Code/yearn_finance.ipynb

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      },
      "source": [
        "# Import Data"
      ]
    },
    {
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      "execution_count": 1,
      "metadata": {
        "id": "tg_UBjQwM7to"
      },
      "outputs": [],
      "source": [
        "import numpy as np"
      ]
    },
    {
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      "execution_count": 2,
      "metadata": {
        "id": "qpIu57kD49p4"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
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    {
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      "execution_count": 3,
      "metadata": {
        "id": "YXqrClC3I3zl"
      },
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      "source": [
        "# Load the CSV file into a DataFrame with the correct encoding\n",
        "yfi = pd.read_csv('/content/Yearn Talk - general - 🗳governance [734805853768777738].csv', encoding='ISO-8859-1')\n",
        "\n",
        "# Assume yfi is the DataFrame you want to remove unnamed columns from\n",
        "yfi = yfi.loc[:, ~yfi.columns.str.startswith('Unnamed:')]"
      ]
    },
    {
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              "       AuthorID        Author          Date_original        Date  \\\n",
              "0  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "1  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "2  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "3  6.466630e+17  Cr3ateD#0001  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "4  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "\n",
              "                                             Content Attachments Reactions  \n",
              "0  https://gov.yearn.finance/t/proposal-0-yfi-sup...         NaN       NaN  \n",
              "1  https://gov.yearn.finance/t/proposal-1-yfi-fee...         NaN       NaN  \n",
              "2  https://gov.yearn.finance/t/proposal-5-reducin...         NaN       NaN  \n",
              "3                      should we, um. fund an audit?         NaN  ð¯ (2)  \n",
              "4                              i would vote for that         NaN       NaN  "
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          "metadata": {},
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      "source": [
        "yfi.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yfi.tail(25)"
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              "           AuthorID           Author          Date_original       Date  \\\n",
              "13598  1.125350e+17             j_._    2023/7/5 ä¸å4:24  2023/7/5    \n",
              "13599  1.125350e+17             j_._   2023/7/19 ä¸å3:56  2023/7/19   \n",
              "13600  8.341010e+17         liptucom    2023/8/2 ä¸å4:16  2023/8/2    \n",
              "13601  3.979630e+17  blockenthusiast   2023/8/16 ä¸å2:31  2023/8/16   \n",
              "13602  3.979630e+17  blockenthusiast   2023/8/16 ä¸å2:48  2023/8/16   \n",
              "13603  6.910390e+17    _barrypooter_   2023/8/16 ä¸å2:54  2023/8/16   \n",
              "13604  1.125350e+17             j_._  2023/8/25 ä¸å10:54  2023/8/25   \n",
              "13605  1.125350e+17             j_._  2023/8/25 ä¸å10:55  2023/8/25   \n",
              "13606  1.125350e+17             j_._  2023/8/25 ä¸å10:56  2023/8/25   \n",
              "13607  1.125350e+17             j_._  2023/8/25 ä¸å10:58  2023/8/25   \n",
              "13608  3.991790e+17        defiglenn   2023/8/25 ä¸å4:53  2023/8/25   \n",
              "13609  3.991790e+17        defiglenn   2023/8/25 ä¸å4:53  2023/8/25   \n",
              "13610  6.910390e+17    _barrypooter_   2023/8/26 ä¸å4:25  2023/8/26   \n",
              "13611  6.910390e+17    _barrypooter_   2023/8/26 ä¸å4:25  2023/8/26   \n",
              "13612  6.910390e+17    _barrypooter_   2023/8/26 ä¸å4:41  2023/8/26   \n",
              "13613  6.910390e+17    _barrypooter_   2023/8/26 ä¸å4:41  2023/8/26   \n",
              "13614  6.910390e+17    _barrypooter_   2023/8/29 ä¸å4:05  2023/8/29   \n",
              "13615  6.910390e+17    _barrypooter_   2023/8/29 ä¸å5:02  2023/8/29   \n",
              "13616  4.148710e+17       fav_truffe    2023/9/8 ä¸å8:48  2023/9/8    \n",
              "13617  3.573170e+17         .skyfoxx  2023/9/11 ä¸å11:23  2023/9/11   \n",
              "13618  1.125350e+17             j_._  2023/9/12 ä¸å12:42  2023/9/12   \n",
              "13619  4.154720e+17         .drose01   2023/9/18 ä¸å8:29  2023/9/18   \n",
              "13620  1.125350e+17             j_._   2023/9/18 ä¸å8:44  2023/9/18   \n",
              "13621  1.125350e+17             j_._   2023/9/18 ä¸å8:44  2023/9/18   \n",
              "13622  4.154720e+17         .drose01  2023/9/18 ä¸å10:07  2023/9/18   \n",
              "\n",
              "                                                 Content Attachments  \\\n",
              "13598                                    see #ð§§budget         NaN   \n",
              "13599  no sorry. working on something. also this not ...         NaN   \n",
              "13600  I locked my YFI to be part of the Yearn govern...         NaN   \n",
              "13601  https://twitter.com/BlockEnthusiast/status/169...         NaN   \n",
              "13602  not super familiar with the new yCRV contract....         NaN   \n",
              "13603                                   #yip-74-rejected         NaN   \n",
              "13604  https://discord.com/channels/73480444635303131...         NaN   \n",
              "13605  https://gov.yearn.finance/t/proposal-dwf-labs-...         NaN   \n",
              "13606    i'll open a channel and put some comments there         NaN   \n",
              "13607                      #dwf-labs-crv-yfi-partnership         NaN   \n",
              "13608                            Launch V3 snapshot live         NaN   \n",
              "13609  https://snapshot.org/#/veyfi.eth/proposal/0xdb...         NaN   \n",
              "13610  If you hold Stakedao YFI you can vote for wint...         NaN   \n",
              "13611  ð¢ New YFI proposal: YIP-74: Wintermute YFI ...         NaN   \n",
              "13612                                Also YIP 75 is live         NaN   \n",
              "13613  ð¢ New YFI proposal: YIP-75: Launch V3\\nEnd ...         NaN   \n",
              "13614                             StakeDAO voted against         NaN   \n",
              "13615                                 Stakedao voted for         NaN   \n",
              "13616  Hey Yearn frens! How is it going?\\n\\nFav from ...         NaN   \n",
              "13617  Hey guys, we're working on a web3 forum to hel...         NaN   \n",
              "13618                             Why? Discourse is fine         NaN   \n",
              "13619        Does yearn finance not develop mobile apps?         NaN   \n",
              "13620  nope. should be responsive on mobile anyway so...         NaN   \n",
              "13621                    also, wrong channel to ask this         NaN   \n",
