ch13 test03.ipynb

{
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   "source": [
    "# 실전 종합 문제3\n",
    "\n",
    "## 01 시나리오\n",
    "DS 마트의 경영부서는 기존의 매출 데이터를 기반으로 마케팅 팀에 전달한 집중 홍보 상품 목록 선정과 매장 매대 진열 및 소비자 동선 수정을 이번 달 목표로 잡았다. 이를 위해 매출 데이터, 상품 정보, 고객정보를 전산팀으로부터 인계 받아 분석을 준비하였다. 정제된 로그 데이터를 활용하여 다음의 분석을 실시하시오.\n",
    "\n",
    "## 02 데이터 설명\n",
    "\n",
    "### 마트 매출 데이터 - association_rules_mart.csv\n",
    "\n",
    "### 고객 데이터 - association_rules_customers.csv\n",
    "\n",
    "### 품목 데이터 - association_rules_products.csv\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a7234586",
   "metadata": {},
   "source": [
    "## 03 문제\n",
    "\n",
    "### 1번\n",
    "\n",
    "2014년에는 매출이 발생했으나 2015년에는 매출이 발생하지 않은 품목은 총 몇 개 인가?(정답 예시: 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "158428fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from mlxtend.frequent_patterns import apriori\n",
    "from mlxtend.frequent_patterns import association_rules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "47b2f496",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>ID</th>\n",
       "      <th>Item</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>citrus fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>curd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1440kn258</td>\n",
       "      <td>other vegetables</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date         ID              Item\n",
       "0  2014-01-01  1249in804      citrus fruit\n",
       "1  2014-01-01  1249in804            coffee\n",
       "2  2014-01-01  1381ht273              curd\n",
       "3  2014-01-01  1381ht273              soda\n",
       "4  2014-01-01  1440kn258  other vegetables"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"실습파일/association_rules_mart.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9994cdb8",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000ol738</td>\n",
       "      <td>F</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1001sf480</td>\n",
       "      <td>M</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1002nj599</td>\n",
       "      <td>M</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1003cq947</td>\n",
       "      <td>M</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1004jh583</td>\n",
       "      <td>F</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID Gender  Age\n",
       "0  1000ol738      F   51\n",
       "1  1001sf480      M   55\n",
       "2  1002nj599      M   35\n",
       "3  1003cq947      M   39\n",
       "4  1004jh583      F   35"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cus = pd.read_csv(\"실습파일/association_rules_customers.csv\")\n",
    "df_cus.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6954c1ff",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>product</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>citrus fruit</td>\n",
       "      <td>3100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>coffee</td>\n",
       "      <td>1500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>curd</td>\n",
       "      <td>1400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>soda</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>other vegetables</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            product  price\n",
       "0      citrus fruit   3100\n",
       "1            coffee   1500\n",
       "2              curd   1400\n",
       "3              soda    300\n",
       "4  other vegetables   1700"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prod = pd.read_csv(\"실습파일/association_rules_products.csv\")\n",
    "df_prod.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c0e889f",
   "metadata": {},
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>citrus fruit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>coffee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>curd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>soda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1440kn258</td>\n",
       "      <td>other vegetables</td>\n",
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      ],
      "text/plain": [
       "         Date         ID              Item\n",
       "0  2014-01-01  1249in804      citrus fruit\n",
       "1  2014-01-01  1249in804            coffee\n",
       "2  2014-01-01  1381ht273              curd\n",
       "3  2014-01-01  1381ht273              soda\n",
       "4  2014-01-01  1440kn258  other vegetables"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   ],
   "source": [
    "df_q1 = df.copy()\n",
    "df_q1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2eb9f89e",
   "metadata": {},
   "outputs": [
    {
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       "      <th>yesr</th>\n",
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       "      <th>0</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>citrus fruit</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1249in804</td>\n",
       "      <td>coffee</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>curd</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1381ht273</td>\n",
       "      <td>soda</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-01-01</td>\n",
       "      <td>1440kn258</td>\n",
