인기대여소와 고장의 연관성 분석.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a25c3d3f",
   "metadata": {},
   "source": [
    "# 인기대여소(대여소별 이용횟수)와 고장의 연관성 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3bf3ae99",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.rcParams['font.family'] ='Malgun Gothic'\n",
    "matplotlib.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c68aae5",
   "metadata": {},
   "source": [
    "## 데이터 로드 및 전처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a01a2c3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rent = pd.read_csv(\"training_data7~12.csv\")\n",
    "df_office = pd.read_csv(\"서울특별시 공공자전거 대여소별 이용정보(월별)_22.7-22.12.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ae85d12e",
   "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>자치구</th>\n",
       "      <th>대여소명</th>\n",
       "      <th>기준년월</th>\n",
       "      <th>대여건수</th>\n",
       "      <th>반납건수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강남구</td>\n",
       "      <td>2301</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>2170</td>\n",
       "      <td>2183</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>강남구</td>\n",
       "      <td>2302</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>1140</td>\n",
       "      <td>1143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>강남구</td>\n",
       "      <td>2303</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>1329</td>\n",
       "      <td>1196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강남구</td>\n",
       "      <td>2304</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>288</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>강남구</td>\n",
       "      <td>2305</td>\n",
       "      <td>2022-07-01</td>\n",
       "      <td>334</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   자치구  대여소명       기준년월  대여건수  반납건수\n",
       "0  강남구  2301 2022-07-01  2170  2183\n",
       "1  강남구  2302 2022-07-01  1140  1143\n",
       "2  강남구  2303 2022-07-01  1329  1196\n",
       "3  강남구  2304 2022-07-01   288   104\n",
       "4  강남구  2305 2022-07-01   334   342"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "# 기준년월 열을 datetime 타입으로 변환\n",
    "df_office['기준년월'] = pd.to_datetime(df_office['기준년월'], format=\"%Y%m\")\n",
    "\n",
    "# 대여소명에서 숫자만 추출하여 int 타입으로 변환\n",
    "df_office['대여소명'] = df_office['대여소명'].str.extract('(\\d+)').astype(int)\n",
    "\n",
    "# 숫자 이외의 문자 제거하고 int 타입으로 변환하는 함수\n",
    "def remove_non_digits_and_convert_to_int(value):\n",
    "    cleaned_value = re.sub('[^0-9]', '', value)\n",
    "    return int(cleaned_value) if cleaned_value else 0\n",
    "\n",
    "# 대여건수와 반납건수에서 숫자 이외의 문자 제거 및 int 타입으로 변환\n",
    "df_office['대여건수'] = df_office['대여건수'].apply(remove_non_digits_and_convert_to_int)\n",
    "df_office['반납건수'] = df_office['반납건수'].apply(remove_non_digits_and_convert_to_int)\n",
    "df_office.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fbce99fe",
   "metadata": {},
   "source": [
    "## 인기대여소, 비인기대여소 추출\n",
    "\n",
    "- **`인기대여소`** : 사람들의 이용이 많은 상위 100개 대여소\n",
    "\n",
    "- **`비인기대여소`** : 사람들의 이용이 적은 하위 1000개 대여소\n",
    "\n",
    "- **`이용건수`** : 대여건수 + 반납건수\n",
    "\n",
    "상위100개, 하위 1000개의 비율로 해야 데이터 수가 비슷합니다.\n",
    "\n",
    "대여와 반납이 모두 인기대여소(비인기대여소) 에 속한 대여이력을 추출하였습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5b759d65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "총 대여소 수 : 2735\n"
     ]
    }
   ],
   "source": [
    "# 대여소명으로 묶어 대여건수와 반납건수 합산\n",
    "df_grouped = df_office.groupby('대여소명').agg({'대여건수': 'sum', '반납건수': 'sum'}).reset_index()\n",
    "# '이용건수' 열 추가\n",
    "df_grouped['이용건수'] = df_grouped['대여건수'] + df_grouped['반납건수']\n",