              "13622                                             I see.         NaN   \n",
              "\n",
              "        Reactions  \n",
              "13598         NaN  \n",
              "13599         NaN  \n",
              "13600   mmc13 (2)  \n",
              "13601         NaN  \n",
              "13602         NaN  \n",
              "13603  prayer (3)  \n",
              "13604   waifu (3)  \n",
              "13605         NaN  \n",
              "13606         NaN  \n",
              "13607         NaN  \n",
              "13608         NaN  \n",
              "13609         NaN  \n",
              "13610         NaN  \n",
              "13611         NaN  \n",
              "13612         NaN  \n",
              "13613         NaN  \n",
              "13614         NaN  \n",
              "13615         NaN  \n",
              "13616         NaN  \n",
              "13617         NaN  \n",
              "13618         NaN  \n",
              "13619         NaN  \n",
              "13620         NaN  \n",
              "13621         NaN  \n",
              "13622         NaN  "
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AuthorID</th>\n",
              "      <th>Author</th>\n",
              "      <th>Date_original</th>\n",
              "      <th>Date</th>\n",
              "      <th>Content</th>\n",
              "      <th>Attachments</th>\n",
              "      <th>Reactions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>13598</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/7/5 ä¸å4:24</td>\n",
              "      <td>2023/7/5</td>\n",
              "      <td>see #ð§§budget</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13599</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/7/19 ä¸å3:56</td>\n",
              "      <td>2023/7/19</td>\n",
              "      <td>no sorry. working on something. also this not ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13600</th>\n",
              "      <td>8.341010e+17</td>\n",
              "      <td>liptucom</td>\n",
              "      <td>2023/8/2 ä¸å4:16</td>\n",
              "      <td>2023/8/2</td>\n",
              "      <td>I locked my YFI to be part of the Yearn govern...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mmc13 (2)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13601</th>\n",
              "      <td>3.979630e+17</td>\n",
              "      <td>blockenthusiast</td>\n",
              "      <td>2023/8/16 ä¸å2:31</td>\n",
              "      <td>2023/8/16</td>\n",
              "      <td>https://twitter.com/BlockEnthusiast/status/169...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13602</th>\n",
              "      <td>3.979630e+17</td>\n",
              "      <td>blockenthusiast</td>\n",
              "      <td>2023/8/16 ä¸å2:48</td>\n",
              "      <td>2023/8/16</td>\n",
              "      <td>not super familiar with the new yCRV contract....</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13603</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/16 ä¸å2:54</td>\n",
              "      <td>2023/8/16</td>\n",
              "      <td>#yip-74-rejected</td>\n",
              "      <td>NaN</td>\n",
              "      <td>prayer (3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13604</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/8/25 ä¸å10:54</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>https://discord.com/channels/73480444635303131...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>waifu (3)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13605</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/8/25 ä¸å10:55</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>https://gov.yearn.finance/t/proposal-dwf-labs-...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13606</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/8/25 ä¸å10:56</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>i'll open a channel and put some comments there</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13607</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/8/25 ä¸å10:58</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>#dwf-labs-crv-yfi-partnership</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13608</th>\n",
              "      <td>3.991790e+17</td>\n",
              "      <td>defiglenn</td>\n",
              "      <td>2023/8/25 ä¸å4:53</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>Launch V3 snapshot live</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13609</th>\n",
              "      <td>3.991790e+17</td>\n",
              "      <td>defiglenn</td>\n",
              "      <td>2023/8/25 ä¸å4:53</td>\n",
              "      <td>2023/8/25</td>\n",
              "      <td>https://snapshot.org/#/veyfi.eth/proposal/0xdb...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13610</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/26 ä¸å4:25</td>\n",
              "      <td>2023/8/26</td>\n",
              "      <td>If you hold Stakedao YFI you can vote for wint...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13611</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/26 ä¸å4:25</td>\n",
              "      <td>2023/8/26</td>\n",
              "      <td>ð¢ New YFI proposal: YIP-74: Wintermute YFI ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13612</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/26 ä¸å4:41</td>\n",
              "      <td>2023/8/26</td>\n",
              "      <td>Also YIP 75 is live</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13613</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/26 ä¸å4:41</td>\n",
              "      <td>2023/8/26</td>\n",
              "      <td>ð¢ New YFI proposal: YIP-75: Launch V3\\nEnd ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13614</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/29 ä¸å4:05</td>\n",
              "      <td>2023/8/29</td>\n",
              "      <td>StakeDAO voted against</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13615</th>\n",
              "      <td>6.910390e+17</td>\n",
              "      <td>_barrypooter_</td>\n",
              "      <td>2023/8/29 ä¸å5:02</td>\n",
              "      <td>2023/8/29</td>\n",
              "      <td>Stakedao voted for</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13616</th>\n",
              "      <td>4.148710e+17</td>\n",
              "      <td>fav_truffe</td>\n",
              "      <td>2023/9/8 ä¸å8:48</td>\n",
              "      <td>2023/9/8</td>\n",
              "      <td>Hey Yearn frens! How is it going?\\n\\nFav from ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13617</th>\n",
              "      <td>3.573170e+17</td>\n",
              "      <td>.skyfoxx</td>\n",
              "      <td>2023/9/11 ä¸å11:23</td>\n",
              "      <td>2023/9/11</td>\n",
              "      <td>Hey guys, we're working on a web3 forum to hel...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13618</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/9/12 ä¸å12:42</td>\n",
              "      <td>2023/9/12</td>\n",
