Multi-Dimensional_Analysis.ipynb

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   "cell_type": "markdown",
   "id": "21832e72-643a-48c6-8650-61c150df6240",
   "metadata": {},
   "source": [
    "# 3. Multi-Dimensional Analysis(다차원 분석)에서 통계분석\n",
    "- Proposed by Biber (1988) and used in various text analytic research\n",
    "- Useful to identify genre and stylistic features of texts\n",
    "- Six dimensions of lexico-grammatical patterns of texts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6da7f0ce-b647-4f3e-9503-2be6a7ac4db8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\sumihan\\\\Desktop\\\\NLP 연구\\\\AI 소설 텍스트 분석 연구'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "11ab4f62-c58a-49d6-bf7c-da230b078218",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지\n",
    "\n",
    "# !pip install seaborn\n",
    "# !pip install factor_analyzer  \n",
    "# from factor_analyzer import FactorAnalyzer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a6c9566-487c-4ad5-a90b-79686e76bcbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# !pip install statsmodels\n",
    "# from statsmodels.multivariate.manova import MANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6c263e48-acce-48ae-a8a8-84d19848d9ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Publication Year</th>\n",
       "      <th>Dimension1</th>\n",
       "      <th>Dimension2</th>\n",
       "      <th>Dimension3</th>\n",
       "      <th>Dimension4</th>\n",
       "      <th>Dimension5</th>\n",
       "      <th>Dimension6</th>\n",
       "      <th>Closest Text Type</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>C114_Dagon</td>\n",
       "      <td>Male</td>\n",
       "      <td>1917.0</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.96</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.16</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>C115_Nyarlathotep_H_P_Lovecraft</td>\n",
       "      <td>Male</td>\n",
       "      <td>1920.0</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-6.51</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>0.48</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>C116_The_Whisper_in_the_Darkness</td>\n",
       "      <td>Male</td>\n",
       "      <td>1930.0</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.78</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1.17</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Filename Gender  Publication Year  Dimension1  \\\n",
       "113                        C114_Dagon   Male            1917.0       -7.39   \n",
       "114   C115_Nyarlathotep_H_P_Lovecraft   Male            1920.0       -5.55   \n",
       "115  C116_The_Whisper_in_the_Darkness   Male            1930.0       -3.99   \n",
       "\n",
       "     Dimension2  Dimension3  Dimension4  Dimension5  Dimension6  \\\n",
       "113        1.42        3.96       -3.42        0.38        0.16   \n",
       "114        0.42        4.21       -6.51       -2.42        0.48   \n",
       "115        1.82        2.78       -0.02       -0.20        1.17   \n",
       "\n",
       "                Closest Text Type Type  \n",
       "113  General narrative exposition   SF  \n",
       "114  General narrative exposition   SF  \n",
       "115  General narrative exposition   SF  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MDA_dimensions 데이터 읽기\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('data_analysis_MDA.csv')\n",
    "df.head()\n",
    "df = df[:212]\n",
    "df[113:116]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "05b29b89-79f3-4ed2-a05d-34561d58c897",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Publication Year</th>\n",
       "      <th>Dimension1</th>\n",
       "      <th>Dimension2</th>\n",
       "      <th>Dimension3</th>\n",
       "      <th>Dimension4</th>\n",
       "      <th>Dimension5</th>\n",
       "      <th>Dimension6</th>\n",
       "      <th>Closest Text Type</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>C114_Dagon</td>\n",
       "      <td>Male</td>\n",
       "      <td>1917</td>\n",
       "      <td>-7.39</td>\n",
       "      <td>1.42</td>\n",
       "      <td>3.96</td>\n",
       "      <td>-3.42</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.16</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>C115_Nyarlathotep_H_P_Lovecraft</td>\n",
       "      <td>Male</td>\n",
       "      <td>1920</td>\n",
       "      <td>-5.55</td>\n",
       "      <td>0.42</td>\n",
       "      <td>4.21</td>\n",
       "      <td>-6.51</td>\n",
       "      <td>-2.42</td>\n",
       "      <td>0.48</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>115</th>\n",