    "df_grouped = df_grouped.sort_values(by='이용건수', ascending=False)\n",
    "df_grouped = df_grouped.reset_index(drop=True)  # 인덱스를 0부터 순서대로 다시 설정\n",
    "print(\"총 대여소 수 :\", len(df_grouped))\n",
    "\n",
    "popular = df_grouped.head(100) # 인기대여소 상위 100개\n",
    "unpopular = df_grouped.tail(1000) # 인기없는 대여소 하위 1000개"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b817541c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 이용건수에 대한 바 그래프 그리기\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# 인기대여소와 비인기대여소 구간에배경색 설정\n",
    "popul = plt.axvspan(0, 100, alpha=0.2, color='red', label='인기대여소(상위 100)')\n",
    "unpopul = plt.axvspan(1735, 2735, alpha=0.2, color='blue', label='비인기대여소(하위 1000)')\n",
    "\n",
    "plt.bar(df_grouped.index, df_grouped['이용건수'])\n",
    "plt.xlabel('이용건수로 정렬된 대여소')\n",
    "plt.ylabel('이용건수')\n",
    "plt.title('대여소별 이용건수 분포')\n",
    "\n",
    "# 범례 추가\n",
    "plt.legend(handles=[popul, unpopul])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "71a66df0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "대여와 반납이 인기 상위 100개 대여소에서 이루어진 대여이력 개수 : 739\n",
      "고장 414개,  정상 325개\n"
     ]
    }
   ],
   "source": [
    "popular_stations = popular['대여소명'].tolist()\n",
    "# df_rent에서 대여와 반납이 popular_stations에 속한 행만 추출\n",
    "popular_rent = df_rent[df_rent['대여 대여소번호'].isin(popular_stations) & df_rent['반납대여소번호'].isin(popular_stations)]\n",
    "\n",
    "# 결과 출력\n",
    "print(\"대여와 반납이 인기 상위 100개 대여소에서 이루어진 대여이력 개수 :\",len(popular_rent))\n",
    "\n",
    "broken = popular_rent[popular_rent['고장'] == 1]\n",
    "normal = popular_rent[popular_rent['고장'] == 0]\n",
    "\n",
    "print(f\"고장 {len(broken)}개,  정상 {len(normal)}개\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "61955888",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "대여와 반납이 인기 하위 1000개 대여소에서 이루어진 대여이력 개수 : 831\n",
      "고장 358개,  정상 473개\n"
     ]
    }
   ],
   "source": [
    "unpopular_stations = unpopular['대여소명'].tolist()\n",
    "# df_rent에서 대여와 반납이 unpopular_stations에 속한 행만 추출\n",
    "unpopular_rent = df_rent[df_rent['대여 대여소번호'].isin(unpopular_stations) & df_rent['반납대여소번호'].isin(unpopular_stations)]\n",
    "\n",
    "print(\"대여와 반납이 인기 하위 1000개 대여소에서 이루어진 대여이력 개수 :\", len(unpopular_rent))\n",
    "\n",
    "broken2 = unpopular_rent[unpopular_rent['고장'] == 1]\n",
    "normal2 = unpopular_rent[unpopular_rent['고장'] == 0]\n",
    "\n",
    "print(f\"고장 {len(broken2)}개,  정상 {len(normal2)}개\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8345acd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['고장', '정상']\n",
    "colors = ['orange', 'green']\n",
    "\n",
    "fig, axs = plt.subplots(1, 2)\n",
    "\n",
    "axs[0].pie([len(broken), len(normal)], labels=labels, colors=colors, autopct='%1.1f%%')\n",
    "axs[0].set_title('인기 상위 100개 대여소')\n",
    "\n",
    "axs[1].pie([len(broken2), len(normal2)], labels=labels, colors=colors, autopct='%1.1f%%')\n",
    "axs[1].set_title('인기 하위 1000개 대여소')\n",
    "\n",
    "plt.subplots_adjust(wspace=1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "292c3b1f",
   "metadata": {},
   "source": [
    "## 분석결과 및 해석\n",
    "\n",
    "**인기대여소에서 대여 반납이 이루어진 데이터들은 고장이 정상보다 많았고, 반대로 비인기대여소는 정상이 훨씬 많았습니다.**\n",
    "\n",
    "아무래도 인기 대여소 주변은 자전거가 많고 혼잡하여 고장이 더 잦다고 추측해 볼 수 있겠습니다.\n",
    "\n",
    "하지만 이전에 했던 대여소명과 고장의 연관성 분석에서 '광장', '광화문'등 사람들이 많아 혼잡하다고 여겨지는 곳 주변은 고장이 오히려 적었습니다.\n",
    "\n",
    "**'사람'** 이 많은 장소와 **'자전거'** 가 많은 장소는 뭔가 다른 점이 있어보입니다."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:2023-DS-B] *",
   "language": "python",
   "name": "conda-env-2023-DS-B-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}