lPF6MH1t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m2 = np.array(df_male[\"Likes\"])\n",
        "f2 = np.array(df_female[\"Likes\"])"
      ],
      "metadata": {
        "id": "4Q-h-dppMJxW"
      },
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m2), np.var(f2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pNyNnvXLMJ0T",
        "outputId": "e6bf2562-9661-4922-b190-c1ad4be88309"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(972107.768888889, 117753458.45582224)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m2))\n",
        "var_f.append(np.var(f2))"
      ],
      "metadata": {
        "id": "e8W3HR2d87yE"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(f2)/ np.var(m2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2TAFoIHPMJ3y",
        "outputId": "32d8a650-06df-4431-ed21-af75bd9653e9"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "121.13210306961486"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m2, b=f2, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D1WoYWeuMJ7i",
        "outputId": "b8bbec56-1155-463a-eb7b-88d2888b37a4"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-1.7567193181771177, pvalue=0.08103377548827567)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result2 = stats.ttest_ind(a=m2, b=f2, equal_var=True)\n",
        "t_statistic.append(result2[0])\n",
        "p_value.append(result2[1])"
      ],
      "metadata": {
        "id": "IT4a4_fZ92ta"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Comments"
      ],
      "metadata": {
        "id": "Wwoo6QnkLqqa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m3 = np.array(df_male[\"Comment\"])\n",
        "f3 = np.array(df_female[\"Comment\"])"
      ],
      "metadata": {
        "id": "qM1zH3x-Lgkt"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m3), np.var(f3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R7UgOZ42Lyyh",
        "outputId": "10f001a4-b8c7-4aa3-ddf3-4963dc8cb71b"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10048.047288888889, 234292.66595555554)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m3))\n",
        "var_f.append(np.var(f3))"
      ],
      "metadata": {
        "id": "_sjOzmRj8-pQ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(f3)/ np.var(m3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nl6TrUchL17z",
        "outputId": "13cfd07e-ff79-47fc-ae39-d292666e6982"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.31723360962243"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m3, b=f3, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0QjYJp8pL2-6",
        "outputId": "848b0573-3f7e-4953-bca0-a95aeee898d3"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-2.4419542166601707, pvalue=0.015786600841859642)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result3 = stats.ttest_ind(a=m3, b=f3, equal_var=True)\n",
        "t_statistic.append(result3[0])\n",
        "p_value.append(result3[1])"
      ],
      "metadata": {
        "id": "bmydmFFE95d5"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### GenBit Score"
      ],
      "metadata": {
        "id": "FnhilvWFMC_Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m4 = np.array(df_male[\"Genbit Score\"])\n",
        "f4 = np.array(df_female[\"Genbit Score\"])"
      ],
      "metadata": {
        "id": "0iPY5Do7MCXd"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m4), np.var(f4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V_UIaq1LMBDA",
        "outputId": "2028600c-302b-420c-9653-96270f5958c9"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.17914357680950205, 0.1519825033289676)"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m4))\n",
        "var_f.append(np.var(f4))"
      ],
      "metadata": {
        "id": "Ain_YhP_9Agv"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m4)/ np.var(f4)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kQwUAuMvPcey",
        "outputId": "1b861bf4-5f67-4959-a8af-396701c0385d"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.1787118443610844"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m4, b=f4, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aO3d93OBPd-s",
        "outputId": "9c571eab-a3c6-4d84-8adf-dcbfb4e8ce27"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=2.139977442366143, pvalue=0.03399592703353841)"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result4 = stats.ttest_ind(a=m4, b=f4, equal_var=True)\n",
        "t_statistic.append(result4[0])\n",
        "p_value.append(result4[1])"
      ],
      "metadata": {
        "id": "Sl7ViUsw978G"
      },
      "execution_count": 76,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Percentage Female Definition Words"
      ],
      "metadata": {
        "id": "ExG1IufvRT8I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m5 = np.array(df_male[\"% Female Words\"])\n",
        "f5 = np.array(df_female[\"% Female Words\"])"
      ],
      "metadata": {
        "id": "ec5g4171RTMG"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m5), np.var(f5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wehcEcHXRiTf",
        "outputId": "c681ae13-8d67-44d3-e4d2-88c42fa2e227"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.028627552785296793, 0.09862543307983328)"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m5))\n",
        "var_f.append(np.var(f5))"
      ],
      "metadata": {
        "id": "dmi4gY4z9DlC"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(f5)/ np.var(m5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BRN6gpvwRjpl",
        "outputId": "7fa2502b-9c43-4ec9-89dd-b18a02543c51"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.445122739603772"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m5, b=f5, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A8XEJb3ARlOi",
        "outputId": "2da70f0e-6a1a-48ae-94bb-a39d02e2a9ff"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=-5.618700535582312, pvalue=9.268314181297536e-08)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result5 = stats.ttest_ind(a=m5, b=f5, equal_var=True)\n",
        "t_statistic.append(result5[0])\n",
        "p_value.append(result5[1])"
      ],
      "metadata": {
        "id": "9iB2_Iv49_Ba"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Percentage Male Definition Words"
      ],
      "metadata": {
        "id": "XIAyx_LORrko"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m6 = np.array(df_male[\"% Male Words\"])\n",
        "f6 = np.array(df_female[\"% Male Words\"])"
      ],
      "metadata": {
        "id": "74gP8CpERqgb"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m6), np.var(f6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DC9YwMpBRxNY",
        "outputId": "2d2a2675-591f-4801-8f30-b881578dc940"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.12881677840717945, 0.05370031821105933)"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m6))\n",
        "var_f.append(np.var(f6))"
      ],
      "metadata": {
        "id": "ct_p2y599FgK"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m6)/ np.var(f6)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M3MwfVj_RybN",
        "outputId": "515662d5-dc97-48e5-deb0-26b97b01ff19"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.398808474484053"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m6, b=f6, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M-31yi6ER0XH",
        "outputId": "09d1e199-d6c7-4480-ae0b-e4e57726b4a7"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=7.095021091510648, pvalue=4.975125030132194e-11)"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result6 = stats.ttest_ind(a=m6, b=f6, equal_var=True)\n",
        "t_statistic.append(result6[0])\n",
        "p_value.append(result6[1])"
      ],
      "metadata": {
        "id": "OpQURA5S-CWZ"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Average Bias Ratio"
      ],
      "metadata": {
        "id": "4hJRvfV_R_Xf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m7 = np.array(df_male[\"Avg Bias Ratio\"])\n",
        "f7 = np.array(df_female[\"Avg Bias Ratio\"])"
      ],
      "metadata": {
        "id": "VcryfrNFR-Zw"
      },
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m7), np.var(f7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZoYlelE9SKg2",
        "outputId": "d49befb3-c168-4bc1-8350-3269eba2af1a"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.3327574561243729, 0.2402789563701514)"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m7))\n",
        "var_f.append(np.var(f7))"
      ],
      "metadata": {
