Demo.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "blIHB4avUkBO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "b7eb1c46-9d4f-440f-b31f-cdcc72063ecb"
      },
      "source": [
        "from IPython.display import clear_output\n",
        "!git clone https://github.com/jroakes/querycat.git\n",
        "!pip install -r querycat/requirements_colab.txt\n",
        "\n",
        "clear_output()\n",
        "print('All loaded.')\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "All loaded.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYtdNvkmUkBm",
        "colab_type": "text"
      },
      "source": [
        "## Import QueryCat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z7pfn8lIUkBu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 181
        },
        "outputId": "de32bff5-9f2e-4189-e33c-0daa678e8b5b"
      },
      "source": [
        "import querycat"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/wordnet.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "htHSjMUbUkB2",
        "colab_type": "text"
      },
      "source": [
        "## Load Datset of comparative data from Google Search Console"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yLk5m5y2UkB6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 764
        },
        "outputId": "8ee50df9-a136-45c5-ec48-043128c63793"
      },
      "source": [
        "df = querycat.pd.read_csv('querycat/data/loco_compare.csv')\n",
        "df.head(20)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Query</th>\n",
              "      <th>Last 28 days Clicks</th>\n",
              "      <th>Previous 28 days Clicks</th>\n",
              "      <th>Last 28 days Impressions</th>\n",
              "      <th>Previous 28 days Impressions</th>\n",
              "      <th>Last 28 days CTR</th>\n",
              "      <th>Previous 28 days CTR</th>\n",
              "      <th>Last 28 days Position</th>\n",
              "      <th>Previous 28 days Position</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>google analytics export more than 5000 rows</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "      <td>48</td>\n",
              "      <td>29</td>\n",
              "      <td>62.50%</td>\n",
              "      <td>68.97%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adapt partners</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>55</td>\n",
              "      <td>51</td>\n",
              "      <td>50.91%</td>\n",
              "      <td>39.22%</td>\n",
              "      <td>1.22</td>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>export more than 5000 rows google analytics</td>\n",
              "      <td>27</td>\n",
              "      <td>11</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>55.10%</td>\n",
              "      <td>55%</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>locomotive agency</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>383</td>\n",
              "      <td>130</td>\n",
              "      <td>6.27%</td>\n",
              "      <td>3.08%</td>\n",
              "      <td>3.80</td>\n",
              "      <td>5.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>locomotive agency apex</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>6</td>\n",
              "      <td>60%</td>\n",
              "      <td>0%</td>\n",
              "      <td>4.17</td>\n",
              "      <td>5.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>locomotive agency apex nc</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>35</td>\n",
              "      <td>22</td>\n",
              "      <td>57.14%</td>\n",
              "      <td>45.45%</td>\n",
              "      <td>3.57</td>\n",
              "      <td>4.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>locomotive seo</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>80%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>google analytics export all rows</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>39</td>\n",
              "      <td>25</td>\n",
              "      <td>33.33%</td>\n",
              "      <td>48%</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>google analytics export more than 5000</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>134</td>\n",
              "      <td>139</td>\n",
              "      <td>8.96%</td>\n",
              "      <td>10.79%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>how to export more than 5000 rows in google an...</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>27.27%</td>\n",
              "      <td>33.33%</td>\n",
              "      <td>2.12</td>\n",
              "      <td>2.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>google analytics export all data</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>139</td>\n",
              "      <td>123</td>\n",
              "      <td>4.32%</td>\n",
              "      <td>2.44%</td>\n",
              "      <td>7.69</td>\n",
              "      <td>8.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>seo agency</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>13518</td>\n",
              "      <td>6647</td>\n",
              "      <td>0.04%</td>\n",
              "      <td>0.02%</td>\n",
              "      <td>67.94</td>\n",
              "      <td>65.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>technical seo agency</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>711</td>\n",
              "      <td>791</td>\n",
              "      <td>0.56%</td>\n",
              "      <td>0%</td>\n",
              "      <td>50.44</td>\n",