              "      <td>Why? Discourse is fine</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13619</th>\n",
              "      <td>4.154720e+17</td>\n",
              "      <td>.drose01</td>\n",
              "      <td>2023/9/18 ä¸å8:29</td>\n",
              "      <td>2023/9/18</td>\n",
              "      <td>Does yearn finance not develop mobile apps?</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13620</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/9/18 ä¸å8:44</td>\n",
              "      <td>2023/9/18</td>\n",
              "      <td>nope. should be responsive on mobile anyway so...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13621</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/9/18 ä¸å8:44</td>\n",
              "      <td>2023/9/18</td>\n",
              "      <td>also, wrong channel to ask this</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13622</th>\n",
              "      <td>4.154720e+17</td>\n",
              "      <td>.drose01</td>\n",
              "      <td>2023/9/18 ä¸å10:07</td>\n",
              "      <td>2023/9/18</td>\n",
              "      <td>I see.</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-51396d70-352e-43c1-abb4-819bf8efee25 button');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "yfi=yfi[:13601]"
      ],
      "metadata": {
        "id": "hFiEuZSCbx-w"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "yfi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9SrNb-u6b1f-",
        "outputId": "ba0ffce2-83e1-4200-ffe5-2956728a057f"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
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              "           AuthorID        Author          Date_original        Date  \\\n",
              "0      1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "1      1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "2      1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "3      6.466630e+17  Cr3ateD#0001  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "4      1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "...             ...           ...                    ...         ...   \n",
              "13596  7.369610e+17  Twisted#8812    2023/7/5 ä¸å4:03   2023/7/5    \n",
              "13597  7.369610e+17  Twisted#8812    2023/7/5 ä¸å4:04   2023/7/5    \n",
              "13598  1.125350e+17          j_._    2023/7/5 ä¸å4:24   2023/7/5    \n",
              "13599  1.125350e+17          j_._   2023/7/19 ä¸å3:56   2023/7/19   \n",
              "13600  8.341010e+17      liptucom    2023/8/2 ä¸å4:16   2023/8/2    \n",
              "\n",
              "                                                 Content Attachments  \\\n",
              "0      https://gov.yearn.finance/t/proposal-0-yfi-sup...         NaN   \n",
              "1      https://gov.yearn.finance/t/proposal-1-yfi-fee...         NaN   \n",
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              "...                                                  ...         ...   \n",
              "13596                                        Hello guys.         NaN   \n",
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              "13598                                    see #ð§§budget         NaN   \n",
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              "13596        NaN  \n",
              "13597        NaN  \n",
              "13598        NaN  \n",
              "13599        NaN  \n",
              "13600  mmc13 (2)  \n",
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              "      <td>mrdunkirk</td>\n",
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              "      <td>2020/7/20</td>\n",
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              "      <td>mrdunkirk</td>\n",
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              "      <td>2020/7/20</td>\n",
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              "      <td>Cr3ateD#0001</td>\n",
              "      <td>2020/7/20 ä¸å12:27</td>\n",
              "      <td>2020/7/20</td>\n",
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              "      <td>2023/7/5</td>\n",
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              "      <th>13599</th>\n",
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              "      <td>j_._</td>\n",
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              "      <td>2023/7/19</td>\n",
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              "      <td>liptucom</td>\n",
              "      <td>2023/8/2 ä¸å4:16</td>\n",
              "      <td>2023/8/2</td>\n",
              "      <td>I locked my YFI to be part of the Yearn govern...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mmc13 (2)</td>\n",
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              "<p>13601 rows × 7 columns</p>\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b8ZXpJ619A55"
      },
      "source": [
        "# Text Processing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xRodc9K79qfq",
        "outputId": "f9410743-819a-4745-cb57-2604aba6dd66"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
          ]
        }
      ],
      "source": [
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import PorterStemmer\n",
        "import re\n",
        "\n",
        "# Download stopwords and stemmer\n",
        "nltk.download('stopwords')\n",
        "nltk.download('punkt')\n",
        "\n",
        "# Define the preprocessing function\n",
        "stop_words = set(stopwords.words('english'))\n",
        "def preprocess_text(text):\n",
        "    # Check if text is NaN\n",
        "    if pd.isna(text):\n",
        "        return \"\"\n",
        "\n",
        "    # Convert to lowercase\n",
        "    text = text.lower()\n",
        "\n",
        "    # Remove URLs\n",
        "    text = re.sub(r'http\\S+', '', text)\n",
        "\n",
        "    # Remove non-alphabetic characters\n",
        "    text = re.sub(r'[^a-zA-Z\\s]', '', text)\n",
        "\n",
        "    # Tokenize the text\n",
        "    words = word_tokenize(text)\n",
        "\n",
        "    # Remove stop words and punctuation\n",
        "    words = [word for word in words if word not in stop_words and word.isalpha()]\n",
        "\n",
        "    # Join the remaining words into a string\n",
        "    text = ' '.join(words)\n",
        "\n",
        "    return text\n",
        "\n",
        "# Apply the preprocessing function to the 'Content' column\n",
        "yfi['Preprocessed'] = yfi['Content'].apply(preprocess_text)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "vih871AjIX3S",
        "outputId": "9e76476f-2630-4159-edd3-b0afa4a01890"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           AuthorID        Author         Date_original       Date  \\\n",
              "13596  7.369610e+17  Twisted#8812   2023/7/5 ä¸å4:03  2023/7/5    \n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "yfi.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "HoozrWHiIddy"
      },
      "outputs": [],
      "source": [
        "# Save the cleaned DataFrame to a new CSV file\n",