        "id": "DnFsW4dG9HSK"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m7)/ np.var(f7)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ui7xvwv4SYll",
        "outputId": "38af82bc-ab1c-4c3a-ac9c-59d07f561bf8"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.3848797295912911"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m7, b=f7, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6rTSSDAjSZkv",
        "outputId": "a57be346-857a-4d79-815e-95588dec59f8"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=8.421339825963038, pvalue=2.9742988451388084e-14)"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result7 = stats.ttest_ind(a=m7, b=f7, equal_var=True)\n",
        "t_statistic.append(result7[0])\n",
        "p_value.append(result7[1])"
      ],
      "metadata": {
        "id": "m_uatpFu-FG7"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Frequency of Female Definition Words"
      ],
      "metadata": {
        "id": "1pDLxrWRSoOK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m8 = np.array(df_male[\"Freq of Female Words\"])\n",
        "f8 = np.array(df_female[\"Freq of Female Words\"])"
      ],
      "metadata": {
        "id": "8OZGaxOySfLW"
      },
      "execution_count": 95,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m8), np.var(f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0qkcZmabS6bY",
        "outputId": "690adec4-c6ca-4507-9286-5a03b50f3f90"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(953.9758222222223, 793.6824888888888)"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m8))\n",
        "var_f.append(np.var(f8))"
      ],
      "metadata": {
        "id": "DAgz1GiQ9JTc"
      },
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m8)/ np.var(f8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2tIpU7AVS84F",
        "outputId": "c243511f-2c2c-460a-ec58-a27af1c6c380"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.2019615344641599"
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m8, b=f8, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2M1786hnTAWn",
        "outputId": "26a90d5d-bb88-4ec3-f66e-9773cab8cb46"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=0.0, pvalue=1.0)"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result8 = stats.ttest_ind(a=m8, b=f8, equal_var=True)\n",
        "t_statistic.append(result8[0])\n",
        "p_value.append(result8[1])"
      ],
      "metadata": {
        "id": "tvib5l10-JGm"
      },
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Frequency of Male Definition Words"
      ],
      "metadata": {
        "id": "HXNGpUgSTNHS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m9 = np.array(df_male[\"Freq of Male Words\"])\n",
        "f9 = np.array(df_female[\"Freq of Male Words\"])"
      ],
      "metadata": {
        "id": "xry01R1WTMOe"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m9), np.var(f9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7HAgZ-rTS37",
        "outputId": "dc8bb2f6-6cba-497e-e362-5b31e7f430fa"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1569.2632888888893, 65.8424888888889)"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m9))\n",
        "var_f.append(np.var(f9))"
      ],
      "metadata": {
        "id": "uqf2mEfu9LXI"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m9)/ np.var(f9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRHj91DTTXZM",
        "outputId": "740fd6fb-5598-489d-898d-01d89bf7bbcd"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "23.833596137853576"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m9, b=f9, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vu91UCU6Tapa",
        "outputId": "55d6ea13-49f7-42f4-d194-6dbf4c0583f6"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=3.5257604236226223, pvalue=0.0005624108115331523)"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result9 = stats.ttest_ind(a=m9, b=f9, equal_var=True)\n",
        "t_statistic.append(result9[0])\n",
        "p_value.append(result9[1])"
      ],
      "metadata": {
        "id": "q3uML5_H-P2e"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Title GenBit Score"
      ],
      "metadata": {
        "id": "4xiC-4x2DNxY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m10 = np.array(df_male[\"Title GenBit Score\"])\n",
        "f10 = np.array(df_female[\"Title GenBit Score\"])"
      ],
      "metadata": {
        "id": "FBxHBoWlDS9f"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m10), np.var(f10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_X3NvegxDWPG",
        "outputId": "2b97c9ad-af49-4f79-c8b3-b1527092ac02"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.08059803039960986, 0.010545120005485204)"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m10))\n",
        "var_f.append(np.var(f10))"
      ],
      "metadata": {
        "id": "F6MmnSh8DdPd"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m10)/ np.var(f10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JcODVdeMDgaF",
        "outputId": "66ada168-c6c6-43ed-bd03-0a8cf17d44f7"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.6431591444844225"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m10, b=f10, equal_var=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E3pt4ZNxDjY9",
        "outputId": "4eb8e1f4-58de-4321-9a4a-6cb317cc3a09"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ttest_indResult(statistic=6.681892830082853, pvalue=4.4800124438216827e-10)"
            ]
          },
          "metadata": {},
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result10 = stats.ttest_ind(a=m10, b=f10, equal_var=True)\n",
        "t_statistic.append(result10[0])\n",
        "p_value.append(result10[1])"
      ],
      "metadata": {
        "id": "Un1FxNXtDl6N"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Title Avg Bias Ratio"
      ],
      "metadata": {
        "id": "PvUFOQ8zDYaK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "m11 = np.array(df_male[\"Title Avg Bias Ratio\"])\n",
        "f11 = np.array(df_female[\"Title Avg Bias Ratio\"])"
      ],
      "metadata": {
        "id": "iwwj5nMwDUZI"
      },
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m11), np.var(f11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZpOup9OdDeFV",
        "outputId": "30a78618-27a8-4962-cd5e-d1a3bb49af6a"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.1575405669145613, 0.017126249121592076)"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "var_m.append(np.var(m11))\n",
        "var_f.append(np.var(f11))"
      ],
      "metadata": {
        "id": "KdZkaiSNDenY"
      },
      "execution_count": 115,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "np.var(m11)/ np.var(f11)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyuyH2WxDhT-",
        "outputId": "2cdda422-132c-4c3f-aea7-1b9b23808c71"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9.198778191071657"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "stats.ttest_ind(a=m11, b=f11, equal_var=True)"
      ],
      "metadata": {
        "id": "acpu3p1xDkG2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "result11 = stats.ttest_ind(a=m11, b=f11, equal_var=True)\n",
        "t_statistic.append(result11[0])\n",
        "p_value.append(result11[1])"
      ],
      "metadata": {
        "id": "ApUA56UyDmeR"
      },
      "execution_count": 117,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Final"
      ],
      "metadata": {
        "id": "2TBjeDgJToTs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame()"
      ],
      "metadata": {
        "id": "wwTie3uITphr"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"Metric\"] = metrics\n",
        "results[\"M Variance\"] = var_m\n",
        "results[\"F Variance\"] = var_f\n",
        "results[\"T-Statistic\"] = t_statistic\n",
        "results[\"P-Value\"] = p_value"
      ],
      "metadata": {
        "id": "p3a3HBzMgArg"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "T-statistic measures the difference between the means of the two groups relative to the variability within each group.\n",
        "P-value indicates the probability of observing the obtained t-statistic or more extreme values under the null hypothesis (the assumption that there is no difference between the groups).\n",
        "If the p-value is below a predetermined significance level (commonly 0.05), it suggests that there is sufficient evidence to reject the null hypothesis and conclude that there is a significant difference between the groups.\n",
        "If the p-value is above the significance level, it suggests that there is not enough evidence to reject the null hypothesis, indicating that there is no significant difference between the groups."
