11.6.1.슬라이딩 윈도우 데이터 가공.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "from dateutil.relativedelta import relativedelta\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# plt.rcParams['figure.dpi'] = 300"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>dt</th>\n",
       "      <th>ym</th>\n",
       "      <th>sale_amt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG4127094465</td>\n",
       "      <td>2021-12-09</td>\n",
       "      <td>202112</td>\n",
       "      <td>300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG4127094465</td>\n",
       "      <td>2022-04-22</td>\n",
       "      <td>202204</td>\n",
       "      <td>45000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG4136725677</td>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>202204</td>\n",
       "      <td>47800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG4140129314</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>202202</td>\n",
       "      <td>39200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG4144923657</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>202205</td>\n",
       "      <td>110000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "         cust_id          dt      ym  sale_amt\n",
       "0  AFG4127094465  2021-12-09  202112    300000\n",
       "1  AFG4127094465  2022-04-22  202204     45000\n",
       "2  AFG4136725677  2022-04-07  202204     47800\n",
       "3  AFG4140129314  2022-02-07  202202     39200\n",
       "4  AFG4144923657  2022-05-11  202205    110000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/sewonghwang/shopping-mall\n",
    "df_raw=pd.read_csv(\"datasets/shopping_mall.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>dt</th>\n",
       "      <th>ym</th>\n",
       "      <th>sale_amt</th>\n",
       "      <th>Date2</th>\n",
       "      <th>YM_M0</th>\n",
       "      <th>YM_M1</th>\n",
       "      <th>YM_M2</th>\n",
       "      <th>YM_M3</th>\n",
       "      <th>YM_M4</th>\n",
       "      <th>YM_M5</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>AFG4127094465</td>\n",
       "      <td>2021-12-09</td>\n",
       "      <td>202112</td>\n",
       "      <td>300000</td>\n",
       "      <td>2021-12-09</td>\n",
       "      <td>M5</td>\n",
       "      <td>M4</td>\n",
       "      <td>M3</td>\n",
       "      <td>M2</td>\n",
       "      <td>M1</td>\n",
       "      <td>M0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG4127094465</td>\n",
       "      <td>2022-04-22</td>\n",
       "      <td>202204</td>\n",
       "      <td>45000</td>\n",
       "      <td>2022-04-22</td>\n",
       "      <td>M1</td>\n",
       "      <td>M0</td>\n",
       "      <td>M-1</td>\n",
       "      <td>M-2</td>\n",
       "      <td>M-3</td>\n",
       "      <td>M-4</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG4136725677</td>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>202204</td>\n",
       "      <td>47800</td>\n",
       "      <td>2022-04-07</td>\n",
       "      <td>M1</td>\n",
       "      <td>M0</td>\n",
       "      <td>M-1</td>\n",
       "      <td>M-2</td>\n",
       "      <td>M-3</td>\n",
       "      <td>M-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG4140129314</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>202202</td>\n",
       "      <td>39200</td>\n",
       "      <td>2022-02-07</td>\n",
       "      <td>M3</td>\n",
       "      <td>M2</td>\n",
       "      <td>M1</td>\n",
       "      <td>M0</td>\n",
       "      <td>M-1</td>\n",
       "      <td>M-2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG4144923657</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>202205</td>\n",
       "      <td>110000</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>M0</td>\n",
       "      <td>M-1</td>\n",
       "      <td>M-2</td>\n",
       "      <td>M-3</td>\n",
       "      <td>M-4</td>\n",
       "      <td>M-5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cust_id          dt      ym  sale_amt      Date2 YM_M0 YM_M1 YM_M2  \\\n",
       "0  AFG4127094465  2021-12-09  202112    300000 2021-12-09    M5    M4    M3   \n",
       "1  AFG4127094465  2022-04-22  202204     45000 2022-04-22    M1    M0   M-1   \n",
       "2  AFG4136725677  2022-04-07  202204     47800 2022-04-07    M1    M0   M-1   \n",