        "yfi.to_csv('cleaned_discord_yfi_governance.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X2bgAMsW_l9q"
      },
      "source": [
        "# Sentiment Analysis (VADER)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Date-based Sentiment"
      ],
      "metadata": {
        "id": "HJrttzRZTLs8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3-M3z4NMjbc_",
        "outputId": "f9ab0a49-84f3-40cd-d627-3d91803950f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting vaderSentiment\n",
            "  Downloading vaderSentiment-3.3.2-py2.py3-none-any.whl (125 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/126.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m112.6/126.0 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m126.0/126.0 kB\u001b[0m \u001b[31m2.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from vaderSentiment) (2.31.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (3.3.1)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->vaderSentiment) (2023.7.22)\n",
            "Installing collected packages: vaderSentiment\n",
            "Successfully installed vaderSentiment-3.3.2\n"
          ]
        }
      ],
      "source": [
        "! pip install vaderSentiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "8hqaYhtLBCwL"
      },
      "outputs": [],
      "source": [
        "yfi.dropna(subset=['Date'], inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "KeoL6P7bPbpy",
        "outputId": "64841fa1-b074-40d3-8ce9-5ec8bb19f047"
      },
      "outputs": [
        {
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              "      <td>2023/7/5</td>\n",
              "      <td>I'm a DeFi developer and I want to know where ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>im defi developer want know get yearns grants</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13598</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/7/5 ä¸å4:24</td>\n",
              "      <td>2023/7/5</td>\n",
              "      <td>see #ð§§budget</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>see budget</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13599</th>\n",
              "      <td>1.125350e+17</td>\n",
              "      <td>j_._</td>\n",
              "      <td>2023/7/19 ä¸å3:56</td>\n",
              "      <td>2023/7/19</td>\n",
              "      <td>no sorry. working on something. also this not ...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>sorry working something also proper channel tr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13600</th>\n",
              "      <td>8.341010e+17</td>\n",
              "      <td>liptucom</td>\n",
              "      <td>2023/8/2 ä¸å4:16</td>\n",
              "      <td>2023/8/2</td>\n",
              "      <td>I locked my YFI to be part of the Yearn govern...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>mmc13 (2)</td>\n",
              "      <td>locked yfi part yearn governance read twitter ...</td>\n",
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cd243cb9-5402-4b47-a5ae-1b7e529b9319 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-cd243cb9-5402-4b47-a5ae-1b7e529b9319');\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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              "        element.appendChild(docLink);\n",
              "      }\n",
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              "\n",
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              "\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-668fb1ed-61d7-496d-add7-6c4d953774c2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "yfi.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "bWBcmYQTPOdR",
        "outputId": "37a05986-5ebd-482b-8728-ca42e78f9ced"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       AuthorID        Author          Date_original        Date  \\\n",
              "0  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "1  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "2  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:26  2020/7/20    \n",
              "3  6.466630e+17  Cr3ateD#0001  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "4  1.777620e+17     mrdunkirk  2020/7/20 ä¸å12:27  2020/7/20    \n",
              "\n",
              "                                             Content Attachments Reactions  \\\n",
              "0  https://gov.yearn.finance/t/proposal-0-yfi-sup...         NaN       NaN   \n",
              "1  https://gov.yearn.finance/t/proposal-1-yfi-fee...         NaN       NaN   \n",
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              "4                              i would vote for that         NaN       NaN   \n",
              "\n",
              "    Preprocessed  \n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "YFI = yfi\n",
        "YFI.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "qMrsk4XIAb6k"
      },
      "outputs": [],
      "source": [
        "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
        "\n",
        "# Initialize the sentiment analyzer\n",
        "analyzer = SentimentIntensityAnalyzer()\n",
        "\n",
        "# Convert the 'Date' column to a datetime object and set it as the index\n",
        "\n",
        "YFI['Date'] = pd.to_datetime(YFI['Date'], errors='coerce')\n",
        "YFI.set_index('Date', inplace=True)\n",
        "\n",
        "# Define a function to compute the sentiment score for each day\n",
        "def daily_sentiment_score(text):\n",
        "    sentiment = analyzer.polarity_scores(text)\n",
        "    return sentiment['compound']\n",
        "\n",
        "# Compute the daily sentiment score using resample and apply\n",
        "yfi_daily = YFI['Preprocessed'].resample('D').apply(lambda x: x.apply(daily_sentiment_score).mean())\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WWwnaAZ3BZoU",
        "outputId": "b54e6bbb-d5c1-4d71-f4e9-09910c75fda5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2020-07-20    0.104253\n",
              "2020-07-21    0.104855\n",
              "2020-07-22    0.151472\n",
              "2020-07-23    0.149307\n",
              "2020-07-24    0.132579\n",
              "                ...   \n",
              "2023-07-29         NaN\n",
              "2023-07-30         NaN\n",
              "2023-07-31         NaN\n",
              "2023-08-01         NaN\n",
              "2023-08-02    0.202300\n",
              "Freq: D, Name: Preprocessed, Length: 1109, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "yfi_daily"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "70HeLMjTDORw"
      },
      "outputs": [],
      "source": [
        "yfi_daily_sentiment = yfi_daily.to_frame(name='Sentiment Score').reset_index()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ELyMiDJ5CrzQ",
        "outputId": "d63c4401-a4d9-4355-975b-a385ccefd7e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date', 'Sentiment Score'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "yfi_daily_sentiment.columns"
      ]
    },
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      "execution_count": 19,