              "      <td>54.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>adapt seo</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>21.43%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>how do i export more than 5000 rows in google ...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>42.86%</td>\n",
              "      <td>0%</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>export from google analytics</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>12.50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8.94</td>\n",
              "      <td>9.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>export more than 5000 rows in google analytics</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>how to extract data from google analytics usin...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>23.75</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>best seo agency</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1111</td>\n",
              "      <td>627</td>\n",
              "      <td>0.09%</td>\n",
              "      <td>0%</td>\n",
              "      <td>79.66</td>\n",
              "      <td>83.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>export google analytics data</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>413</td>\n",
              "      <td>365</td>\n",
              "      <td>0.24%</td>\n",
              "      <td>0.27%</td>\n",
              "      <td>11.36</td>\n",
              "      <td>12.53</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Query  ...  Previous 28 days Position\n",
              "0         google analytics export more than 5000 rows  ...                       1.00\n",
              "1                                      adapt partners  ...                       1.22\n",
              "2         export more than 5000 rows google analytics  ...                       1.00\n",
              "3                                   locomotive agency  ...                       5.52\n",
              "4                              locomotive agency apex  ...                       5.00\n",
              "5                           locomotive agency apex nc  ...                       4.05\n",
              "6                                      locomotive seo  ...                       0.00\n",
              "7                    google analytics export all rows  ...                       2.40\n",
              "8              google analytics export more than 5000  ...                       1.00\n",
              "9   how to export more than 5000 rows in google an...  ...                       2.20\n",
              "10                   google analytics export all data  ...                       8.23\n",
              "11                                         seo agency  ...                      65.99\n",
              "12                               technical seo agency  ...                      54.91\n",
              "13                                          adapt seo  ...                       1.78\n",
              "14  how do i export more than 5000 rows in google ...  ...                       0.00\n",
              "15                       export from google analytics  ...                       9.75\n",
              "16     export more than 5000 rows in google analytics  ...                       0.00\n",
              "17  how to extract data from google analytics usin...  ...                       0.00\n",
              "18                                    best seo agency  ...                      83.02\n",
              "19                       export google analytics data  ...                      12.53\n",
              "\n",
              "[20 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "olh1wXFaUkCG",
        "colab_type": "text"
      },
      "source": [
        "##  Categorize the queries\n",
        "\n",
        "The `querycat.Categorize` function, takes the following arguments.\n",
        "\n",
        "* **Position 1**: A Pandas Dataframe that includes at least a column of search queries.\n",
        "* **col**: The name of the query column from the DataFrame\n",
        "* **alg**: Either `apriori` (Apriori Algorithm), or `fpgrowth` (FP Growth Algorithm)\n",
        "* **min_support**: The number of times the subset terms should be found in the dataset to be included.\n",
        "* **min_probablity**: FP Growth only. Finds patterns of associated terms above this minimum probability 0-1 (float)\n",
        "* **min_lift**: Apriori only. Finds patterns of associated terms. <1 less probable, >1 more probable  (float)\n",
        "\n",
        "After it runs, it will print the number of categories found, as well as the frequencies of queries in each category."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JsBok9aBUkCJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 830
        },
        "outputId": "d5b7d0dd-44e8-48e2-806e-22a3424f5854"
      },
      "source": [
        "catz = querycat.Categorize(df, 'Query', min_support=10,  alg='apriori')\n",
        "catz.counts.head(20)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Converting to transactions.\n",
            "Normalizing the keyword corpus.\n",
            "Total queries: 1364\n",
            "Total unique queries: 987\n",
            "Total transactions: 986\n",
            "Running Apriori\n",
            "Making Categories\n",
            "Total Categories: 101\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>seo</th>\n",
              "      <td>252</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>##other##</th>\n",
              "      <td>63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seo technical</th>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google business</th>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google analytics</th>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google analytics data</th>\n",