       "3  AFG4140129314  2022-02-07  202202     39200 2022-02-07    M3    M2    M1   \n",
       "4  AFG4144923657  2022-05-11  202205    110000 2022-05-11    M0   M-1   M-2   \n",
       "\n",
       "  YM_M3 YM_M4 YM_M5  \n",
       "0    M2    M1    M0  \n",
       "1   M-2   M-3   M-4  \n",
       "2   M-2   M-3   M-4  \n",
       "3    M0   M-1   M-2  \n",
       "4   M-3   M-4   M-5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 시점 기준 컬럼 생성\n",
    "\n",
    "# 날짜형으로 변환\n",
    "df_raw['Date2']= pd.to_datetime(df_raw['dt']) \n",
    "\n",
    "# 시점변수 생성 \n",
    "ym_li = [f\"YM_M{i}\" for i in range(6)]\n",
    "ym_li\n",
    "\n",
    "for i in range(len(ym_li)):\n",
    "    col = ym_li[i]\n",
    "    now = pd.to_datetime('2022-05-01') + relativedelta(months=-i)\n",
    "# 현재시점 기준 미래(-) 과거(+)\n",
    "    df_raw[col] = 'M' + (-np.floor((df_raw.Date2 - now)\n",
    "                                   /np.timedelta64(1, 'M'))).astype(int).astype(str) \n",
    "    \n",
    "df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cust_id</th>\n",
       "      <th>slae_amt_M0</th>\n",
       "      <th>slae_amt_M1</th>\n",
       "      <th>slae_amt_M2</th>\n",
       "      <th>slae_amt_M3</th>\n",
       "      <th>slae_amt_M4</th>\n",
       "      <th>slae_amt_M5</th>\n",
       "      <th>MM_DIFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>300000.0</td>\n",
       "      <td>YM_M0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG4136725677</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG4140129314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG4144923657</td>\n",
       "      <td>110000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG4154711536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         cust_id  slae_amt_M0  slae_amt_M1  slae_amt_M2  slae_amt_M3  \\\n",
       "0  AFG4127094465          0.0      45000.0          0.0          0.0   \n",
       "1  AFG4136725677          0.0      47800.0          0.0          0.0   \n",
       "2  AFG4140129314          0.0          0.0          0.0      39200.0   \n",
       "3  AFG4144923657     110000.0          0.0          0.0          0.0   \n",
       "4  AFG4154711536          0.0          0.0          0.0      45000.0   \n",
       "\n",
       "   slae_amt_M4  slae_amt_M5 MM_DIFF  \n",
       "0          0.0     300000.0   YM_M0  \n",
       "1          0.0          0.0   YM_M0  \n",
       "2          0.0          0.0   YM_M0  \n",
       "3          0.0          0.0   YM_M0  \n",
       "4          0.0          0.0   YM_M0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 슬라이딩 윈도우 형태로 변환\n",
    "\n",
    "m_col = [\"M{}\".format(i) for i in range(6)]   # M0~M5 목록 생성\n",
    "df_li = []   # 임시테이블 저장할 목록\n",
    "\n",
    "for n, ym in enumerate(ym_li):  # YM_M0 ~ YM_M5 반복\n",
    "# STD_YM_M0 변수 기준 M0~M5 & 구매금액 0원 초과         \n",
    "    tmp = df_raw[(df_raw[ym].isin(m_col)) & (df_raw['sale_amt'] > 0)] \n",
    "# YM_M0 기준 pivot\n",
    "    tmp = tmp.pivot_table(index='cust_id', \n",
    "                          columns=ym, values='sale_amt', \n",
    "                          aggfunc='sum') \n",
    "    \n",
    "    # M0~M12 중 누락된 컬럼 생성\n",
    "    # 추후 테이블 union을 위해 pivot시 누락된 컬럼을 별도로 생성해줌\n",
    "    missing_col = list(set(m_col) - set(tmp.columns)) \n",
    "    for col in missing_col :\n",
    "        tmp[col] = 0\n",
    "    \n",
    "    # 컬럼이름 변경\n",
    "    tmp.columns = [f'slae_amt_{c}' for c in tmp.columns] # 생략 가능\n",
    "    \n",
    "    tmp['MM_DIFF'] = ym\n",
    "    tmp = tmp.fillna(0)\n",
    "\n",
    "    df_li.append(tmp)\n",
    "\n",
    "final_df = pd.concat(df_li).reset_index()\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "      <th></th>\n",
       "      <th>cust_id</th>\n",
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       "    <tr>\n",
       "      <th>832</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>YM_M0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1442</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1849</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2166</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2399</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cust_id  slae_amt_M0  slae_amt_M1  slae_amt_M2  slae_amt_M3  \\\n",
       "832   AFG6825009314          0.0          0.0          0.0          0.0   \n",