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        "id": "fLKlfgt1DWBe",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Date  Sentiment Score\n",
              "0 2020-07-20         0.104253\n",
              "1 2020-07-21         0.104855\n",
              "2 2020-07-22         0.151472\n",
              "3 2020-07-23         0.149307\n",
              "4 2020-07-24         0.132579"
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              "                                                    [key], {});\n",
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              "        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",
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          },
          "metadata": {},
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      "source": [
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      ]
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      "execution_count": 20,
      "metadata": {
        "id": "VwLvWsR1DxAV"
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      "source": [
        "yfi_daily_sentiment['Sentiment Score'].fillna(0.000000, inplace=True)"
      ]
    },
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      "execution_count": 21,
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        "id": "pT3cxoLlDya7",
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            "text/plain": [
              "           Date  Sentiment Score\n",
              "0    2020-07-20         0.104253\n",
              "1    2020-07-21         0.104855\n",
              "2    2020-07-22         0.151472\n",
              "3    2020-07-23         0.149307\n",
              "4    2020-07-24         0.132579\n",
              "...         ...              ...\n",
              "1104 2023-07-29         0.000000\n",
              "1105 2023-07-30         0.000000\n",
              "1106 2023-07-31         0.000000\n",
              "1107 2023-08-01         0.000000\n",
              "1108 2023-08-02         0.202300\n",
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              "[1109 rows x 2 columns]"
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              "      <th>2</th>\n",
              "      <td>2020-07-22</td>\n",
              "      <td>0.151472</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-07-23</td>\n",
              "      <td>0.149307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-07-24</td>\n",
              "      <td>0.132579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1104</th>\n",
              "      <td>2023-07-29</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1105</th>\n",
              "      <td>2023-07-30</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1106</th>\n",
              "      <td>2023-07-31</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1107</th>\n",
              "      <td>2023-08-01</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1108</th>\n",
              "      <td>2023-08-02</td>\n",
              "      <td>0.202300</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>1109 rows × 2 columns</p>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ed482ab6-90d9-4f67-8dcc-ec0adeda4ae4');\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",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        element.appendChild(docLink);\n",
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              "  </button>\n",
              "\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-85656787-d54d-4c50-895c-c42936976ea4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "yfi_daily_sentiment"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "8X8BluGRR7H2"
      },
      "outputs": [],
      "source": [
        "yfi_daily_sentiment.to_csv('sentiment_discord_yfi_governance.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "A-_kCcdgEHZy",
        "outputId": "99245bac-9287-45e8-df6e-8038cf3d1257"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('042ec025-cae1-45db-8780-f1bb37d7361f');\n",
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              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
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              "    x.observe(outputEl, {childList: true});\n",
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.graph_objs as go\n",
        "\n",
        "# Create a line graph using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add a line trace to the figure\n",
        "fig.add_trace(go.Scatter(x=yfi_daily_sentiment['Date'], y=yfi_daily_sentiment['Sentiment Score'], mode='lines'))\n",
        "\n",
        "# Customize the layout of the figure\n",
        "fig.update_layout(\n",
        "    xaxis_title='Date',\n",
        "    yaxis_title='Sentiment Score',\n",
        "    xaxis=dict(title_font=dict(size=20), tickfont=dict(size=14), showline=True, linewidth=2, linecolor='black'),\n",
        "    yaxis=dict(title_font=dict(size=20), tickfont=dict(size=14), showline=True, linewidth=2, linecolor='black')\n",
        ")\n",
        "\n",
        "# Display the figure\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "0Z9xHhNTUlvI"
      },
      "outputs": [],
      "source": [
        "positive_count = len(yfi_daily_sentiment[yfi_daily_sentiment['Sentiment Score'] > 0])\n",
        "negative_count = len(yfi_daily_sentiment[yfi_daily_sentiment['Sentiment Score'] < 0])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "rtodeps6U39V",
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              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Calculate positive and negative sentiment counts\n",
        "positive_count = (yfi_daily_sentiment['Sentiment Score'] > 0).sum()\n",
        "negative_count = (yfi_daily_sentiment['Sentiment Score'] < 0).sum()\n",
        "\n",
        "# Create a dataframe for histogram\n",
        "hist_yfi = pd.DataFrame({\n",
        "    'Sentiment': ['Positive', 'Negative'],\n",
        "    'Count': [positive_count, negative_count]\n",
        "})\n",
        "\n",
        "# Create a plotly histogram\n",
        "fig = px.histogram(hist_yfi, x='Sentiment', y='Count', color='Sentiment',\n",
        "                   title='yearn.finance Discord Sentiment Distribution')\n",
        "\n",
        "# Set y-axis label\n",
        "fig.update_layout(yaxis_title='Count (days)')\n",
        "\n",
        "# Add count number on each bar\n",
        "fig.update_traces(texttemplate='%{y}', textposition='outside')\n",
        "\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate overall statistics on the sentiment scores\n",
        "overall_sentiment_stats = yfi_daily_sentiment['Sentiment Score'].describe()\n",
        "\n",
        "# Output overall sentiment statistics\n",
        "print(\"Overall Sentiment Statistics:\")\n",
        "print(overall_sentiment_stats)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EyRwpP4DWhsk",
        "outputId": "5fbb5ae0-a91a-4fff-ad60-6255c0884f8b"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overall Sentiment Statistics:\n",
            "count    1109.000000\n",