              "      <td>43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google analytics export</th>\n",
              "      <td>42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>agency</th>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>google search</th>\n",
              "      <td>35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>website</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seo local</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>link building</th>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>api analytics google</th>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>report google analytics</th>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seo service</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tool</th>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seo software</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>post</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>site</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                         category\n",
              "seo                           252\n",
              "##other##                      63\n",
              "seo technical                  52\n",
              "google business                52\n",
              "google analytics               48\n",
              "google analytics data          43\n",
              "google analytics export        42\n",
              "google                         36\n",
              "agency                         36\n",
              "google search                  35\n",
              "website                        32\n",
              "seo local                      31\n",
              "link building                  30\n",
              "api analytics google           25\n",
              "report google analytics        24\n",
              "seo service                    22\n",
              "tool                           21\n",
              "seo software                   19\n",
              "post                           19\n",
              "site                           18"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1cLOFkcQUkCS",
        "colab_type": "text"
      },
      "source": [
        "## Show original data\n",
        "Below we print the original DataFrame with categories added.\n",
        "\n",
        "This can be saved to CSV using `catz.df.to_csv('filename.csv', index=False)` to save and use in Excel."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tUpKqduCUkCV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 885
        },
        "outputId": "ac1d2484-22f2-4a2f-b5cb-a8214f24c6c2"
      },
      "source": [
        "catz.df.head(20)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Query</th>\n",
              "      <th>Last 28 days Clicks</th>\n",
              "      <th>Previous 28 days Clicks</th>\n",
              "      <th>Last 28 days Impressions</th>\n",
              "      <th>Previous 28 days Impressions</th>\n",
              "      <th>Last 28 days CTR</th>\n",
              "      <th>Previous 28 days CTR</th>\n",
              "      <th>Last 28 days Position</th>\n",
              "      <th>Previous 28 days Position</th>\n",
              "      <th>match_queries</th>\n",
              "      <th>category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>google analytics export more than 5000 rows</td>\n",
              "      <td>30</td>\n",
              "      <td>20</td>\n",
              "      <td>48</td>\n",
              "      <td>29</td>\n",
              "      <td>62.50%</td>\n",
              "      <td>68.97%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>adapt partners</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>55</td>\n",
              "      <td>51</td>\n",
              "      <td>50.91%</td>\n",
              "      <td>39.22%</td>\n",
              "      <td>1.22</td>\n",
              "      <td>1.22</td>\n",
              "      <td>adapt partner</td>\n",
              "      <td>partner</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>export more than 5000 rows google analytics</td>\n",
              "      <td>27</td>\n",
              "      <td>11</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>55.10%</td>\n",
              "      <td>55%</td>\n",
              "      <td>1.02</td>\n",
              "      <td>1.00</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>locomotive agency</td>\n",
              "      <td>24</td>\n",
              "      <td>4</td>\n",
              "      <td>383</td>\n",
              "      <td>130</td>\n",
              "      <td>6.27%</td>\n",
              "      <td>3.08%</td>\n",
              "      <td>3.80</td>\n",
              "      <td>5.52</td>\n",
              "      <td>agency locomotive</td>\n",
              "      <td>locomotive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>locomotive agency apex</td>\n",
              "      <td>21</td>\n",
              "      <td>0</td>\n",
              "      <td>35</td>\n",
              "      <td>6</td>\n",
              "      <td>60%</td>\n",
              "      <td>0%</td>\n",
              "      <td>4.17</td>\n",
              "      <td>5.00</td>\n",
              "      <td>agency apex locomotive</td>\n",
              "      <td>locomotive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>locomotive agency apex nc</td>\n",
              "      <td>20</td>\n",
              "      <td>10</td>\n",
              "      <td>35</td>\n",
              "      <td>22</td>\n",
              "      <td>57.14%</td>\n",