      ],
      "metadata": {
        "id": "THqnbrTFiqZ1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "tKr7EVnzgi8E",
        "outputId": "874b4700-f294-4cc8-ddbc-704eef537e72"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Metric    M Variance    F Variance  T-Statistic  \\\n",
              "0                  Views  1.414368e+14  3.699822e+11     2.739743   \n",
              "1                  Likes  9.721078e+05  1.177535e+08    -1.756719   \n",
              "2               Comments  1.004805e+04  2.342927e+05    -2.441954   \n",
              "3           GenBit Score  1.791436e-01  1.519825e-01     2.139977   \n",
              "4         % Female Words  2.862755e-02  9.862543e-02    -5.618701   \n",
              "5           % Male Words  1.288168e-01  5.370032e-02     7.095021   \n",
              "6         Avg Bias Ratio  3.327575e-01  2.402790e-01     8.421340   \n",
              "7      Freq Female Words  9.539758e+02  7.936825e+02     0.000000   \n",
              "8        Freq Male Words  1.569263e+03  6.584249e+01     3.525760   \n",
              "9     Title GenBit Score  8.059803e-02  1.054512e-02     6.681893   \n",
              "10  Title Avg Bias Ratio  1.575406e-01  1.712625e-02     6.748580   \n",
              "\n",
              "         P-Value  \n",
              "0   6.905239e-03  \n",
              "1   8.103378e-02  \n",
              "2   1.578660e-02  \n",
              "3   3.399593e-02  \n",
              "4   9.268314e-08  \n",
              "5   4.975125e-11  \n",
              "6   2.974299e-14  \n",
              "7   1.000000e+00  \n",
              "8   5.624108e-04  \n",
              "9   4.480012e-10  \n",
              "10  3.155575e-10  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fdb8959b-d618-4396-8ea5-8a940074e0cd\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>M Variance</th>\n",
              "      <th>F Variance</th>\n",
              "      <th>T-Statistic</th>\n",
              "      <th>P-Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Views</td>\n",
              "      <td>1.414368e+14</td>\n",
              "      <td>3.699822e+11</td>\n",
              "      <td>2.739743</td>\n",
              "      <td>6.905239e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Likes</td>\n",
              "      <td>9.721078e+05</td>\n",
              "      <td>1.177535e+08</td>\n",
              "      <td>-1.756719</td>\n",
              "      <td>8.103378e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Comments</td>\n",
              "      <td>1.004805e+04</td>\n",
              "      <td>2.342927e+05</td>\n",
              "      <td>-2.441954</td>\n",
              "      <td>1.578660e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GenBit Score</td>\n",
              "      <td>1.791436e-01</td>\n",
              "      <td>1.519825e-01</td>\n",
              "      <td>2.139977</td>\n",
              "      <td>3.399593e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>% Female Words</td>\n",
              "      <td>2.862755e-02</td>\n",
              "      <td>9.862543e-02</td>\n",
              "      <td>-5.618701</td>\n",
              "      <td>9.268314e-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>% Male Words</td>\n",
              "      <td>1.288168e-01</td>\n",
              "      <td>5.370032e-02</td>\n",
              "      <td>7.095021</td>\n",
              "      <td>4.975125e-11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Avg Bias Ratio</td>\n",
              "      <td>3.327575e-01</td>\n",
              "      <td>2.402790e-01</td>\n",
              "      <td>8.421340</td>\n",
              "      <td>2.974299e-14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Freq Female Words</td>\n",
              "      <td>9.539758e+02</td>\n",
              "      <td>7.936825e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Freq Male Words</td>\n",
              "      <td>1.569263e+03</td>\n",
              "      <td>6.584249e+01</td>\n",
              "      <td>3.525760</td>\n",
              "      <td>5.624108e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Title GenBit Score</td>\n",
              "      <td>8.059803e-02</td>\n",
              "      <td>1.054512e-02</td>\n",
              "      <td>6.681893</td>\n",
              "      <td>4.480012e-10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Title Avg Bias Ratio</td>\n",
              "      <td>1.575406e-01</td>\n",
              "      <td>1.712625e-02</td>\n",
              "      <td>6.748580</td>\n",
              "      <td>3.155575e-10</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-1779729d-2a10-41e9-8980-7f7ceddc960e')\"\n",
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              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-1779729d-2a10-41e9-8980-7f7ceddc960e button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fdb8959b-d618-4396-8ea5-8a940074e0cd button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fdb8959b-d618-4396-8ea5-8a940074e0cd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"p < 0.05\"] = results[\"P-Value\"] < 0.05"
      ],
      "metadata": {
        "id": "B7aip2VXiIl_"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "TscY3MWVjHMb",
        "outputId": "d0d04c78-6fef-4df8-b564-e5f0d2fa5957"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  Metric    M Variance    F Variance  T-Statistic  \\\n",
              "0                  Views  1.414368e+14  3.699822e+11     2.739743   \n",
              "1                  Likes  9.721078e+05  1.177535e+08    -1.756719   \n",
              "2               Comments  1.004805e+04  2.342927e+05    -2.441954   \n",
              "3           GenBit Score  1.791436e-01  1.519825e-01     2.139977   \n",
              "4         % Female Words  2.862755e-02  9.862543e-02    -5.618701   \n",
              "5           % Male Words  1.288168e-01  5.370032e-02     7.095021   \n",
              "6         Avg Bias Ratio  3.327575e-01  2.402790e-01     8.421340   \n",
              "7      Freq Female Words  9.539758e+02  7.936825e+02     0.000000   \n",
              "8        Freq Male Words  1.569263e+03  6.584249e+01     3.525760   \n",
              "9     Title GenBit Score  8.059803e-02  1.054512e-02     6.681893   \n",
              "10  Title Avg Bias Ratio  1.575406e-01  1.712625e-02     6.748580   \n",
              "\n",
              "         P-Value  p < 0.05  \n",
              "0   6.905239e-03      True  \n",
              "1   8.103378e-02     False  \n",
              "2   1.578660e-02      True  \n",
              "3   3.399593e-02      True  \n",
              "4   9.268314e-08      True  \n",
              "5   4.975125e-11      True  \n",
              "6   2.974299e-14      True  \n",
              "7   1.000000e+00     False  \n",
              "8   5.624108e-04      True  \n",
              "9   4.480012e-10      True  \n",
              "10  3.155575e-10      True  "
            ],
            "text/html": [
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Metric</th>\n",
              "      <th>M Variance</th>\n",
              "      <th>F Variance</th>\n",
              "      <th>T-Statistic</th>\n",
              "      <th>P-Value</th>\n",
              "      <th>p &lt; 0.05</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Views</td>\n",
              "      <td>1.414368e+14</td>\n",
              "      <td>3.699822e+11</td>\n",
              "      <td>2.739743</td>\n",
              "      <td>6.905239e-03</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Likes</td>\n",
              "      <td>9.721078e+05</td>\n",
              "      <td>1.177535e+08</td>\n",
              "      <td>-1.756719</td>\n",
              "      <td>8.103378e-02</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Comments</td>\n",
              "      <td>1.004805e+04</td>\n",
              "      <td>2.342927e+05</td>\n",
              "      <td>-2.441954</td>\n",
              "      <td>1.578660e-02</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>GenBit Score</td>\n",
              "      <td>1.791436e-01</td>\n",
              "      <td>1.519825e-01</td>\n",
              "      <td>2.139977</td>\n",
              "      <td>3.399593e-02</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>% Female Words</td>\n",
              "      <td>2.862755e-02</td>\n",
              "      <td>9.862543e-02</td>\n",
              "      <td>-5.618701</td>\n",
              "      <td>9.268314e-08</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>% Male Words</td>\n",
              "      <td>1.288168e-01</td>\n",
              "      <td>5.370032e-02</td>\n",
