read_csv.ipynb

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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>タイムスタンプ</th>\n",
       "      <th>q1</th>\n",
       "      <th>q2</th>\n",
       "      <th>q3</th>\n",
       "      <th>q4</th>\n",
       "      <th>q5</th>\n",
       "      <th>q6</th>\n",
       "      <th>q7</th>\n",
       "      <th>q8</th>\n",
       "      <th>q9</th>\n",
       "      <th>...</th>\n",
       "      <th>q84</th>\n",
       "      <th>q85</th>\n",
       "      <th>q86</th>\n",
       "      <th>q87</th>\n",
       "      <th>q88</th>\n",
       "      <th>q89</th>\n",
       "      <th>q90</th>\n",
       "      <th>q91</th>\n",
       "      <th>q92</th>\n",
       "      <th>q93</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024/02/01 10:20:36</td>\n",
       "      <td>ゲームに出てきそうなキャラクターが正面を向いている</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ユニコーンが歩いている</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>回る観覧車</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い橋を駆け抜ける車</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>たくさんの犬</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>スパイ風の男性</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024/02/01 10:20:46</td>\n",
       "      <td>中国部将風の女性がこちらを向いている</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ユニコーンが砂浜を歩いている</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>観覧車（なんか伸びてる）</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>海外にありそうな橋</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>たくさんの犬が草原で寝そべっている</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>スーツを着てサングラスをかけた男性が腕組みをしてたっている</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024/02/01 10:21:05</td>\n",
       "      <td>青髪の碧眼の女性</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>歩くユニコーン</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>回る観覧車</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>明石海峡大橋</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>たぶん犬の集団もしくはバケモノ</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>できる風の男</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024/02/01 10:24:08</td>\n",
       "      <td>青い髪の女性と黒い影</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>ゆっくり歩くユニコーン</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>観覧車に近づく様子</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い巨大な橋に何台もの車が走っている</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>草原に寝転ぶ10数匹の犬</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>色黒のサラリーマン</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2024/02/01 10:24:26</td>\n",
       "      <td>ファンタジーな格好をした女性が前を向いている。</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>晴れた日にユニコーンが草原を歩いている。</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>遊園地と赤い観覧車。</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>大きな赤い橋と街並み</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>草原と沢山の犬</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>サングラスをかけたスーツ姿の若い男性</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2024/02/01 10:25:02</td>\n",
       "      <td>エンシェントな女</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>歩くユニコーン</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>傾く観覧車</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>都市の橋</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>異形の集い</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>エリートスパイ</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2024/02/01 10:30:06</td>\n",
       "      <td>コスプレイヤーの撮影会</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>デブのユニコーンが湿地を走っている</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ゴールデンゲートブリッジの余りで作りました</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>ゴールデンゲートブリッジ</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>犬とワームの融合</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>投資銀行に勤めて3年目</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2024/02/02 10:17:08</td>\n",
       "      <td>異世界の女性の髪がなびいている</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>ユニコーンが海辺を歩いている</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い観覧車が動いている</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い橋とその上を走る車</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>野原の上でじゃれあう大量の犬</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>軍事品開発会社社長</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2024/02/02 10:21:37</td>\n",