              "      <td>45.45%</td>\n",
              "      <td>3.57</td>\n",
              "      <td>4.05</td>\n",
              "      <td>agency apex locomotive nc</td>\n",
              "      <td>locomotive</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>locomotive seo</td>\n",
              "      <td>16</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>80%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>locomotive seo</td>\n",
              "      <td>seo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>google analytics export all rows</td>\n",
              "      <td>13</td>\n",
              "      <td>12</td>\n",
              "      <td>39</td>\n",
              "      <td>25</td>\n",
              "      <td>33.33%</td>\n",
              "      <td>48%</td>\n",
              "      <td>2.36</td>\n",
              "      <td>2.40</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>google analytics export more than 5000</td>\n",
              "      <td>12</td>\n",
              "      <td>15</td>\n",
              "      <td>134</td>\n",
              "      <td>139</td>\n",
              "      <td>8.96%</td>\n",
              "      <td>10.79%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.00</td>\n",
              "      <td>analytics export google</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>how to export more than 5000 rows in google an...</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>33</td>\n",
              "      <td>30</td>\n",
              "      <td>27.27%</td>\n",
              "      <td>33.33%</td>\n",
              "      <td>2.12</td>\n",
              "      <td>2.20</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>google analytics export all data</td>\n",
              "      <td>6</td>\n",
              "      <td>3</td>\n",
              "      <td>139</td>\n",
              "      <td>123</td>\n",
              "      <td>4.32%</td>\n",
              "      <td>2.44%</td>\n",
              "      <td>7.69</td>\n",
              "      <td>8.23</td>\n",
              "      <td>analytics data export google</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>seo agency</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>13518</td>\n",
              "      <td>6647</td>\n",
              "      <td>0.04%</td>\n",
              "      <td>0.02%</td>\n",
              "      <td>67.94</td>\n",
              "      <td>65.99</td>\n",
              "      <td>agency seo</td>\n",
              "      <td>seo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>technical seo agency</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>711</td>\n",
              "      <td>791</td>\n",
              "      <td>0.56%</td>\n",
              "      <td>0%</td>\n",
              "      <td>50.44</td>\n",
              "      <td>54.91</td>\n",
              "      <td>agency seo technical</td>\n",
              "      <td>seo technical</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>adapt seo</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>14</td>\n",
              "      <td>9</td>\n",
              "      <td>21.43%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.79</td>\n",
              "      <td>1.78</td>\n",
              "      <td>adapt seo</td>\n",
              "      <td>seo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>how do i export more than 5000 rows in google ...</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>42.86%</td>\n",
              "      <td>0%</td>\n",
              "      <td>2.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>export from google analytics</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>16</td>\n",
              "      <td>8</td>\n",
              "      <td>12.50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>8.94</td>\n",
              "      <td>9.75</td>\n",
              "      <td>analytics export google</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>export more than 5000 rows in google analytics</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>1.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>analytics export google row</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>how to extract data from google analytics usin...</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>50%</td>\n",
              "      <td>0%</td>\n",
              "      <td>23.75</td>\n",
              "      <td>0.00</td>\n",
              "      <td>analytics data extract google python use</td>\n",
              "      <td>python google analytics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>best seo agency</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1111</td>\n",
              "      <td>627</td>\n",
              "      <td>0.09%</td>\n",
              "      <td>0%</td>\n",
              "      <td>79.66</td>\n",
              "      <td>83.02</td>\n",
              "      <td>agency best seo</td>\n",
              "      <td>seo best</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>export google analytics data</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>413</td>\n",
              "      <td>365</td>\n",
              "      <td>0.24%</td>\n",
              "      <td>0.27%</td>\n",
              "      <td>11.36</td>\n",
              "      <td>12.53</td>\n",
              "      <td>analytics data export google</td>\n",
              "      <td>google analytics export</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                Query  ...                 category\n",
              "0         google analytics export more than 5000 rows  ...  google analytics export\n",
              "1                                      adapt partners  ...                  partner\n",