              "      <td>7.095021</td>\n",
              "      <td>4.975125e-11</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Avg Bias Ratio</td>\n",
              "      <td>3.327575e-01</td>\n",
              "      <td>2.402790e-01</td>\n",
              "      <td>8.421340</td>\n",
              "      <td>2.974299e-14</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Freq Female Words</td>\n",
              "      <td>9.539758e+02</td>\n",
              "      <td>7.936825e+02</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Freq Male Words</td>\n",
              "      <td>1.569263e+03</td>\n",
              "      <td>6.584249e+01</td>\n",
              "      <td>3.525760</td>\n",
              "      <td>5.624108e-04</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Title GenBit Score</td>\n",
              "      <td>8.059803e-02</td>\n",
              "      <td>1.054512e-02</td>\n",
              "      <td>6.681893</td>\n",
              "      <td>4.480012e-10</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Title Avg Bias Ratio</td>\n",
              "      <td>1.575406e-01</td>\n",
              "      <td>1.712625e-02</td>\n",
              "      <td>6.748580</td>\n",
              "      <td>3.155575e-10</td>\n",
              "      <td>True</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f16612e-c32a-46b6-b52d-7223fe9a8a04')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-08b3d5b9-6b06-4350-b4a6-ac15e0da798f')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-08b3d5b9-6b06-4350-b4a6-ac15e0da798f button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7f16612e-c32a-46b6-b52d-7223fe9a8a04 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7f16612e-c32a-46b6-b52d-7223fe9a8a04');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.to_excel(\"/content/Internship/T_Test1.xlsx\")"
      ],
      "metadata": {
        "id": "EgjTXqjjgjtb"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Regression Analysis"
      ],
      "metadata": {
        "id": "l7QON_4WqIAI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coEfficients = []\n",
        "pValues = []"
      ],
      "metadata": {
        "id": "Y_-1q6drxbne"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = np.column_stack([\n",
        "    df[\"Genbit Score\"].values,\n",
        "    df[\"% Female Words\"].values,\n",
        "    df[\"% Male Words\"].values,\n",
        "    df[\"Freq of Female Words\"].values,\n",
        "    df[\"Freq of Male Words\"].values,\n",
        "    df[\"Avg Bias Ratio\"].values,\n",
        "    (df[\"Genbit Score\"] * df[\"Group\"]).values,\n",
        "    (df[\"% Female Words\"] * df[\"Group\"]).values,\n",
        "    (df[\"% Male Words\"] * df[\"Group\"]).values,\n",
        "    (df[\"Freq of Female Words\"] * df[\"Group\"]).values,\n",
        "    (df[\"Freq of Male Words\"] * df[\"Group\"]).values,\n",
        "    (df[\"Avg Bias Ratio\"] * df[\"Group\"]).values,\n",
        "    df[\"Length\"].values,\n",
        "    df[\"Days Old\"].values,\n",
        "    df[\"Title GenBit Score\"].values,\n",
        "    df[\"Title Avg Bias Ratio\"].values\n",
        "])"
      ],
      "metadata": {
        "id": "NvRsTqOY5kYJ"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = sm.add_constant(X)"
      ],
      "metadata": {
        "id": "C7hItLEV8HA8"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Views"
      ],
      "metadata": {
        "id": "RAzdHNWaq13r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y1 = df[\"Views\"]"
      ],
      "metadata": {
        "id": "_EyXLUJLqN0P"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1 = sm.OLS(y1, X)\n",
        "results1 = model1.fit()"
      ],
      "metadata": {
        "id": "7MITcXEcqeLp"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(results1.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MfGb8N409L8F",
        "outputId": "ca54ff82-b70e-42ed-f8a8-0e21314e0bcf"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.2604126993547379\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coEfficients.append(np.array(results1.params))\n",
        "pValues.append(np.array(results1.pvalues))"
      ],
      "metadata": {
        "id": "QCV4CGqdwRdc"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Likes"
      ],
      "metadata": {
        "id": "PbFAhyKQ5asG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y2 = df[\"Likes\"]/df[\"Views\"]"
      ],
      "metadata": {
        "id": "etxSkTtmxpE2"
      },
      "execution_count": 154,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model2 = sm.OLS(y2, X)\n",
        "results2 = model2.fit()"
      ],
      "metadata": {
        "id": "A0vGXUUw5vrh"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(results2.rsquared_adj)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FKZ6HOS9QpA",
        "outputId": "9935bec2-85e1-4c74-8301-9d133fd3ddd2"
      },
      "execution_count": 156,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.013281824854768143\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "coEfficients.append(np.array(results2.params))\n",
        "pValues.append(np.array(results2.pvalues))"
      ],
      "metadata": {
        "id": "vzPqQ3j75tLC"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Final"
      ],
      "metadata": {
        "id": "1NQ8KMca51SO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coEfficients"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hqC34qWv5x0e",
        "outputId": "cf8425af-3119-4c83-d66b-f64bacf97a62"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([ 7.49184347e+05, -3.66504353e+07,  1.08424923e+08, -3.51407427e+06,\n",
              "         1.06492882e+05, -1.30975604e+05,  3.02846485e+07,  1.73150565e+07,\n",
              "        -5.46784188e+07,  8.78337407e+05, -5.16420058e+04,  8.99577044e+04,\n",
              "        -1.54083286e+07, -5.69592111e+01,  1.78568697e+04, -3.29566676e+06,\n",
              "         1.39413432e+06]),\n",
              " array([ 4.80760529e-02, -2.41037985e-02, -4.24012790e-02, -3.91149128e-02,\n",
              "         1.87154907e-05,  1.09333365e-03,  3.41353979e-02,  4.55506083e-03,\n",
              "         4.03048080e-02,  2.19821696e-02,  2.05509925e-05, -1.32472602e-03,\n",
              "        -1.59197717e-02,  8.05194522e-07, -5.21900197e-05,  6.78595235e-04,\n",
              "        -8.68397327e-03])]"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pValues"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Qu4xS5153UP",
        "outputId": "55c46caa-73df-4ec4-ac29-c8a66aa492f9"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[array([5.60416474e-01, 2.08647626e-02, 5.08492672e-06, 6.45146738e-01,\n",
              "        4.93730750e-01, 5.29396602e-01, 2.73472294e-02, 4.66087512e-02,\n",
              "        1.35044553e-05, 8.64529291e-01, 5.71350545e-01, 6.75121137e-01,\n",
              "        4.34458073e-02, 6.71609725e-01, 4.71873783e-05, 3.84535639e-01,\n",
              "        6.01376667e-01]),\n",
              " array([1.23134277e-06, 8.34997872e-01, 8.01142579e-01, 4.86854164e-01,\n",
              "        9.86964752e-01, 4.76237592e-01, 7.33414424e-01, 9.42939401e-01,\n",
              "        6.51848588e-01, 5.62480729e-01, 9.75595968e-01, 4.02685978e-01,\n",
              "        7.75406482e-01, 4.16444128e-01, 9.74870860e-02, 9.80585744e-01,\n",
              "        6.58703248e-01])]"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results = pd.DataFrame()"
      ],
      "metadata": {
        "id": "umz4Rdhn89u0"
      },
      "execution_count": 160,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"Variable\"] = [\n",
        "    \"Constant\",\n",
        "    \"Genbit Score\",\n",
        "    \"% Female Words\",\n",
        "    \"% Male Words\",\n",
        "    \"Freq of Female Words\",\n",
        "    \"Freq of Male Words\",\n",
        "    \"Avg Bias Ratio\",\n",
        "    \"Genbit Score * Binary Variable\",\n",
        "    \"% Female Words * Binary Variable\",\n",
        "    \"% Male Words * Binary Variable\",\n",
        "    \"Freq of Female Words * Binary Variable\",\n",
        "    \"Freq of Male Words * Binary Variable\",\n",
        "    \"Avg Bias Ratio * Binary Variable\",\n",
        "    \"Length\",\n",
        "    \"Days Old\",\n",
        "    \"Title GenBit Score\",\n",
        "    \"Title Avg Bias Ratio\"\n",
        "]"
      ],
      "metadata": {
        "id": "iv3IslKR9-Iu"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results[\"Co-Efficient for Views\"] = coEfficients[0]\n",
        "results[\"p-Value for Views\"] = pValues[0]\n",
        "results[\"Co-Efficient for Likes\"] = coEfficients[1]\n",
        "results[\"p-Value for Likes\"] = pValues[1]"