            "mean        0.062604\n",
            "std         0.150326\n",
            "min        -0.510600\n",
            "25%         0.000000\n",
            "50%         0.000000\n",
            "75%         0.011276\n",
            "max         0.975800\n",
            "Name: Sentiment Score, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## User-Sentiment\n"
      ],
      "metadata": {
        "id": "eFTJviPs9trd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a new column to store sentiment scores\n",
        "YFI['Sentiment_Score'] = 0.0\n",
        "\n",
        "# Group the data based on AuthorID\n",
        "grouped = YFI.groupby('AuthorID')\n",
        "\n",
        "# Iterate through each group\n",
        "for author, group in grouped:\n",
        "    # Perform sentiment analysis on the content of each group and store sentiment scores\n",
        "    sentiment_scores = []\n",
        "    for content in group['Content']:\n",
        "        # Check if the content is a string; if not, convert it to a string\n",
        "        if not isinstance(content, str):\n",
        "            content = str(content)\n",
        "        sentiment = analyzer.polarity_scores(content)\n",
        "        sentiment_scores.append(sentiment['compound'])\n",
        "\n",
        "    # Assign the average sentiment score to all rows in each group\n",
        "    YFI.loc[group.index, 'Sentiment_Score'] = sum(sentiment_scores) / len(sentiment_scores)\n",
        "\n",
        "# Now, your DataFrame will have a new column named 'Sentiment_Score' containing the average sentiment score for each user\n",
        "# You can further analyze or visualize these sentiment scores as needed"
      ],
      "metadata": {
        "id": "PgZZDCnq9tY9"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate the number of messages per user\n",
        "message_count_per_user = YFI['AuthorID'].value_counts()\n",
        "\n",
        "# Calculate user count\n",
        "user_count = len(message_count_per_user)\n",
        "\n",
        "# Calculate 25th, median (50th), 75th, minimum, and maximum percentiles of message counts\n",
        "message_count_percentiles = message_count_per_user.describe(percentiles=[0.25, 0.5, 0.75])\n",
        "\n",
        "# Calculate the mean number of messages per user\n",
        "mean_messages_per_user = message_count_per_user.mean()\n",
        "\n",
        "# Calculate the minimum and maximum message counts\n",
        "min_message_count = message_count_per_user.min()\n",
        "max_message_count = message_count_per_user.max()\n",
        "\n",
        "# Calculate the distribution of average sentiment scores per user's message\n",
        "sentiment_scores_per_user = YFI.groupby('AuthorID')['Sentiment_Score'].mean()\n",
        "\n",
        "# Print the results\n",
        "print(\"User Count:\", user_count)\n",
        "print(\"25th Percentile of Message Counts:\", message_count_percentiles['25%'])\n",
        "print(\"Median (50th Percentile) of Message Counts:\", message_count_percentiles['50%'])\n",
        "print(\"75th Percentile of Message Counts:\", message_count_percentiles['75%'])\n",
        "print(\"Minimum Message Count:\", min_message_count)\n",
        "print(\"Maximum Message Count:\", max_message_count)\n",
        "print(\"Mean Number of Messages per User:\", mean_messages_per_user)\n",
        "print(\"Distribution of Average Sentiment Scores per User's Message:\")\n",
        "print(sentiment_scores_per_user)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdGVtn2J-txX",
        "outputId": "6446fac5-768f-4997-9627-cb340162f2f4"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "User Count: 722\n",
            "25th Percentile of Message Counts: 1.0\n",
            "Median (50th Percentile) of Message Counts: 3.0\n",
            "75th Percentile of Message Counts: 11.0\n",
            "Minimum Message Count: 1\n",
            "Maximum Message Count: 647\n",
            "Mean Number of Messages per User: 18.837950138504155\n",
            "Distribution of Average Sentiment Scores per User's Message:\n",
            "AuthorID\n",
            "9.130660e+16    0.179146\n",
            "9.468930e+16    0.243327\n",
            "1.031540e+17    0.063218\n",
            "1.048220e+17    0.027200\n",
            "1.071180e+17    0.134075\n",
            "                  ...   \n",
            "9.367030e+17    0.817600\n",
            "9.637870e+17    0.621900\n",
            "9.775880e+17   -0.057375\n",
            "1.044740e+18    0.000000\n",
            "1.105180e+18    0.169725\n",
            "Name: Sentiment_Score, Length: 722, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the Series into a DataFrame with a named column\n",
        "sentiment_scores_df = sentiment_scores_per_user.reset_index()\n",
        "sentiment_scores_df.columns = ['AuthorID', 'Average_Sentiment_Score']\n",
        "\n",
        "# Print the resulting DataFrame\n",
        "sentiment_scores_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "h4CNW2om_Toc",
        "outputId": "431ba28e-ed6c-4177-aace-9b469e027092"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         AuthorID  Average_Sentiment_Score\n",
              "0    9.130660e+16                 0.179146\n",
              "1    9.468930e+16                 0.243327\n",
              "2    1.031540e+17                 0.063218\n",
              "3    1.048220e+17                 0.027200\n",
              "4    1.071180e+17                 0.134075\n",
              "..            ...                      ...\n",
              "717  9.367030e+17                 0.817600\n",
              "718  9.637870e+17                 0.621900\n",
              "719  9.775880e+17                -0.057375\n",
              "720  1.044740e+18                 0.000000\n",
              "721  1.105180e+18                 0.169725\n",
              "\n",
              "[722 rows x 2 columns]"
            ],
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              "\n",
              "  <div id=\"df-193ee233-cd82-4bda-98cb-19093ff333a0\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AuthorID</th>\n",
              "      <th>Average_Sentiment_Score</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.130660e+16</td>\n",
              "      <td>0.179146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.468930e+16</td>\n",
              "      <td>0.243327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.031540e+17</td>\n",
              "      <td>0.063218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.048220e+17</td>\n",
              "      <td>0.027200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.071180e+17</td>\n",
              "      <td>0.134075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>9.367030e+17</td>\n",
              "      <td>0.817600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>9.637870e+17</td>\n",
              "      <td>0.621900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>9.775880e+17</td>\n",
              "      <td>-0.057375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>720</th>\n",