              "2         export more than 5000 rows google analytics  ...  google analytics export\n",
              "3                                   locomotive agency  ...               locomotive\n",
              "4                              locomotive agency apex  ...               locomotive\n",
              "5                           locomotive agency apex nc  ...               locomotive\n",
              "6                                      locomotive seo  ...                      seo\n",
              "7                    google analytics export all rows  ...  google analytics export\n",
              "8              google analytics export more than 5000  ...  google analytics export\n",
              "9   how to export more than 5000 rows in google an...  ...  google analytics export\n",
              "10                   google analytics export all data  ...  google analytics export\n",
              "11                                         seo agency  ...                      seo\n",
              "12                               technical seo agency  ...            seo technical\n",
              "13                                          adapt seo  ...                      seo\n",
              "14  how do i export more than 5000 rows in google ...  ...  google analytics export\n",
              "15                       export from google analytics  ...  google analytics export\n",
              "16     export more than 5000 rows in google analytics  ...  google analytics export\n",
              "17  how to extract data from google analytics usin...  ...  python google analytics\n",
              "18                                    best seo agency  ...                 seo best\n",
              "19                       export google analytics data  ...  google analytics export\n",
              "\n",
              "[20 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4O4lkCcUkCc",
        "colab_type": "text"
      },
      "source": [
        "## Make use of original data\n",
        "\n",
        "This cell will aggregate data from your original CSV to produce an updated DataFrame that has current and previous period data consolidated by category.  We also include a `diff` column that shows the period change."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sRddqx2wUkCg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "afcdc0b6-9ec6-44a4-eb67-64b88c35884d"
      },
      "source": [
        "df = catz.df\n",
        "df.head()\n",
        "\n",
        "# Goup by category and aggregate the other metrics\n",
        "df = df.groupby(['category'], as_index=False).agg({'Query':'count', 'Last 28 days Clicks':'sum', 'Previous 28 days Clicks': 'sum' })\n",
        "\n",
        "# Create a diff column\n",
        "df['Diff'] = df['Last 28 days Clicks'] - df['Previous 28 days Clicks']\n",
        "\n",
        "# Rename the columns\n",
        "df.columns = ['category', 'count of queries', 'current', 'prior', 'diff']\n",
        "\n",
        "# Sort by the `diff` columnn\n",
        "df.sort_values(by='diff', ascending=False, inplace=True)\n",
        "\n",
        "df.head(10)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>count of queries</th>\n",
              "      <th>current</th>\n",
              "      <th>prior</th>\n",
              "      <th>diff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>locomotive</td>\n",
              "      <td>16</td>\n",
              "      <td>65</td>\n",
              "      <td>14</td>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>google analytics export</td>\n",
              "      <td>42</td>\n",
              "      <td>112</td>\n",
              "      <td>76</td>\n",
              "      <td>36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>seo</td>\n",
              "      <td>252</td>\n",
              "      <td>25</td>\n",
              "      <td>1</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>partner</td>\n",
              "      <td>6</td>\n",
              "      <td>28</td>\n",
              "      <td>20</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>seo technical</td>\n",
              "      <td>52</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>python google analytics</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>console google search</td>\n",
              "      <td>17</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>content</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>link building</td>\n",
              "      <td>30</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>72</th>\n",
              "      <td>seo best</td>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   category  count of queries  current  prior  diff\n",
              "52               locomotive                16       65     14    51\n",
              "41  google analytics export                42      112     76    36\n",
              "70                      seo               252       25      1    24\n",
              "57                  partner                 6       28     20     8\n",
              "79            seo technical                52        5      0     5\n",
              "62  python google analytics                16        2      0     2\n",
              "24    console google search                17        2      0     2\n",
              "26                  content                 3        1      0     1\n",
              "49            link building                30        1      0     1\n",
              "72                 seo best                 8        1      0     1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWpHdAWXUkCo",
        "colab_type": "text"