       "      <td>C116_The_Whisper_in_the_Darkness</td>\n",
       "      <td>Male</td>\n",
       "      <td>1930</td>\n",
       "      <td>-3.99</td>\n",
       "      <td>1.82</td>\n",
       "      <td>2.78</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>1.17</td>\n",
       "      <td>General narrative exposition</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             Filename Gender  Publication Year  Dimension1  \\\n",
       "113                        C114_Dagon   Male              1917       -7.39   \n",
       "114   C115_Nyarlathotep_H_P_Lovecraft   Male              1920       -5.55   \n",
       "115  C116_The_Whisper_in_the_Darkness   Male              1930       -3.99   \n",
       "\n",
       "     Dimension2  Dimension3  Dimension4  Dimension5  Dimension6  \\\n",
       "113        1.42        3.96       -3.42        0.38        0.16   \n",
       "114        0.42        4.21       -6.51       -2.42        0.48   \n",
       "115        1.82        2.78       -0.02       -0.20        1.17   \n",
       "\n",
       "                Closest Text Type Type  \n",
       "113  General narrative exposition   SF  \n",
       "114  General narrative exposition   SF  \n",
       "115  General narrative exposition   SF  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Publication Year'] = df['Publication Year'].astype('int')\n",
    "df.dtypes\n",
    "df[113:116]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d081b9de-af61-4ac8-94a0-4b3733ae8f69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Publication Year\n",
       "1826        1\n",
       "1896        1\n",
       "1901        1\n",
       "1904        1\n",
       "1906        2\n",
       "           ..\n",
       "2019        6\n",
       "2020        5\n",
       "2022        1\n",
       "19602015    1\n",
       "19862013    1\n",
       "Name: count, Length: 84, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Publication Year'].value_counts().sort_index()   # 20, 21세기 소설이나 예외있음(2권)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b4909326-f032-4c92-926c-878fe48d997f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Publication Year']>2000]['Publication Year'].count()       #106개?    # 19602015    19862013    => 이 경우 두개 빼고, 104개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5aec0e2-8e86-481e-8ae9-ff8623c91164",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      2017\n",
       "1      2020\n",
       "2      2017\n",
       "8      2011\n",
       "9      2003\n",
       "       ... \n",
       "202    2011\n",
       "203    2014\n",
       "209    2013\n",
       "210    2017\n",
       "211    2018\n",
       "Name: Publication Year, Length: 106, dtype: int32"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Publication Year']>2000]['Publication Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c6f6e1e4-04eb-40e1-8656-0f3a4b199f03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Publication Year'] <= 2000]['Publication Year'].count()   #106개 +2개 =>   108개 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8b2294d6-95af-450b-9f44-79247085784c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3      1964\n",
       "4      1962\n",
       "5      1952\n",
       "6      1983\n",
       "7      1991\n",
       "       ... \n",
       "204    1927\n",
       "205    1929\n",
       "206    1954\n",
       "207    1954\n",
       "208    1955\n",
       "Name: Publication Year, Length: 106, dtype: int32"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Publication Year']<=2000]['Publication Year'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d907d62a-f06a-4fdb-bd9d-4bff03843354",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 212 entries, 0 to 211\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Filename           212 non-null    object \n",
      " 1   Gender             212 non-null    object \n",
      " 2   Publication Year   212 non-null    int32  \n",
      " 3   Dimension1         212 non-null    float64\n",
      " 4   Dimension2         212 non-null    float64\n",
      " 5   Dimension3         212 non-null    float64\n",
      " 6   Dimension4         212 non-null    float64\n",
      " 7   Dimension5         212 non-null    float64\n",
      " 8   Dimension6         212 non-null    float64\n",
      " 9   Closest Text Type  212 non-null    object \n",
      " 10  Type               212 non-null    object \n",
      "dtypes: float64(6), int32(1), object(4)\n",
      "memory usage: 17.5+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "15717c76-5801-452f-8941-ae1a87463af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Publication Year</th>\n",
       "      <th>Dimension1</th>\n",
