data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas \n",
    "import matplotlib.pyplot as pyplot\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tic 그룹의 머리흔들기(0~1) 평균: 0.47\n",
      "Nontic 그룹의 머리흔들기(0~1) 평균: 0.07\n",
      "Tic 그룹의 평균 눈 감는 횟수(0~1) 평균: 0.83\n",
      "Nontic 그룹의 평균 눈 감는 횟수(0~1) 평균:0.43\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 주어진 데이터프레임 생성\n",
    "data = {'점수': ['tic-1', 'tic-2', 'tic-3', 'tic-4', 'tic-5', 'tic-6', 'nontic-1', 'nontic-2', 'nontic-3', 'nontic-4', 'nontic-5', 'nontic-6'],\n",
    "        '머리흔들기(0~1)': [0.4, 0.6, 0.6, 0.2, 0.2, 0.8, 0.2, 0, 0.2, 0, 0, 0],\n",
    "        '비대칭 눈 점수(0~1)': [0.2, 1.0, 0.4, 1.0, 0.6, 1.0, 0, 0, 0.2, 0.2, 0, 0.2],\n",
    "        '평균 눈 감는 횟수(0~1)': [1.0, 1.0, 0.6, 1.0, 0.4, 1.0, 0.8, 1.0, 0, 0.2, 0.2, 0.4],\n",
    "        '그룹': ['Tic', 'Tic', 'Tic', 'Tic', 'Tic', 'Tic', 'Nontic', 'Nontic', 'Nontic', 'Nontic', 'Nontic', 'Nontic']}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Tic 그룹과 Nontic 그룹의 머리흔들기(0~1) 값의 평균 계산\n",
    "tic_mean = df[df['그룹'] == 'Tic']['머리흔들기(0~1)'].mean()\n",
    "nontic_mean = df[df['그룹'] == 'Nontic']['머리흔들기(0~1)'].mean()\n",
    "blink_tic_mean = df[df['그룹'] == 'Tic']['평균 눈 감는 횟수(0~1)'].mean()\n",
    "blink_nontic_mean = df[df['그룹'] == 'Nontic']['평균 눈 감는 횟수(0~1)'].mean()\n",
    " \n",
    "print(f\"Tic 그룹의 머리흔들기(0~1) 평균: {tic_mean:.2f}\",)\n",
    "print(f\"Nontic 그룹의 머리흔들기(0~1) 평균: {nontic_mean:.2f}\")\n",
    "print(f\"Tic 그룹의 평균 눈 감는 횟수(0~1) 평균: {blink_tic_mean:.2f}\")\n",
    "print(f\"Nontic 그룹의 평균 눈 감는 횟수(0~1) 평균:{blink_nontic_mean:.2f}\" )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.        , 1.        , 1.        , 0.46473029, 0.49294606],\n",
       "       [0.        , 0.2       , 1.        , 0.20338983, 0.20067797],\n",
       "       [0.        , 0.2       , 1.        , 0.66206897, 0.29241379],\n",
       "       [0.        , 0.2       , 1.        , 0.03947368, 0.16789474],\n",
       "       [0.        , 1.        , 1.        , 0.37912088, 0.47582418],\n",
       "       [0.        , 0.        , 0.        , 0.03962704, 0.00792541],\n",
       "       [0.2       , 1.        , 0.8       , 0.17624521, 0.49524904],\n",
       "       [0.        , 0.2       , 1.        , 0.        , 0.16      ],\n",
       "       [0.        , 0.        , 1.        , 0.35616438, 0.17123288],\n",
       "       [0.        , 1.        , 1.        , 0.01190476, 0.40238095],\n",
       "       [0.2       , 0.4       , 1.        , 0.26612903, 0.35322581],\n",
       "       [0.        , 0.2       , 0.8       , 0.00310559, 0.14062112],\n",
       "       [0.2       , 0.4       , 1.        , 0.26612903, 0.35322581],\n",
       "       [0.2       , 1.        , 0.8       , 0.15625   , 0.49125   ],\n",
       "       [0.        , 0.4       , 1.        , 0.01901141, 0.22380228],\n",
       "       [0.        , 0.2       , 1.        , 0.45042017, 0.25008403]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 47672 (\\N{HANGUL SYLLABLE MEO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 47532 (\\N{HANGUL SYLLABLE RI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 55124 (\\N{HANGUL SYLLABLE HEUN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 46308 (\\N{HANGUL SYLLABLE DEUL}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 44592 (\\N{HANGUL SYLLABLE GI}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 48712 (\\N{HANGUL SYLLABLE BIN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 46020 (\\N{HANGUL SYLLABLE DO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 44536 (\\N{HANGUL SYLLABLE GEU}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 47353 (\\N{HANGUL SYLLABLE RUB}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 44050 (\\N{HANGUL SYLLABLE GABS}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 48516 (\\N{HANGUL SYLLABLE BUN}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "/Users/cdh/anaconda3/envs/dh/lib/python3.8/site-packages/IPython/core/pylabtools.py:152: UserWarning: Glyph 54252 (\\N{HANGUL SYLLABLE PO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dh",
   "language": "python",
   "name": "dh"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}