       "      <td>青髪の女性が真正面を向いている</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>一角獣が歩いている</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>赤い観覧車がゆっくりと動いている</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>赤く大きな橋を行き交う車</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>犬とも羊とも形容しがたい動物の群れ</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>サングラスとスーツを着用し決めポーズを取っている男性</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2024/02/02 10:25:13</td>\n",
       "      <td>整いたての眉毛をみんなに見せびらかしている</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>ユニコーンが散歩している</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>観覧車に向かって歩いている</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>車通りの多い橋</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>派遣犬たちが大変な研修を終えてお互いを労っている様子</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>映画監督が見下してきている</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2024/02/02 12:49:29</td>\n",
       "      <td>ゲームのキャラクターみたいな女の人がこっちを見ている</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>ユニコーンが歩いている</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>観覧車を撮影しながら移動している</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>赤い大きな橋の上を車が走っている</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>大量の犬が草原にいる</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>サングラスをかけたスーツの男が腕を組んで立っている</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2024/02/02 12:50:13</td>\n",
       "      <td>風が吹いている</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ユニコーン闊歩</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>動く観覧車</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>赤い橋の上の道路</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>101匹わんちゃん</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>腕を組んで立つスーツの男</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2024/02/02 12:51:04</td>\n",
       "      <td>APEXに居そうなキャラ</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>ホワイトタイガーとユニコーンのキメラ</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い観覧車</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>赤い橋の道路</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>エビフライの犬</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>黒人の男がスーツを着ている</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2024/02/02 12:52:09</td>\n",
       "      <td>青髪のツインテールの女性が正面を向いてる</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>太り気味にユニコーンが走っている</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>赤い色の観覧車</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>赤い大きな橋</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>群がる沢山の犬</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>仕事できる男性</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2024/02/02 12:52:51</td>\n",
       "      <td>気の強そうな女</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>走るユニコーン</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>赤い観覧車</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>赤い橋</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>犬の大家族</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>黒人のイケメンスパイ</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2024/02/02 12:53:11</td>\n",
       "      <td>正面を向いた女性の髪が靡いている。</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>太ったユニコーンが走っている</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>変形する観覧車</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>高い位置にある赤い橋</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>戯れる10匹の犬</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>スパイの男</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2024/02/02 12:54:14</td>\n",
       "      <td>青髪の女性が証明写真のように正面を向いている</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>ユニコーンが大地を走り回ってる</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>遊園地の中にある観覧車</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>レインボーブリッジ</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>母親の犬がミルクを上げている</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>武器を作っている会社のイケてるスーツの男</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2024/02/02 12:56:05</td>\n",