      },
      "source": [
        "## BERT\n",
        "\n",
        "By default, we use the `distilbert-base-uncased` model from the [Huggingface Transformers repo](https://github.com/huggingface/transformers).  \n",
        "This can be edited in the `querycat/config.py` file.\n",
        "\n",
        "The BERTSim class has a few functions that are useful:\n",
        "\n",
        "* **read_df**: Reads a dataframe where the `term_col` and `diff_col` are specified as named parameters.\n",
        "* **get_similar_df**: After running `read_df`, you can provide a term as a parameter to get the top matching categories, by cosine similarity.\n",
        "* **diff_plot**: After running `read_df`, plots the output in 2d vector space, using either `umap`, `pca`, or `tsne` as vector reduction strategies. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-6RdxTXwUkCq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171,
          "referenced_widgets": [
            "a964826733f047159623dde558eb65d9",
            "2db97e8d992b485498d393595749087a",
            "14ebe8e65ff645b09dfdedfa7a3966e8",
            "24475f2dc4bb4c2e89fdacaf4f44bf77",
            "e4d44a5da92a4c57ad2b2f2e55b8f27b",
            "2905aa58fc6f4232988f708d4461b5b6",
            "6f43103bed90452cb90905b766925b44",
            "3d1dcb776c1940cbbcf25f9c1954a839",
            "5dc4e3e3d0724cccb639fcde750bf2ec",
            "3addbe5cb75c4c9db1e53e1fad4307ee",
            "cb5a93fe9f7245cc9f0a36d600be792c",
            "7a45944cc33649b1ab71cd62053a34ba",
            "a7bc4dbce54a4a7f99dc9d70f1c602b3",
            "a9e8de5ac5784d58af5245a39a32c89a",
            "b60c5b0536d041bdb54c3df6a43072ee",
            "47c23bee26c948058a788e2333cab189",
            "13b76c05432246b0a1f99c5dd2134085",
            "7449b7e6ab0d4f9ea4381b5a9d4394be",
            "acc0a61abb7a4f128a3f90d9ff813074",
            "6ce3e66e953448e79ecdddaabdc5a56c",
            "04eb739fcc0e44278f2c643b7d1596e7",
            "0d3d238c451248748c9c247462f0d95f",
            "8dcfe4d18e0d4d28a5c0179dedf93c9d",
            "6b83abc693814155b0c7ea7bdb51b8c0"
          ]
        },
        "outputId": "16601f70-459b-4d45-a931-6ec508516bb7"
      },
      "source": [
        "bsim = querycat.BERTSim()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a964826733f047159623dde558eb65d9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=546, style=ProgressStyle(description_width=…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5dc4e3e3d0724cccb639fcde750bf2ec",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=267967963, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "13b76c05432246b0a1f99c5dd2134085",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(IntProgress(value=0, description='Downloading', max=231508, style=ProgressStyle(description_wid…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sr0Z6XdEUkCy",
        "colab_type": "text"
      },
      "source": [
        "### Read DataFrame into Class"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MouR3jiNUkCz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bsim.read_df(df, term_col = 'category', diff_col = 'diff')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A0bPfh-YUkC6",
        "colab_type": "text"
      },
      "source": [
        "### Print similar categories to a given term."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_fDu2TXGUkC8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "d875fd20-6ddd-462b-e811-7593ad43ec91"
      },
      "source": [
        "df_sim = bsim.get_similar_df('train')\n",
        "df_sim.sort_values(by='sim', ascending=False, inplace=True)\n",
        "df_sim.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>terms</th>\n",
              "      <th>sim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>locomotive</td>\n",
              "      <td>0.846860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>analytics data</td>\n",
              "      <td>0.693261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>google data</td>\n",
              "      <td>0.678765</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>google analytics data</td>\n",
              "      <td>0.652805</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>post</td>\n",
              "      <td>0.649012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    terms       sim\n",
              "0              locomotive  0.846860\n",
              "58         analytics data  0.693261\n",
              "65            google data  0.678765\n",
              "76  google analytics data  0.652805\n",
              "11                   post  0.649012"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JiER4oqJUkDH",
        "colab_type": "text"
      },
      "source": [
        "### Plot Categories\n",
        "\n",
        "This cell plots the categories in 2D space, with reduction from BERT summed 768 dimension vectors to 2 dimension, based on the given reduction strategy.  Green is increase PoP, red is descrease.  Size is the magnitude of the change.\n",
        "\n",
        "Reduction strategies available are `umap`, `tsne`, and `pca`\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hDRU7DpqUkDJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 918
        },
        "outputId": "a2099a07-384d-4e8e-d357-5cd5723f9362"
      },
      "source": [
        "bsim.diff_plot(reduction='umap')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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/n02bNvH1118zZswYzGYze/furXCeiIiI3HyUgBUREbkKZiXNoqS0xNlhXFdKSkuYlaRT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\nOvIWAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1728x1152 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}