              "      <td>1.044740e+18</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>1.105180e+18</td>\n",
              "      <td>0.169725</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>722 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-193ee233-cd82-4bda-98cb-19093ff333a0')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-193ee233-cd82-4bda-98cb-19093ff333a0 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-193ee233-cd82-4bda-98cb-19093ff333a0');\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",
              "\n",
              "\n",
              "<div id=\"df-ed8953e1-1837-4acf-8252-a2670e8faa2a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ed8953e1-1837-4acf-8252-a2670e8faa2a')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ed8953e1-1837-4acf-8252-a2670e8faa2a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sentiment_scores_df.to_csv('user_sentiment_yfi.csv', index=False)"
      ],
      "metadata": {
        "id": "O-UB6EjpFlnW"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Count the number of records with sentiment score equal to 0\n",
        "count_zero_sentiment = (sentiment_scores_df['Average_Sentiment_Score'] == 0).sum()\n",
        "\n",
        "# Print the count\n",
        "print(\"Number of records with sentiment score equal to 0:\", count_zero_sentiment)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qbYs2cpfCywh",
        "outputId": "de888fc8-5fae-4cef-8995-9b9b67c1f17e"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of records with sentiment score equal to 0: 51\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Drop rows where Average_Sentiment_Score is 0\n",
        "sentiment_scores_df_new = sentiment_scores_df[sentiment_scores_df['Average_Sentiment_Score'] != 0]\n",
        "\n",
        "# Print the resulting DataFrame\n",
        "sentiment_scores_df_new"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zDZcLPOnCWwp",
        "outputId": "ab0732d3-9b59-42cf-b836-ad3d49bfa58c"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         AuthorID  Average_Sentiment_Score\n",
              "0    9.130660e+16                 0.179146\n",
              "1    9.468930e+16                 0.243327\n",
              "2    1.031540e+17                 0.063218\n",
              "3    1.048220e+17                 0.027200\n",
              "4    1.071180e+17                 0.134075\n",
              "..            ...                      ...\n",
              "716  9.332940e+17                 0.146800\n",
              "717  9.367030e+17                 0.817600\n",
              "718  9.637870e+17                 0.621900\n",
              "719  9.775880e+17                -0.057375\n",
              "721  1.105180e+18                 0.169725\n",
              "\n",
              "[671 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca06d292-122d-4989-8efb-a3b927658347\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AuthorID</th>\n",
              "      <th>Average_Sentiment_Score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>9.130660e+16</td>\n",
              "      <td>0.179146</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.468930e+16</td>\n",
              "      <td>0.243327</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.031540e+17</td>\n",
              "      <td>0.063218</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.048220e+17</td>\n",
              "      <td>0.027200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.071180e+17</td>\n",
              "      <td>0.134075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>716</th>\n",
              "      <td>9.332940e+17</td>\n",
              "      <td>0.146800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>717</th>\n",
              "      <td>9.367030e+17</td>\n",
              "      <td>0.817600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>718</th>\n",
              "      <td>9.637870e+17</td>\n",
              "      <td>0.621900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>719</th>\n",
              "      <td>9.775880e+17</td>\n",
              "      <td>-0.057375</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>721</th>\n",
              "      <td>1.105180e+18</td>\n",
              "      <td>0.169725</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>671 rows × 2 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-ca06d292-122d-4989-8efb-a3b927658347 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-ca06d292-122d-4989-8efb-a3b927658347');\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",
              "\n",
              "\n",
              "<div id=\"df-459f9f76-c732-43cf-8e35-fd9edac489f5\">\n",
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              "            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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-459f9f76-c732-43cf-8e35-fd9edac489f5 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import plotly.express as px\n",
        "\n",
        "# Assuming you have a DataFrame named sentiment_scores_df_new\n",
        "# If you haven't already created it, please follow the previous response\n",
        "\n",
        "# Create a histogram\n",
        "fig = px.histogram(sentiment_scores_df_new, x='Average_Sentiment_Score', title='Distribution of Average Sentiment Scores per User\\'s Message')\n",
        "\n",
        "# Show the histogram\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "FZbBWONR_6Hx",
        "outputId": "8df68f6a-988d-4523-a87e-5a8f41f34c3d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# Define the bins, excluding 0 as a separate interval\n",
        "bins = [-1.0, -0.5, 0.0, 0.5, 1.0]\n",
        "hist, bin_edges = pd.cut(sentiment_scores_df_new['Average_Sentiment_Score'], bins, right=False, retbins=True)\n",
        "\n",
        "# Create a DataFrame to display the frequency table\n",
        "frequency_table = pd.value_counts(hist, sort=False).reset_index()\n",
        "frequency_table.columns = ['Sentiment_Score_Range', 'Frequency']\n",
        "\n",
        "# Calculate the proportion of each bin\n",
        "total_records = len(sentiment_scores_df_new)\n",
        "frequency_table['Proportion'] = frequency_table['Frequency'] / total_records\n",
        "\n",
        "# Display the frequency table\n",
        "print(frequency_table)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZXCV_NRBUxw",
        "outputId": "087f7cae-0920-4934-b3d1-41025016a683"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Sentiment_Score_Range  Frequency  Proportion\n",
            "0          [-1.0, -0.5)          1    0.001490\n",
            "1           [-0.5, 0.0)         38    0.056632\n",
            "2            [0.0, 0.5)        550    0.819672\n",
            "3            [0.5, 1.0)         82    0.122206\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YzmZLuddTiUc"
      },
      "source": [
        "# Discord Volume"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L9jiJsuDTi74",
        "outputId": "12523bd9-2049-44ba-987d-02fc204544ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2020-07-20     934\n",