      ],
      "metadata": {
        "id": "GYe8QfB59CAi"
      },
      "execution_count": 162,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = results.set_index(\"Variable\")"
      ],
      "metadata": {
        "id": "7rjpD_ny95xj"
      },
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "EeM0zhjo96q7",
        "outputId": "5c0e0928-e206-4364-9ef9-3afd4cb9a534"
      },
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        Co-Efficient for Views  \\\n",
              "Variable                                                         \n",
              "Constant                                          7.491843e+05   \n",
              "Genbit Score                                     -3.665044e+07   \n",
              "% Female Words                                    1.084249e+08   \n",
              "% Male Words                                     -3.514074e+06   \n",
              "Freq of Female Words                              1.064929e+05   \n",
              "Freq of Male Words                               -1.309756e+05   \n",
              "Avg Bias Ratio                                    3.028465e+07   \n",
              "Genbit Score * Binary Variable                    1.731506e+07   \n",
              "% Female Words * Binary Variable                 -5.467842e+07   \n",
              "% Male Words * Binary Variable                    8.783374e+05   \n",
              "Freq of Female Words * Binary Variable           -5.164201e+04   \n",
              "Freq of Male Words * Binary Variable              8.995770e+04   \n",
              "Avg Bias Ratio * Binary Variable                 -1.540833e+07   \n",
              "Length                                           -5.695921e+01   \n",
              "Days Old                                          1.785687e+04   \n",
              "Title GenBit Score                               -3.295667e+06   \n",
              "Title Avg Bias Ratio                              1.394134e+06   \n",
              "\n",
              "                                        p-Value for Views  \\\n",
              "Variable                                                    \n",
              "Constant                                         0.560416   \n",
              "Genbit Score                                     0.020865   \n",
              "% Female Words                                   0.000005   \n",
              "% Male Words                                     0.645147   \n",
              "Freq of Female Words                             0.493731   \n",
              "Freq of Male Words                               0.529397   \n",
              "Avg Bias Ratio                                   0.027347   \n",
              "Genbit Score * Binary Variable                   0.046609   \n",
              "% Female Words * Binary Variable                 0.000014   \n",
              "% Male Words * Binary Variable                   0.864529   \n",
              "Freq of Female Words * Binary Variable           0.571351   \n",
              "Freq of Male Words * Binary Variable             0.675121   \n",
              "Avg Bias Ratio * Binary Variable                 0.043446   \n",
              "Length                                           0.671610   \n",
              "Days Old                                         0.000047   \n",
              "Title GenBit Score                               0.384536   \n",
              "Title Avg Bias Ratio                             0.601377   \n",
              "\n",
              "                                        Co-Efficient for Likes  \\\n",
              "Variable                                                         \n",
              "Constant                                          4.807605e-02   \n",
              "Genbit Score                                     -2.410380e-02   \n",
              "% Female Words                                   -4.240128e-02   \n",
              "% Male Words                                     -3.911491e-02   \n",
              "Freq of Female Words                              1.871549e-05   \n",
              "Freq of Male Words                                1.093334e-03   \n",
              "Avg Bias Ratio                                    3.413540e-02   \n",
              "Genbit Score * Binary Variable                    4.555061e-03   \n",
              "% Female Words * Binary Variable                  4.030481e-02   \n",
              "% Male Words * Binary Variable                    2.198217e-02   \n",
              "Freq of Female Words * Binary Variable            2.055099e-05   \n",
              "Freq of Male Words * Binary Variable             -1.324726e-03   \n",
              "Avg Bias Ratio * Binary Variable                 -1.591977e-02   \n",
              "Length                                            8.051945e-07   \n",
              "Days Old                                         -5.219002e-05   \n",
              "Title GenBit Score                                6.785952e-04   \n",
              "Title Avg Bias Ratio                             -8.683973e-03   \n",
              "\n",
              "                                        p-Value for Likes  \n",
              "Variable                                                   \n",
              "Constant                                         0.000001  \n",
              "Genbit Score                                     0.834998  \n",
              "% Female Words                                   0.801143  \n",
              "% Male Words                                     0.486854  \n",
              "Freq of Female Words                             0.986965  \n",
              "Freq of Male Words                               0.476238  \n",
              "Avg Bias Ratio                                   0.733414  \n",
              "Genbit Score * Binary Variable                   0.942939  \n",
              "% Female Words * Binary Variable                 0.651849  \n",
              "% Male Words * Binary Variable                   0.562481  \n",
              "Freq of Female Words * Binary Variable           0.975596  \n",
              "Freq of Male Words * Binary Variable             0.402686  \n",
              "Avg Bias Ratio * Binary Variable                 0.775406  \n",
              "Length                                           0.416444  \n",
              "Days Old                                         0.097487  \n",
              "Title GenBit Score                               0.980586  \n",
              "Title Avg Bias Ratio                             0.658703  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-0f5b1f8b-8f78-42dc-a5f3-e9e16166b9b1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Co-Efficient for Views</th>\n",
              "      <th>p-Value for Views</th>\n",
              "      <th>Co-Efficient for Likes</th>\n",
              "      <th>p-Value for Likes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Variable</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Constant</th>\n",
              "      <td>7.491843e+05</td>\n",
              "      <td>0.560416</td>\n",
              "      <td>4.807605e-02</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score</th>\n",
              "      <td>-3.665044e+07</td>\n",
              "      <td>0.020865</td>\n",
              "      <td>-2.410380e-02</td>\n",
              "      <td>0.834998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Female Words</th>\n",
              "      <td>1.084249e+08</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-4.240128e-02</td>\n",
              "      <td>0.801143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words</th>\n",
              "      <td>-3.514074e+06</td>\n",
              "      <td>0.645147</td>\n",
              "      <td>-3.911491e-02</td>\n",
              "      <td>0.486854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words</th>\n",
              "      <td>1.064929e+05</td>\n",
              "      <td>0.493731</td>\n",
              "      <td>1.871549e-05</td>\n",
              "      <td>0.986965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words</th>\n",
              "      <td>-1.309756e+05</td>\n",
              "      <td>0.529397</td>\n",
              "      <td>1.093334e-03</td>\n",
              "      <td>0.476238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Bias Ratio</th>\n",
              "      <td>3.028465e+07</td>\n",
              "      <td>0.027347</td>\n",
              "      <td>3.413540e-02</td>\n",
              "      <td>0.733414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score * Binary Variable</th>\n",