       "      <td>ゲームのキャラ</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>ユニコーン</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>観覧車</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>橋を渡る車</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>大量の犬</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>飛行機業者の偉い人</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2024/02/02 12:58:39</td>\n",
       "      <td>女の人が真正面（私の方）を向いている</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>ユニコーンが坂を恐る恐る降っている</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>遊園地の観覧車</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>赤い大きな橋</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>沢山の犬がいる</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>スーツの男の人と船や椅子</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2024/02/02 12:59:41</td>\n",
       "      <td>美少女がこっちを見ている</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>ユニコーンが歩いている</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>観覧車の真ん中から何かが伸びている</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>巨大な橋の上を走る車</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>たくさんの犬のようななにか</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>黒髪でスーツにサングラスのスパイ風の男</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2024/02/02 13:01:18</td>\n",
       "      <td>青い髪と瞳で額に石を持つ女性が正面を見ているアップのイラスト</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ユニコーンが空を走っている</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>観覧車が回っている</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>大きな橋を抜ける車ら</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>イラスト調で、たくさんの子犬に乳を与える母犬</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>なんらかの組織の男</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 94 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                タイムスタンプ                              q1  q2  q3  \\\n",
       "0   2024/02/01 10:20:36       ゲームに出てきそうなキャラクターが正面を向いている   3   2   \n",
       "1   2024/02/01 10:20:46              中国部将風の女性がこちらを向いている   1   2   \n",
       "2   2024/02/01 10:21:05                        青髪の碧眼の女性   3   2   \n",
       "3   2024/02/01 10:24:08                      青い髪の女性と黒い影   2   3   \n",
       "4   2024/02/01 10:24:26         ファンタジーな格好をした女性が前を向いている。   3   3   \n",
       "5   2024/02/01 10:25:02                        エンシェントな女   2   3   \n",
       "6   2024/02/01 10:30:06                     コスプレイヤーの撮影会   2   4   \n",
       "7   2024/02/02 10:17:08                 異世界の女性の髪がなびいている   4   5   \n",
       "8   2024/02/02 10:21:37                 青髪の女性が真正面を向いている   1   4   \n",
       "9   2024/02/02 10:25:13           整いたての眉毛をみんなに見せびらかしている   1   5   \n",
       "10  2024/02/02 12:49:29      ゲームのキャラクターみたいな女の人がこっちを見ている   2   5   \n",
       "11  2024/02/02 12:50:13                         風が吹いている   3   2   \n",
       "12  2024/02/02 12:51:04                    APEXに居そうなキャラ   4   5   \n",
       "13  2024/02/02 12:52:09            青髪のツインテールの女性が正面を向いてる   2   4   \n",
       "14  2024/02/02 12:52:51                         気の強そうな女   2   4   \n",
       "15  2024/02/02 12:53:11               正面を向いた女性の髪が靡いている。   2   4   \n",
       "16  2024/02/02 12:54:14          青髪の女性が証明写真のように正面を向いている   3   4   \n",
       "17  2024/02/02 12:56:05                         ゲームのキャラ   3   4   \n",
       "18  2024/02/02 12:58:39              女の人が真正面（私の方）を向いている   1   5   \n",
       "19  2024/02/02 12:59:41                    美少女がこっちを見ている   2   2   \n",
       "20  2024/02/02 13:01:18  青い髪と瞳で額に石を持つ女性が正面を見ているアップのイラスト   3   2   \n",
       "\n",
       "                      q4  q5  q6                     q7  q8  q9  ... q84  \\\n",
       "0            ユニコーンが歩いている   3   2                  回る観覧車   5   4  ...   5   \n",
       "1         ユニコーンが砂浜を歩いている   2   2           観覧車（なんか伸びてる）   3   2  ...   5   \n",
       "2                歩くユニコーン   3   3                  回る観覧車   5   4  ...   3   \n",
       "3            ゆっくり歩くユニコーン   3   4              観覧車に近づく様子   5   4  ...   5   \n",
       "4   晴れた日にユニコーンが草原を歩いている。   3   2             遊園地と赤い観覧車。   5   3  ...   3   \n",
       "5                歩くユニコーン   4   3                  傾く観覧車   2   2  ...   4   \n",
       "6      デブのユニコーンが湿地を走っている   1   2  ゴールデンゲートブリッジの余りで作りました   5   2  ...   3   \n",