            "2020-07-21    1092\n",
            "2020-07-22    1727\n",
            "2020-07-23     504\n",
            "2020-07-24     573\n",
            "              ... \n",
            "2023-07-29       0\n",
            "2023-07-30       0\n",
            "2023-07-31       0\n",
            "2023-08-01       0\n",
            "2023-08-02       1\n",
            "Freq: D, Name: Content, Length: 1109, dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Group the discord messages by day and count the number of discord messages in each day\n",
        "daily_discord_count = YFI['Content'].resample('D').count()\n",
        "\n",
        "# Print the daily discord message count\n",
        "print(daily_discord_count)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "JmsSseddTu7d"
      },
      "outputs": [],
      "source": [
        "daily_discord_count = daily_discord_count.to_frame(name='Discord Volume').reset_index()"
      ]
    },
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              "           Date  Discord Volume\n",
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              "4    2020-07-24             573\n",
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              "1104 2023-07-29               0\n",
              "1105 2023-07-30               0\n",
              "1106 2023-07-31               0\n",
              "1107 2023-08-01               0\n",
              "1108 2023-08-02               1\n",
              "\n",
              "[1109 rows x 2 columns]"
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              "      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-9f2467ef-e879-4cd3-b973-d53c837a22f8 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-9f2467ef-e879-4cd3-b973-d53c837a22f8');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34c11b1c-9723-4adb-baf6-8f22b92bc0fd')\"\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",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34c11b1c-9723-4adb-baf6-8f22b92bc0fd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "daily_discord_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "7qLjofhCWwyb"
      },
      "outputs": [],
      "source": [
        "daily_discord_count.to_csv('discord_volume_yfi_governance.csv', index=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "YRx2tNLzT7s5",
        "outputId": "49059e24-d932-4ae2-f836-765050fac6c3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                            \n",
              "var gd = document.getElementById('cfd54d7a-781a-4062-a67c-ff8d5004179e');\n",
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              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })                };                            </script>        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import plotly.graph_objs as go\n",
        "\n",
        "# Create a line graph using Plotly\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add a line trace to the figure\n",
        "fig.add_trace(go.Scatter(x=daily_discord_count['Date'], y=daily_discord_count['Discord Volume'], mode='lines'))\n",
        "\n",
        "# Customize the layout of the figure\n",
        "fig.update_layout(title='yearn.finance Discord Daily Message Volume', xaxis_title='Date', yaxis_title='Discord Message Volume')\n",
        "\n",
        "# Display the figure\n",
        "fig.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Discord Volume\n",
        "\n",
        "# Group the discord messages by day and count the number of discord messages in each day\n",
        "daily_discord_count = YFI['Content'].resample('D').count()\n",
        "\n",
        "# Function to detect outliers in the daily Discord message count\n",
        "def detect_outliers(data):\n",
        "    Q1 = data.quantile(0.25)\n",
        "    Q3 = data.quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    lower_bound = Q1 - 1.5 * IQR\n",
        "    upper_bound = Q3 + 1.5 * IQR\n",
        "    outliers = (data < lower_bound) | (data > upper_bound)\n",
        "    return outliers\n",
        "\n",
        "# Detect outliers in Discord message count using the above function\n",
        "outliers = detect_outliers(daily_discord_count)\n",
        "\n",
        "# Remove outliers from the daily Discord message count\n",
        "daily_discord_count_cleaned = daily_discord_count[~outliers]\n",
        "\n",
        "# Save the cleaned data to a CSV\n",
        "daily_discord_count_cleaned.to_csv('cleaned_discord_volume_yfi_governance.csv', index=False)"
      ],
      "metadata": {
        "id": "yQFGX9NELq61"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "daily_discord_count_cleaned = daily_discord_count_cleaned.to_frame(name='Discord Volume').reset_index()"
      ],
      "metadata": {
        "id": "YM6FtQpnLsYV"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "daily_discord_count_cleaned"
      ],
      "metadata": {
        "id": "mQQzNlmiT8Wk",
        "outputId": "8d75a512-abe4-4a6f-dea3-05388549cd03",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        }
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date  Discord Volume\n",
              "0   2020-08-12               3\n",
              "1   2020-08-13               4\n",
              "2   2020-08-14               2\n",
              "3   2020-09-28               1\n",
              "4   2020-10-01               1\n",
              "..         ...             ...\n",
              "920 2023-07-29               0\n",
              "921 2023-07-30               0\n",
              "922 2023-07-31               0\n",
              "923 2023-08-01               0\n",
              "924 2023-08-02               1\n",
              "\n",
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              "      <th>Date</th>\n",
              "      <th>Discord Volume</th>\n",
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              "    </div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a line graph for Discord message count with and without outliers\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add a line without outliers\n",
        "fig.add_trace(go.Scatter(x=daily_discord_count_cleaned['Date'], y=daily_discord_count_cleaned['Discord Volume'], mode='lines', name='Cleaned'))\n",
        "\n",
        "# Customize the layout of the figure with increased font sizes\n",
        "fig.update_layout(\n",
        "    xaxis_title='Date',\n",
        "    yaxis_title='Discord Message Volume (Count)',\n",
        "    xaxis=dict(title_font=dict(size=20), tickfont=dict(size=14), showline=True, linewidth=2, linecolor='black'),\n",
        "    yaxis=dict(title_font=dict(size=20), tickfont=dict(size=14), showline=True, linewidth=2, linecolor='black')\n",
        ")\n",
        "\n",
        "# Display the figure\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "auTSXGvPJc4Y",
        "outputId": "e52a47bf-c10c-4368-d644-26989b414112"
      },
      "execution_count": 43,
      "outputs": [
        {
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          "data": {
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