              "      <td>1.731506e+07</td>\n",
              "      <td>0.046609</td>\n",
              "      <td>4.555061e-03</td>\n",
              "      <td>0.942939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Female Words * Binary Variable</th>\n",
              "      <td>-5.467842e+07</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>4.030481e-02</td>\n",
              "      <td>0.651849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words * Binary Variable</th>\n",
              "      <td>8.783374e+05</td>\n",
              "      <td>0.864529</td>\n",
              "      <td>2.198217e-02</td>\n",
              "      <td>0.562481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words * Binary Variable</th>\n",
              "      <td>-5.164201e+04</td>\n",
              "      <td>0.571351</td>\n",
              "      <td>2.055099e-05</td>\n",
              "      <td>0.975596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words * Binary Variable</th>\n",
              "      <td>8.995770e+04</td>\n",
              "      <td>0.675121</td>\n",
              "      <td>-1.324726e-03</td>\n",
              "      <td>0.402686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Bias Ratio * Binary Variable</th>\n",
              "      <td>-1.540833e+07</td>\n",
              "      <td>0.043446</td>\n",
              "      <td>-1.591977e-02</td>\n",
              "      <td>0.775406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Length</th>\n",
              "      <td>-5.695921e+01</td>\n",
              "      <td>0.671610</td>\n",
              "      <td>8.051945e-07</td>\n",
              "      <td>0.416444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Days Old</th>\n",
              "      <td>1.785687e+04</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>-5.219002e-05</td>\n",
              "      <td>0.097487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title GenBit Score</th>\n",
              "      <td>-3.295667e+06</td>\n",
              "      <td>0.384536</td>\n",
              "      <td>6.785952e-04</td>\n",
              "      <td>0.980586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title Avg Bias Ratio</th>\n",
              "      <td>1.394134e+06</td>\n",
              "      <td>0.601377</td>\n",
              "      <td>-8.683973e-03</td>\n",
              "      <td>0.658703</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f5b1f8b-8f78-42dc-a5f3-e9e16166b9b1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-7a809710-2f58-43f5-9a68-04da7f469309\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7a809710-2f58-43f5-9a68-04da7f469309')\"\n",
              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
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              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
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              "      </button>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-7a809710-2f58-43f5-9a68-04da7f469309 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
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              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f5b1f8b-8f78-42dc-a5f3-e9e16166b9b1 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f5b1f8b-8f78-42dc-a5f3-e9e16166b9b1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.sort_values(by='Co-Efficient for Views', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "yiI36WV1OLrL",
        "outputId": "7a6afdb2-887d-4488-949f-7d2190024123"
      },
      "execution_count": 165,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        Co-Efficient for Views  \\\n",
              "Variable                                                         \n",
              "% Female Words                                    1.084249e+08   \n",
              "Avg Bias Ratio                                    3.028465e+07   \n",
              "Genbit Score * Binary Variable                    1.731506e+07   \n",
              "Title Avg Bias Ratio                              1.394134e+06   \n",
              "% Male Words * Binary Variable                    8.783374e+05   \n",
              "Constant                                          7.491843e+05   \n",
              "Freq of Female Words                              1.064929e+05   \n",
              "Freq of Male Words * Binary Variable              8.995770e+04   \n",
              "Days Old                                          1.785687e+04   \n",
              "Length                                           -5.695921e+01   \n",
              "Freq of Female Words * Binary Variable           -5.164201e+04   \n",
              "Freq of Male Words                               -1.309756e+05   \n",
              "Title GenBit Score                               -3.295667e+06   \n",
              "% Male Words                                     -3.514074e+06   \n",
              "Avg Bias Ratio * Binary Variable                 -1.540833e+07   \n",
              "Genbit Score                                     -3.665044e+07   \n",
              "% Female Words * Binary Variable                 -5.467842e+07   \n",
              "\n",
              "                                        p-Value for Views  \\\n",
              "Variable                                                    \n",
              "% Female Words                                   0.000005   \n",
              "Avg Bias Ratio                                   0.027347   \n",
              "Genbit Score * Binary Variable                   0.046609   \n",
              "Title Avg Bias Ratio                             0.601377   \n",
              "% Male Words * Binary Variable                   0.864529   \n",
              "Constant                                         0.560416   \n",
              "Freq of Female Words                             0.493731   \n",
              "Freq of Male Words * Binary Variable             0.675121   \n",
              "Days Old                                         0.000047   \n",
              "Length                                           0.671610   \n",
              "Freq of Female Words * Binary Variable           0.571351   \n",
              "Freq of Male Words                               0.529397   \n",
              "Title GenBit Score                               0.384536   \n",
              "% Male Words                                     0.645147   \n",
              "Avg Bias Ratio * Binary Variable                 0.043446   \n",
              "Genbit Score                                     0.020865   \n",
              "% Female Words * Binary Variable                 0.000014   \n",
              "\n",
              "                                        Co-Efficient for Likes  \\\n",
              "Variable                                                         \n",
              "% Female Words                                   -4.240128e-02   \n",
              "Avg Bias Ratio                                    3.413540e-02   \n",
              "Genbit Score * Binary Variable                    4.555061e-03   \n",
              "Title Avg Bias Ratio                             -8.683973e-03   \n",
              "% Male Words * Binary Variable                    2.198217e-02   \n",
              "Constant                                          4.807605e-02   \n",
              "Freq of Female Words                              1.871549e-05   \n",
              "Freq of Male Words * Binary Variable             -1.324726e-03   \n",
              "Days Old                                         -5.219002e-05   \n",
              "Length                                            8.051945e-07   \n",
              "Freq of Female Words * Binary Variable            2.055099e-05   \n",
              "Freq of Male Words                                1.093334e-03   \n",
              "Title GenBit Score                                6.785952e-04   \n",
              "% Male Words                                     -3.911491e-02   \n",
              "Avg Bias Ratio * Binary Variable                 -1.591977e-02   \n",
              "Genbit Score                                     -2.410380e-02   \n",
              "% Female Words * Binary Variable                  4.030481e-02   \n",
              "\n",
              "                                        p-Value for Likes  \n",
              "Variable                                                   \n",
              "% Female Words                                   0.801143  \n",
              "Avg Bias Ratio                                   0.733414  \n",
              "Genbit Score * Binary Variable                   0.942939  \n",