       "7         ユニコーンが海辺を歩いている   4   5            赤い観覧車が動いている   3   5  ...   5   \n",
       "8              一角獣が歩いている   2   2       赤い観覧車がゆっくりと動いている   5   2  ...   3   \n",
       "9           ユニコーンが散歩している   4   5          観覧車に向かって歩いている   5   3  ...   3   \n",
       "10           ユニコーンが歩いている   4   4       観覧車を撮影しながら移動している   4   4  ...   4   \n",
       "11               ユニコーン闊歩   2   4                  動く観覧車   2   2  ...   2   \n",
       "12    ホワイトタイガーとユニコーンのキメラ   2   5                  赤い観覧車   3   4  ...   5   \n",
       "13      太り気味にユニコーンが走っている   5   2                赤い色の観覧車   5   4  ...   4   \n",
       "14               走るユニコーン   4   4                  赤い観覧車   5   5  ...   3   \n",
       "15        太ったユニコーンが走っている   2   1                変形する観覧車   3   2  ...   4   \n",
       "16       ユニコーンが大地を走り回ってる   1   2            遊園地の中にある観覧車   2   3  ...   3   \n",
       "17                 ユニコーン   4   2                    観覧車   4   3  ...   4   \n",
       "18     ユニコーンが坂を恐る恐る降っている   3   4                遊園地の観覧車   5   1  ...   4   \n",
       "19           ユニコーンが歩いている   5   5      観覧車の真ん中から何かが伸びている   5   5  ...   4   \n",
       "20         ユニコーンが空を走っている   3   4              観覧車が回っている   5   1  ...   3   \n",
       "\n",
       "                   q85  q86 q87                         q88  q89 q90  \\\n",
       "0           赤い橋を駆け抜ける車    3   5                      たくさんの犬    1   5   \n",
       "1            海外にありそうな橋    5   5           たくさんの犬が草原で寝そべっている    3   2   \n",
       "2               明石海峡大橋    4   5             たぶん犬の集団もしくはバケモノ    3   1   \n",
       "3   赤い巨大な橋に何台もの車が走っている    5   5                草原に寝転ぶ10数匹の犬    4   4   \n",
       "4           大きな赤い橋と街並み    4   4                     草原と沢山の犬    3   2   \n",
       "5                 都市の橋    4   5                       異形の集い    2   1   \n",
       "6         ゴールデンゲートブリッジ    5   3                    犬とワームの融合    4   2   \n",
       "7          赤い橋とその上を走る車    4   5              野原の上でじゃれあう大量の犬    3   5   \n",
       "8         赤く大きな橋を行き交う車    4   5           犬とも羊とも形容しがたい動物の群れ    2   1   \n",
       "9              車通りの多い橋    4   4  派遣犬たちが大変な研修を終えてお互いを労っている様子    3   3   \n",
       "10    赤い大きな橋の上を車が走っている    3   3                  大量の犬が草原にいる    3   3   \n",
       "11            赤い橋の上の道路    3   4                   101匹わんちゃん    2   1   \n",
       "12              赤い橋の道路    2   3                     エビフライの犬    2   3   \n",
       "13              赤い大きな橋    4   4                     群がる沢山の犬    2   2   \n",
       "14                 赤い橋    4   4                       犬の大家族    3   2   \n",
       "15          高い位置にある赤い橋    4   4                    戯れる10匹の犬    3   2   \n",
       "16           レインボーブリッジ    4   4              母親の犬がミルクを上げている    2   3   \n",
       "17               橋を渡る車    4   4                        大量の犬    2   2   \n",
       "18              赤い大きな橋    4   4                     沢山の犬がいる    3   2   \n",
       "19          巨大な橋の上を走る車    4   4               たくさんの犬のようななにか    3   2   \n",
       "20          大きな橋を抜ける車ら    3   3      イラスト調で、たくさんの子犬に乳を与える母犬    2   2   \n",
       "\n",
       "                              q91  q92 q93  \n",
       "0                         スパイ風の男性    5   5  \n",
       "1   スーツを着てサングラスをかけた男性が腕組みをしてたっている    5   3  \n",
       "2                          できる風の男    5   3  \n",
       "3                       色黒のサラリーマン    5   5  \n",
       "4              サングラスをかけたスーツ姿の若い男性    3   3  \n",
       "5                         エリートスパイ    5   5  \n",
       "6                     投資銀行に勤めて3年目    4   4  \n",
       "7                       軍事品開発会社社長    5   3  \n",
       "8      サングラスとスーツを着用し決めポーズを取っている男性    5   3  \n",
       "9                   映画監督が見下してきている    5   5  \n",
       "10      サングラスをかけたスーツの男が腕を組んで立っている    5   4  \n",
       "11                   腕を組んで立つスーツの男    3   2  \n",
       "12                  黒人の男がスーツを着ている    5   4  \n",
       "13                        仕事できる男性    4   4  \n",
       "14                     黒人のイケメンスパイ    4   4  \n",
       "15                          スパイの男    5   2  \n",
       "16           武器を作っている会社のイケてるスーツの男    5   5  \n",
       "17                      飛行機業者の偉い人    3   1  \n",
       "18                   スーツの男の人と船や椅子    5   4  \n",
       "19            黒髪でスーツにサングラスのスパイ風の男    5   4  \n",