       "      <th>Dimension2</th>\n",
       "      <th>Dimension3</th>\n",
       "      <th>Dimension4</th>\n",
       "      <th>Dimension5</th>\n",
       "      <th>Dimension6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.120000e+02</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "      <td>212.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.881136e+05</td>\n",
       "      <td>-0.096085</td>\n",
       "      <td>4.147972</td>\n",
       "      <td>-0.171462</td>\n",
       "      <td>-0.304858</td>\n",
       "      <td>-0.770094</td>\n",
       "      <td>-1.061321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.911846e+06</td>\n",
       "      <td>5.997934</td>\n",
       "      <td>1.940585</td>\n",
       "      <td>1.781122</td>\n",
       "      <td>1.789196</td>\n",
       "      <td>0.903952</td>\n",
       "      <td>0.794887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.826000e+03</td>\n",
       "      <td>-16.750000</td>\n",
       "      <td>-1.210000</td>\n",
       "      <td>-3.780000</td>\n",
       "      <td>-6.510000</td>\n",
       "      <td>-3.290000</td>\n",
       "      <td>-2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.955750e+03</td>\n",
       "      <td>-3.605000</td>\n",
       "      <td>3.090000</td>\n",
       "      <td>-1.237500</td>\n",
       "      <td>-1.320000</td>\n",
       "      <td>-1.390000</td>\n",
       "      <td>-1.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.999500e+03</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>4.465000</td>\n",
       "      <td>-0.550000</td>\n",
       "      <td>-0.210000</td>\n",
       "      <td>-0.915000</td>\n",
       "      <td>-1.170000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.014000e+03</td>\n",
       "      <td>4.002500</td>\n",
       "      <td>5.442500</td>\n",
       "      <td>0.545000</td>\n",
       "      <td>0.752500</td>\n",
       "      <td>-0.375000</td>\n",
       "      <td>-0.670000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.986201e+07</td>\n",
       "      <td>14.380000</td>\n",
       "      <td>8.910000</td>\n",
       "      <td>7.350000</td>\n",
       "      <td>6.530000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>4.580000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Publication Year  Dimension1  Dimension2  Dimension3  Dimension4  \\\n",
       "count      2.120000e+02  212.000000  212.000000  212.000000  212.000000   \n",
       "mean       1.881136e+05   -0.096085    4.147972   -0.171462   -0.304858   \n",
       "std        1.911846e+06    5.997934    1.940585    1.781122    1.789196   \n",
       "min        1.826000e+03  -16.750000   -1.210000   -3.780000   -6.510000   \n",
       "25%        1.955750e+03   -3.605000    3.090000   -1.237500   -1.320000   \n",
       "50%        1.999500e+03    0.060000    4.465000   -0.550000   -0.210000   \n",
       "75%        2.014000e+03    4.002500    5.442500    0.545000    0.752500   \n",
       "max        1.986201e+07   14.380000    8.910000    7.350000    6.530000   \n",
       "\n",
       "       Dimension5  Dimension6  \n",
       "count  212.000000  212.000000  \n",
       "mean    -0.770094   -1.061321  \n",
       "std      0.903952    0.794887  \n",
       "min     -3.290000   -2.500000  \n",
       "25%     -1.390000   -1.640000  \n",
       "50%     -0.915000   -1.170000  \n",
       "75%     -0.375000   -0.670000  \n",
       "max      3.500000    4.580000  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "003b3645-360b-4caf-85ed-2c23e561482e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 136 entries, 0 to 209\n",
      "Data columns (total 11 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   Filename           136 non-null    object \n",
      " 1   Gender             136 non-null    object \n",
      " 2   Publication Year   136 non-null    int32  \n",
      " 3   Dimension1         136 non-null    float64\n",
      " 4   Dimension2         136 non-null    float64\n",
      " 5   Dimension3         136 non-null    float64\n",
      " 6   Dimension4         136 non-null    float64\n",
      " 7   Dimension5         136 non-null    float64\n",
      " 8   Dimension6         136 non-null    float64\n",
      " 9   Closest Text Type  136 non-null    object \n",
      " 10  Type               136 non-null    object \n",
      "dtypes: float64(6), int32(1), object(4)\n",
      "memory usage: 12.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# 남녀 작가 SF 소설 코퍼스 비교/대조\n",
    "df_m = df[df['Gender'] == 'Male']  \n",
    "df_f = df[df['Gender'] == 'Female']            \n",
    "\n",