              "Title Avg Bias Ratio                             0.658703  \n",
              "% Male Words * Binary Variable                   0.562481  \n",
              "Constant                                         0.000001  \n",
              "Freq of Female Words                             0.986965  \n",
              "Freq of Male Words * Binary Variable             0.402686  \n",
              "Days Old                                         0.097487  \n",
              "Length                                           0.416444  \n",
              "Freq of Female Words * Binary Variable           0.975596  \n",
              "Freq of Male Words                               0.476238  \n",
              "Title GenBit Score                               0.980586  \n",
              "% Male Words                                     0.486854  \n",
              "Avg Bias Ratio * Binary Variable                 0.775406  \n",
              "Genbit Score                                     0.834998  \n",
              "% Female Words * Binary Variable                 0.651849  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-f2f0e1b5-010d-471b-b15b-dba4a3bd5c5b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Co-Efficient for Views</th>\n",
              "      <th>p-Value for Views</th>\n",
              "      <th>Co-Efficient for Likes</th>\n",
              "      <th>p-Value for Likes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Variable</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>% Female Words</th>\n",
              "      <td>1.084249e+08</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-4.240128e-02</td>\n",
              "      <td>0.801143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Bias Ratio</th>\n",
              "      <td>3.028465e+07</td>\n",
              "      <td>0.027347</td>\n",
              "      <td>3.413540e-02</td>\n",
              "      <td>0.733414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score * Binary Variable</th>\n",
              "      <td>1.731506e+07</td>\n",
              "      <td>0.046609</td>\n",
              "      <td>4.555061e-03</td>\n",
              "      <td>0.942939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title Avg Bias Ratio</th>\n",
              "      <td>1.394134e+06</td>\n",
              "      <td>0.601377</td>\n",
              "      <td>-8.683973e-03</td>\n",
              "      <td>0.658703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words * Binary Variable</th>\n",
              "      <td>8.783374e+05</td>\n",
              "      <td>0.864529</td>\n",
              "      <td>2.198217e-02</td>\n",
              "      <td>0.562481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Constant</th>\n",
              "      <td>7.491843e+05</td>\n",
              "      <td>0.560416</td>\n",
              "      <td>4.807605e-02</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words</th>\n",
              "      <td>1.064929e+05</td>\n",
              "      <td>0.493731</td>\n",
              "      <td>1.871549e-05</td>\n",
              "      <td>0.986965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words * Binary Variable</th>\n",
              "      <td>8.995770e+04</td>\n",
              "      <td>0.675121</td>\n",
              "      <td>-1.324726e-03</td>\n",
              "      <td>0.402686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Days Old</th>\n",
              "      <td>1.785687e+04</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>-5.219002e-05</td>\n",
              "      <td>0.097487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Length</th>\n",
              "      <td>-5.695921e+01</td>\n",
              "      <td>0.671610</td>\n",
              "      <td>8.051945e-07</td>\n",
              "      <td>0.416444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words * Binary Variable</th>\n",
              "      <td>-5.164201e+04</td>\n",
              "      <td>0.571351</td>\n",
              "      <td>2.055099e-05</td>\n",
              "      <td>0.975596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words</th>\n",
              "      <td>-1.309756e+05</td>\n",
              "      <td>0.529397</td>\n",
              "      <td>1.093334e-03</td>\n",
              "      <td>0.476238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title GenBit Score</th>\n",
              "      <td>-3.295667e+06</td>\n",
              "      <td>0.384536</td>\n",
              "      <td>6.785952e-04</td>\n",
              "      <td>0.980586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words</th>\n",
              "      <td>-3.514074e+06</td>\n",
              "      <td>0.645147</td>\n",
              "      <td>-3.911491e-02</td>\n",
              "      <td>0.486854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Bias Ratio * Binary Variable</th>\n",
              "      <td>-1.540833e+07</td>\n",
              "      <td>0.043446</td>\n",
              "      <td>-1.591977e-02</td>\n",
              "      <td>0.775406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score</th>\n",
              "      <td>-3.665044e+07</td>\n",
              "      <td>0.020865</td>\n",
              "      <td>-2.410380e-02</td>\n",
              "      <td>0.834998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Female Words * Binary Variable</th>\n",
              "      <td>-5.467842e+07</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>4.030481e-02</td>\n",
              "      <td>0.651849</td>\n",
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              "  </tbody>\n",
              "</table>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f2f0e1b5-010d-471b-b15b-dba4a3bd5c5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.sort_values(by='Co-Efficient for Likes', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 613
        },
        "id": "PG-cnEFmOtkF",
        "outputId": "d7cad551-6d0d-4637-eeaa-b7f4d643c37d"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        Co-Efficient for Views  \\\n",
              "Variable                                                         \n",
              "Constant                                          7.491843e+05   \n",
              "% Female Words * Binary Variable                 -5.467842e+07   \n",
              "Avg Bias Ratio                                    3.028465e+07   \n",
              "% Male Words * Binary Variable                    8.783374e+05   \n",
              "Genbit Score * Binary Variable                    1.731506e+07   \n",
              "Freq of Male Words                               -1.309756e+05   \n",
              "Title GenBit Score                               -3.295667e+06   \n",
              "Freq of Female Words * Binary Variable           -5.164201e+04   \n",
              "Freq of Female Words                              1.064929e+05   \n",
              "Length                                           -5.695921e+01   \n",
              "Days Old                                          1.785687e+04   \n",
              "Freq of Male Words * Binary Variable              8.995770e+04   \n",
              "Title Avg Bias Ratio                              1.394134e+06   \n",
              "Avg Bias Ratio * Binary Variable                 -1.540833e+07   \n",
              "Genbit Score                                     -3.665044e+07   \n",
              "% Male Words                                     -3.514074e+06   \n",
              "% Female Words                                    1.084249e+08   \n",
              "\n",
              "                                        p-Value for Views  \\\n",
              "Variable                                                    \n",
              "Constant                                         0.560416   \n",
              "% Female Words * Binary Variable                 0.000014   \n",
              "Avg Bias Ratio                                   0.027347   \n",
              "% Male Words * Binary Variable                   0.864529   \n",
              "Genbit Score * Binary Variable                   0.046609   \n",
              "Freq of Male Words                               0.529397   \n",
              "Title GenBit Score                               0.384536   \n",
              "Freq of Female Words * Binary Variable           0.571351   \n",
              "Freq of Female Words                             0.493731   \n",
              "Length                                           0.671610   \n",
              "Days Old                                         0.000047   \n",
              "Freq of Male Words * Binary Variable             0.675121   \n",