       "20                      なんらかの組織の男    4   2  \n",
       "\n",
       "[21 rows x 94 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import japanize_matplotlib\n",
    "import pandas as pd\n",
    "from pylab import rcParams\n",
    "\n",
    "csv = pd.read_csv(\"./Ground_Truth.csv\")\n",
    "csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "q2     2.333333\n",
       "q3     3.523810\n",
       "q5     3.047619\n",
       "q6     3.190476\n",
       "q8     4.095238\n",
       "         ...   \n",
       "q87    4.142857\n",
       "q89    2.619048\n",
       "q90    2.380952\n",
       "q92    4.523810\n",
       "q93    3.571429\n",
       "Length: 62, dtype: float64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv.select_dtypes(include=['number']).mean(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     3\n",
       "1     1\n",
       "2     3\n",
       "3     2\n",
       "4     3\n",
       "5     2\n",
       "6     2\n",
       "7     4\n",
       "8     1\n",
       "9     1\n",
       "10    2\n",
       "11    3\n",
       "12    4\n",
       "13    2\n",
       "14    2\n",
       "15    2\n",
       "16    3\n",
       "17    3\n",
       "18    1\n",
       "19    2\n",
       "20    3\n",
       "Name: q2, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv[\"q2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.3333333333333335"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(csv[\"q2\"]) / len(csv[\"q2\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(csv[\"q1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 64 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = 10, 10\n",
    "csv.select_dtypes(include=['number']).hist(range=(0, 6), bins=5)\n",
    "plt.tight_layout()\n",
    "plt.xlim([0,5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "csv.plot.box(showmeans=True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          ゲームに出てきそうなキャラクターが正面を向いている\n",
       "1                 中国部将風の女性がこちらを向いている\n",
       "2                           青髪の碧眼の女性\n",
       "3                         青い髪の女性と黒い影\n",
       "4            ファンタジーな格好をした女性が前を向いている。\n",
       "5                           エンシェントな女\n",
       "6                        コスプレイヤーの撮影会\n",
       "7                    異世界の女性の髪がなびいている\n",
       "8                    青髪の女性が真正面を向いている\n",
       "9              整いたての眉毛をみんなに見せびらかしている\n",
       "10        ゲームのキャラクターみたいな女の人がこっちを見ている\n",
       "11                           風が吹いている\n",
       "12                      APEXに居そうなキャラ\n",
       "13              青髪のツインテールの女性が正面を向いてる\n",
       "14                           気の強そうな女\n",
       "15                 正面を向いた女性の髪が靡いている。\n",
       "16            青髪の女性が証明写真のように正面を向いている\n",
       "17                           ゲームのキャラ\n",
       "18                女の人が真正面（私の方）を向いている\n",
       "19                      美少女がこっちを見ている\n",
       "20    青い髪と瞳で額に石を持つ女性が正面を見ているアップのイラスト\n",
       "Name: q1, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "csv[\"q1\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:2.3333333333333335\n",
      "2:3.0476190476190474\n",
      "3:4.095238095238095\n",
      "4:2.0476190476190474\n",
      "5:3.9523809523809526\n",
      "6:2.619047619047619\n",
      "7:3.9523809523809526\n",
      "8:4.142857142857143\n",
      "9:3.619047619047619\n",
      "10:1.5714285714285714\n",
      "11:4.095238095238095\n",
      "12:4.380952380952381\n",
      "13:1.1904761904761905\n",
      "14:2.8095238095238093\n",
      "15:3.619047619047619\n",
      "16:3.4285714285714284\n",
      "17:4.095238095238095\n",
      "18:2.6666666666666665\n",
      "19:2.238095238095238\n",
      "20:3.9047619047619047\n",
      "21:4.285714285714286\n",
      "22:4.523809523809524\n",
      "23:4.285714285714286\n",
      "24:2.6666666666666665\n",
      "25:3.761904761904762\n",
      "26:4.380952380952381\n",
      "27:3.0476190476190474\n",
      "28:3.8095238095238093\n",
      "29:3.857142857142857\n",
      "30:2.619047619047619\n",
      "31:4.523809523809524\n"
     ]
    }
   ],
   "source": [
    "for i in range(31):\n",
    "    index = 'q' + str(3 * i + 2)\n",
    "    print(f\"{i+1}:{csv[index].mean()}\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}