       "1442  AFG6825009314          0.0          0.0          0.0          0.0   \n",
       "1849  AFG6825009314          0.0          0.0          0.0      39200.0   \n",
       "2166  AFG6825009314          0.0          0.0      39200.0          0.0   \n",
       "2399  AFG6825009314          0.0      39200.0          0.0          0.0   \n",
       "2529  AFG6825009314      39200.0          0.0          0.0          0.0   \n",
       "\n",
       "      slae_amt_M4  slae_amt_M5 MM_DIFF  \n",
       "832           0.0      39200.0   YM_M0  \n",
       "1442      39200.0          0.0   YM_M1  \n",
       "1849          0.0          0.0   YM_M2  \n",
       "2166          0.0          0.0   YM_M3  \n",
       "2399          0.0          0.0   YM_M4  \n",
       "2529          0.0          0.0   YM_M5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 특정 고객의 시점 별 형태 확인\n",
    "df1 = final_df[(final_df['cust_id']=='AFG6825009314')]\n",
    "\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>cust_id</th>\n",
       "      <th>slae_amt_M0</th>\n",
       "      <th>slae_amt_M1</th>\n",
       "      <th>slae_amt_M2</th>\n",
       "      <th>slae_amt_M3</th>\n",
       "      <th>slae_amt_M4</th>\n",
       "      <th>slae_amt_M5</th>\n",
       "      <th>MM_DIFF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2523</th>\n",
       "      <td>AFG6544053758</td>\n",
       "      <td>78400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2524</th>\n",
       "      <td>AFG6555046283</td>\n",
       "      <td>278000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2525</th>\n",
       "      <td>AFG6575203354</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2526</th>\n",
       "      <td>AFG6683774364</td>\n",
       "      <td>28000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2527</th>\n",
       "      <td>AFG6728543455</td>\n",
       "      <td>89000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2528</th>\n",
       "      <td>AFG6777275172</td>\n",
       "      <td>149000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2529</th>\n",
       "      <td>AFG6825009314</td>\n",
       "      <td>39200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2530</th>\n",
       "      <td>AFG6838532849</td>\n",
       "      <td>99000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2531</th>\n",
       "      <td>AFG6846099516</td>\n",
       "      <td>129000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2532</th>\n",
       "      <td>AFG6860414162</td>\n",
       "      <td>139000.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>YM_M5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            cust_id  slae_amt_M0  slae_amt_M1  slae_amt_M2  slae_amt_M3  \\\n",
       "2523  AFG6544053758      78400.0          0.0          0.0          0.0   \n",
       "2524  AFG6555046283     278000.0          0.0          0.0          0.0   \n",
       "2525  AFG6575203354      99000.0          0.0          0.0          0.0   \n",
       "2526  AFG6683774364      28000.0          0.0          0.0          0.0   \n",
       "2527  AFG6728543455      89000.0          0.0          0.0          0.0   \n",
       "2528  AFG6777275172     149000.0          0.0          0.0          0.0   \n",
       "2529  AFG6825009314      39200.0          0.0          0.0          0.0   \n",
       "2530  AFG6838532849      99000.0          0.0          0.0          0.0   \n",
       "2531  AFG6846099516     129000.0          0.0          0.0          0.0   \n",
       "2532  AFG6860414162     139000.0          0.0          0.0          0.0   \n",
       "\n",
       "      slae_amt_M4  slae_amt_M5 MM_DIFF  \n",
       "2523          0.0          0.0   YM_M5  \n",
       "2524          0.0          0.0   YM_M5  \n",
       "2525          0.0          0.0   YM_M5  \n",
       "2526          0.0          0.0   YM_M5  \n",
       "2527          0.0          0.0   YM_M5  \n",
       "2528          0.0          0.0   YM_M5  \n",
       "2529          0.0          0.0   YM_M5  \n",
       "2530          0.0          0.0   YM_M5  \n",
       "2531          0.0          0.0   YM_M5  \n",
       "2532          0.0          0.0   YM_M5  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 마지막 시점의 데이터 형태 확인\n",
    "final_df.tail(10)"
   ]
  }
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