              "Title Avg Bias Ratio                             0.601377   \n",
              "Avg Bias Ratio * Binary Variable                 0.043446   \n",
              "Genbit Score                                     0.020865   \n",
              "% Male Words                                     0.645147   \n",
              "% Female Words                                   0.000005   \n",
              "\n",
              "                                        Co-Efficient for Likes  \\\n",
              "Variable                                                         \n",
              "Constant                                          4.807605e-02   \n",
              "% Female Words * Binary Variable                  4.030481e-02   \n",
              "Avg Bias Ratio                                    3.413540e-02   \n",
              "% Male Words * Binary Variable                    2.198217e-02   \n",
              "Genbit Score * Binary Variable                    4.555061e-03   \n",
              "Freq of Male Words                                1.093334e-03   \n",
              "Title GenBit Score                                6.785952e-04   \n",
              "Freq of Female Words * Binary Variable            2.055099e-05   \n",
              "Freq of Female Words                              1.871549e-05   \n",
              "Length                                            8.051945e-07   \n",
              "Days Old                                         -5.219002e-05   \n",
              "Freq of Male Words * Binary Variable             -1.324726e-03   \n",
              "Title Avg Bias Ratio                             -8.683973e-03   \n",
              "Avg Bias Ratio * Binary Variable                 -1.591977e-02   \n",
              "Genbit Score                                     -2.410380e-02   \n",
              "% Male Words                                     -3.911491e-02   \n",
              "% Female Words                                   -4.240128e-02   \n",
              "\n",
              "                                        p-Value for Likes  \n",
              "Variable                                                   \n",
              "Constant                                         0.000001  \n",
              "% Female Words * Binary Variable                 0.651849  \n",
              "Avg Bias Ratio                                   0.733414  \n",
              "% Male Words * Binary Variable                   0.562481  \n",
              "Genbit Score * Binary Variable                   0.942939  \n",
              "Freq of Male Words                               0.476238  \n",
              "Title GenBit Score                               0.980586  \n",
              "Freq of Female Words * Binary Variable           0.975596  \n",
              "Freq of Female Words                             0.986965  \n",
              "Length                                           0.416444  \n",
              "Days Old                                         0.097487  \n",
              "Freq of Male Words * Binary Variable             0.402686  \n",
              "Title Avg Bias Ratio                             0.658703  \n",
              "Avg Bias Ratio * Binary Variable                 0.775406  \n",
              "Genbit Score                                     0.834998  \n",
              "% Male Words                                     0.486854  \n",
              "% Female Words                                   0.801143  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Co-Efficient for Views</th>\n",
              "      <th>p-Value for Views</th>\n",
              "      <th>Co-Efficient for Likes</th>\n",
              "      <th>p-Value for Likes</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Variable</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Constant</th>\n",
              "      <td>7.491843e+05</td>\n",
              "      <td>0.560416</td>\n",
              "      <td>4.807605e-02</td>\n",
              "      <td>0.000001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Female Words * Binary Variable</th>\n",
              "      <td>-5.467842e+07</td>\n",
              "      <td>0.000014</td>\n",
              "      <td>4.030481e-02</td>\n",
              "      <td>0.651849</td>\n",
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              "      <th>Avg Bias Ratio</th>\n",
              "      <td>3.028465e+07</td>\n",
              "      <td>0.027347</td>\n",
              "      <td>3.413540e-02</td>\n",
              "      <td>0.733414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words * Binary Variable</th>\n",
              "      <td>8.783374e+05</td>\n",
              "      <td>0.864529</td>\n",
              "      <td>2.198217e-02</td>\n",
              "      <td>0.562481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score * Binary Variable</th>\n",
              "      <td>1.731506e+07</td>\n",
              "      <td>0.046609</td>\n",
              "      <td>4.555061e-03</td>\n",
              "      <td>0.942939</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words</th>\n",
              "      <td>-1.309756e+05</td>\n",
              "      <td>0.529397</td>\n",
              "      <td>1.093334e-03</td>\n",
              "      <td>0.476238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title GenBit Score</th>\n",
              "      <td>-3.295667e+06</td>\n",
              "      <td>0.384536</td>\n",
              "      <td>6.785952e-04</td>\n",
              "      <td>0.980586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words * Binary Variable</th>\n",
              "      <td>-5.164201e+04</td>\n",
              "      <td>0.571351</td>\n",
              "      <td>2.055099e-05</td>\n",
              "      <td>0.975596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Female Words</th>\n",
              "      <td>1.064929e+05</td>\n",
              "      <td>0.493731</td>\n",
              "      <td>1.871549e-05</td>\n",
              "      <td>0.986965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Length</th>\n",
              "      <td>-5.695921e+01</td>\n",
              "      <td>0.671610</td>\n",
              "      <td>8.051945e-07</td>\n",
              "      <td>0.416444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Days Old</th>\n",
              "      <td>1.785687e+04</td>\n",
              "      <td>0.000047</td>\n",
              "      <td>-5.219002e-05</td>\n",
              "      <td>0.097487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Freq of Male Words * Binary Variable</th>\n",
              "      <td>8.995770e+04</td>\n",
              "      <td>0.675121</td>\n",
              "      <td>-1.324726e-03</td>\n",
              "      <td>0.402686</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Title Avg Bias Ratio</th>\n",
              "      <td>1.394134e+06</td>\n",
              "      <td>0.601377</td>\n",
              "      <td>-8.683973e-03</td>\n",
              "      <td>0.658703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Avg Bias Ratio * Binary Variable</th>\n",
              "      <td>-1.540833e+07</td>\n",
              "      <td>0.043446</td>\n",
              "      <td>-1.591977e-02</td>\n",
              "      <td>0.775406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genbit Score</th>\n",
              "      <td>-3.665044e+07</td>\n",
              "      <td>0.020865</td>\n",
              "      <td>-2.410380e-02</td>\n",
              "      <td>0.834998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Male Words</th>\n",
              "      <td>-3.514074e+06</td>\n",
              "      <td>0.645147</td>\n",
              "      <td>-3.911491e-02</td>\n",
              "      <td>0.486854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>% Female Words</th>\n",
              "      <td>1.084249e+08</td>\n",
              "      <td>0.000005</td>\n",
              "      <td>-4.240128e-02</td>\n",
              "      <td>0.801143</td>\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-5ae127d3-efb0-408b-bc9d-41c682386ed4 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5ae127d3-efb0-408b-bc9d-41c682386ed4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "results.to_excel(\"/content/Internship/RegressionResults1.xlsx\")"
      ],
      "metadata": {
        "id": "lQmJ6ZfnAYPF"
      },
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results.to_csv(\"/content/Internship/RegressionResults1.csv\")"
      ],
      "metadata": {
        "id": "SsN4AURwBCcz"
      },
      "execution_count": 168,
      "outputs": []
    }
  ]
}