       "      <td>other vegetables</td>\n",
       "      <td>2014</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        Date         ID              Item  yesr  count\n",
       "0 2014-01-01  1249in804      citrus fruit  2014      1\n",
       "1 2014-01-01  1249in804            coffee  2014      1\n",
       "2 2014-01-01  1381ht273              curd  2014      1\n",
       "3 2014-01-01  1381ht273              soda  2014      1\n",
       "4 2014-01-01  1440kn258  other vegetables  2014      1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q1[\"Date\"] = pd.to_datetime(df_q1[\"Date\"])\n",
    "df_q1[\"yesr\"] = df_q1[\"Date\"].dt.year\n",
    "df_q1[\"count\"] = 1\n",
    "df_q1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "73a124c1",
   "metadata": {},
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>2014</td>\n",
       "      <td>Instant food products</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014</td>\n",
       "      <td>UHT-milk</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014</td>\n",
       "      <td>abrasive cleaner</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014</td>\n",
       "      <td>artif. sweetener</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014</td>\n",
       "      <td>baby cosmetics</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "   yesr                   Item  count\n",
       "0  2014  Instant food products     37\n",
       "1  2014               UHT-milk    160\n",
       "2  2014       abrasive cleaner     12\n",
       "3  2014       artif. sweetener     13\n",
       "4  2014         baby cosmetics      1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
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    }
   ],
   "source": [
    "df_q1_cnt = df_q1.groupby([\"yesr\", \"Item\"])[\"count\"].sum().reset_index()\n",
    "df_q1_cnt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cfbc9389",
   "metadata": {},
   "outputs": [
    {
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       "      <th>yesr</th>\n",
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       "      <th>2014</th>\n",
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       "      <th>2016</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Instant food products</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UHT-milk</td>\n",
       "      <td>160.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abrasive cleaner</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>artif. sweetener</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby cosmetics</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "yesr                   Item   2014   2015  2016\n",
       "0     Instant food products   37.0   23.0   1.0\n",
       "1                  UHT-milk  160.0  169.0   5.0\n",
       "2          abrasive cleaner   12.0   10.0   1.0\n",
       "3          artif. sweetener   13.0   17.0   2.0\n",
       "4            baby cosmetics    1.0    2.0   NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q1_cnt_pivot = df_q1_cnt.pivot(index = \"Item\", columns = \"yesr\", values = \"count\").reset_index()\n",
    "df_q1_cnt_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1efd61f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>yesr</th>\n",
       "      <th>Item</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Instant food products</td>\n",
       "      <td>37.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UHT-milk</td>\n",
       "      <td>160.0</td>\n",
       "      <td>169.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>abrasive cleaner</td>\n",
       "      <td>12.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>artif. sweetener</td>\n",
       "      <td>13.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>baby cosmetics</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "yesr                   Item   2014   2015  2016\n",
       "0     Instant food products   37.0   23.0   1.0\n",
       "1                  UHT-milk  160.0  169.0   5.0\n",
       "2          abrasive cleaner   12.0   10.0   1.0\n",
       "3          artif. sweetener   13.0   17.0   2.0\n",
       "4            baby cosmetics    1.0    2.0   0.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q1_cnt_pivot = df_q1_cnt_pivot.fillna(0)\n",
    "df_q1_cnt_pivot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3dceb2fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>yesr</th>\n",
       "      <th>Item</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>kitchen utensil</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>preservation products</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>toilet cleaner</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "yesr                   Item  2014  2015  2016\n",
       "79          kitchen utensil   1.0   0.0   0.0\n",
       "114   preservation products   1.0   0.0   0.0\n",
       "155          toilet cleaner   5.0   0.0   0.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q1_cnt_pivot.loc[(df_q1_cnt_pivot[2014] > 0) & (df_q1_cnt_pivot[2015] == 0), ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "256efaab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5e43b1b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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