    "df_m.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8fb6e63c-cec2-4bd1-ba6b-b922d1864f96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Filename</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Publication Year</th>\n",
       "      <th>Dimension1</th>\n",
       "      <th>Dimension2</th>\n",
       "      <th>Dimension3</th>\n",
       "      <th>Dimension4</th>\n",
       "      <th>Dimension5</th>\n",
       "      <th>Dimension6</th>\n",
       "      <th>Closest Text Type</th>\n",
       "      <th>Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C003_The_Power</td>\n",
       "      <td>Female</td>\n",
       "      <td>2017</td>\n",
       "      <td>7.14</td>\n",
       "      <td>3.27</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>0.07</td>\n",
       "      <td>-1.34</td>\n",
       "      <td>-0.69</td>\n",
       "      <td>Imaginative narrative</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>C008_A_Woman_of_the_Iron_People</td>\n",
       "      <td>Female</td>\n",
       "      <td>1991</td>\n",
       "      <td>5.15</td>\n",
       "      <td>4.87</td>\n",
       "      <td>0.45</td>\n",
       "      <td>-0.77</td>\n",
       "      <td>-1.86</td>\n",
       "      <td>-1.93</td>\n",
       "      <td>Imaginative narrative</td>\n",
       "      <td>SF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Filename  Gender  Publication Year  Dimension1  \\\n",
       "2                   C003_The_Power  Female              2017        7.14   \n",
       "7  C008_A_Woman_of_the_Iron_People  Female              1991        5.15   \n",
       "\n",
       "   Dimension2  Dimension3  Dimension4  Dimension5  Dimension6  \\\n",
       "2        3.27       -0.24        0.07       -1.34       -0.69   \n",
       "7        4.87        0.45       -0.77       -1.86       -1.93   \n",
       "\n",
       "       Closest Text Type Type  \n",
       "2  Imaginative narrative   SF  \n",
       "7  Imaginative narrative   SF  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "16263796-0e44-4f19-8191-26cd9a9cd87f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Dimension1', 'Dimension2', 'Dimension3', 'Dimension4', 'Dimension5',\n",
       "       'Dimension6'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f.columns[3:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b2b444b0-8b4c-4d03-a634-c66c8250d329",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    136.000000\n",
       "mean      -1.186029\n",
       "std        5.827917\n",
       "min      -16.750000\n",
       "25%       -4.335000\n",
       "50%       -1.065000\n",
       "75%        2.107500\n",
       "max       14.380000\n",
       "Name: Dimension1, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 남여 평균 내기\n",
    "\n",
    "# dimension 1\n",
    "df_m_d1 = df_m['Dimension1']\n",
    "df_f_d1 = df_f['Dimension1']\n",
    "\n",
    "df_m_d1.describe() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a2cf4f40-49d1-40cd-a1e2-6fdffda9032b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    76.000000\n",
       "mean      1.854342\n",
       "std       5.836510\n",
       "min     -10.150000\n",
       "25%      -1.832500\n",
       "50%       2.455000\n",
       "75%       5.575000\n",
       "max      13.920000\n",
       "Name: Dimension1, dtype: float64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_f_d1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0c8e5a7a-88c5-4ad0-b7c2-96511125455d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 4))\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension1\", hue=df.Type.tolist(), ax=axs[0])\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension2\", hue=df.Type.tolist(), ax=axs[1])              # SD가 높은 편;mean에서 +-2SD를 제외할 수 있음...outliers(이상치제거 필요)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e71c37a7-f392-44ee-b398-5d85b598c13b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 4))\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension3\", hue=df.Type.tolist(), ax=axs[0])\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension4\", hue=df.Type.tolist(), ax=axs[1])           # Female의 Dimension 3의 경우 outliers가 많음; variance 작음\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "952e3e8c-8396-4a6d-b51e-5d5c0836820c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(10, 4))\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension5\", hue=df.Type.tolist(), ax=axs[0])\n",
    "sns.boxplot(data=df, x=\"Gender\", y=\"Dimension6\", hue=df.Type.tolist(), ax=axs[1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05c4e4ae-ddb7-48fa-a614-46aaae4dcc9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8355e64-16e5-44f6-b959-46e8282b126c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}